Geographies of child trafficking in Guangdong Province, China (1980–2020): Spatial dynamics and socio-economic drivers

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This study focuses on child trafficking in Guangdong Province, China, a region characterized by significant economic disparities, large floating populations, and deeply rooted cultural preferences. Utilizing data from the 'Baby Come Home' public welfare website and statistical yearbooks, this research employs the GeoDetector method to analyze the spatial distribution, temporal trends, and socio-economic factors driving child trafficking from 1980 to 2020. The findings reveal that child trafficking in Guangdong Province is predominantly influenced by the region's socio-economic dynamics, particularly the presence of large migrant populations and a cultural preference for male children. The spatial analysis identifies economically developed cities as hotspots for trafficking due to their substantial migrant populations. Additionally, the study highlights the significant role of population mobility in shaping the temporal and spatial patterns of these crimes. This research contributes to the existing literature by providing a comprehensive spatial and temporal analysis of child trafficking in a region that has been underexplored. It echoes and advances current trends in geographical approaches to child trafficking, particularly the integration of child-focused studies, mixed methods, and comparative perspectives. The study’s findings offer important implications for policy development and law enforcement, providing insights that can guide targeted interventions to prevent child trafficking and protect vulnerable children. Furthermore, the methodological approach employed in this research offers a model for future studies on child trafficking in other regions. Social science/Criminology Social science/Geography child trafficking spatial analysis population mobility geographical approaches Guangdong Province Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Child trafficking is a grave global issue that affects millions of children worldwide, manifesting in various forms, including forced labor, sexual exploitation, and illegal adoption (Frederick et al., 2010). It is a severe violation of fundamental human rights and poses significant challenges to governments, communities, and families alike (Yea, 2013). The complexity of child trafficking lies not only in its clandestine nature but also in the diverse factors that contribute to its persistence across different regions and cultures (Martinho et al., 2020). China, as the world's most populous country, has long grappled with the issue of child trafficking, a problem exacerbated by its rapid economic development, vast migration flows, and deeply rooted cultural preferences (Yang et al., 2021). The phenomenon of child trafficking in China is intricately linked to historical and socio-cultural factors, such as the traditional preference for male offspring and the one-child policy, both of which have created environments conducive to the exploitation and trafficking of vulnerable children (Yang et al., 2023). Within China, Guangdong Province stands out as a critical region for studying child trafficking due to its significant economic disparities, large floating population, and dual role as both a source and destination of trafficked children (Yao et al., 2021). Despite the severity of the issue, research specifically focusing on the geographical and socio-economic factors driving child trafficking in this region remains limited. Recent studies have begun to shed light on the patterns of child trafficking in China, focusing on aspects such as the age and gender of victims (Zhou et al., 2023), the route of migration (Wang et al., 2018), and the influence of cultural preferences on trafficking dynamics (Yang et al., 2023). However, much of the existing research has been limited in scope, often concentrating on adult trafficking or lacking a detailed spatial analysis of child trafficking within specific provinces like Guangdong. In response to these gaps, this study aligns with emerging trends in geographical approaches to human trafficking, which increasingly emphasize the need for child-focused research, the integration of mixed methods, and the adoption of comparative perspectives (Corsaro and Everitt, 2023). This study provides a detailed analysis of child trafficking in Guangdong Province by employing a geographical perspective that examines the spatial distribution of trafficking routes, the temporal evolution of these crimes, and the socio-economic factors driving them. By focusing on a region with significant floating populations, this research not only advances the understanding of child trafficking in China but also contributes to the broader field of geographical research on trafficking by applying innovative methods that combine quantitative and qualitative analyses and compare regional variations to uncover complex socio-cultural dynamics. The central argument of this research is that child trafficking in Guangdong Province is deeply influenced by the region’s socio-economic dynamics, particularly the presence of large floating populations and the cultural preference for male children. The spatial and temporal patterns of child trafficking reflect the interplay between these factors and the broader economic and social changes occurring in the province. To explore this argument, the study utilizes data from the ‘Baby Come Home’ public welfare website, along with statistical yearbooks from Guangdong Province and various prefecture-level cities. The research employs the GeoDetector method to analyze the spatial distribution of child trafficking crimes, assess the temporal evolution from 1980 to 2020, and identify the key factors influencing these patterns. The findings reveal a concentration of trafficking in economically developed cities with large migrant populations, highlighting the significant role of population mobility in driving these crimes. This research fills a critical gap in the existing literature by providing a comprehensive spatial and temporal analysis of child trafficking in Guangdong Province, a region that has been underexplored in previous studies. By focusing on child victims and utilizing a robust geographical methodology, this study contributes to a deeper understanding of the factors that make certain regions more susceptible to child trafficking. The findings have significant implications for policy and intervention strategies. By identifying high-risk areas and key influencing factors, the study provides valuable insights that can guide law enforcement efforts and inform the development of targeted policies aimed at preventing child trafficking and protecting vulnerable children. Moreover, the study’s methodological approach offers a model for future research on child trafficking in other regions. This article is structured as follows: The next section reviews the relevant literature, discussing the geographical approaches to human trafficking and the current state of research on child trafficking in China. This is followed by a detailed description of the research data and methods. The subsequent sections present the findings of the spatial and temporal analyses, as well as the GeoDetector results on the driving factors of child trafficking. The paper concludes with a discussion of the implications of these findings, the study’s limitations, and suggestions for future research. Literature review Human Trafficking Revisited: Multi-Disciplinary Perspectives and Geographical Approaches Human trafficking has a long and troubling history, attracting the attention of scholars across various disciplines (Olisah et al., 2024;Weitzer, 2014; Cullen-DuPont, 2009; Tyldum, 2010). Sociologists, criminologists, public health experts, and psychologists have all contributed to understanding the complex dynamics of this global issue (Cottingham et al., 2013; Decker, 2015; Recknor et al., 2022;Cunha et al., 2022). Sociological research delves into the social structures and inequalities that underpin human trafficking, with theories such as structural violence and social network theory playing pivotal roles in explaining how systemic inequalities and social networks facilitate trafficking (Esson, 2020; Breuil and Gerasimov, 2021). Structural violence theory suggests that entrenched social hierarchies, poverty, and discrimination create environments where trafficking can flourish(Shimazaki, 2021), while social network theory elaborates on how traffickers exploit these networks for recruitment and exploitation(Gezinski and Gonzalez-Pons, 2024). Criminology provides further insight into the legal and policy frameworks governing trafficking(Pajón and Walsh, 2020), with routine activity theory positing that trafficking occurs when a motivated offender encounters a suitable target in the absence of a capable guardian, and crime pattern theory offering an understanding of the spatial behaviors of traffickers, which contributes to the identification of trafficking hotspots(Mahalingam and Sidhu, 2021). From a public health standpoint, trafficking is seen as a significant issue due to its profound physical and psychological impacts on victims, with theories related to the social determinants of health exploring how socio-economic factors contribute to vulnerabilities that traffickers exploit(Greenbaum, 2020; Recknor et al., 2022). Public health research emphasizes the critical role of health systems in identifying victims and providing necessary care(De Shalit et al., 2021; Sprang et al., 2022). Psychological research complements these perspectives by focusing on the trauma experienced by trafficking victims and the psychological control mechanisms traffickers employ, with trauma theory examining the long-term psychological effects of trafficking and behavioral psychology providing insights into how traffickers use coercion and manipulation to maintain control over their victims (Cecchet and Thoburn, 2014; Pascual-Leone et al., 2017). Despite the valuable contributions of various disciplines, geographical approaches to human trafficking are increasingly attracting scholarly attention(Cockbain et al., 2022; Smith, 2018; Blazek et al., 2019). This growing interest stems from the recognition of gaps in the traditional perspectives, particularly their limited consideration of the spatial and environmental factors that significantly influence trafficking patterns(Yea, 2021). Human trafficking is fundamentally a spatially illicit activity, involving involuntary or forced mobility across local, regional, national, and international boundaries (Cockbain et al., 2022). The diverse spatial dimensions of human trafficking crimes allow for a more profound and detailed analysis when approached from a geographical perspective(Cockbain et al., 2024). Geographical research has made significant contributions to understanding these spatial dimensions, particularly through the exploration of how the location and movement of traffickers and victims are influenced by various geographic factors, including urbanization, migration, and regional inequalities(Chong and Clark., 2017; Eargle and Doucet 2021). Limoncelli (2009) emphasizes the influence of external drivers, such as globalization and urbanization, in shaping trafficking dynamics. Similarly, Shelley (2010) highlights how geopolitical shifts and economic disparities between regions can affect the flow of human trafficking, while Zimmerman et al. (2008) reveal that economic stressors, such as job insecurity and poverty, can heighten vulnerability to trafficking (see also Cho, 2015). A prominent trend in recent research is the focus on unraveling the intricate spatial patterns and routes of trafficking networks within a globalized context(Cockbain et al., 2022; Yao et al., 2021). Indeed, understanding these spatial dynamics is crucial for effective policy formulation and intervention strategies, as trafficking often involves the movement of victims across multiple geographical regions(Lopez and Truesdale-Moore, 2020). By employing spatial analysis techniques such as Geographic Information Systems (GIS), geographers have successfully mapped trafficking routes, identified hotspots, and analyzed the spatial distribution of victims and traffickers (Sarrica, 2022). Such studies provide valuable insights into the geographical intricacies of trafficking networks, which frequently transcend national boundaries. In addition to mapping trafficking routes, geographers have investigated the environmental and socio-economic conditions that create vulnerabilities to trafficking(Biswas, 2015; Russell, 2014). Dakua and Rahaman (2024) found that regions with high poverty rates and limited access to education in rural India are more susceptible to trafficking, as traffickers exploit these vulnerabilities to recruit victims. Similarly, research by Musto (2019) on trafficking within Eastern Europe revealed that local socio-economic conditions significantly impact the prevalence of trafficking networks in the region. These studies collectively underscore the importance of spatial analysis in understanding the complex interplay between geographic factors and human trafficking. However, while geographical research has made significant contributions, it has also faced criticism for its narrow focus on spatial patterns(Yea, 2021; Mahalingam, 2019). This approach sometimes overlooks the broader socio-economic, cultural, and psychological factors that also play critical roles in trafficking dynamics (O'Brien et al., 2013). For instance, while mapping trafficking routes can provide valuable insights, it does not fully capture the complex interplay of social and cultural factors that influence why certain populations are more vulnerable to trafficking than others (Van Liempt, 2011). Moreover, there is a pressing need for more mixed-methods research that combines spatial analysis with qualitative approaches to offer a more comprehensive understanding of trafficking(Palmiotto, 2014). Additionally, geographical research has often focused predominantly on adult trafficking, with relatively little attention paid to child trafficking(Abu-Ali and Al-Bahar, 2011; Martinho et al., 2020), particularly in non-Western contexts such as China(Sidun and Flores, 2020). This represents a significant gap in the literature, given the unique vulnerabilities and experiences of child trafficking victims. Trends in Geographical Approaches to Human (child) Trafficking Increased Focus on Child Trafficking One significant trend in current geographical studies on human trafficking is the growing recognition of the need to focus more extensively on child trafficking (O'Connell Davidson, 2011; Cockbain and Olver, 2019). Historically, geographical research has predominantly concentrated on adult trafficking, particularly in contexts of labor and sexual exploitation (Ricard-Guay and Hanley, 2020). However, as awareness of the unique vulnerabilities and experiences of trafficked children increases, there is a corresponding shift in research priorities (Martinho et al., 2020). Children, due to their age, dependence, and limited agency, are particularly susceptible to exploitation (Beyrer, 2004), yet they have been underrepresented in trafficking studies. Recent geographical research is beginning to rectify this oversight by exploring the spatial dynamics of child trafficking, including the identification of trafficking routes and hotspots specific to child victims, as well as the socio-economic and environmental factors that heighten their vulnerability (Sax, 2017). This shift is critical, as it aligns research efforts with the need to protect one of the most vulnerable populations from the harms of trafficking. Integration of Quantitative and Qualitative Methods Another notable trend is the increasing use of mixed-methods approaches that combine quantitative and qualitative research (Kiss et al., 2022). Traditional geographical studies on trafficking have often relied heavily on either spatial analysis through GIS and other quantitative tools or on qualitative methods like interviews and ethnography. However, these approaches, when used in isolation, have limitations (Lanier and Farrell, 2015). Quantitative methods can provide broad overviews and identify patterns on a larger scale but may miss the nuanced, local-level insights that qualitative methods can offer. Conversely, qualitative research provides depth and context but may lack the statistical rigor needed to generalize findings across broader contexts. In regions where trafficking data is incomplete or difficult to obtain—common in studies of child trafficking, for instance, like China in this study —mixed-methods approaches can bridge this gap (Jiang and Sánchez-Barricarte, 2013; Cody et al., 2024). By integrating spatial data with in-depth qualitative insights, researchers can achieve a more comprehensive understanding of trafficking patterns and the lived experiences of victims. This methodological trend is particularly pertinent in societies where human and child trafficking data are fragmented or underreported, making mixed methods an essential tool for robust trafficking research (Di Nicola, 2013). Adoption of Comparative Perspectives A third trend is the adoption of comparative perspectives to better understand the complex social and cultural biases that influence trafficking (Lanier et al., 2014). Trafficking is not a uniform phenomenon; it varies significantly across different social, cultural, and temporal contexts (Foot et al., 2021). Factors such as gender, age, and the location and timing of trafficking events play critical roles in shaping the experiences of victims and the strategies of traffickers (Duong, 2012; Lin, 2021). Comparative research, which examines trafficking across different regions, cultures, or demographic groups, is increasingly seen as essential for uncovering these complexities (Reid 2012). For instance, comparing trafficking patterns in urban versus rural settings, or in different cultural contexts, can reveal how local norms and socio-economic conditions contribute to trafficking (Olisah et al., 2024). Moreover, understanding how these factors intersect—such as how gender and age interact to influence trafficking risks—can help develop more targeted and effective interventions. This trend towards comparative research underscores the importance of situating trafficking studies within broader social and cultural frameworks, allowing for a more nuanced analysis of the factors that drive and sustain trafficking. These trends in geographical approaches to human (child) trafficking reflect a growing awareness of the need to address existing research gaps and to develop more comprehensive, nuanced, and methodologically sound approaches to studying this complex issue (Dragiewicz, 2014; Okech et al., 2018; Yea, 2021). In line with the emerging research trends in geographical approaches to human (child) trafficking, this study focuses on the phenomena of child trafficking at the provincial level in China. The research aims to analyze the temporal and spatial characteristics of child trafficking crimes, with particular attention to victims of different ages and genders. This includes an examination of the temporal evolution of these crimes over years and months, as well as the spatial distribution across provinces and cities, and the flow paths of trafficking activities. To further investigate the factors associated with child trafficking, the study employs GeoDetector, a powerful tool for exploring the influencing factors behind these crimes. By doing so, this research not only aligns with but also contributes to the current trends in trafficking research, addressing critical gaps in understanding the spatial dynamics and influencing factors of child trafficking in China. Child trafficking in China: Backgrounds, Challenges and General Landscape In an era influenced by the deeply rooted preference for male offspring and the strict implementation of the one-child policy, child trafficking has emerged as a significant issue in China (Wang et al., 2018; Yik-Yi Chu, 2011). The cultural preference for sons, coupled with restrictive family planning policies, has occasionally led to the occurrence of child trafficking, particularly in regions where these social pressures are most acute (Shen et al., 2013). As a result, children have become the primary focus of research on human trafficking crimes within the Chinese context (Wang et al., 2018). The confluence of these socio-cultural factors has not only contributed to the persistence of child trafficking but has also shaped the research landscape, driving scholars to pay closer attention to the unique dynamics of this form of exploitation in China. Although research on child trafficking from a geographical perspective remains relatively limited in China, there has been a gradual increase in such studies in recent years (Huang and Weng, 2019; Zhou et al., 2024). One of the main challenges faced by researchers in this field is the lack of official data on child trafficking, which complicates efforts to conduct comprehensive analyses. However, alternative data sources have emerged as crucial for studying child trafficking in China. Two of the most significant are ‘Baby Back Home,’ one of China’s largest public-service websites dedicated to reuniting families, and the Chinese Judgment Document Network, which provides judgment documents related to trafficking cases. By analyzing the geographical information associated with both the origins and destinations of trafficking, research has increasingly focused on the spatial dynamics of these crimes. Specifically, studies related to the origins of trafficking delve into the spatial distribution patterns of trafficked children and the perpetrators (Yang et al., 2021), while research on the destinations of trafficking reveals the geographical distribution characteristics of buyers (Li et al., 2019). These studies contribute to a deeper understanding of the regional disparities and socio-economic factors that drive child trafficking in China. Current research also examines the spatial patterns of child trafficking crimes at different geographical scales. At the national level, child trafficking in China exhibits a distinct spatial pattern, with trafficking incidents concentrated in the western regions and a more dispersed distribution in the eastern regions, which primarily serve as destinations for trafficked children (Li et al., 2017). The southwestern region, including provinces such as Guizhou, Sichuan, Yunnan, and Chongqing, is identified as the primary source of trafficked children, whereas the southern coastal regions, particularly Fujian and Guangdong, serve as the main destinations (Huang & Weng, 2019). In studies that focus on specific provinces, researchers have analyzed the criminal characteristics of child trafficking in several high-incidence areas, including Henan (Zhou et al., 2024), Guizhou (Xue et al., 2020), Hubei (Wang et al., 2021), and Yunnan (Yang et al., 2022). However, research on high-incidence destination provinces is relatively scarce, highlighting a significant gap in the literature that needs to be addressed. This study focuses on Guangdong Province, identified as one of China’s high-incidence destination provinces for child trafficking. Despite its significance, Guangdong has not been extensively studied as a research area. Moreover, unlike the prevalent trend of cross-provincial trafficking observed in other regions, Guangdong Province is one of the few provinces where a substantial number of intra-provincial trafficking cases have been recorded (Wang et al., 2018). This unique characteristic provides a robust perspective for conducting a provincial-level spatial analysis of child trafficking. By focusing on Guangdong Province, this study aims to fill the gap in the existing research and offer new insights into the spatial dynamics and influencing factors of child trafficking within the province. Research Data and Methods Source of data The data utilized in this study was sourced from the public welfare website ‘Baby Back Home’ [ https://www.baobeihuijia.com/bbhj/ ]. Established in 2007, ‘Baby Back Home’ is a platform created by the Baby Back Home Volunteer Association, with the primary objective of assisting parents in locating missing children and facilitating the placement of homeless children into adoptive families. The website's sections, ‘Family Searching for Children’ and ‘Successful Cases,’ provide valuable data on the origin (for cases where children have not been found) and both the origin and destination (for successfully located cases) of child trafficking incidents. Data retrieval was conducted in November 2022. In addition to this, the study also incorporates data from statistical yearbooks of Guangdong Province, China, and various prefecture-level cities, which were used for analysis with GeoDetector. The data includes natural factors (e.g., annual average temperature, administrative divisions, and land area), population factors (e.g., sex ratio, migrant population), social factors (e.g., unemployment rate, passenger transport volume, number of individuals with university education), and economic factors (e.g., Engel coefficient). Data cleaning The initial dataset was extracted from the location information of child trafficking incidents retrieved from the database of missing children in Guangdong Province, covering the period from 1980 to 2020. Subsequently, we excluded records related to missing cases, abandonment, adoption, and running away from home. This data cleaning process resulted in a final dataset comprising 2,325 valid records from the ‘Family Searching for Children’ section and 102 valid records from the ‘Successful Cases’ section. These records include crucial details such as the child’s name or nickname, gender, age at the time of abduction, year and month of abduction, origin address, trafficking routes, and other relevant information. Additionally, textual descriptions from the ‘Successful Cases’ section were utilized as supplementary material for analysis. Data quality Given that the ‘Baby Back Home’ website relies on volunteer-provided information, the data may contain errors or omissions. To address potential issues, we implemented several corrective measures: (1) Inaccurate or missing addresses were corrected by inferring approximate locations from case details and cross-referencing with map data. (2) Outdated administrative divisions were updated when discrepancies were identified between registered locations and current administrative boundaries. (3) Address precision was categorized into city-level, county-level, and street-level to align with specific spatial analysis requirements. It is important to note that the ‘Family Searching for Children’ section of the Baby Back Home website registers children who were abducted under the age of 16, and thus this study is focused on this age group. Analytical methods To analyze the distribution of age, gender, routes, and timing of trafficking, we employed descriptive analysis to present the socio-demographic characteristics of trafficked children in Guangdong Province. Following this, we utilized GeoDetector to analyze the driving forces behind these patterns. GeoDetector is a widely used tool for exploring stratified spatial heterogeneity of dependent variables and examining the influence of interactions between independent variables on dependent variables (Liang and Xu, 2023). Specifically, the factor detector within GeoDetector allows for the classification of factors on the same scale under varying conditions and identifies the main factors driving the observed patterns. Furthermore, it provides an intuitive reflection of the determining influence of these factors without the constraints of a linear hypothesis (Liang et al., 2022). In this study, we applied GeoDetector for both factor detection and interaction detection to explore the influencing factors of the spatial distribution of child trafficking crimes in Guangdong Province. Results Socio-demographic characteristics of trafficked children in Guangdong Province Gender distribution In analyzing the gender distribution of abducted individuals, it is evident that boys constitute a significantly larger proportion of the victims. Specifically, 1,580 boys were abducted, representing 68% of the total cases, while 745 girls were abducted, accounting for 32% of the total. This results in a gender ratio of 212.08, indicating a predominance of male victims, as illustrated in Fig. 1 . The higher number of abducted boys compared to girls in the studied province is consistent with the broader trend observed across China, where the incidence of boy abductions surpasses that of girls (Li et al., 2017). The existing literature often interprets this gender disparity through the lens of gender norms and cultural factors, particularly the historical preference for male children in China (Xiong, 2023). This preference is deeply rooted in the patriarchal family structure and traditional marriage systems that have long characterized Chinese society (Wang et al., 2021). In Guangdong Province, these cultural norms are further intensified by the region's strong kinship culture, which places significant emphasis on male lineage and the continuation of the family name (Chen, 2009). In many parts of Guangdong, particularly in rural areas, the cultural and social importance of having male offspring is profound. Male children are often seen as essential for maintaining the family line, especially in the context of traditional Chinese beliefs about ancestor worship and the passing down of family heritage (Guohua, 2004). This cultural expectation can create intense pressure on families to produce male heirs, leading to a unique demand for male children, particularly in families that lack sons. This demand is not just about cultural continuity; it also has practical implications in the socio-economic fabric of rural Guangdong. Sons are traditionally expected to care for their aging parents and are often seen as the primary bearers of family responsibilities (Lei, 2013). As a result, the desire for male children can be so strong that it may contribute to the abduction of boys, as families without sons seek to fulfill these cultural and social expectations (as evidenced by Case 1 ). This cultural bias and the associated 'cultural pressure' contribute to the higher incidence of male abductions observed in this study. Case 1 At the age of 4, W was abducted by a familiar man and taken to the dock under the temptation of snacks and toys. After about seven hours of sailing, W had forgotten everything along the way and was handed over to two women he had never met before during the voyage. He was then taken by train to a small village. A local family gave birth to 7 girls, and in order to continue their popularity, they bought W back home. (Baby Back Home ID: 11914) Age distribution As shown in Fig. 1 , the age distribution of abducted children reveals a pattern characterized by two distinct peaks: a primary peak at ages 0–7 and a secondary peak at ages 13–16. These two age groups represent the periods with the highest incidence of child abductions. Specifically, the number of abducted children aged 0–7 is 1,628, accounting for 70.02% of the total cases, while those aged 13–16 number 466, representing 20.03% of the total. This indicates that younger children, particularly those in the 0–7 age group, are the most vulnerable to abduction. The predominance of younger children among abduction victims can be attributed to their greater physical and psychological vulnerability (Tschann et al., 1996). Due to their incomplete physical and psychological development, young children are less capable of defending themselves and are more easily deceived or overpowered by abductors (Fong and Cardoso, 2010). This vulnerability makes them particularly susceptible to criminal exploitation. Furthermore, the primary objective of child trafficking in China is often illegal adoption, rather than other forms of exploitation such as sexual exploitation or forced labor (Yang et al., 2021). In this context, factors such as health, nationality, gender, and age are critical considerations in the illegal adoption process (Leinaweaver and van Wichelen, 2015). As children grow older, the likelihood of successful integration into new families decreases, with older children facing greater challenges in adjusting to their adoptive environments (Clark et al., 2006). This higher failure rate in family integration may deter buyers from adopting older children. Moreover, younger children, due to their limited memories of their original families, are perceived by buyers as more adaptable to new family settings, making them more desirable targets for illegal adoption (as illustrated in Case 2 ). This preference for younger children further exacerbates their risk of abduction, contributing to the observed trend towards younger ages in child trafficking cases. Case 2 At the age of one, C was secretly taken away from his parents by traffickers. He has no memory of his biological parents or how he came to be raised by another family. The knowledge that he is not the biological child of his adopted family was something he heard in fragments from relatives and friends when he was very young. This made him feel extremely inferior. In the end, he received confirmation from his adopted cousin: his adoptive parents had bought him for 20,000 RMB from the trafficker who falsely claimed to be his biological parents. (Baby Back Home ID: 155631) Gender distribution by age In terms of gender distribution across age groups, the analysis reveals that in the primary peak age group of 0–7 years, boys are more frequently abducted than girls. Conversely, in the secondary peak age group of 13–16 years, girls are more commonly abducted than boys (see Fig. 1 ). These findings suggest that boys are at a higher risk of abduction during early childhood, while girls face a greater risk during adolescence. This pattern is consistent with conclusions drawn in existing literature, which associates the abduction of older girls with feudal traditions such as child marriage and the desire of adolescent girls to escape economic hardship and social or familial disadvantages. These factors often lead to increased social control over girls in this age group (Ghosh, 2014). The higher incidence of abductions among adolescent girls may thus reflect these broader socio-cultural dynamics (as illustrated in Case 3 ). Case 3 In Z’s recollection, she left home at around eleven or twelve years old. It wasn’t due to rebellion or running away, nor was it because she didn’t want to go to school. On the contrary, it was to earn money to support her younger siblings’ education. The moment Z stepped out of the door, she never imagined that she would never be able to return home. After leaving home, Z went to work with fellow villagers, but one day she got separated from them. What she didn’t expect was that everything that happened afterward plunged her into an abyss.Z, a young girl without education and with no familiar faces around, didn’t know the name of her hometown or how to get back home. Misfortune struck again as the clutches of evil reached out to this vulnerable young woman. She was deceived by an old man in his fifties or sixties, who claimed to take her back home. This man coerced Z into marrying him, and amid his abuse, she bore four children. Even in this dire situation, the old man showed her no mercy. Whenever he suspected Z of attempting to find her family, he would brutally assault her. Every time Z sought help from outside, it only resulted in more physical and emotional pain.(Baby Back Home ID: 293751) Temporal characteristics of child trafficking crimes Annual variation characteristics A statistical analysis of child trafficking cases in Guangdong Province from 1980 to 2021 reveals an overall inverted ‘V’-shaped trend in the annual variation of these crimes, which can be broadly divided into three stages (see Fig. 2 ): Slow Increase Stage (1980–1989): During this period, the number of child trafficking cases gradually increased, although the overall number of abducted children remained relatively low. Rapid Increase Stage (1990–1996): This stage witnessed a significant surge in the number of abducted children, peaking in 1996 with 150 reported cases in Guangdong Province. Fluctuating Decline Stage (1997–2020): Beginning in 1997, the number of abducted children entered a phase of fluctuating decline. While minor increases were observed in 2003, 2008, 2013, and 2016, the overall trend was one of decline in subsequent years. The annual variation trends in the number of abducted boys and girls were generally consistent with the overall trend. However, the annual variation curve for boys closely mirrored the overall curve, reflecting the higher proportion of boys among the total number of abducted children. The observed annual variation in child trafficking cases was influenced by several key factors, including the implementation of birth control policies and anti-trafficking efforts (Zhou et al., 2024). The one-child policy, officially established as a fundamental national policy in 1982, imposed restrictions on childbirth that, to some extent, stimulated the occurrence of child trafficking crimes. This period (1980–1989) saw a gradual increase in such crimes. In the 1990s, China began to enforce family planning laws more rigorously, transitioning from merely ‘advocating’ controlled childbirth to ‘strictly enforcing’ it. Traditional Chinese beliefs in ‘more sons, more blessings’ and the strong preference for male offspring faced significant challenges during this time. Coupled with inadequate legal controls over child trafficking, these factors contributed to a surge in trafficking, culminating in a peak in 1996. In 1997, China officially criminalized the trafficking of women and children. Over the following two decades, the Chinese government, including the State Council and the Ministry of Public Security, consistently strengthened the legal framework and anti-trafficking efforts, thereby imposing stricter controls on child trafficking markets. For instance, in 2009, the police department of Guangdong established a DNA database to support anti-trafficking efforts, pioneering a ‘rapid search mechanism’ that connected data nationwide. Since 2016, in response to the needs of the ‘Internet + anti-trafficking’ era, the Ministry of Public Security launched the ‘Reunion’ system, designed for the urgent dissemination of information on missing children. Additionally, in 2017, the Guangdong Public Security Department introduced a green channel for DNA matching, allowing suspected victims and their parents to provide DNA samples at local police stations free of charge. Starting in 2013, China gradually relaxed its birth control policy, moving from a ‘one-child policy’ to a ‘two-child policy,’ and later to a ‘three-child policy,’ which contributed to a reduced demand in the child trafficking market. By the end of 2020, the Ministry of Public Security had organized a nationwide ‘Reunion Operation’ aimed at locating missing children. In conclusion, the increased efforts to combat child trafficking, coupled with improved enforcement and monitoring methods, have effectively curbed these crimes in Guangdong Province. As a result, an increasing number of abducted children have been successfully reunited with their families. Monthly variation characteristics According to Routine Activity Theory, there is a correlation between temporal factors—such as seasons, months, and days—and criminal activities. Certain crimes are more likely to occur at specific times, with variations in their frequency, type, and characteristics depending on the time of year (Cohen and Felson, 2003). To explore this relationship, this study conducted a statistical analysis of the monthly distribution of child trafficking crimes. The analysis reveals that child trafficking crimes were more prevalent in January, May, June, July, August, and October, with the number of incidents in these months exceeding the monthly average(see Fig. 3 ). August recorded the highest number of child trafficking crimes, with 255 cases, making it the month with the highest crime rate. In contrast, February had the lowest number of child trafficking crimes, with 130 cases, marking it as the month with the fewest incidents. The months with higher crime rates were predominantly those with warmer weather, while months with lower crime rates coincided with colder temperatures. When examining the crime mean frequency—a measure that offers a more objective assessment of crime occurrence compared to total counts and averages—the findings closely mirrored the trends observed in total crime counts. August exhibited the highest crime mean frequency at 1.29, whereas February had the lowest at 0.73 (see Fig. 3 ). In terms of gender, the monthly variation curve for male child trafficking cases closely followed the overall trend, while the curve for female child trafficking cases was comparatively smoother. The warmer weather during the high-crime months likely contributed to increased crime rates by facilitating more frequent social interactions, which in turn raised the likelihood of contact between potential victims and criminals. Additionally, during warmer months, caregivers were more prone to leaving children unattended, thereby increasing the risk of child trafficking. Holidays also played a significant role in the occurrence of child trafficking crimes. During holiday periods, children tend to be more active, and parents may be preoccupied with customs and festivities, potentially leading to reduced supervision and increased opportunities for traffickers (as illustrated in Case 4 and Case 5 ). Case 4 One day in July 1995, a father took his 4-year-old child, B, on a bus to Guangzhou to visit a relative. After getting off the bus at Guangzhou Bus Station, the father went to use the restroom, leaving B waiting outside. After about ten minutes, when the father returned from the restroom, he could not find any trace of his child. Despite searching the area, they were unable to locate B. (Baby Back Home ID: 436512) Case 5 On the morning of the Lantern Festival in 2002, C, who was not yet four years old, went to a small convenience store approximately 300 meters from home with a same-aged friend to buy snacks. About ten minutes later, when family members realized that C had not returned, they began searching but could not find him. The day of his disappearance was the Lantern Festival, and everyone was busy with their business, not noticing the child’s absence. (Baby Back Home ID: 436512) Spatial characteristics of child trafficking crimes in Guangdong province Spatial distribution of child trafficking Guangdong Province, a key economic powerhouse in China, exhibits a diverse and complex urban structure characterized by significant regional disparities. Economically advanced cities are primarily concentrated in the geographically central region of the province, known as the Pearl River Delta. This area benefits from its proximity to Hong Kong and Macau, and it was among the first regions to initiate economic reforms and development. As a result, the Pearl River Delta has emerged as an economic engine not only for Guangdong Province but for the entire country. In contrast, the cities located in the eastern, northern, and western parts of Guangdong are largely mountainous and less developed. These regions have historically contributed to the economic growth of the Pearl River Delta by supplying young labor, land, and natural resources. However, due to slower economic development, these peripheral cities often experience higher levels of out-migration as residents seek better opportunities in the more prosperous central cities. This unbalanced development, with its concentration of wealth and population in the Pearl River Delta, significantly influences the spatial distribution of child trafficking crimes across the province. Using cities as the geographical units and the number of child trafficking cases as the variable, the cities in Guangdong Province can be categorized into five levels based on the prevalence of such crimes(see Fig. 4 ). First Level (high incidence): The first level includes Guangzhou and Dongguan, which recorded 421 and 385 child trafficking cases, respectively. According to data provided by the Chinese Ministry of Public Security, the group most vulnerable to trafficking comprises children who accompany their parents to urban areas for work, referred to in this study as ‘urban migrant children.’ Due to household registration restrictions, these children have limited access to education and social services, face challenges in social interaction, and often lack access to public activity spaces, making it difficult for them to integrate into the community. Both Guangzhou and Dongguan have large migrant populations and rank among the highest in the province in terms of the total number of migrants. In Dongguan, migrants constitute 76.0% of the city’s permanent population, totaling over 7.5 million people. In Guangzhou, the migrant population also exceeds 50%. The substantial influx of migrants has led to a significant population of urban migrant children, thereby creating opportunities for child trafficking crimes. Second Level (secondary high incidence): The second level comprises Shenzhen, Huizhou, Jieyang, and Shantou. These cities also have relatively large populations of urban migrant children and are geographically close to Guangzhou and Dongguan, where child trafficking crimes are most prevalent. Furthermore, existing research and statistical data indicate that the regions encompassing Jieyang and Shantou are characterized by more rigid traditional beliefs regarding childbirth and a stronger preference for male offspring, which results in a higher demand for child trafficking and makes these cities secondary high-risk areas for such crimes. Third Level (medium high incidence): The third level includes Zhanjiang, Maoming, Foshan, Shenzhen, and Shaoguan, which are neighboring cities to those with high incidences of child trafficking, either within or outside the province. Fourth (secondary low incidence)and Fifth (low incidence) Levels: The fourth level consists of Jiangmen, Zhaoqing, Qingyuan, Heyuan, Meizhou, Shanwei, and Chaozhou, while the fifth level comprises Yunfu and Yangjiang. This classification into five levels reveals a discernible spatial pattern in the distribution of child trafficking crimes: the prevalence of child trafficking tends to decrease progressively as one moves from cities with larger migrant populations to those with fewer migrants. However, it is important to note that local cultural influences also play a significant role in shaping the incidence of child trafficking crimes. Routes of child trafficking The movement of individuals across geographical locations is a critical element of trafficking crimes. This study analyzed a total of 104 trafficking routes and identified a pattern characterized by "few interprovincial, mostly intraprovincial" routes. Specifically, 30 of these routes (28.85%) involved movement from Guangdong Province to other provinces(see Fig. 5 ). The most frequently observed interprovincial route was from Guangdong Province to Fujian Province, accounting for 18 cases (18.27%). Other interprovincial routes, such as those from Guangdong to Guizhou, Henan, Anhui, Jiangxi, and Guangxi provinces, were less common. Conversely, there were very few trafficking routes leading into Guangdong Province from other provinces, with only the "Guangxi Province–Guangdong Province" and "Sichuan Province–Guangdong Province" routes identified. As is shown in Fig. 5 , intraprovincial trafficking routes were predominant in Guangdong Province, comprising 74 routes (72.55%). From the perspective of cities where children were trafficked, Guangzhou City had the highest number of outbound routes, followed by Dongguan City and Shenzhen City. This finding aligns with the previously discussed spatial distribution of child trafficking cases. Regarding inbound routes, Guangzhou City and Shantou City had the highest number of routes, followed by Jieyang City and Shenzhen City. In summary, Guangzhou City emerged as a major hub for child trafficking, with the highest number of both outbound and inbound routes. Cities with a high number of outbound trafficked children, such as Guangzhou, Dongguan, and Shenzhen, are typically economically developed and host large populations of migrant workers and their children. However, from the perspective of inbound trafficking, children are primarily trafficked into areas such as Shantou City and Jieyang City, where there is a strong cultural preference for male offspring. Driving factors of child trafficking crimes in Guangdong province Selection of factors To investigate the factors influencing child trafficking crimes in Guangdong Province, this study selected eight variables from four categories: natural, population, social, and economic (Zhou et al., 2024). These variables were then used in the GeoDetector analysis to assess their impact on the spatial distribution of child trafficking crimes. In the natural dimension, two factors were considered: annual average temperature and total city land area. Previous research has demonstrated that both temperature and city size are closely related to the occurrence of criminal activities, with temperature influencing social behavior and city size affecting the scale and complexity of urban life (Cohn and Rotton, 2000). The population dimension included two factors: gender ratio and the number of floating populations. The relationship between crime and population dynamics can vary depending on the location (Boivin, 2018). Empirical studies have shown that regions in China with higher gender ratios tend to have elevated crime rates. Additionally, the mobility of the population, particularly the challenges faced by floating populations in destination cities, can contribute to increased crime rates (Ghosh, 2014). In the social dimension, the study considered three factors: the urban registered unemployment rate, passenger traffic volume, and the number of people with a university education or higher per 100,000 population. These factors reflect various aspects of social stability, transportation convenience, and educational attainment, which can influence crime rates (Raphael and Winter-Ebmer, 2001). The economic dimension was represented by the Engel coefficient for all residents, a measure of regional living standards(Lochner and Moretti, 2004).. The selected variables are summarized in Table 1 . Table 1 Detection factors affecting child trafficking crimes in Guangdong province Variable Type and Dimension Influencing Factor Detection Factor Unit Factor Explanation Dependent Variable Number of Child Trafficking Cases S0 Cases Child trafficking crime situation Independent Variables Natural Dimension Annual Average Temperature S1 ℃ Measure of regional surface heat conditions Total City Land Area S2 km2 Measure of regional land area Population Dimension Gender Ratio S3 % Measure of regional social gender differences Number of Floating Population S4 % Measure of regional population mobility Social Dimension Urban Registered Unemployment Rate S5 % Measure of regional urban and rural employment conditions Passenger Traffic Volume S6 Ten thousand person-times Measure of regional transportation convenience Number of People with University Education or Higher per 100,000 Population S7 People Measure of regional education level Economic Dimension Engel Coefficient for All Residents S8 % Measure of regional residents’ living standards Factor detection As is shown in Table 2 , The results of the GeoDetector analysis, based on a 1% significance level, indicate that four factors significantly influence the spatial distribution of child trafficking crimes in Guangdong Province. These factors are: gender ratio and the number of floating populations (population factors), the number of people with a university education or higher per 100,000 population (social factors), and the Engel coefficient for all residents (economic factors). The strength of the influence of each factor is measured by the q-value, which reflects the degree to which the dependent variable is affected by the independent variable. Among the driving factors, the number of floating populations (S4) has the highest q-value at 0.901, indicating it has the greatest impact. The gender ratio (S3) is the second most influential factor, with a q-value of 0.602. The Engel coefficient for all residents (S8) and the number of people with a university education or higher per 100,000 population (S7) rank third and fourth, respectively. Table 2 Detection results of influencing factors for child trafficking crimes in Guangdong province Variable Dimension Influencing Factor Factor Detection Results Explanatory Power Ranking p-value Significance Level q-value Natural Dimension Annual Average Temperature 0.013 - 0.214 Total City Land Area 0.014 - 0.224 Population Dimension Gender Ratio < 0.01 0.01 0.602 2 Number of Floating Population < 0.01 0.01 0.901 1 Social Dimension Urban Registered Unemployment Rate 0.056 - 0.320 Passenger Traffic Volume 0.079 - 0.211 Number of People with University Education or Higher per 100,000 Population < 0.01 0.01 0.306 4 Economic Dimension Engel Coefficient for All Residents < 0.01 0.01 0.342 3 Note : ‘-’ indicates that the q-value did not pass the 1% significance test; only variables passing the 1% significance test are ranked. Given that factors influencing child trafficking often interact, this study employed the interaction detection method in GeoDetector to assess the combined effects of different driving factors on the spatial distribution of child trafficking crimes. The results reveal that the combined factors exert a stronger influence than individual factors, suggesting that the interaction between any two factors has a greater effect than any single factor alone. The types of interaction effects observed were non-linear enhancement or dual-factor enhancement, indicating that no factor operates independently of others. Notably, the interaction between the number of floating populations (S4) and passenger traffic volume (S6) demonstrated the strongest influence, with an interaction q-value of 0.996. The second strongest interaction was between the gender ratio (S3) and passenger traffic volume (S6), with a q-value of 0.973. The third-ranked interaction was between total city land area (S2) and the number of floating populations (S4), with a q-value of 0.967. Although factors such as annual average temperature (S1), total city land area (S2), urban registered unemployment rate (S5), and passenger traffic volume (S6) cannot independently explain the spatial characteristics of child trafficking crimes, their explanatory power significantly increases when interacting with other factors. The interaction between the number of floating populations (S4) and other factors is particularly noteworthy, further confirming that the number of floating populations (S4) is a core factor influencing the spatial distribution of child trafficking crimes.(see Table 3 ) Table 3 Top five ranked interaction factor combinations in the detection of child trafficking crimes in Guangdong province q = A∩B A + B Comparison Result Type of Interaction Effect Explanatory Power Ranking after Interaction S4∩S6 = 0.996 S4(0.901) + S6(0.320) = 1.221 A + B > q > A, B Dual-factor enhancement 1 S3∩S6 = 0.973 S3(0.601) + S6(0.320) = 0.922 q > A + B Non-linear enhancement 2 S2∩S4 = 0.967 S2(0.224) + S4(0.901) = 1.126 A + B > q > A, B Dual-factor enhancement 3 S1∩S4 = 0.966 S1(0.214) + S4(0.901) = 1.116 A + B > q > A, B Dual-factor enhancement 4 S2∩S3 = 0.934 S2(0.224) + S3(0.601) = 0.826 q > A + B Non-linear enhancement 5 Conclusion and discussion This study offers a comprehensive spatial and temporal analysis of child trafficking in Guangdong Province from 1980 to 2020, revealing critical socio-demographic and spatial patterns. Trafficked children are mostly young boys, with cases concentrated in economically developed cities, particularly those with large migrant populations. Trafficking trends followed a significant increase during the 1990s, driven by socio-economic changes and policies, but saw a decline with improved government interventions. These findings provide valuable insights into the complex socio-economic and demographic dynamics that underlie child trafficking, contributing to the broader geographical research on this critical issue. This research addresses key gaps in child trafficking literature, emphasizing the need for geographical studies, mixed methods, and comparative perspectives. By focusing on Guangdong Province, it highlights how socio-economic factors like floating populations influence the spatial distribution of trafficking and reveals vulnerabilities in specific regions. The use of the GeoDetector tool effectively integrates spatial analysis with qualitative insights, demonstrating how factors such as population mobility, gender ratio, and transportation infrastructure interact to shape trafficking patterns. The study also stresses the importance of comparative approaches to uncover social and cultural biases in trafficking. By comparing regions within Guangdong, it shows how economic development, migration, and cultural preferences contribute to varying trafficking risks, offering a basis for targeted interventions. For further exploration, the field of children’s geography offers a promising avenue. Children’s geography, which examines the changing relationship between children and public spaces, can provide new perspectives on child trafficking by focusing on the environments in which these crimes occur. future research could explore how the institutionalization of children’s living spaces—shifting from homes and neighborhoods to organized and specialized environments—impacts the safety of children and their susceptibility to trafficking. Additionally, understanding how abducted children navigate and construct their own spaces within these environments could offer deeper insights into their agency and resilience. Despite its contributions, the study has limitations. Reliance on self-reported data from the 'Baby Come Home' website raises concerns about accuracy, and the hidden nature of trafficking suggests the issue may not be fully captured. Future research should involve interdisciplinary collaboration, incorporating criminology, sociology, and public health, and more comprehensive data sources to deepen understanding and enhance interventions. Declarations This work was supported by the National Natural Science Foundation of China [grant number 42171229]; the Natural Science Foundation of Guangdong Province [grant number 2022B1515020087]; and the Innovative Projects of Department of Education of Guangzhou City [grant numbers 2023A03J0063, 202235209]. This article does not contain any studies with human participants performed by any of the authors. Author Contribution C was primarily responsible for constructing the theoretical framework of this study and writing parts of the revised manuscript. L conducted the initial draft writing and data analysis, laying a solid foundation for the paper. Y was in charge of reconstructing the theoretical framework in the revised manuscript and refining the content to enhance its quality. Each author's contributions were indispensable, jointly ensuring the completion of this paper. Data Availability All data generated or analysed during this study are included in this published article and its supplementary information files "original data" and "data analysis". References Abu-Ali, A., & Al-Bahar, M. (2011). Understanding child survivors of human trafficking: A micro and macro level analysis. Procedia-Social and Behavioral Sciences, 30, 791–796. https://doi.org/10.1016/j.sbspro.2011.10.154 Beyrer, C. (2004). Global child trafficking. The Lancet, 364, 16–17. https://doi.org/10.1016/S0140-6736(04)17624-1 Biswas, M. A. (2015). Human trafficking scenario in Bangladesh: Some concerns. International Journal of Humanities & Social Science Studies, 1(4), 85–90. Blazek, M., Esson, J., & Smith, D. P. (2019). Relational geographies of human trafficking: Inequality, manoeuvring and im/mobility across space and time. Transactions of the Institute of British Geographers, 44(1), 63–78. https://doi.org/10.1111/tran.12271 Boivin, R. (2018). Routine activity, population (s) and crime: Spatial heterogeneity and conflicting Propositions about the neighborhood crime-population link. Applied geography, 95, 79–87. https://doi.org/10.1016/j.apgeog.2018.04.016 Breuil, B. O., & Gerasimov, B. (2021). Trafficking in Minors: Confronting complex realities, structural inequalities, and agency. Anti-trafficking review, (16), 1–9. https://doi.org/10.17269/s41997-022-00642-8 Cecchet, S. J., & Thoburn, J. (2014). The psychological experience of child and adolescent sex trafficking in the United States: Trauma and resilience in survivors. Psychological trauma: theory, research, practice, and policy, 6(5), 482. Chen Youyi (2009). An analysis of the phenomenon of favoring sons over daughters in Chaoshan region. Journal of Guangzhou Panyu Vocational and Technical College, 8 (3), 56–60 Cho, S. Y. (2015). Modelling for determinants of human trafficking. Social Inclusion, 3, 2–21. http://dx.doi.org/10.2139/ssrn.2117838 Chong, N. G., & Clark, J. B. (Eds.). (2017). Human trafficking: a complex phenomenon of globalization and vulnerability. Routledge. Clark, P., Thigpen, S., & Yates, A. M. (2006). Integrating the older/special needs adoptive child into the family. Journal of Marital and Family Therapy, 32(2), 181–194. https://doi.org/10.1111/j.1752-0606.2006.tb01599.x Cockbain, E., & Olver, K. (2019). Child trafficking: characteristics, complexities, and challenges. In Child abuse and neglect (pp. 95–116). Academic Press. https://doi.org/10.1016/B978-0-12-815344-4.00006-4 Cockbain, E., Ashby, M., Bowers, K., & Zhang, S. X. (2024). Concentrations of harm: Geographic and demographic patterning in human trafficking and related victimisation. Criminology & Criminal Justice, 0(0). https://doi.org/10.1177/17488958241245311 Cockbain, E., Bowers, K., & Hutt, O. (2022). Examining the geographies of human trafficking: Methodological challenges in mapping trafficking's complexities and connectivities. Applied Geography, 139, 102643. https://doi.org/10.1016/j.apgeog.2022.102643 Cody, A. M., Okech, D., Yi, H., Aletraris, L., Clay-Warner, J., & Callands, T. (2024). When child trafficking and informal fostering intersect: A mixed methods study. Social Work and Social Sciences Review, 24. https://doi.org/10.1921/swssr.v24i2.2026 Cohen, L. E., & Felson, M. (2003). Social change and crime rate trends: A routine activity approach. Crime: Critical Concepts in Sociology, 1, 316. Cohn, E. G., & Rotton, J. (2000). Weather, seasonal trends and property crimes in Minneapolis, 1987–1988. A moderator-variable time-series analysis of routine activities. Journal of environmental psychology, 20(3), 257–272. https://doi.org/10.1006/jevp.1999.0157 Corsaro, W. A., & Everitt, J. G. (2023). The sociology of childhood. Sage publications. Cottingham, M., Nowak, T., Snyder, K., & Swauger, M. (2013). Sociological perspective: underlying causes. Human trafficking: interdisciplinary perspectives. New York: Routledge, 51–72. Cullen-DuPont, K. (2009). Human trafficking. Infobase publishing. Cunha, A., Martinho, G., Gonçalves, M., & Matos, M. (2022). Addressing the psychological trauma in human trafficking victims: A brief review. Psychological trauma: theory, research, practice, and policy. Dakua, T., Rahaman, M., & Das, K. C. (2024). An analysis of the spatial and temporal variations of human trafficking in India. Cogent Social Sciences, 10(1), 2293315. De Shalit, A., van der Meulen, E., & Guta, A. (2021). Social service responses to human trafficking: The making of a public health problem. Culture, Health & Sexuality, 23(12), 1717–1732. https://doi.org/10.1080/13691058.2020.1802670 Decker, S. H. (2015). Human trafficking: contexts and connections to conventional crime. Journal of crime and justice, 38(3), 291–296. https://doi.org/10.1080/0735648X.2015.1039792 Di Nicola, A. (2013). Researching into human trafficking: Issues and problems. In Human trafficking (pp. 49–72). Willan. Dragiewicz, M. (Ed.). (2014). Global human trafficking: Critical issues and contexts. Routledge. Duong, K. A. (2012). Human trafficking in a globalized world: Gender aspects of the issue and anti-trafficking politics. Journal of research in gender studies, 2(1), 48–65. Eargle, L. A., & Doucet, J. M. (2021). Investigating human trafficking within the United States: a state-level analysis of prevalence and correlates. Sociation, 20(1). Esson, J. (2020). Playing the victim? Human trafficking, African youth, and geographies of structural inequality. Population, Space and Place, 26(6), e2309.https://doi.org/10.1002/psp.2309 Fong, R., & Cardoso, J. B. (2010). Child human trafficking victims: Challenges for the child welfare system. Evaluation and program planning, 33(3), 311–316. https://doi.org/10.1016/j.evalprogplan.2009.06.018 Foot, K., Sworn, H., & Alejano-Steele, A. (2021). An outcome-centered comparative analysis of counter-human trafficking coalitions in the Global South. Management Communication Quarterly, 35(3), 418–444. https://doi.org/10.1177/08933189211017925 Frederick, J., Basnyat, M., & Aguettant, J. L. (2010). Trafficking and exploitation in the entertainment and sex industries in Nepal: A handbook for decision-makers. Kathmandu, Nepal: Terra De homes Foundation. Gezinski, L. B., & Gonzalez-Pons, K. M. (2024). Sex trafficking and technology: A systematic review of recruitment and exploitation. Journal of Human Trafficking, 10(3), 497–511. https://doi.org/10.1080/23322705.2022.2034378 Ghosh, S. (2014). Anti-trafficking and its discontents: women’s migrations and work in an Indian borderland. Gender, Place & Culture, 22(9), 1220–1235. https://doi.org/10.1080/0966369X.2014.970139 Greenbaum, J. (2020). A public health approach to global child sex trafficking. Annual Review of Public Health, 41(1), 481–497. https://doi.org/10.1146/annurev-publhealth-040119-094335 Guohua Z. (2004). Adaptation, revival, and reconstruction: Clan organizations and rural power structures: Two case studies from southern Jiangxi and eastern Guangdong. Chinese Sociology & Anthropology, 37(1), 34–50. https://doi.org/10.1080/21620555.2004.11038340 Huang, Z., & Weng, W. (2019). Analysis on geographical migration networks of child trafficking crime for illegal adoption from 2008 to 2017 in China. Physica A: Statistical Mechanics and Its Applications, 528, 121404. https://doi.org/10.1016/j.physa.2019.121404 Jiang, Q., & Sánchez-Barricarte, J. J. (2013). Child trafficking in China. China Report, 49(3), 317–335. https://doi.org/10.1177/0009445513502268 Kiss, L., Fotheringhame, D., Kyegombe, N., McAlpine, A., Abilio, L., Kyamulabi, A., et al. (2022). Paper:violence, abuse and exploitation among trafficked women and girls: a mixed-methods study in Nigeria and Uganda. BMC public health, 22(1), 794. https://doi.org/10.1186/s12889-022-13021-2 Lanier, M. M., & Farrell, C. T. (2015). A mixed methods analysis of human trafficking. Journal of Social Sciences Research, 66. Lanier, M. M., Farrell, C. T., & Bezuidenhout, C. (2014). A comparative analysis of human trafficking: The United States of America (USA) and the Republic of South Africa (RSA). International Journal of Criminology and Sociology, 3, 275–283. https://doi.org/10.6000/1929-4409.2014.03.23 Lei, L. (2013). Sons, daughters, and intergenerational support in China. Chinese Sociological Review, 45(3), 26–52. https://doi.org/10.2753/CSA2162-0555450302 Leinaweaver, J., & van Wichelen, S. (2015). The geography of transnational adoption: kin and place in globalization. Social & Cultural Geography, 16(5), 499–507. https://doi.org/10.1080/14649365.2015.1033449 Li, G., Tan, R., Wang, H.,Yan, X., & Shao, Y. (2017). Geographic Characteristics of Child Trafficking Crime in China. Scientia Geographica Sinica, 37(7), 1049–1058. https://doi.org/10.13249/j.cnki.sgs.2017.07.010 Li, G., Wang Y., Yang, L., Tan, R., &Gou, S. (2019). Spatio-temporal pattern of child trafficking crime in China: A demand-front perspective. Journal of Shaanxi Normal University (Natural Science Edition), 47(3), 95–102. https://doi.org/10.15983/j.cnki.jsnu.2019.03.431 Liang, Y., & Xu, C. (2023). Knowledge diffusion of Geodetector: A perspective of the literature review and Geotree. Heliyon. https://doi.org/10.1016/j.heliyon.2023.e19651 Liang, Y., Li, S., Zeng, J., & Wu, T. (2022). Examining the impact of multidimensional accessibility on regional poverty in Laos. Applied Geography, 148, 102789. https://doi.org/10.1016/j.apgeog.2022.102789 Limoncelli, S. A. (2009). Human trafficking: Globalization, exploitation, and transnational sociology. Sociology compass, 3(1), 72–91.https://doi.org/10.1111/j.1751-9020.2008.00178.x Lin, L. S. (2021). Human security and gender: a comparative case study of human trafficking in Southeast Asia. RAIS Journal for Social Sciences, 5(2), 1–9. Lochner, L., & Moretti, E. (2004). The effect of education on crime: Evidence from prison inmates, arrests, and self-reports. American economic review, 94(1), 155–189. https://doi.org/10.1257/000282804322970751 Lopez, J. J., & Truesdale-Moore, S. (2020). The use of spatial statistics to control human and sex trafficking. International Journal of Social Science and Humanity, 10(2), 57–61. https://doi.org/10.18178/ijssh.2020.10.2.1014 Mahalingam, R. (2019). Human trafficking from a multidisciplinary perspectives: A literature review. Asian Journal of Social Science Research, 2(2). https://doi.org/10.5281/zenodo.8107042 Mahalingam, R., & Sidhu, J. S. (2021). Inside the crime of sex trafficking in Sabah, Malaysia. Journal of human trafficking, 7(3), 308–324. https://doi.org/10.1080/23322705.2019.1710069 Martinho, G., Gonçalves, M., & Matos, M. (2020). Child trafficking, comprehensive needs and professional practices: A systematic review. Children and Youth Services Review, 119, 105674.https://doi.org/10.1016/j.childyouth.2020.105674 Musto, J. (2019). The Geographies of Human Trafficking: Eastern European Women in the United States. In Geographical Review, 109(4), 441–458. O'Brien, E., Hayes, S., & Carpenter, B. (2013). The politics of sex trafficking: a moral geography. Springer. O'Connell Davidson, J. (2011). Moving children? Child trafficking, child migration, and child rights. Critical social policy, 31(3), 454–477.https://doi.org/10.1177/0261018311405014 Okech, D., Choi, Y. J., Elkins, J., & Burns, A. C. (2018). Seventeen years of human trafficking research in social work: A review of the literature. Journal of evidence-informed social work, 15(2), 103–122. https://doi.org/10.1080/23761407.2017.1415177 Olisah, S., Odooh, C., Efijemue, O., Obunadike, E., Onwuchekwa, J., Owolabi, O., et al. (2024). Unveiling Global Human Trafficking Trends: A Comprehensive Analysis. Journal of Data Analysis and Information Processing, 12(01), 49–75. https://doi.org/10.4236/jdaip.2024.121004 Pajón, L., & Walsh, D. (2020). Proposing a theoretical framework for the criminal investigation of human trafficking crimes. Policing: a journal of policy and practice, 14(2), 493–511. http://10.1093/police/pay031 Palmiotto, M. J. (Ed.). (2014). Combating human trafficking: A multidisciplinary approach. CRC Press. Pascual-Leone, A., Kim, J., & Morrison, O. P. (2017). Working with victims of human trafficking. Journal of Contemporary Psychotherapy, 47, 51–59.https://doi.org/10.1007/s10879-016-9338-3 Raphael, S., & Winter-Ebmer, R. (2001). Identifying the effect of unemployment on crime. The journal of law and economics, 44(1), 259–283. https://doi.org/10.1086/320275 Recknor, F., Di Ruggiero, E., & Jensen, E. (2022). Addressing human trafficking as a public health issue. Canadian journal of public health, 113(4), 607–610.https://doi.org/10.17269/s41997-022-00642-8 Reid, J. A. (2012). Exploratory review of route-specific, gendered, and age-graded dynamics of exploitation: Applying life course theory to victimization in sex trafficking in North America. Aggression and Violent Behavior, 17(3), 257–271. https://doi.org/10.1016/j.avb.2012.02.005 Ricard-Guay, A., Hanley, J. (2020). The Challenge of Addressing Both Forced Labor and Sexual Exploitation. In: Winterdyk, J., Jones, J. (eds) The Palgrave International Handbook of Human Trafficking. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-63058-8_16 Russell, A. M. (2014). “Victims of trafficking”: the feminisation of poverty and migration in the gendered narratives of human trafficking. Societies, 4(4), 532–548.https://doi.org/10.3390/soc4040532 Sarrica, F. (2022). The Use of Human Trafficking Detection Data for Modelling Static and Dynamic Determinants of Human Trafficking Flows. European Journal on Criminal Policy and Research, 28(4), 483–501. https://doi.org/10.1007/s10610-020-09460-5 Sax, H. (2017). Child trafficking–a call for rights-based integrated approaches. In Routledge Handbook of Human Trafficking (pp. 251–260). Routledge. Shelley, L. (2010). Human trafficking: A global perspective. Cambridge University Press. Shen, A., Antonopoulos, G. A., & Papanicolaou, G. (2013). China’s stolen children: Internal child trafficking in the People’s Republic of China. Trends in organized crime, 16, 31–48. https://doi.org/10.1007/s12117-012-9167-z Shimazaki, Y. (2021). Human trafficking and the feminization of poverty: Structural violence in Cambodia. Rowman & Littlefield. Sidun, N. M., & Flores, Y. G. (2020). Human trafficking. The Cambridge handbook of psychology and human rights, 273–287. Smith, D. P. (2018). Population geography I: Human trafficking. Progress in Human Geography, 42(2), 297–308.https://doi.org/10.1177/0309132516685196 Sprang, G., Stoklosa, H., & Greenbaum, J. (2022). The public health response to human trafficking: a look back and a step forward. Public Health Reports, 137(1_suppl), 5S-9S.https://doi.org/10.1177/00333549221085588 Tyldum, G. (2010). Limitations in research on human trafficking. International Migration, 48(5), 1–13. https://doi.org/10.1111/j.1468-2435.2009.00597.x Tschann, J M., Kaiser, P., Chesney, M A., Alkon,A., & Boyece, W T.(1996). Resilience and vulnerability among preschool children: Family functioning, temperament, and behavior problems. Journal of the American Academy of Child & Adolescent Psychiatry, 35(2), 184–192. Van Liempt, I. (2011). Different geographies and experiences of ‘assisted’types of migration: A gendered critique on the distinction between trafficking and smuggling. Gender, place and culture, 18(02), 179–193. https://doi.org/10.1080/0966369X.2011.552316 Wang, J B., Li, G., Zhou, J J., Ma, X Y., & Xu T T. (2021). Spatio-temporal pattern and influencing factors of child trafficking in Hubei Province. Human Geography, 36(1), 73–83. https://doi.org/10.13959/j.issn.1003-2398.2021.01.010 Wang, Z., Wei, L., Peng, S., Deng, L., & Niu, B. (2018). Child-trafficking networks of illegal adoption in China. Nature Suatainability, 1(5), 254–260.https://doi.org/10.1038/s41893-018-0065-5 Weitzer, R. (2014). New directions in research on human trafficking. The ANNALS of the American Academy of Political and Social Science, 653(1), 6–24.https://doi.org/10.1177/0002716214521562 Xiong, W. (2023). Evidence of son preference in the child trafficking market for illegal adoption in China. Journal of human trafficking , 9(2), 242–255. https://doi.org/10.1080/23322705.2021.1874188 Xue, S., Li, G., Ma, X Y., Liu, L., & Yang, Y. (2020). The multidimensional spatio-temporal pattern and influencing factors of child trafficking in Guizhou province, China. Geographic Research , 39(7), 1691–1706. Yang, L., Xu, J H., Chen, Nuo., Li, G., & Zhou, J J. (2022). Spatiotemporal Pattern and Influencing Factors of Minor Trafficking in Yunnan Province from 1958 to 2019. Tropical Geography , 42(9), 1523–1533. http://doi.org/10.13284/j.cnki.rddl.003551 Yang, M., Xia, X. & Zhou, Y.(2023). Abandoned children in China: the son-preference culture and the gender-differentiated impacts of the one-child policy. Humanities and Social Sciences Communications , 10(1), 1–10. https://doi.org/10.1057/s41599-023-02015-z Yang, S., Han, L., & Bi, Y. (2021). Child trafficking in the Yunnan and Guangdong provinces of China. The International Journal of Human Rights , 25(4), 718–742. https://doi.org/10.1080/13642987.2020.1794840 Yao, Y., Liu, Y., Guan, Q., Hong, Y., Wang, R., Wang, R., & Liang, X. (2021). Spatiotemporal distribution of human trafficking in China and predicting the locations of missing persons. Computers, Environment and Urban Systems , 85, 101567.https://doi.org/10.1016/j.compenvurbsys.2020.101567 Yea, S. (2013). Mobilising the child victim: The localisation of human trafficking in Singapore through global activism. Environment and Planning D: Society and Space , 31(6), 988–1003. https://doi.org/10.1068/d15411 Yea, S. (2021). Towards critical geographies of anti-human trafficking: Producing and precluding victimhood through discourses, practices and institutions. Progress in Human Geography , 45(3), 513–530. https://doi.org/10.1177/0309132520923136 Yik-Yi Chu, C. (2011). Human trafficking and smuggling in China. Journal of Contemporary China , 20(68), 39–52. http://dx.doi.org/10.1080/10670564.2011.520842 Zhou, J., Li, G., Wang, J., Xu, T., Chen, Z., Gao, X., & Jin, A. (2024). Patterns, evolution and determinants of child trafficking in Henan Province, China. Children's Geographies , 22(2), 201–216. https://doi.org/10.1080/14733285.2023.2259322 Zhou, J., Li, G., Wang, J., Xu, T., Nie, Q., Gao, X., & Jin, A. (2023). Exploring variations and influencing factors of illegal adoption: A comparison between child trafficking and informal adoption. Child Abuse & Neglect , 140, 106124. https://doi.org/10.1016/j.chiabu.2023.106124 Zimmerman, C., Hossain, M., Yun, K., Roche, B., Morison, L., & Watts, C. (2008). Stolen smiles: A summary report on the physical and psychological health consequences of women and adolescents trafficked in Europe . London: London School of Hygiene and Tropical Medicine. Additional Declarations No competing interests reported. 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01:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6849755/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6849755/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89981889,"identity":"3289619d-dd3c-4a1e-837c-e9a56240710e","added_by":"auto","created_at":"2025-08-27 06:26:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137730,"visible":true,"origin":"","legend":"\u003cp\u003eSocio-demographic characteristics of trafficked children in Guangdong province\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6849755/v1/a4007e64f007ec25e14e4ebd.jpg"},{"id":89979549,"identity":"4c6a1fe4-4c14-4f5a-83e5-2ef922653832","added_by":"auto","created_at":"2025-08-27 06:18:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":173119,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual variation characteristics of trafficked children in Guangdong province\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6849755/v1/6bf49ef00cfa8e44a06313fb.jpg"},{"id":89979552,"identity":"bd1004da-6da5-44b6-a596-949eaf628a03","added_by":"auto","created_at":"2025-08-27 06:18:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137158,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly variation characteristics of trafficked children in Guangdong province\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6849755/v1/75b3f86a72d9e5c05d23c0b1.jpg"},{"id":89979555,"identity":"48f2df6f-d4d9-4c6c-80ba-ddb74dba0588","added_by":"auto","created_at":"2025-08-27 06:18:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":162547,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of child trafficking in Guangdong province\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6849755/v1/145d293f9c06b87ca8ce3383.jpg"},{"id":89979565,"identity":"d062844b-7d7f-49fe-8e9a-e9a4082af633","added_by":"auto","created_at":"2025-08-27 06:18:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":224688,"visible":true,"origin":"","legend":"\u003cp\u003eRoutes of child trafficking in Guangdong province.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6849755/v1/a145147956e879f82ce029a0.png"},{"id":89983869,"identity":"562dac8e-ff1c-45a3-9130-e44389bd3a62","added_by":"auto","created_at":"2025-08-27 06:34:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1780097,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6849755/v1/9bf2961e-df9c-4dc0-89e7-e5da7022aabb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geographies of child trafficking in Guangdong Province, China (1980–2020): Spatial dynamics and socio-economic drivers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChild trafficking is a grave global issue that affects millions of children worldwide, manifesting in various forms, including forced labor, sexual exploitation, and illegal adoption (Frederick et al., 2010). It is a severe violation of fundamental human rights and poses significant challenges to governments, communities, and families alike (Yea, 2013). The complexity of child trafficking lies not only in its clandestine nature but also in the diverse factors that contribute to its persistence across different regions and cultures (Martinho et al., 2020).\u003c/p\u003e\u003cp\u003eChina, as the world's most populous country, has long grappled with the issue of child trafficking, a problem exacerbated by its rapid economic development, vast migration flows, and deeply rooted cultural preferences (Yang et al., 2021). The phenomenon of child trafficking in China is intricately linked to historical and socio-cultural factors, such as the traditional preference for male offspring and the one-child policy, both of which have created environments conducive to the exploitation and trafficking of vulnerable children (Yang et al., 2023). Within China, Guangdong Province stands out as a critical region for studying child trafficking due to its significant economic disparities, large floating population, and dual role as both a source and destination of trafficked children (Yao et al., 2021). Despite the severity of the issue, research specifically focusing on the geographical and socio-economic factors driving child trafficking in this region remains limited.\u003c/p\u003e\u003cp\u003eRecent studies have begun to shed light on the patterns of child trafficking in China, focusing on aspects such as the age and gender of victims (Zhou et al., 2023), the route of migration (Wang et al., 2018), and the influence of cultural preferences on trafficking dynamics (Yang et al., 2023). However, much of the existing research has been limited in scope, often concentrating on adult trafficking or lacking a detailed spatial analysis of child trafficking within specific provinces like Guangdong. In response to these gaps, this study aligns with emerging trends in geographical approaches to human trafficking, which increasingly emphasize the need for child-focused research, the integration of mixed methods, and the adoption of comparative perspectives (Corsaro and Everitt, 2023).\u003c/p\u003e\u003cp\u003eThis study provides a detailed analysis of child trafficking in Guangdong Province by employing a geographical perspective that examines the spatial distribution of trafficking routes, the temporal evolution of these crimes, and the socio-economic factors driving them. By focusing on a region with significant floating populations, this research not only advances the understanding of child trafficking in China but also contributes to the broader field of geographical research on trafficking by applying innovative methods that combine quantitative and qualitative analyses and compare regional variations to uncover complex socio-cultural dynamics.\u003c/p\u003e\u003cp\u003eThe central argument of this research is that child trafficking in Guangdong Province is deeply influenced by the region’s socio-economic dynamics, particularly the presence of large floating populations and the cultural preference for male children. The spatial and temporal patterns of child trafficking reflect the interplay between these factors and the broader economic and social changes occurring in the province. To explore this argument, the study utilizes data from the ‘Baby Come Home’ public welfare website, along with statistical yearbooks from Guangdong Province and various prefecture-level cities. The research employs the GeoDetector method to analyze the spatial distribution of child trafficking crimes, assess the temporal evolution from 1980 to 2020, and identify the key factors influencing these patterns. The findings reveal a concentration of trafficking in economically developed cities with large migrant populations, highlighting the significant role of population mobility in driving these crimes.\u003c/p\u003e\u003cp\u003eThis research fills a critical gap in the existing literature by providing a comprehensive spatial and temporal analysis of child trafficking in Guangdong Province, a region that has been underexplored in previous studies. By focusing on child victims and utilizing a robust geographical methodology, this study contributes to a deeper understanding of the factors that make certain regions more susceptible to child trafficking. The findings have significant implications for policy and intervention strategies. By identifying high-risk areas and key influencing factors, the study provides valuable insights that can guide law enforcement efforts and inform the development of targeted policies aimed at preventing child trafficking and protecting vulnerable children. Moreover, the study’s methodological approach offers a model for future research on child trafficking in other regions.\u003c/p\u003e\u003cp\u003eThis article is structured as follows: The next section reviews the relevant literature, discussing the geographical approaches to human trafficking and the current state of research on child trafficking in China. This is followed by a detailed description of the research data and methods. The subsequent sections present the findings of the spatial and temporal analyses, as well as the GeoDetector results on the driving factors of child trafficking. The paper concludes with a discussion of the implications of these findings, the study’s limitations, and suggestions for future research.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003e\u003cb\u003eHuman Trafficking Revisited: Multi-Disciplinary Perspectives and Geographical Approaches\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHuman trafficking has a long and troubling history, attracting the attention of scholars across various disciplines (Olisah et al., 2024;Weitzer, 2014; Cullen-DuPont, 2009; Tyldum, 2010). Sociologists, criminologists, public health experts, and psychologists have all contributed to understanding the complex dynamics of this global issue (Cottingham et al., 2013; Decker, 2015; Recknor et al., 2022;Cunha et al., 2022).\u003c/p\u003e\u003cp\u003eSociological research delves into the social structures and inequalities that underpin human trafficking, with theories such as structural violence and social network theory playing pivotal roles in explaining how systemic inequalities and social networks facilitate trafficking (Esson, 2020; Breuil and Gerasimov, 2021). Structural violence theory suggests that entrenched social hierarchies, poverty, and discrimination create environments where trafficking can flourish(Shimazaki, 2021), while social network theory elaborates on how traffickers exploit these networks for recruitment and exploitation(Gezinski and Gonzalez-Pons, 2024). Criminology provides further insight into the legal and policy frameworks governing trafficking(Pajón and Walsh, 2020), with routine activity theory positing that trafficking occurs when a motivated offender encounters a suitable target in the absence of a capable guardian, and crime pattern theory offering an understanding of the spatial behaviors of traffickers, which contributes to the identification of trafficking hotspots(Mahalingam and Sidhu, 2021). From a public health standpoint, trafficking is seen as a significant issue due to its profound physical and psychological impacts on victims, with theories related to the social determinants of health exploring how socio-economic factors contribute to vulnerabilities that traffickers exploit(Greenbaum, 2020; Recknor et al., 2022). Public health research emphasizes the critical role of health systems in identifying victims and providing necessary care(De Shalit et al., 2021; Sprang et al., 2022). Psychological research complements these perspectives by focusing on the trauma experienced by trafficking victims and the psychological control mechanisms traffickers employ, with trauma theory examining the long-term psychological effects of trafficking and behavioral psychology providing insights into how traffickers use coercion and manipulation to maintain control over their victims (Cecchet and Thoburn, 2014; Pascual-Leone et al., 2017).\u003c/p\u003e\u003cp\u003eDespite the valuable contributions of various disciplines, geographical approaches to human trafficking are increasingly attracting scholarly attention(Cockbain et al., 2022; Smith, 2018; Blazek et al., 2019). This growing interest stems from the recognition of gaps in the traditional perspectives, particularly their limited consideration of the spatial and environmental factors that significantly influence trafficking patterns(Yea, 2021). Human trafficking is fundamentally a spatially illicit activity, involving involuntary or forced mobility across local, regional, national, and international boundaries (Cockbain et al., 2022). The diverse spatial dimensions of human trafficking crimes allow for a more profound and detailed analysis when approached from a geographical perspective(Cockbain et al., 2024).\u003c/p\u003e\u003cp\u003eGeographical research has made significant contributions to understanding these spatial dimensions, particularly through the exploration of how the location and movement of traffickers and victims are influenced by various geographic factors, including urbanization, migration, and regional inequalities(Chong and Clark., 2017; Eargle and Doucet 2021). Limoncelli (2009) emphasizes the influence of external drivers, such as globalization and urbanization, in shaping trafficking dynamics. Similarly, Shelley (2010) highlights how geopolitical shifts and economic disparities between regions can affect the flow of human trafficking, while Zimmerman et al. (2008) reveal that economic stressors, such as job insecurity and poverty, can heighten vulnerability to trafficking (see also Cho, 2015).\u003c/p\u003e\u003cp\u003eA prominent trend in recent research is the focus on unraveling the intricate spatial patterns and routes of trafficking networks within a globalized context(Cockbain et al., 2022; Yao et al., 2021). Indeed, understanding these spatial dynamics is crucial for effective policy formulation and intervention strategies, as trafficking often involves the movement of victims across multiple geographical regions(Lopez and Truesdale-Moore, 2020). By employing spatial analysis techniques such as Geographic Information Systems (GIS), geographers have successfully mapped trafficking routes, identified hotspots, and analyzed the spatial distribution of victims and traffickers (Sarrica, 2022). Such studies provide valuable insights into the geographical intricacies of trafficking networks, which frequently transcend national boundaries.\u003c/p\u003e\u003cp\u003eIn addition to mapping trafficking routes, geographers have investigated the environmental and socio-economic conditions that create vulnerabilities to trafficking(Biswas, 2015; Russell, 2014). Dakua and Rahaman (2024) found that regions with high poverty rates and limited access to education in rural India are more susceptible to trafficking, as traffickers exploit these vulnerabilities to recruit victims. Similarly, research by Musto (2019) on trafficking within Eastern Europe revealed that local socio-economic conditions significantly impact the prevalence of trafficking networks in the region. These studies collectively underscore the importance of spatial analysis in understanding the complex interplay between geographic factors and human trafficking.\u003c/p\u003e\u003cp\u003eHowever, while geographical research has made significant contributions, it has also faced criticism for its narrow focus on spatial patterns(Yea, 2021; Mahalingam, 2019). This approach sometimes overlooks the broader socio-economic, cultural, and psychological factors that also play critical roles in trafficking dynamics (O'Brien et al., 2013). For instance, while mapping trafficking routes can provide valuable insights, it does not fully capture the complex interplay of social and cultural factors that influence why certain populations are more vulnerable to trafficking than others (Van Liempt, 2011). Moreover, there is a pressing need for more mixed-methods research that combines spatial analysis with qualitative approaches to offer a more comprehensive understanding of trafficking(Palmiotto, 2014). Additionally, geographical research has often focused predominantly on adult trafficking, with relatively little attention paid to child trafficking(Abu-Ali and Al-Bahar, 2011; Martinho et al., 2020), particularly in non-Western contexts such as China(Sidun and Flores, 2020). This represents a significant gap in the literature, given the unique vulnerabilities and experiences of child trafficking victims.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrends in Geographical Approaches to Human (child) Trafficking\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncreased Focus on Child Trafficking\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOne significant trend in current geographical studies on human trafficking is the growing recognition of the need to focus more extensively on child trafficking (O'Connell Davidson, 2011; Cockbain and Olver, 2019). Historically, geographical research has predominantly concentrated on adult trafficking, particularly in contexts of labor and sexual exploitation (Ricard-Guay and Hanley, 2020). However, as awareness of the unique vulnerabilities and experiences of trafficked children increases, there is a corresponding shift in research priorities (Martinho et al., 2020). Children, due to their age, dependence, and limited agency, are particularly susceptible to exploitation (Beyrer, 2004), yet they have been underrepresented in trafficking studies. Recent geographical research is beginning to rectify this oversight by exploring the spatial dynamics of child trafficking, including the identification of trafficking routes and hotspots specific to child victims, as well as the socio-economic and environmental factors that heighten their vulnerability (Sax, 2017). This shift is critical, as it aligns research efforts with the need to protect one of the most vulnerable populations from the harms of trafficking.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntegration of Quantitative and Qualitative Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnother notable trend is the increasing use of mixed-methods approaches that combine quantitative and qualitative research (Kiss et al., 2022). Traditional geographical studies on trafficking have often relied heavily on either spatial analysis through GIS and other quantitative tools or on qualitative methods like interviews and ethnography. However, these approaches, when used in isolation, have limitations (Lanier and Farrell, 2015). Quantitative methods can provide broad overviews and identify patterns on a larger scale but may miss the nuanced, local-level insights that qualitative methods can offer. Conversely, qualitative research provides depth and context but may lack the statistical rigor needed to generalize findings across broader contexts. In regions where trafficking data is incomplete or difficult to obtain—common in studies of child trafficking, for instance, like China in this study —mixed-methods approaches can bridge this gap (Jiang and Sánchez-Barricarte, 2013; Cody et al., 2024). By integrating spatial data with in-depth qualitative insights, researchers can achieve a more comprehensive understanding of trafficking patterns and the lived experiences of victims. This methodological trend is particularly pertinent in societies where human and child trafficking data are fragmented or underreported, making mixed methods an essential tool for robust trafficking research (Di Nicola, 2013).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAdoption of Comparative Perspectives\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA third trend is the adoption of comparative perspectives to better understand the complex social and cultural biases that influence trafficking (Lanier et al., 2014). Trafficking is not a uniform phenomenon; it varies significantly across different social, cultural, and temporal contexts (Foot et al., 2021). Factors such as gender, age, and the location and timing of trafficking events play critical roles in shaping the experiences of victims and the strategies of traffickers (Duong, 2012; Lin, 2021). Comparative research, which examines trafficking across different regions, cultures, or demographic groups, is increasingly seen as essential for uncovering these complexities (Reid 2012). For instance, comparing trafficking patterns in urban versus rural settings, or in different cultural contexts, can reveal how local norms and socio-economic conditions contribute to trafficking (Olisah et al., 2024). Moreover, understanding how these factors intersect—such as how gender and age interact to influence trafficking risks—can help develop more targeted and effective interventions. This trend towards comparative research underscores the importance of situating trafficking studies within broader social and cultural frameworks, allowing for a more nuanced analysis of the factors that drive and sustain trafficking.\u003c/p\u003e\u003cp\u003eThese trends in geographical approaches to human (child) trafficking reflect a growing awareness of the need to address existing research gaps and to develop more comprehensive, nuanced, and methodologically sound approaches to studying this complex issue (Dragiewicz, 2014; Okech et al., 2018; Yea, 2021). In line with the emerging research trends in geographical approaches to human (child) trafficking, this study focuses on the phenomena of child trafficking at the provincial level in China. The research aims to analyze the temporal and spatial characteristics of child trafficking crimes, with particular attention to victims of different ages and genders. This includes an examination of the temporal evolution of these crimes over years and months, as well as the spatial distribution across provinces and cities, and the flow paths of trafficking activities. To further investigate the factors associated with child trafficking, the study employs GeoDetector, a powerful tool for exploring the influencing factors behind these crimes. By doing so, this research not only aligns with but also contributes to the current trends in trafficking research, addressing critical gaps in understanding the spatial dynamics and influencing factors of child trafficking in China.\u003c/p\u003e\u003cp\u003e\u003cb\u003eChild trafficking in China: Backgrounds, Challenges and General Landscape\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn an era influenced by the deeply rooted preference for male offspring and the strict implementation of the one-child policy, child trafficking has emerged as a significant issue in China (Wang et al., 2018; Yik-Yi Chu, 2011). The cultural preference for sons, coupled with restrictive family planning policies, has occasionally led to the occurrence of child trafficking, particularly in regions where these social pressures are most acute (Shen et al., 2013). As a result, children have become the primary focus of research on human trafficking crimes within the Chinese context (Wang et al., 2018). The confluence of these socio-cultural factors has not only contributed to the persistence of child trafficking but has also shaped the research landscape, driving scholars to pay closer attention to the unique dynamics of this form of exploitation in China.\u003c/p\u003e\u003cp\u003eAlthough research on child trafficking from a geographical perspective remains relatively limited in China, there has been a gradual increase in such studies in recent years (Huang and Weng, 2019; Zhou et al., 2024). One of the main challenges faced by researchers in this field is the lack of official data on child trafficking, which complicates efforts to conduct comprehensive analyses. However, alternative data sources have emerged as crucial for studying child trafficking in China. Two of the most significant are ‘Baby Back Home,’ one of China’s largest public-service websites dedicated to reuniting families, and the Chinese Judgment Document Network, which provides judgment documents related to trafficking cases. By analyzing the geographical information associated with both the origins and destinations of trafficking, research has increasingly focused on the spatial dynamics of these crimes. Specifically, studies related to the origins of trafficking delve into the spatial distribution patterns of trafficked children and the perpetrators (Yang et al., 2021), while research on the destinations of trafficking reveals the geographical distribution characteristics of buyers (Li et al., 2019). These studies contribute to a deeper understanding of the regional disparities and socio-economic factors that drive child trafficking in China.\u003c/p\u003e\u003cp\u003eCurrent research also examines the spatial patterns of child trafficking crimes at different geographical scales. At the national level, child trafficking in China exhibits a distinct spatial pattern, with trafficking incidents concentrated in the western regions and a more dispersed distribution in the eastern regions, which primarily serve as destinations for trafficked children (Li et al., 2017). The southwestern region, including provinces such as Guizhou, Sichuan, Yunnan, and Chongqing, is identified as the primary source of trafficked children, whereas the southern coastal regions, particularly Fujian and Guangdong, serve as the main destinations (Huang \u0026amp; Weng, 2019). In studies that focus on specific provinces, researchers have analyzed the criminal characteristics of child trafficking in several high-incidence areas, including Henan (Zhou et al., 2024), Guizhou (Xue et al., 2020), Hubei (Wang et al., 2021), and Yunnan (Yang et al., 2022). However, research on high-incidence destination provinces is relatively scarce, highlighting a significant gap in the literature that needs to be addressed.\u003c/p\u003e\u003cp\u003eThis study focuses on Guangdong Province, identified as one of China’s high-incidence destination provinces for child trafficking. Despite its significance, Guangdong has not been extensively studied as a research area. Moreover, unlike the prevalent trend of cross-provincial trafficking observed in other regions, Guangdong Province is one of the few provinces where a substantial number of intra-provincial trafficking cases have been recorded (Wang et al., 2018). This unique characteristic provides a robust perspective for conducting a provincial-level spatial analysis of child trafficking. By focusing on Guangdong Province, this study aims to fill the gap in the existing research and offer new insights into the spatial dynamics and influencing factors of child trafficking within the province.\u003c/p\u003e"},{"header":"Research Data and Methods","content":"\u003cp\u003e\u003cb\u003eSource of data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data utilized in this study was sourced from the public welfare website ‘Baby Back Home’ [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.baobeihuijia.com/bbhj/\u003c/span\u003e\u003cspan address=\"https://www.baobeihuijia.com/bbhj/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. Established in 2007, ‘Baby Back Home’ is a platform created by the Baby Back Home Volunteer Association, with the primary objective of assisting parents in locating missing children and facilitating the placement of homeless children into adoptive families. The website's sections, ‘Family Searching for Children’ and ‘Successful Cases,’ provide valuable data on the origin (for cases where children have not been found) and both the origin and destination (for successfully located cases) of child trafficking incidents. Data retrieval was conducted in November 2022.\u003c/p\u003e\u003cp\u003eIn addition to this, the study also incorporates data from statistical yearbooks of Guangdong Province, China, and various prefecture-level cities, which were used for analysis with GeoDetector. The data includes natural factors (e.g., annual average temperature, administrative divisions, and land area), population factors (e.g., sex ratio, migrant population), social factors (e.g., unemployment rate, passenger transport volume, number of individuals with university education), and economic factors (e.g., Engel coefficient).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData cleaning\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe initial dataset was extracted from the location information of child trafficking incidents retrieved from the database of missing children in Guangdong Province, covering the period from 1980 to 2020. Subsequently, we excluded records related to missing cases, abandonment, adoption, and running away from home. This data cleaning process resulted in a final dataset comprising 2,325 valid records from the ‘Family Searching for Children’ section and 102 valid records from the ‘Successful Cases’ section. These records include crucial details such as the child’s name or nickname, gender, age at the time of abduction, year and month of abduction, origin address, trafficking routes, and other relevant information. Additionally, textual descriptions from the ‘Successful Cases’ section were utilized as supplementary material for analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData quality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven that the ‘Baby Back Home’ website relies on volunteer-provided information, the data may contain errors or omissions. To address potential issues, we implemented several corrective measures: (1) Inaccurate or missing addresses were corrected by inferring approximate locations from case details and cross-referencing with map data. (2) Outdated administrative divisions were updated when discrepancies were identified between registered locations and current administrative boundaries. (3) Address precision was categorized into city-level, county-level, and street-level to align with specific spatial analysis requirements. It is important to note that the ‘Family Searching for Children’ section of the Baby Back Home website registers children who were abducted under the age of 16, and thus this study is focused on this age group.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalytical methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo analyze the distribution of age, gender, routes, and timing of trafficking, we employed descriptive analysis to present the socio-demographic characteristics of trafficked children in Guangdong Province. Following this, we utilized GeoDetector to analyze the driving forces behind these patterns. GeoDetector is a widely used tool for exploring stratified spatial heterogeneity of dependent variables and examining the influence of interactions between independent variables on dependent variables (Liang and Xu, 2023). Specifically, the factor detector within GeoDetector allows for the classification of factors on the same scale under varying conditions and identifies the main factors driving the observed patterns. Furthermore, it provides an intuitive reflection of the determining influence of these factors without the constraints of a linear hypothesis (Liang et al., 2022). In this study, we applied GeoDetector for both factor detection and interaction detection to explore the influencing factors of the spatial distribution of child trafficking crimes in Guangdong Province.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSocio-demographic characteristics of trafficked children in Guangdong Province\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGender distribution\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn analyzing the gender distribution of abducted individuals, it is evident that boys constitute a significantly larger proportion of the victims. Specifically, 1,580 boys were abducted, representing 68% of the total cases, while 745 girls were abducted, accounting for 32% of the total. This results in a gender ratio of 212.08, indicating a predominance of male victims, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The higher number of abducted boys compared to girls in the studied province is consistent with the broader trend observed across China, where the incidence of boy abductions surpasses that of girls (Li et al., 2017).\u003c/p\u003e\u003cp\u003eThe existing literature often interprets this gender disparity through the lens of gender norms and cultural factors, particularly the historical preference for male children in China (Xiong, 2023). This preference is deeply rooted in the patriarchal family structure and traditional marriage systems that have long characterized Chinese society (Wang et al., 2021). In Guangdong Province, these cultural norms are further intensified by the region's strong kinship culture, which places significant emphasis on male lineage and the continuation of the family name (Chen, 2009).\u003c/p\u003e\u003cp\u003eIn many parts of Guangdong, particularly in rural areas, the cultural and social importance of having male offspring is profound. Male children are often seen as essential for maintaining the family line, especially in the context of traditional Chinese beliefs about ancestor worship and the passing down of family heritage (Guohua, 2004). This cultural expectation can create intense pressure on families to produce male heirs, leading to a unique demand for male children, particularly in families that lack sons.\u003c/p\u003e\u003cp\u003eThis demand is not just about cultural continuity; it also has practical implications in the socio-economic fabric of rural Guangdong. Sons are traditionally expected to care for their aging parents and are often seen as the primary bearers of family responsibilities (Lei, 2013). As a result, the desire for male children can be so strong that it may contribute to the abduction of boys, as families without sons seek to fulfill these cultural and social expectations (as evidenced by Case \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This cultural bias and the associated 'cultural pressure' contribute to the higher incidence of male abductions observed in this study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCase 1\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAt the age of 4, W was abducted by a familiar man and taken to the dock under the temptation of snacks and toys. After about seven hours of sailing, W had forgotten everything along the way and was handed over to two women he had never met before during the voyage. He was then taken by train to a small village. A local family gave birth to 7 girls, and in order to continue their popularity, they bought W back home. (Baby Back Home ID: 11914)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAge distribution\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the age distribution of abducted children reveals a pattern characterized by two distinct peaks: a primary peak at ages 0–7 and a secondary peak at ages 13–16. These two age groups represent the periods with the highest incidence of child abductions. Specifically, the number of abducted children aged 0–7 is 1,628, accounting for 70.02% of the total cases, while those aged 13–16 number 466, representing 20.03% of the total. This indicates that younger children, particularly those in the 0–7 age group, are the most vulnerable to abduction.\u003c/p\u003e\u003cp\u003eThe predominance of younger children among abduction victims can be attributed to their greater physical and psychological vulnerability (Tschann et al., 1996). Due to their incomplete physical and psychological development, young children are less capable of defending themselves and are more easily deceived or overpowered by abductors (Fong and Cardoso, 2010). This vulnerability makes them particularly susceptible to criminal exploitation.\u003c/p\u003e\u003cp\u003eFurthermore, the primary objective of child trafficking in China is often illegal adoption, rather than other forms of exploitation such as sexual exploitation or forced labor (Yang et al., 2021). In this context, factors such as health, nationality, gender, and age are critical considerations in the illegal adoption process (Leinaweaver and van Wichelen, 2015). As children grow older, the likelihood of successful integration into new families decreases, with older children facing greater challenges in adjusting to their adoptive environments (Clark et al., 2006). This higher failure rate in family integration may deter buyers from adopting older children.\u003c/p\u003e\u003cp\u003eMoreover, younger children, due to their limited memories of their original families, are perceived by buyers as more adaptable to new family settings, making them more desirable targets for illegal adoption (as illustrated in Case \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This preference for younger children further exacerbates their risk of abduction, contributing to the observed trend towards younger ages in child trafficking cases.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCase 2\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAt the age of one, C was secretly taken away from his parents by traffickers. He has no memory of his biological parents or how he came to be raised by another family. The knowledge that he is not the biological child of his adopted family was something he heard in fragments from relatives and friends when he was very young. This made him feel extremely inferior. In the end, he received confirmation from his adopted cousin: his adoptive parents had bought him for 20,000 RMB from the trafficker who falsely claimed to be his biological parents. (Baby Back Home ID: 155631)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGender distribution by age\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn terms of gender distribution across age groups, the analysis reveals that in the primary peak age group of 0–7 years, boys are more frequently abducted than girls. Conversely, in the secondary peak age group of 13–16 years, girls are more commonly abducted than boys (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings suggest that boys are at a higher risk of abduction during early childhood, while girls face a greater risk during adolescence.\u003c/p\u003e\u003cp\u003eThis pattern is consistent with conclusions drawn in existing literature, which associates the abduction of older girls with feudal traditions such as child marriage and the desire of adolescent girls to escape economic hardship and social or familial disadvantages. These factors often lead to increased social control over girls in this age group (Ghosh, 2014). The higher incidence of abductions among adolescent girls may thus reflect these broader socio-cultural dynamics (as illustrated in Case \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCase 3\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIn Z’s recollection, she left home at around eleven or twelve years old. It wasn’t due to rebellion or running away, nor was it because she didn’t want to go to school. On the contrary, it was to earn money to support her younger siblings’ education. The moment Z stepped out of the door, she never imagined that she would never be able to return home. After leaving home, Z went to work with fellow villagers, but one day she got separated from them. What she didn’t expect was that everything that happened afterward plunged her into an abyss.Z, a young girl without education and with no familiar faces around, didn’t know the name of her hometown or how to get back home. Misfortune struck again as the clutches of evil reached out to this vulnerable young woman. She was deceived by an old man in his fifties or sixties, who claimed to take her back home. This man coerced Z into marrying him, and amid his abuse, she bore four children. Even in this dire situation, the old man showed her no mercy. Whenever he suspected Z of attempting to find her family, he would brutally assault her. Every time Z sought help from outside, it only resulted in more physical and emotional pain.(Baby Back Home ID: 293751)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTemporal characteristics of child trafficking crimes\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAnnual variation characteristics\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA statistical analysis of child trafficking cases in Guangdong Province from 1980 to 2021 reveals an overall inverted ‘V’-shaped trend in the annual variation of these crimes, which can be broadly divided into three stages (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSlow Increase Stage (1980–1989): During this period, the number of child trafficking cases gradually increased, although the overall number of abducted children remained relatively low.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRapid Increase Stage (1990–1996): This stage witnessed a significant surge in the number of abducted children, peaking in 1996 with 150 reported cases in Guangdong Province.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFluctuating Decline Stage (1997–2020): Beginning in 1997, the number of abducted children entered a phase of fluctuating decline. While minor increases were observed in 2003, 2008, 2013, and 2016, the overall trend was one of decline in subsequent years.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe annual variation trends in the number of abducted boys and girls were generally consistent with the overall trend. However, the annual variation curve for boys closely mirrored the overall curve, reflecting the higher proportion of boys among the total number of abducted children.\u003c/p\u003e\u003cp\u003eThe observed annual variation in child trafficking cases was influenced by several key factors, including the implementation of birth control policies and anti-trafficking efforts (Zhou et al., 2024). The one-child policy, officially established as a fundamental national policy in 1982, imposed restrictions on childbirth that, to some extent, stimulated the occurrence of child trafficking crimes. This period (1980–1989) saw a gradual increase in such crimes.\u003c/p\u003e\u003cp\u003eIn the 1990s, China began to enforce family planning laws more rigorously, transitioning from merely ‘advocating’ controlled childbirth to ‘strictly enforcing’ it. Traditional Chinese beliefs in ‘more sons, more blessings’ and the strong preference for male offspring faced significant challenges during this time. Coupled with inadequate legal controls over child trafficking, these factors contributed to a surge in trafficking, culminating in a peak in 1996.\u003c/p\u003e\u003cp\u003eIn 1997, China officially criminalized the trafficking of women and children. Over the following two decades, the Chinese government, including the State Council and the Ministry of Public Security, consistently strengthened the legal framework and anti-trafficking efforts, thereby imposing stricter controls on child trafficking markets. For instance, in 2009, the police department of Guangdong established a DNA database to support anti-trafficking efforts, pioneering a ‘rapid search mechanism’ that connected data nationwide. Since 2016, in response to the needs of the ‘Internet + anti-trafficking’ era, the Ministry of Public Security launched the ‘Reunion’ system, designed for the urgent dissemination of information on missing children. Additionally, in 2017, the Guangdong Public Security Department introduced a green channel for DNA matching, allowing suspected victims and their parents to provide DNA samples at local police stations free of charge.\u003c/p\u003e\u003cp\u003eStarting in 2013, China gradually relaxed its birth control policy, moving from a ‘one-child policy’ to a ‘two-child policy,’ and later to a ‘three-child policy,’ which contributed to a reduced demand in the child trafficking market. By the end of 2020, the Ministry of Public Security had organized a nationwide ‘Reunion Operation’ aimed at locating missing children.\u003c/p\u003e\u003cp\u003eIn conclusion, the increased efforts to combat child trafficking, coupled with improved enforcement and monitoring methods, have effectively curbed these crimes in Guangdong Province. As a result, an increasing number of abducted children have been successfully reunited with their families.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMonthly variation characteristics\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAccording to Routine Activity Theory, there is a correlation between temporal factors—such as seasons, months, and days—and criminal activities. Certain crimes are more likely to occur at specific times, with variations in their frequency, type, and characteristics depending on the time of year (Cohen and Felson, 2003). To explore this relationship, this study conducted a statistical analysis of the monthly distribution of child trafficking crimes.\u003c/p\u003e\u003cp\u003eThe analysis reveals that child trafficking crimes were more prevalent in January, May, June, July, August, and October, with the number of incidents in these months exceeding the monthly average(see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). August recorded the highest number of child trafficking crimes, with 255 cases, making it the month with the highest crime rate. In contrast, February had the lowest number of child trafficking crimes, with 130 cases, marking it as the month with the fewest incidents. The months with higher crime rates were predominantly those with warmer weather, while months with lower crime rates coincided with colder temperatures.\u003c/p\u003e\u003cp\u003eWhen examining the crime mean frequency—a measure that offers a more objective assessment of crime occurrence compared to total counts and averages—the findings closely mirrored the trends observed in total crime counts. August exhibited the highest crime mean frequency at 1.29, whereas February had the lowest at 0.73 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn terms of gender, the monthly variation curve for male child trafficking cases closely followed the overall trend, while the curve for female child trafficking cases was comparatively smoother. The warmer weather during the high-crime months likely contributed to increased crime rates by facilitating more frequent social interactions, which in turn raised the likelihood of contact between potential victims and criminals. Additionally, during warmer months, caregivers were more prone to leaving children unattended, thereby increasing the risk of child trafficking.\u003c/p\u003e\u003cp\u003eHolidays also played a significant role in the occurrence of child trafficking crimes. During holiday periods, children tend to be more active, and parents may be preoccupied with customs and festivities, potentially leading to reduced supervision and increased opportunities for traffickers (as illustrated in Case \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Case \u003cspan refid=\"FPar5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCase 4\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOne day in July 1995, a father took his 4-year-old child, B, on a bus to Guangzhou to visit a relative. After getting off the bus at Guangzhou Bus Station, the father went to use the restroom, leaving B waiting outside. After about ten minutes, when the father returned from the restroom, he could not find any trace of his child. Despite searching the area, they were unable to locate B. (Baby Back Home ID: 436512)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCase 5\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOn the morning of the Lantern Festival in 2002, C, who was not yet four years old, went to a small convenience store approximately 300 meters from home with a same-aged friend to buy snacks. About ten minutes later, when family members realized that C had not returned, they began searching but could not find him. The day of his disappearance was the Lantern Festival, and everyone was busy with their business, not noticing the child’s absence. (Baby Back Home ID: 436512)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial characteristics of child trafficking crimes in Guangdong province\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSpatial distribution of child trafficking\u003c/em\u003e\u003c/p\u003e\u003cp\u003eGuangdong Province, a key economic powerhouse in China, exhibits a diverse and complex urban structure characterized by significant regional disparities. Economically advanced cities are primarily concentrated in the geographically central region of the province, known as the Pearl River Delta. This area benefits from its proximity to Hong Kong and Macau, and it was among the first regions to initiate economic reforms and development. As a result, the Pearl River Delta has emerged as an economic engine not only for Guangdong Province but for the entire country.\u003c/p\u003e\u003cp\u003eIn contrast, the cities located in the eastern, northern, and western parts of Guangdong are largely mountainous and less developed. These regions have historically contributed to the economic growth of the Pearl River Delta by supplying young labor, land, and natural resources. However, due to slower economic development, these peripheral cities often experience higher levels of out-migration as residents seek better opportunities in the more prosperous central cities. This unbalanced development, with its concentration of wealth and population in the Pearl River Delta, significantly influences the spatial distribution of child trafficking crimes across the province.\u003c/p\u003e\u003cp\u003eUsing cities as the geographical units and the number of child trafficking cases as the variable, the cities in Guangdong Province can be categorized into five levels based on the prevalence of such crimes(see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFirst Level (high incidence): The first level includes Guangzhou and Dongguan, which recorded 421 and 385 child trafficking cases, respectively. According to data provided by the Chinese Ministry of Public Security, the group most vulnerable to trafficking comprises children who accompany their parents to urban areas for work, referred to in this study as ‘urban migrant children.’ Due to household registration restrictions, these children have limited access to education and social services, face challenges in social interaction, and often lack access to public activity spaces, making it difficult for them to integrate into the community. Both Guangzhou and Dongguan have large migrant populations and rank among the highest in the province in terms of the total number of migrants. In Dongguan, migrants constitute 76.0% of the city’s permanent population, totaling over 7.5\u0026nbsp;million people. In Guangzhou, the migrant population also exceeds 50%. The substantial influx of migrants has led to a significant population of urban migrant children, thereby creating opportunities for child trafficking crimes.\u003c/p\u003e\u003cp\u003eSecond Level (secondary high incidence): The second level comprises Shenzhen, Huizhou, Jieyang, and Shantou. These cities also have relatively large populations of urban migrant children and are geographically close to Guangzhou and Dongguan, where child trafficking crimes are most prevalent. Furthermore, existing research and statistical data indicate that the regions encompassing Jieyang and Shantou are characterized by more rigid traditional beliefs regarding childbirth and a stronger preference for male offspring, which results in a higher demand for child trafficking and makes these cities secondary high-risk areas for such crimes.\u003c/p\u003e\u003cp\u003eThird Level (medium high incidence): The third level includes Zhanjiang, Maoming, Foshan, Shenzhen, and Shaoguan, which are neighboring cities to those with high incidences of child trafficking, either within or outside the province.\u003c/p\u003e\u003cp\u003eFourth (secondary low incidence)and Fifth (low incidence) Levels: The fourth level consists of Jiangmen, Zhaoqing, Qingyuan, Heyuan, Meizhou, Shanwei, and Chaozhou, while the fifth level comprises Yunfu and Yangjiang.\u003c/p\u003e\u003cp\u003eThis classification into five levels reveals a discernible spatial pattern in the distribution of child trafficking crimes: the prevalence of child trafficking tends to decrease progressively as one moves from cities with larger migrant populations to those with fewer migrants. However, it is important to note that local cultural influences also play a significant role in shaping the incidence of child trafficking crimes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eRoutes of child trafficking\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe movement of individuals across geographical locations is a critical element of trafficking crimes. This study analyzed a total of 104 trafficking routes and identified a pattern characterized by \"few interprovincial, mostly intraprovincial\" routes. Specifically, 30 of these routes (28.85%) involved movement from Guangdong Province to other provinces(see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The most frequently observed interprovincial route was from Guangdong Province to Fujian Province, accounting for 18 cases (18.27%). Other interprovincial routes, such as those from Guangdong to Guizhou, Henan, Anhui, Jiangxi, and Guangxi provinces, were less common. Conversely, there were very few trafficking routes leading into Guangdong Province from other provinces, with only the \"Guangxi Province–Guangdong Province\" and \"Sichuan Province–Guangdong Province\" routes identified.\u003c/p\u003e\u003cp\u003eAs is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, intraprovincial trafficking routes were predominant in Guangdong Province, comprising 74 routes (72.55%). From the perspective of cities where children were trafficked, Guangzhou City had the highest number of outbound routes, followed by Dongguan City and Shenzhen City. This finding aligns with the previously discussed spatial distribution of child trafficking cases. Regarding inbound routes, Guangzhou City and Shantou City had the highest number of routes, followed by Jieyang City and Shenzhen City. In summary, Guangzhou City emerged as a major hub for child trafficking, with the highest number of both outbound and inbound routes.\u003c/p\u003e\u003cp\u003eCities with a high number of outbound trafficked children, such as Guangzhou, Dongguan, and Shenzhen, are typically economically developed and host large populations of migrant workers and their children. However, from the perspective of inbound trafficking, children are primarily trafficked into areas such as Shantou City and Jieyang City, where there is a strong cultural preference for male offspring.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDriving factors of child trafficking crimes in Guangdong province\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSelection of factors\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo investigate the factors influencing child trafficking crimes in Guangdong Province, this study selected eight variables from four categories: natural, population, social, and economic (Zhou et al., 2024). These variables were then used in the GeoDetector analysis to assess their impact on the spatial distribution of child trafficking crimes.\u003c/p\u003e\u003cp\u003eIn the natural dimension, two factors were considered: annual average temperature and total city land area. Previous research has demonstrated that both temperature and city size are closely related to the occurrence of criminal activities, with temperature influencing social behavior and city size affecting the scale and complexity of urban life (Cohn and Rotton, 2000).\u003c/p\u003e\u003cp\u003eThe population dimension included two factors: gender ratio and the number of floating populations. The relationship between crime and population dynamics can vary depending on the location (Boivin, 2018). Empirical studies have shown that regions in China with higher gender ratios tend to have elevated crime rates. Additionally, the mobility of the population, particularly the challenges faced by floating populations in destination cities, can contribute to increased crime rates (Ghosh, 2014).\u003c/p\u003e\u003cp\u003eIn the social dimension, the study considered three factors: the urban registered unemployment rate, passenger traffic volume, and the number of people with a university education or higher per 100,000 population. These factors reflect various aspects of social stability, transportation convenience, and educational attainment, which can influence crime rates (Raphael and Winter-Ebmer, 2001).\u003c/p\u003e\u003cp\u003eThe economic dimension was represented by the Engel coefficient for all residents, a measure of regional living standards(Lochner and Moretti, 2004).. The selected variables are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetection factors affecting child trafficking crimes in Guangdong province\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Type and Dimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eInfluencing Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetection Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFactor Explanation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependent Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNumber of Child Trafficking Cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChild trafficking crime situation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eIndependent Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNatural Dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual Average Temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e℃\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMeasure of regional surface heat conditions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal City Land Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ekm2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMeasure of regional land area\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePopulation Dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGender Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMeasure of regional social gender differences\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of Floating Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMeasure of regional population mobility\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSocial Dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban Registered Unemployment Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMeasure of regional urban and rural employment conditions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePassenger Traffic Volume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTen thousand person-times\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMeasure of regional transportation convenience\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of People with University Education or Higher per 100,000 Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePeople\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMeasure of regional education level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEconomic Dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEngel Coefficient for All Residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMeasure of regional residents’ living standards\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eFactor detection\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, The results of the GeoDetector analysis, based on a 1% significance level, indicate that four factors significantly influence the spatial distribution of child trafficking crimes in Guangdong Province. These factors are: gender ratio and the number of floating populations (population factors), the number of people with a university education or higher per 100,000 population (social factors), and the Engel coefficient for all residents (economic factors).\u003c/p\u003e\u003cp\u003eThe strength of the influence of each factor is measured by the q-value, which reflects the degree to which the dependent variable is affected by the independent variable. Among the driving factors, the number of floating populations (S4) has the highest q-value at 0.901, indicating it has the greatest impact. The gender ratio (S3) is the second most influential factor, with a q-value of 0.602. The Engel coefficient for all residents (S8) and the number of people with a university education or higher per 100,000 population (S7) rank third and fourth, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetection results of influencing factors for child trafficking crimes in Guangdong province\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable Dimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eInfluencing Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eFactor Detection Results\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExplanatory Power Ranking\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificance Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eq-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNatural Dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual Average Temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal City Land Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePopulation Dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Floating Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSocial Dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban Registered Unemployment Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePassenger Traffic Volume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of People with University Education or Higher per 100,000 Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic Dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEngel Coefficient for All Residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: ‘-’ indicates that the q-value did not pass the 1% significance test; only variables passing the 1% significance test are ranked.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGiven that factors influencing child trafficking often interact, this study employed the interaction detection method in GeoDetector to assess the combined effects of different driving factors on the spatial distribution of child trafficking crimes. The results reveal that the combined factors exert a stronger influence than individual factors, suggesting that the interaction between any two factors has a greater effect than any single factor alone. The types of interaction effects observed were non-linear enhancement or dual-factor enhancement, indicating that no factor operates independently of others.\u003c/p\u003e\u003cp\u003eNotably, the interaction between the number of floating populations (S4) and passenger traffic volume (S6) demonstrated the strongest influence, with an interaction q-value of 0.996. The second strongest interaction was between the gender ratio (S3) and passenger traffic volume (S6), with a q-value of 0.973. The third-ranked interaction was between total city land area (S2) and the number of floating populations (S4), with a q-value of 0.967. Although factors such as annual average temperature (S1), total city land area (S2), urban registered unemployment rate (S5), and passenger traffic volume (S6) cannot independently explain the spatial characteristics of child trafficking crimes, their explanatory power significantly increases when interacting with other factors. The interaction between the number of floating populations (S4) and other factors is particularly noteworthy, further confirming that the number of floating populations (S4) is a core factor influencing the spatial distribution of child trafficking crimes.(see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop five ranked interaction factor combinations in the detection of child trafficking crimes in Guangdong province\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eq = A∩B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA + B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComparison Result\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eType of Interaction Effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExplanatory Power Ranking after Interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS4∩S6 = 0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS4(0.901) + S6(0.320) = 1.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA + B \u0026gt; q \u0026gt; A, B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDual-factor enhancement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS3∩S6 = 0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS3(0.601) + S6(0.320) = 0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eq \u0026gt; A + B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-linear enhancement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS2∩S4 = 0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS2(0.224) + S4(0.901) = 1.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA + B \u0026gt; q \u0026gt; A, B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDual-factor enhancement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS1∩S4 = 0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS1(0.214) + S4(0.901) = 1.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA + B \u0026gt; q \u0026gt; A, B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDual-factor enhancement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS2∩S3 = 0.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS2(0.224) + S3(0.601) = 0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eq \u0026gt; A + B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-linear enhancement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Conclusion and discussion","content":"\u003cp\u003eThis study offers a comprehensive spatial and temporal analysis of child trafficking in Guangdong Province from 1980 to 2020, revealing critical socio-demographic and spatial patterns. Trafficked children are mostly young boys, with cases concentrated in economically developed cities, particularly those with large migrant populations. Trafficking trends followed a significant increase during the 1990s, driven by socio-economic changes and policies, but saw a decline with improved government interventions. These findings provide valuable insights into the complex socio-economic and demographic dynamics that underlie child trafficking, contributing to the broader geographical research on this critical issue.\u003c/p\u003e\u003cp\u003eThis research addresses key gaps in child trafficking literature, emphasizing the need for geographical studies, mixed methods, and comparative perspectives. By focusing on Guangdong Province, it highlights how socio-economic factors like floating populations influence the spatial distribution of trafficking and reveals vulnerabilities in specific regions. The use of the GeoDetector tool effectively integrates spatial analysis with qualitative insights, demonstrating how factors such as population mobility, gender ratio, and transportation infrastructure interact to shape trafficking patterns. The study also stresses the importance of comparative approaches to uncover social and cultural biases in trafficking. By comparing regions within Guangdong, it shows how economic development, migration, and cultural preferences contribute to varying trafficking risks, offering a basis for targeted interventions.\u003c/p\u003e\u003cp\u003eFor further exploration, the field of children’s geography offers a promising avenue. Children’s geography, which examines the changing relationship between children and public spaces, can provide new perspectives on child trafficking by focusing on the environments in which these crimes occur. future research could explore how the institutionalization of children’s living spaces—shifting from homes and neighborhoods to organized and specialized environments—impacts the safety of children and their susceptibility to trafficking. Additionally, understanding how abducted children navigate and construct their own spaces within these environments could offer deeper insights into their agency and resilience.\u003c/p\u003e\u003cp\u003eDespite its contributions, the study has limitations. Reliance on self-reported data from the 'Baby Come Home' website raises concerns about accuracy, and the hidden nature of trafficking suggests the issue may not be fully captured. Future research should involve interdisciplinary collaboration, incorporating criminology, sociology, and public health, and more comprehensive data sources to deepen understanding and enhance interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [grant number 42171229]; the Natural Science Foundation of Guangdong Province [grant number 2022B1515020087]; and the Innovative Projects of Department of Education of Guangzhou City [grant numbers 2023A03J0063, 202235209].\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC was primarily responsible for constructing the theoretical framework of this study and writing parts of the revised manuscript. L conducted the initial draft writing and data analysis, laying a solid foundation for the paper. Y was in charge of reconstructing the theoretical framework in the revised manuscript and refining the content to enhance its quality. Each author's contributions were indispensable, jointly ensuring the completion of this paper.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files \"original data\" and \"data analysis\".\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbu-Ali, A., \u0026amp; Al-Bahar, M. (2011). Understanding child survivors of human trafficking: A micro and macro level analysis. Procedia-Social and Behavioral Sciences, 30, 791\u0026ndash;796. https://doi.org/10.1016/j.sbspro.2011.10.154\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeyrer, C. (2004). Global child trafficking. The Lancet, 364, 16\u0026ndash;17. https://doi.org/10.1016/S0140-6736(04)17624-1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBiswas, M. A. (2015). Human trafficking scenario in Bangladesh: Some concerns. International Journal of Humanities \u0026amp; Social Science Studies, 1(4), 85\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlazek, M., Esson, J., \u0026amp; Smith, D. P. (2019). Relational geographies of human trafficking: Inequality, manoeuvring and im/mobility across space and time. Transactions of the Institute of British Geographers, 44(1), 63\u0026ndash;78. https://doi.org/10.1111/tran.12271\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoivin, R. (2018). Routine activity, population (s) and crime: Spatial heterogeneity and conflicting Propositions about the neighborhood crime-population link. Applied geography, 95, 79\u0026ndash;87. https://doi.org/10.1016/j.apgeog.2018.04.016\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBreuil, B. O., \u0026amp; Gerasimov, B. (2021). Trafficking in Minors: Confronting complex realities, structural inequalities, and agency. Anti-trafficking review, (16), 1\u0026ndash;9. https://doi.org/10.17269/s41997-022-00642-8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCecchet, S. J., \u0026amp; Thoburn, J. (2014). The psychological experience of child and adolescent sex trafficking in the United States: Trauma and resilience in survivors. Psychological trauma: theory, research, practice, and policy, 6(5), 482.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Youyi (2009). An analysis of the phenomenon of favoring sons over daughters in Chaoshan region. Journal of Guangzhou Panyu Vocational and Technical College, 8 (3), 56\u0026ndash;60\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCho, S. Y. (2015). Modelling for determinants of human trafficking. Social Inclusion, 3, 2\u0026ndash;21. http://dx.doi.org/10.2139/ssrn.2117838\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChong, N. G., \u0026amp; Clark, J. B. (Eds.). (2017). Human trafficking: a complex phenomenon of globalization and vulnerability. Routledge.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClark, P., Thigpen, S., \u0026amp; Yates, A. M. (2006). Integrating the older/special needs adoptive child into the family. Journal of Marital and Family Therapy, 32(2), 181\u0026ndash;194. https://doi.org/10.1111/j.1752-0606.2006.tb01599.x\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCockbain, E., \u0026amp; Olver, K. (2019). Child trafficking: characteristics, complexities, and challenges. In Child abuse and neglect (pp. 95\u0026ndash;116). Academic Press. https://doi.org/10.1016/B978-0-12-815344-4.00006-4\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCockbain, E., Ashby, M., Bowers, K., \u0026amp; Zhang, S. X. (2024). Concentrations of harm: Geographic and demographic patterning in human trafficking and related victimisation. Criminology \u0026amp; Criminal Justice, 0(0). https://doi.org/10.1177/17488958241245311\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCockbain, E., Bowers, K., \u0026amp; Hutt, O. (2022). Examining the geographies of human trafficking: Methodological challenges in mapping trafficking's complexities and connectivities. Applied Geography, 139, 102643. https://doi.org/10.1016/j.apgeog.2022.102643\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCody, A. M., Okech, D., Yi, H., Aletraris, L., Clay-Warner, J., \u0026amp; Callands, T. (2024). When child trafficking and informal fostering intersect: A mixed methods study. Social Work and Social Sciences Review, 24. https://doi.org/10.1921/swssr.v24i2.2026\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen, L. E., \u0026amp; Felson, M. (2003). Social change and crime rate trends: A routine activity approach. Crime: Critical Concepts in Sociology, 1, 316.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohn, E. G., \u0026amp; Rotton, J. (2000). Weather, seasonal trends and property crimes in Minneapolis, 1987\u0026ndash;1988. A moderator-variable time-series analysis of routine activities. Journal of environmental psychology, 20(3), 257\u0026ndash;272. https://doi.org/10.1006/jevp.1999.0157\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorsaro, W. A., \u0026amp; Everitt, J. G. (2023). The sociology of childhood. Sage publications.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCottingham, M., Nowak, T., Snyder, K., \u0026amp; Swauger, M. (2013). Sociological perspective: underlying causes. Human trafficking: interdisciplinary perspectives. New York: Routledge, 51\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCullen-DuPont, K. (2009). Human trafficking. Infobase publishing.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCunha, A., Martinho, G., Gon\u0026ccedil;alves, M., \u0026amp; Matos, M. (2022). Addressing the psychological trauma in human trafficking victims: A brief review. Psychological trauma: theory, research, practice, and policy.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDakua, T., Rahaman, M., \u0026amp; Das, K. C. (2024). An analysis of the spatial and temporal variations of human trafficking in India. Cogent Social Sciences, 10(1), 2293315.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Shalit, A., van der Meulen, E., \u0026amp; Guta, A. (2021). Social service responses to human trafficking: The making of a public health problem. Culture, Health \u0026amp; Sexuality, 23(12), 1717\u0026ndash;1732. https://doi.org/10.1080/13691058.2020.1802670\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDecker, S. H. (2015). Human trafficking: contexts and connections to conventional crime. Journal of crime and justice, 38(3), 291\u0026ndash;296. https://doi.org/10.1080/0735648X.2015.1039792\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDi Nicola, A. (2013). Researching into human trafficking: Issues and problems. In Human trafficking (pp. 49\u0026ndash;72). Willan.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDragiewicz, M. (Ed.). (2014). Global human trafficking: Critical issues and contexts. Routledge.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuong, K. A. (2012). Human trafficking in a globalized world: Gender aspects of the issue and anti-trafficking politics. Journal of research in gender studies, 2(1), 48\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEargle, L. A., \u0026amp; Doucet, J. M. (2021). Investigating human trafficking within the United States: a state-level analysis of prevalence and correlates. Sociation, 20(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEsson, J. (2020). Playing the victim? Human trafficking, African youth, and geographies of structural inequality. Population, Space and Place, 26(6), e2309.https://doi.org/10.1002/psp.2309\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFong, R., \u0026amp; Cardoso, J. B. (2010). Child human trafficking victims: Challenges for the child welfare system. Evaluation and program planning, 33(3), 311\u0026ndash;316. https://doi.org/10.1016/j.evalprogplan.2009.06.018\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFoot, K., Sworn, H., \u0026amp; Alejano-Steele, A. (2021). An outcome-centered comparative analysis of counter-human trafficking coalitions in the Global South. Management Communication Quarterly, 35(3), 418\u0026ndash;444. https://doi.org/10.1177/08933189211017925\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrederick, J., Basnyat, M., \u0026amp; Aguettant, J. L. (2010). Trafficking and exploitation in the entertainment and sex industries in Nepal: A handbook for decision-makers. Kathmandu, Nepal: Terra De homes Foundation.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGezinski, L. B., \u0026amp; Gonzalez-Pons, K. M. (2024). Sex trafficking and technology: A systematic review of recruitment and exploitation. Journal of Human Trafficking, 10(3), 497\u0026ndash;511. https://doi.org/10.1080/23322705.2022.2034378\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhosh, S. (2014). Anti-trafficking and its discontents: women\u0026rsquo;s migrations and work in an Indian borderland. Gender, Place \u0026amp; Culture, 22(9), 1220\u0026ndash;1235. https://doi.org/10.1080/0966369X.2014.970139\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreenbaum, J. (2020). A public health approach to global child sex trafficking. Annual Review of Public Health, 41(1), 481\u0026ndash;497. https://doi.org/10.1146/annurev-publhealth-040119-094335\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuohua Z. (2004). Adaptation, revival, and reconstruction: Clan organizations and rural power structures: Two case studies from southern Jiangxi and eastern Guangdong. Chinese Sociology \u0026amp; Anthropology, 37(1), 34\u0026ndash;50. https://doi.org/10.1080/21620555.2004.11038340\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, Z., \u0026amp; Weng, W. (2019). Analysis on geographical migration networks of child trafficking crime for illegal adoption from 2008 to 2017 in China. Physica A: Statistical Mechanics and Its Applications, 528, 121404. https://doi.org/10.1016/j.physa.2019.121404\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang, Q., \u0026amp; S\u0026aacute;nchez-Barricarte, J. J. (2013). Child trafficking in China. China Report, 49(3), 317\u0026ndash;335. https://doi.org/10.1177/0009445513502268\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKiss, L., Fotheringhame, D., Kyegombe, N., McAlpine, A., Abilio, L., Kyamulabi, A., et al. (2022). Paper:violence, abuse and exploitation among trafficked women and girls: a mixed-methods study in Nigeria and Uganda. BMC public health, 22(1), 794. https://doi.org/10.1186/s12889-022-13021-2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLanier, M. M., \u0026amp; Farrell, C. T. (2015). A mixed methods analysis of human trafficking. Journal of Social Sciences Research, 66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLanier, M. M., Farrell, C. T., \u0026amp; Bezuidenhout, C. (2014). A comparative analysis of human trafficking: The United States of America (USA) and the Republic of South Africa (RSA). International Journal of Criminology and Sociology, 3, 275\u0026ndash;283. https://doi.org/10.6000/1929-4409.2014.03.23\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLei, L. (2013). Sons, daughters, and intergenerational support in China. Chinese Sociological Review, 45(3), 26\u0026ndash;52. https://doi.org/10.2753/CSA2162-0555450302\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeinaweaver, J., \u0026amp; van Wichelen, S. (2015). The geography of transnational adoption: kin and place in globalization. Social \u0026amp; Cultural Geography, 16(5), 499\u0026ndash;507. https://doi.org/10.1080/14649365.2015.1033449\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, G., Tan, R., Wang, H.,Yan, X., \u0026amp; Shao, Y. (2017). Geographic Characteristics of Child Trafficking Crime in China. Scientia Geographica Sinica, 37(7), 1049\u0026ndash;1058. https://doi.org/10.13249/j.cnki.sgs.2017.07.010\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, G., Wang Y., Yang, L., Tan, R., \u0026amp;Gou, S. (2019). Spatio-temporal pattern of child trafficking crime in China: A demand-front perspective. Journal of Shaanxi Normal University (Natural Science Edition), 47(3), 95\u0026ndash;102. https://doi.org/10.15983/j.cnki.jsnu.2019.03.431\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang, Y., \u0026amp; Xu, C. (2023). Knowledge diffusion of Geodetector: A perspective of the literature review and Geotree. Heliyon. https://doi.org/10.1016/j.heliyon.2023.e19651\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang, Y., Li, S., Zeng, J., \u0026amp; Wu, T. (2022). Examining the impact of multidimensional accessibility on regional poverty in Laos. Applied Geography, 148, 102789. https://doi.org/10.1016/j.apgeog.2022.102789\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLimoncelli, S. A. (2009). Human trafficking: Globalization, exploitation, and transnational sociology. Sociology compass, 3(1), 72\u0026ndash;91.https://doi.org/10.1111/j.1751-9020.2008.00178.x\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin, L. S. (2021). Human security and gender: a comparative case study of human trafficking in Southeast Asia. RAIS Journal for Social Sciences, 5(2), 1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLochner, L., \u0026amp; Moretti, E. (2004). The effect of education on crime: Evidence from prison inmates, arrests, and self-reports. American economic review, 94(1), 155\u0026ndash;189. https://doi.org/10.1257/000282804322970751\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLopez, J. J., \u0026amp; Truesdale-Moore, S. (2020). The use of spatial statistics to control human and sex trafficking. International Journal of Social Science and Humanity, 10(2), 57\u0026ndash;61. https://doi.org/10.18178/ijssh.2020.10.2.1014\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahalingam, R. (2019). Human trafficking from a multidisciplinary perspectives: A literature review. Asian Journal of Social Science Research, 2(2). https://doi.org/10.5281/zenodo.8107042\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahalingam, R., \u0026amp; Sidhu, J. S. (2021). Inside the crime of sex trafficking in Sabah, Malaysia. Journal of human trafficking, 7(3), 308\u0026ndash;324. https://doi.org/10.1080/23322705.2019.1710069\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartinho, G., Gon\u0026ccedil;alves, M., \u0026amp; Matos, M. (2020). Child trafficking, comprehensive needs and professional practices: A systematic review. Children and Youth Services Review, 119, 105674.https://doi.org/10.1016/j.childyouth.2020.105674\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMusto, J. (2019). The Geographies of Human Trafficking: Eastern European Women in the United States. In Geographical Review, 109(4), 441\u0026ndash;458.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO'Brien, E., Hayes, S., \u0026amp; Carpenter, B. (2013). The politics of sex trafficking: a moral geography. Springer.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO'Connell Davidson, J. (2011). Moving children? Child trafficking, child migration, and child rights. Critical social policy, 31(3), 454\u0026ndash;477.https://doi.org/10.1177/0261018311405014\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOkech, D., Choi, Y. J., Elkins, J., \u0026amp; Burns, A. C. (2018). Seventeen years of human trafficking research in social work: A review of the literature. Journal of evidence-informed social work, 15(2), 103\u0026ndash;122. https://doi.org/10.1080/23761407.2017.1415177\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlisah, S., Odooh, C., Efijemue, O., Obunadike, E., Onwuchekwa, J., Owolabi, O., et al. (2024). Unveiling Global Human Trafficking Trends: A Comprehensive Analysis. Journal of Data Analysis and Information Processing, 12(01), 49\u0026ndash;75. https://doi.org/10.4236/jdaip.2024.121004\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaj\u0026oacute;n, L., \u0026amp; Walsh, D. (2020). Proposing a theoretical framework for the criminal investigation of human trafficking crimes. Policing: a journal of policy and practice, 14(2), 493\u0026ndash;511. http://10.1093/police/pay031\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalmiotto, M. J. (Ed.). (2014). Combating human trafficking: A multidisciplinary approach. CRC Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePascual-Leone, A., Kim, J., \u0026amp; Morrison, O. P. (2017). Working with victims of human trafficking. Journal of Contemporary Psychotherapy, 47, 51\u0026ndash;59.https://doi.org/10.1007/s10879-016-9338-3\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaphael, S., \u0026amp; Winter-Ebmer, R. (2001). Identifying the effect of unemployment on crime. The journal of law and economics, 44(1), 259\u0026ndash;283. https://doi.org/10.1086/320275\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRecknor, F., Di Ruggiero, E., \u0026amp; Jensen, E. (2022). Addressing human trafficking as a public health issue. Canadian journal of public health, 113(4), 607\u0026ndash;610.https://doi.org/10.17269/s41997-022-00642-8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReid, J. A. (2012). Exploratory review of route-specific, gendered, and age-graded dynamics of exploitation: Applying life course theory to victimization in sex trafficking in North America. Aggression and Violent Behavior, 17(3), 257\u0026ndash;271. https://doi.org/10.1016/j.avb.2012.02.005\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRicard-Guay, A., Hanley, J. (2020). The Challenge of Addressing Both Forced Labor and Sexual Exploitation. In: Winterdyk, J., Jones, J. (eds) The Palgrave International Handbook of Human Trafficking. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-63058-8_16\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRussell, A. M. (2014). \u0026ldquo;Victims of trafficking\u0026rdquo;: the feminisation of poverty and migration in the gendered narratives of human trafficking. Societies, 4(4), 532\u0026ndash;548.https://doi.org/10.3390/soc4040532\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSarrica, F. (2022). The Use of Human Trafficking Detection Data for Modelling Static and Dynamic Determinants of Human Trafficking Flows. European Journal on Criminal Policy and Research, 28(4), 483\u0026ndash;501. https://doi.org/10.1007/s10610-020-09460-5\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSax, H. (2017). Child trafficking\u0026ndash;a call for rights-based integrated approaches. In Routledge Handbook of Human Trafficking (pp. 251\u0026ndash;260). Routledge.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShelley, L. (2010). Human trafficking: A global perspective. Cambridge University Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShen, A., Antonopoulos, G. A., \u0026amp; Papanicolaou, G. (2013). China\u0026rsquo;s stolen children: Internal child trafficking in the People\u0026rsquo;s Republic of China. Trends in organized crime, 16, 31\u0026ndash;48. https://doi.org/10.1007/s12117-012-9167-z\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShimazaki, Y. (2021). Human trafficking and the feminization of poverty: Structural violence in Cambodia. Rowman \u0026amp; Littlefield.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSidun, N. M., \u0026amp; Flores, Y. G. (2020). Human trafficking. The Cambridge handbook of psychology and human rights, 273\u0026ndash;287.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith, D. P. (2018). Population geography I: Human trafficking. Progress in Human Geography, 42(2), 297\u0026ndash;308.https://doi.org/10.1177/0309132516685196\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSprang, G., Stoklosa, H., \u0026amp; Greenbaum, J. (2022). The public health response to human trafficking: a look back and a step forward. Public Health Reports, 137(1_suppl), 5S-9S.https://doi.org/10.1177/00333549221085588\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTyldum, G. (2010). Limitations in research on human trafficking. International Migration, 48(5), 1\u0026ndash;13. https://doi.org/10.1111/j.1468-2435.2009.00597.x\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTschann, J M., Kaiser, P., Chesney, M A., Alkon,A., \u0026amp; Boyece, W T.(1996). Resilience and vulnerability among preschool children: Family functioning, temperament, and behavior problems. Journal of the American Academy of Child \u0026amp; Adolescent Psychiatry, 35(2), 184\u0026ndash;192.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Liempt, I. (2011). Different geographies and experiences of \u0026lsquo;assisted\u0026rsquo;types of migration: A gendered critique on the distinction between trafficking and smuggling. Gender, place and culture, 18(02), 179\u0026ndash;193. https://doi.org/10.1080/0966369X.2011.552316\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, J B., Li, G., Zhou, J J., Ma, X Y., \u0026amp; Xu T T. (2021). Spatio-temporal pattern and influencing factors of child trafficking in Hubei Province. Human Geography, 36(1), 73\u0026ndash;83. https://doi.org/10.13959/j.issn.1003-2398.2021.01.010\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, Z., Wei, L., Peng, S., Deng, L., \u0026amp; Niu, B. (2018). Child-trafficking networks of illegal adoption in China. Nature Suatainability, 1(5), 254\u0026ndash;260.https://doi.org/10.1038/s41893-018-0065-5\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeitzer, R. (2014). New directions in research on human trafficking. The ANNALS of the American Academy of Political and Social Science, 653(1), 6\u0026ndash;24.https://doi.org/10.1177/0002716214521562\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiong, W. (2023). Evidence of son preference in the child trafficking market for illegal adoption in China. \u003cem\u003eJournal of human trafficking\u003c/em\u003e, 9(2), 242\u0026ndash;255. https://doi.org/10.1080/23322705.2021.1874188\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXue, S., Li, G., Ma, X Y., Liu, L., \u0026amp; Yang, Y. (2020). The multidimensional spatio-temporal pattern and influencing factors of child trafficking in Guizhou province, China. \u003cem\u003eGeographic Research\u003c/em\u003e, 39(7), 1691\u0026ndash;1706.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, L., Xu, J H., Chen, Nuo., Li, G., \u0026amp; Zhou, J J. (2022). Spatiotemporal Pattern and Influencing Factors of Minor Trafficking in Yunnan Province from 1958 to 2019. \u003cem\u003eTropical Geography\u003c/em\u003e, 42(9), 1523\u0026ndash;1533. http://doi.org/10.13284/j.cnki.rddl.003551\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, M., Xia, X. \u0026amp; Zhou, Y.(2023). Abandoned children in China: the son-preference culture and the gender-differentiated impacts of the one-child policy. \u003cem\u003eHumanities and Social Sciences Communications\u003c/em\u003e, 10(1), 1\u0026ndash;10. https://doi.org/10.1057/s41599-023-02015-z\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, S., Han, L., \u0026amp; Bi, Y. (2021). Child trafficking in the Yunnan and Guangdong provinces of China. \u003cem\u003eThe International Journal of Human Rights\u003c/em\u003e, 25(4), 718\u0026ndash;742. https://doi.org/10.1080/13642987.2020.1794840\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao, Y., Liu, Y., Guan, Q., Hong, Y., Wang, R., Wang, R., \u0026amp; Liang, X. (2021). Spatiotemporal distribution of human trafficking in China and predicting the locations of missing persons. Computers, \u003cem\u003eEnvironment and Urban Systems\u003c/em\u003e, 85, 101567.https://doi.org/10.1016/j.compenvurbsys.2020.101567\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYea, S. (2013). Mobilising the child victim: The localisation of human trafficking in Singapore through global activism. \u003cem\u003eEnvironment and Planning D: Society and Space\u003c/em\u003e, 31(6), 988\u0026ndash;1003. https://doi.org/10.1068/d15411\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYea, S. (2021). Towards critical geographies of anti-human trafficking: Producing and precluding victimhood through discourses, practices and institutions. \u003cem\u003eProgress in Human Geography\u003c/em\u003e, 45(3), 513\u0026ndash;530. https://doi.org/10.1177/0309132520923136\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYik-Yi Chu, C. (2011). Human trafficking and smuggling in China. \u003cem\u003eJournal of Contemporary China\u003c/em\u003e, 20(68), 39\u0026ndash;52. http://dx.doi.org/10.1080/10670564.2011.520842\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, J., Li, G., Wang, J., Xu, T., Chen, Z., Gao, X., \u0026amp; Jin, A. (2024). Patterns, evolution and determinants of child trafficking in Henan Province, China. \u003cem\u003eChildren's Geographies\u003c/em\u003e, 22(2), 201\u0026ndash;216. https://doi.org/10.1080/14733285.2023.2259322\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, J., Li, G., Wang, J., Xu, T., Nie, Q., Gao, X., \u0026amp; Jin, A. (2023). Exploring variations and influencing factors of illegal adoption: A comparison between child trafficking and informal adoption. \u003cem\u003eChild Abuse \u0026amp; Neglect\u003c/em\u003e, 140, 106124. https://doi.org/10.1016/j.chiabu.2023.106124\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZimmerman, C., Hossain, M., Yun, K., Roche, B., Morison, L., \u0026amp; Watts, C. (2008). \u003cem\u003eStolen smiles: A summary report on the physical and psychological health consequences of women and adolescents trafficked in Europe\u003c/em\u003e. London: London School of Hygiene and Tropical Medicine.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"child trafficking, spatial analysis, population mobility, geographical approaches, Guangdong Province","lastPublishedDoi":"10.21203/rs.3.rs-6849755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6849755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChild trafficking is a pervasive global issue that poses significant challenges to governments and societies worldwide. This study focuses on child trafficking in Guangdong Province, China, a region characterized by significant economic disparities, large floating populations, and deeply rooted cultural preferences. Utilizing data from the 'Baby Come Home' public welfare website and statistical yearbooks, this research employs the GeoDetector method to analyze the spatial distribution, temporal trends, and socio-economic factors driving child trafficking from 1980 to 2020. The findings reveal that child trafficking in Guangdong Province is predominantly influenced by the region's socio-economic dynamics, particularly the presence of large migrant populations and a cultural preference for male children. The spatial analysis identifies economically developed cities as hotspots for trafficking due to their substantial migrant populations. Additionally, the study highlights the significant role of population mobility in shaping the temporal and spatial patterns of these crimes. This research contributes to the existing literature by providing a comprehensive spatial and temporal analysis of child trafficking in a region that has been underexplored. It echoes and advances current trends in geographical approaches to child trafficking, particularly the integration of child-focused studies, mixed methods, and comparative perspectives. The study\u0026rsquo;s findings offer important implications for policy development and law enforcement, providing insights that can guide targeted interventions to prevent child trafficking and protect vulnerable children. Furthermore, the methodological approach employed in this research offers a model for future studies on child trafficking in other regions.\u003c/p\u003e","manuscriptTitle":"Geographies of child trafficking in Guangdong Province, China (1980–2020): Spatial dynamics and socio-economic drivers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:18:42","doi":"10.21203/rs.3.rs-6849755/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-07T12:21:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T14:46:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T00:14:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T10:30:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194025013393975238554450033206504705442","date":"2025-08-18T10:20:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133965529944042932001909991692868738807","date":"2025-08-17T13:53:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178286116094164851623554073251335137835","date":"2025-08-17T07:54:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-17T04:49:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T06:52:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-22T05:56:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T07:03:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-07-15T06:58:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2e3882ce-ba84-43d2-b174-9b6c1d72ff7d","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53622243,"name":"Social science/Criminology"},{"id":53622244,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-03-09T07:23:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 06:18:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6849755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6849755","identity":"rs-6849755","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}