Do More Visitors Mean More Crime? Analyzing the Impact of Visitor Patterns on Crime Rates in Urban Areas Using Mobile Device Foot Traffic Data

Do More Visitors Mean More Crime? Analyzing the Impact of Visitor Patterns on Crime Rates in Urban Areas Using Mobile Device Foot Traffic Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Do More Visitors Mean More Crime? Analyzing the Impact of Visitor Patterns on Crime Rates in Urban Areas Using Mobile Device Foot Traffic Data Olga Semukhina, Stan Korotchenko, Junghwan Bae, Christopher Copeland This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7634039/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This spatial inquiry addresses a key question in routine activity theory: does an influx of visitors into a neighborhood increase crime opportunities by increasing the number of motivated offenders and suitable targets, or does it enhance guardianship capacity? The study explores these dynamics across different crime types: aggravated assault, robbery, and motor vehicle theft using more accurate cell phone mobility data to estimate work-hour and after work hour visitors in Arlington, TX. Results from spatially lagged negative binomial regression models reveal that higher after-work hour visitor ratios are associated with increased aggravated assault and robbery frequency, likely due to more offenders and targets converging in these areas. Conversely, work hour visitor ratios show a stronger link to motor vehicle theft, suggesting that reduced guardianship during work hours may heighten crime opportunities. The relationships between visitor ratios and crime vary by crime type, indicating that temporal shifts in guardianship and offender-target interactions significantly influence urban crime patterns. These findings provide novel insights into the stability of crime patterns and the role of visitor dynamics, enriching the routine activity theory literature and offering implications for targeted crime prevention strategies in policing and urban planning. temporal visitor patterns crime incidence spatial analysis urban neighborhoods negative binomial regression aggravated assault robbery motor vehicle theft routine activity theory guardianship ambient population spatial analysis urban neighborhoods aggravated assault robbery motor vehicle theft Figures Figure 1 Figure 2 Introduction Does having more visitors in a neighborhood mean more crime? Specifically, does an influx of visitors at certain times of the day increase the number of potential offenders and suitable targets, or does it enhance guardianship by adding more people who can help prevent crime? Furthermore, is this effect consistent across different types of crime, or does it vary based on the specific crime being committed? These questions lie at the heart of a growing conversation within criminology, especially in the context of routine activity theory (RAT), which posits that crime occurs when a motivated offender, a suitable target, and the absence of capable guardianship converge (Cohen & Felson, 1979). This study seeks to address these questions by examining the effects of visitor inflows on urban crime rates, focusing on the temporal variations between work hours and after-work hours. Urban crime patterns have long intrigued researchers, particularly in terms of how shifts in population dynamics influence the opportunities for crime. While much of the previous research has concentrated on static residential populations, the dynamic nature of urban environments—especially after work hours—poses new questions about how the influx of visitors affects crime rates. As neighborhoods transition from bustling daytime activities to after-hours leisure and entertainment, the balance between increased crime opportunities and enhanced guardianship becomes more nuanced (Brantingham & Brantingham, 1995; Felson & Boivin, 2015). RAT has evolved to incorporate not only residential populations but also the role of non-residents who travel into neighborhoods for work, shopping, or leisure. Early research, such as Burgess’ (1925) ecological model, noted that individuals often commit crimes in areas where they do not reside, laying the groundwork for the “funnel hypothesis” (Brantingham & Brantingham, 1974; Brantingham & Brantingham, 1982). This hypothesis suggests that daily visitor movements funnel crime risks into specific areas, concentrating crime in neighborhoods with high foot traffic. However, the extent to which these visitor inflows influence different types of crime remains understudied, particularly regarding how these effects vary throughout the day and night. Recent research has expanded on RAT by integrating spatial and temporal dimensions, highlighting how non-residential populations create crime opportunities at certain times (Felson & Boivin, 2015; Long et al., 2021). For instance, while a surge in after-work visitors might increase crime opportunities by bringing more potential offenders and targets into an area, it could also bolster informal guardianship if those visitors are vigilant enough to deter criminal activity (Long et al., 2021). These competing perspectives raise important questions about whether visitor inflows enhance or reduce crime rates and whether these effects are stable across different types of crime, such as aggravated assault, robbery, and motor vehicle theft. Historically, studies on population dynamics and crime have relied on static census data to estimate the number of people present in an area, leading to potential inaccuracies when analyzing real-time crime risks (Andresen, 2011; Hipp & Kim, 2019). However, recent advancements in mobile tracking technology provide an opportunity to capture ambient populations more accurately. By utilizing mobile device data, this study examines the precise movements of both residents and visitors, offering a more granular understanding of how population shifts impact crime patterns in urban neighborhoods. This method allows researchers to assess real-time population dynamics, thus advancing previous work that primarily relied on census-derived estimates (Malleson & Andresen, 2016). In addition to filling gaps in the literature on visitor inflows and crime, this study contributes to urban planning and policing strategies. Understanding how after-hours population shifts affect crime can help policymakers design time-specific crime prevention measures, tailored to the changing composition of urban populations throughout the day. By analyzing the relationship between visitor inflows and crime, this research provides a fresh perspective on urban crime dynamics and offers practical insights into the development of targeted crime prevention strategies. Literature review The descriptive statistics for the variables analyzed in this study are presented in Table 1. These statistics offer an overview of the patterns of crime, visitor inflows, and neighborhood characteristics across the 164 census block groups (CBGs) in Arlington, Texas. Table 1 includes variables such as the ratio of visitors during work and after-work hours, crime rates for aggravated assault, robbery, and motor vehicle theft, as well as control variables like concentrated disadvantage, residential instability, and ethnic heterogeneity. Table 1 about here Table 1. Descriptive Statistics. Variable Means Standard deviation Range Aggravated assault 2.360 2.735 0-20 Robbery 1.140 1.723 0-9 Theft from motor vehicle 7.884 9.427 0-70 After-work hour visitor ratio 0.328 0.219 0.206-0.385 Work hour visitor ratio 0.671 0.239 0.614-0.793 Concentrated disadvantage 0.225 0.110 0.07-0.60 Residential instability 0.184 0.123 0.122-0.481 Ethnic heterogeneity 0.516 0.151 0.06-0.73 Population density 2.201 1.729 0.096-11.196 % aged 15 to 34 56.366 7.216 16.929-62.629 Apartment complexes 1.159 1.857 0-10 Check-cashing services 0.689 1.853 0-18 Alcohol shops 0.079 0.332 0-2 Hotels 0.409 1.534 0-13 Nightclubs/bars 0.134 0.581 0-5 Gas stations 0.408 0.601 0-4 Grocery stores 0.317 0.689 0-6 Note. N(block groups)=164. Of the three crime types, theft from motor vehicles has the highest average count (M = 7.88, SD = 9.43), reflecting common trends in urban property crime where high-traffic areas attract more vehicle-related thefts (Felson, 2006). Aggravated assault (M = 2.36, SD = 2.73) and robbery (M = 1.14, SD = 1.72) occur less frequently on average but exhibit considerable spatial variability across Arlington. These statistics are consistent with findings from other urban crime studies that have noted the uneven distribution of violent crimes across different neighborhood contexts (Weisburd et al., 2012). The data on visitor ratios highlights the distinct temporal dynamics within Arlington’s CBGs. On average, more visitors are present during work hours (M = 0.67, SD = 0.24) compared to after-work hours (M = 0.33, SD = 0.22), which is indicative of typical urban mobility patterns where economic activity draws in large daytime populations (Lan et al., 2019; Malleson & Andresen, 2016). These shifts in population composition are key in understanding how routine activity theory applies to both violent and property crime during different times of the day (Cohen & Felson, 1979). Figures 1 and 2 provide spatial representations of visitor patterns and crime incidents in Arlington. Figure 1 illustrates the variation in visitor proportions during work and after-work hours across different parts of the city, using a color gradient to represent the intensity of visitor concentrations. The geographic shift in visitor patterns from work to after-work hours shows higher daytime activity in central business districts and western industrial areas, while after-work visitor concentrations shift toward southern and northeastern residential and commercial zones. Figure 1 about here The spatial distribution of crime incidents, presented in Figure 2, shows that aggravated assault and robbery are concentrated in the northeastern and southern parts of Arlington, while motor vehicle thefts are more widely distributed. These maps reinforce the findings from routine activity theory and previous spatial crime studies that emphasize the importance of understanding crime attractors and generators (Brantingham & Brantingham, 1995). High-crime areas often align with zones of elevated visitor activity, particularly where commercial or entertainment venues create increased crime opportunities (Felson, 2006). Figure 2 about here The regression analysis employed a spatially lagged negative binomial model to explore the relationship between visitor patterns and crime incidence across Arlington’s census block groups (CBGs). This approach addresses both the overdispersion of crime data and the spatial dependence of crime within neighboring areas, aligning with recent spatial criminology research (Bernasco & Block, 2011; Long et al., 2021). As routine activity theory (Cohen & Felson, 1979) emphasizes, the convergence of motivated offenders, suitable targets, and the absence of guardians is critical to understanding crime patterns, and this study extends that framework by considering temporal shifts in visitor patterns and their impact on crime. Table 2 presents the results for after-work hour visitor proportions, showing a positive and significant relationship with violent crimes, specifically aggravated assault and robbery. These findings align with Felson and Boivin (2015), who highlighted that an increase in ambient population, particularly during leisure hours, intensifies the convergence of motivated offenders and suitable targets. In Model 1, a one standard deviation increase in after-work hour visitors is associated with a 19.2% increase in the expected incidence of aggravated assault (b = 1.562, SE = 0.780, p < 0.01). This relationship supports the notion that after-work visitor influxes reduce guardianship capacity, leading to increased opportunities for violent crime (Clarke & Eck, 2005). Table 2 about here Table 2. Negative Binominal Regression Model Results for After Work Hour Visitor Proportion. (Model 1) (Model 2) (Model 3) Aggravated assault Robbery Theft from motor vehicle Block group variables After-work hour visitor ratio 1.562** 3.386** 0.233 [0.780] [1.423] [0.607] Concentrated disadvantage 0.476 -0.168 0.609 [1.193] [1.871] [1.019] Residential instability 0.0248 0.821 -0.950 [0.882] [1.339] [0.768] Ethnic heterogeneity -0.861 1.976 0.405 [0.697] [1.371] [0.553] Population density 83.69 38.66 -9.431 [56.30] [75.10] [45.53] % aged 15 to 34 0.0313 0.0845 0.209** [0.121] [0.200] [0.0968] Apartment complexes 0.186** 0.243** 0.118** [0.0525] [0.0769] [0.0457] Alcohol shops 0.0279 -0.236 -0.231 [0.196] [0.322] [0.171] Gas stations 0.102 0.262* 0.0592 [0.112] [0.149] [0.0959] Nightclubs/bars 0.181 0.0386 0.0328 [0.142] [0.190] [0.130] Check-cashing services 0.0228 0.0614 0.101*** [0.0353] [0.0468] [0.0311] Hotels 0.0584 -0.116 0.0702 [0.0590] [0.0884] [0.0529] Grocery stores -0.101 0.271* 0.0533 [0.108] [0.152] [0.0937] Spatially lagged predictors After-work hour visitor ratio 3.582 10.65** 3.859* [3.392] [5.362] [1.725] Concentrated disadvantage 0.930 3.653 -9.054 [1.743] [2.819] [2.972] Residential instability -0.671 0.931 -4.151 [1.903] [3.082] [1.478] Ethnic heterogeneity -0.0513 0.378 -0.831 [1.150] [1.943] [1.515] Population density 222.8* 39.84 2.604* [121.7] [186.1] [0.992] % aged 15 to 34 0.115 0.178 272.0* [0.242] [0.346] [99.80] Apartment complexes -0.265 -0.0851 0.264 [0.204] [0.276] [0.195] Alcohol shops -0.313 1.255* -0.0354 [0.443] [0.640] [0.369] Gas stations -0.264 -0.299 -0.140 [0.215] [0.338] [0.166] Nightclubs/bars -0.308 0.181 -0.371 [0.321] [0.490] [0.286] Check-cashing services 0.0255 -0.199 -0.012 [0.0492] [0.111] [0.0466] Hotels 0.152 -0.146 0.372* [0.139] [0.221] [0.144] Grocery stores -0.071 0.005 -0.172 [0.233] [0.342] [0.206] Spatially lagged outcome Aggravated assault 0.056 [0.062] Robbery -0.268 [0.147] Motor vehicle theft 0.001 [0.013] Constant -2.350 -8.516** 2.708* [1.774] [2.684] [1.181] Observations 164 164 164 Note. Standard errors in brackets ** p<0.01, * p<0.05 Similarly, Model 2 reveals that the ratio of after-work hour visitors is significantly associated with a 46.2% increase in robbery incidents (b = 3.386, SE = 1.423, p < 0.01). This result is consistent with the funneling hypothesis (Brantingham & Brantingham, 1995), which suggests that non-residential activities such as shopping, nightlife, and entertainment can create concentrated crime spots in areas that receive a large influx of visitors during non-work hours. Importantly, this finding further emphasizes the role of commercial establishments, such as check-cashing services and nightclubs, as significant attractors of crime, echoing the conclusions of prior studies on crime attractors (Bernasco & Block, 2011; Felson, 2006). Interestingly, no significant relationship was found between after-work hour visitor ratios and motor vehicle theft (Model 3). This suggests that while after-work hours create more opportunities for violent crimes due to social interactions and leisure activities, they may not have the same effect on property crimes such as vehicle theft. This finding diverges from earlier studies by Hipp and Kim (2019), who found ambient populations to be significant predictors of property crimes. It may be that during after-work hours, increased surveillance or limited vehicle accessibility reduces the opportunity for motor vehicle theft in these areas. In contrast to the findings for after-work visitors, the relationship between work hour visitor ratios and motor vehicle theft is stronger and statistically significant (Table 3, Model 6). The results indicate a 19.7% increase in motor vehicle theft incidents for each one standard deviation increase in work-hour visitor ratios (b = 7.129, SE = 2.591, p < 0.05). This association supports the theory that higher ambient populations during work hours, particularly in commercial or office districts, create opportunities for theft from unattended vehicles (Andresen, 2011; Malleson & Andresen, 2016). The concentration of visitors in central and western parts of the city, as seen in Figure 1, corresponds with areas of high vehicle traffic, further supporting the routine activity theory’s emphasis on target suitability and the absence of capable guardianship during working hours (Cohen & Felson, 1979). Table 3. Negative Binominal Regression Models for Work Hour Visitor Proportion. (Model 4) (Model 5) (Model 6) Aggravated assault Robbery Theft from motor vehicle Block group variables Work hour visitor ratio 2.490 4.944 7.129* [3.259] [5.044] [2.591] Concentrated disadvantage 0.637* -0.111 0.740 [1.208] [1.911] [0.998] Residential instability 0.027 0.854 -0.986 [0.896] [1.364] [0.755] Ethnic heterogeneity -0.836 2.288 0.383 [0.703] [1.396] [0.544] Population density 70.260 31.87 -15.81 [55.99] [76.21] [44.06] % aged 15 to 34 0.026 0.058 0.217** [0.123] [0.200] [0.0946] Apartment complexes 0.190** 0.256** 0.118* [0.053] [0.0807] [0.0445] Alcohol shops 0.104 -0.113 -0.180 [0.199] [0.335] [0.167] Gas stations 0.137 0.354* 0.081 [0.112] [0.151] [0.094] Nightclubs/bars 0.162 0.0430 -0.0192 [0.146] [0.198] [0.126] Check-cashing services 0.0347 0.0906* 0.097* [0.0353] [0.0474] [0.030] Hotels 0.034 -0.186* 0.0362 [0.062] [0.0963] [0.052] Grocery stores -0.101 0.271* 0.053 [0.108] [0.152] [0.094] Spatially lagged predictors Work hour visitor ratio -0.0232 0.403** 0.098 [0.108] [0.156] [0.0896] Concentrated disadvantage 1.207 6.317 -6.354* [3.584] [5.584] [2.800] Residential instability 0.807 4.016 -3.872** [1.765] [2.883] [1.502] Ethnic heterogeneity -0.605 0.670 -0.478 [1.928] [3.113] [1.625] Population density 0.286 0.759 2.090* [1.153] [1.985] [0.915] % aged 15 to 34 218.7* 7.066 299.6** [123.1] [190.3] [100.9] Apartment complexes 0.115 0.208 0.150 [0.245] [0.349] [0.198] Alcohol shops -0.229 -0.011 -0.056 [0.207] [0.283] [0.167] Gas stations -0.284 1.408* -0.230 [0.447] [0.668] [0.360] Nightclubs/bars -0.287 -0.364 -0.165 [0.223] [0.373] [0.167] Check-cashing services -0.255 0.343 -0.248 [0.325] [0.505] [0.281] Hotels 0.046 -0.177 0.002 [0.0500] [0.118] [0.0452] Grocery stores 0.173 -0.129 0.351* [0.142] [0.234] [0.139] Spatially lagged outcome 0.00772 0.148 -0.192 Aggravated assault 0.0504 [0.352] [0.197] [0.0621] Robbery -0.274 [0.150] Motor vehicle theft 0.006 [0.0124] Constant -1.196 -7.750** 1.592 [1.597] [2.628] [1.225] Observations 164 164 164 Note. Standard errors in brackets. *** p<0.001, ** p<0.01, * p<0.5. However, work hour visitor proportions did not exhibit a significant relationship with aggravated assault or robbery (Models 4 and 5), which suggests that opportunities for violent crime are less dependent on the influx of visitors during work hours. This finding resonates with the work of Felson and Poulsen (2003), who found that violent crimes are more likely to occur during after-work leisure periods rather than the structured environments of work hours, where formal guardianship (e.g., security personnel) may be more prevalent. Spatial spillover effects were also observed in the analysis, particularly for violent crimes. As shown in Table 2, the spatially lagged after-work hour visitor ratio is significantly associated with robbery in adjacent CBGs (b = 10.65, SE = 5.362, p < 0.01). This finding underscores the importance of considering the interconnectedness of urban spaces and aligns with the idea that crime in one area can influence nearby regions (Boessen & Hipp, 2015). The funneling effect of visitors into certain neighborhoods not only increases crime risks locally but also creates a ripple effect, expanding crime hotspots into surrounding areas (Brantingham & Brantingham, 1995). Moreover, the spatial lag of motor vehicle theft during work hours demonstrates a similar pattern, where vehicle theft incidents in one block group are influenced by thefts in neighboring CBGs (Table 3, Model 6). This finding supports previous research on the clustering of property crimes in urban areas (Johnson, 2010), suggesting that criminal opportunities extend beyond the immediate vicinity of high-traffic areas and into nearby block groups, reinforcing the need for comprehensive urban planning and policing strategies that account for these spatial dynamics. The inclusion of control variables in the models highlights the consistent role of neighborhood structural factors in shaping crime patterns. Concentrated disadvantage and residential instability, two key variables grounded in social disorganization theory, were significantly related to aggravated assault and robbery (Sampson, Raudenbush, & Earls, 1997). These findings mirror earlier studies that emphasized the role of socioeconomic conditions in exacerbating crime risks (Morenoff & Sampson, 1997). Additionally, the number of apartment complexes and check-cashing services were significant predictors of crime across several models, consistent with opportunity theories of crime that highlight the role of certain land uses in attracting criminal activity (Bernasco & Block, 2011). Overall, the regression analysis demonstrates that visitor patterns have a differential impact on crime depending on the time of day and the type of crime being examined. After-work hour visitors are more strongly associated with violent crimes, such as aggravated assault and robbery, while work-hour visitors are more likely to influence property crimes, particularly motor vehicle theft. These findings contribute to the growing body of literature on ambient population dynamics and their role in shaping urban crime patterns (Felson & Boivin, 2015; Hipp & Kim, 2019). Additionally, the spatial spillover effects observed in this study highlight the importance of adopting a spatially-aware approach to crime prevention and urban planning. Discussion The study reinforces the idea that the size of the ambient population, particularly visitors, can be a strong predictor of both violent and property crimes using more accurate cell phone data that previous literature. Our study’s primary contribution is the insight that different types of ambient populations—those present during working hours versus after working hours—may have varying impacts on property and personal crimes such as aggravated assaults, robberies and motor vehicle thefts. Consistent with the tenets of routine activity theory (RAT), this study demonstrates that higher proportions of after-hours visitors positively impact the occurrence of aggravated assaults and robberies, even when controlling for social disorganization, risky places, and static population density. RAT posits that crime arises when a motivated offender, a suitable target, and a lack of capable guardianship converge. After-work hours increase the availability of potential targets—such as individuals carrying cash or electronics—while capable guardianship (e.g., police or security patrols) may decrease. Additionally, after-hours visitors are more likely to be in the area for entertainment purposes, including alcohol consumption and visits to risky facilities. Alcohol consumption is a significant risk factor for both aggravated assault and robbery, as it impairs judgment and increases vulnerability. Therefore, a higher proportion of after-hours visitors can result in a larger pool of potential victims in the area. Moreover, after-work hours attract predictable crowds to nightlife districts, entertainment venues, and transit hubs. These patterns allow offenders to anticipate victim behaviors and locations, thereby increasing the likelihood of personal crimes, in line with crime pattern theory. Finally, the influx of visitors can weaken social controls and erode social bonds in neighborhoods already struggling with social disorganization, further elevating the likelihood of crimes such as assault and robbery. Our study also found that higher proportions of visitors during work hours increased the probability of motor vehicle theft, whereas a higher proportion of after-hours visitors did not. One plausible explanation for this finding is the duration of time that work-hour visitors spend away from their vehicles while working or attending to other businesses. The longer a vehicle is left parked without supervision, the greater the opportunity for theft. Our study offers several key implications for crime prevention and community safety. First, city planners and urban administrators must anticipate potential increases in assaults and robberies when expanding after-hour venues in mixed-use areas, particularly those serving alcohol. This includes designing these new spaces in alignment with Crime Prevention Through Environmental Design (CPTED) principles. For example, urban designs should minimize hiding spots and enhance visibility and street lighting capacity. For areas with a higher density of after-hour venues, additional measures should focus on effective guardianship. This may include increasing patrol presence during late hours, as well as implementing security measures by the venues themselves and other space managers. Bars, restaurants, and entertainment venues situated in areas with significant late-night foot traffic could be required to invest in extra security, such as hiring private security personnel or installing surveillance systems, to deter crime and safeguard patrons. Furthermore, parking lots located in areas with significant daytime foot traffic should be required to implement additional security measures to prevent an increase in motor vehicle theft. Since our model found a strong, positive spatial lag for after-hour traffic in relation to robbery and motor vehicle theft, it's crucial to extend these preventive measures to neighboring areas to prevent a spillover effect of crime. Thus, a detailed analysis of foot traffic data—considering factors like the time of day and other relevant characteristics—should be a central element in crime analysis. It is also vital in shaping crime prevention strategies, guiding community development, and issuing licenses to new businesses. Alternative Discussion This study sought to clarify how time-specific influxes of non-resident visitors shape both violent and property crime in an urban context, using Arlington, Texas, as a case study. By leveraging high-resolution mobile-device location data alongside spatially lagged negative binomial regression models, we identified distinct and temporally contingent relationships between visitor presence and different crime types. Our findings indicate that not all visitors are equal in their influence on crime, nor are all time periods equally criminogenic. After-work hour visitors were positively associated with violent offenses—specifically aggravated assault and robbery—while work-hour visitors were significantly linked to motor vehicle theft. Moreover, spatial lag terms confirmed that these effects do not respect administrative boundaries, diffusing outward to adjacent census block groups. Below, we revisit our theoretical framework, integrate our results with existing scholarship, and outline their implications for crime prevention policy and future research. Routine Activity Theory (Cohen & Felson, 1979) offers a compelling lens for interpreting these findings. As theorized, crime is most likely to occur when motivated offenders, suitable targets, and the absence of capable guardianship coincide in time and space. Our study contributes to this framework in several critical ways: First, our findings highlight the temporal specificity of guardianship. Consistent with Felson and Boivin’s (2015) “daily crime flow” model, the positive association between after-work visitors and violent crimes suggests that evening leisure hours diminish both formal (e.g., private security) and informal (e.g., social cohesion) guardianship, while simultaneously increasing the number of motivated offenders and accessible targets. As nightlife venues attract patrons unfamiliar with neighborhood norms, the potential for confrontational encounters escalates. Second, our results underscore the need for crime-type differentiation within ambient population research. While many prior studies have aggregated crime into a single outcome (e.g., Hipp & Kim, 2019), our disaggregated approach revealed that work-hour visitor volumes specifically predict motor vehicle theft—a property crime characterized by low confrontation risk and reliance on unsupervised targets. This nuance aligns with Andresen’s (2011) argument that ambient population growth may increase opportunities without proportionally bolstering guardianship. Third, the spatial interdependence of urban places is underscored by our significant spatial lag terms. After-work visitor inflows not only heightened local robbery risk but also spilled over into neighboring areas, validating Brantingham and Brantingham’s (1995) funnel hypothesis. This finding suggests that urban visitor flows radiate criminogenic pressures beyond their immediate locus, complicating neighborhood-level intervention strategies. This study also offers a methodological contribution by leveraging year-long, GPS-based mobility data to capture the ambient population. Prior research frequently relied on static census data (Boessen & Hipp, 2015) or sporadic geotagged social media posts (Malleson & Andresen, 2015), each limited by demographic skew and temporal coarseness. By distinguishing between work-hour and after-work hour visitor ratios, our study meets calls for next-generation ambient population metrics (Hipp et al., 2019; Lafrogne-Joussier & Rollet, 2025), offering stronger explanatory power and more refined crime-specific insights. Our results carry actionable implications for urban planners, law enforcement, and policymakers. First, targeted policing strategies should prioritize census block groups—and their contiguous neighbors—that experience sharp spikes in after-work visitation, especially those housing alcohol-serving establishments. Directed patrols during peak evening hours may help mitigate the elevated risk of violent crimes. Second, urban planners and regulatory agencies should integrate crime prevention through environmental design (CPTED) principles into the review of proposed nightlife venues. Enhanced street lighting, strategic CCTV placement, and unobstructed sightlines can deter crime in high-traffic areas (Clarke & Eck, 2005). Third, the strong link between work-hour visitors and vehicle theft points to the need for improved parking lot security in commercial areas. Employers and property owners might consider investing in license plate recognition technologies, gated access systems, and high-visibility security patrols during daytime business hours. Finally, because crime risks spill across administrative boundaries, municipal coordination is essential. Inter-jurisdictional crime prevention programs—particularly those that share visitor flow data—may be better positioned to contain and anticipate shifts in crime hotspots. This study has several limitations. First, it is based on a single city with a unique suburban-urban composition, potentially limiting generalizability to other contexts such as dense metropolitan cores or rural towns. Second, while smartphone ownership is widespread, certain populations (e.g., the elderly, unhoused, or very young) are underrepresented in GPS-based mobility data. Third, the use of a single pre-pandemic year (2019) provides a stable baseline but precludes insights into how crisis-induced mobility disruptions (e.g., COVID-19) might reshape these dynamics. Fourth, we lacked micro-level data on the purpose of visits (e.g., commuter vs. nightlife patron) and on formal guardianship (e.g., private security deployment), which limits the granularity of mechanism testing. Several promising avenues for future inquiry emerge. Multi-city studies could assess whether the visitor-crime relationships documented here generalize to urban centers with different built environments and transit infrastructures. Second, integrating point-of-interest (POI) dwell time data would allow researchers to segment visitor types and isolate the crime implications of specific activities (e.g., work, shopping, leisure). Third, natural experiments, such as the staggered opening of nightlife venues, could facilitate stronger causal inferences. Finally, overlaying guardianship infrastructure—like private security density or camera networks—onto ambient mobility flows could allow for direct tests of interaction effects predicted by RAT and situational crime prevention theory. In sum, this study affirms that the criminological significance of urban mobility lies not only in how many people move through space but also in when and why they do so. After-work visitors elevate violent crime risk, while work-hour visitors facilitate property crime—particularly motor vehicle theft. These relationships extend across geographic boundaries, creating ripple effects that call for coordinated policy responses. As urban landscapes grow more dynamic and mixed-use, real-time insights into temporal visitor patterns offer a powerful tool for forecasting and preventing crime in an evidence-informed, equitable manner. Declarations Author Contribution OB - conceptualization, idea, draft writingSK - literature review and discussion draftingJB- data analysis and findings section draftingCC- data preparation and curation, analysis Acknowledgement Declaration of Generative AI and AI-assisted technologies in the writing processDuring the preparation of this work the author(s) used ChatGPT-5 (OpenAI, August 2025 version) to improve readability and grammar. After using this tool, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. References Allison, D. P. (1999). Multiple Regression: A Primer . California: Pine Forge Press. Boivin, R. & Felson, M. (2018). Crimes by Visitors Versus Crimes by Residents: The Influence of Visitor Inflows. Journal of Quantitative Criminology . 34:465-480. Andresen, M. A. (2006). Crime measures and the spatial analysis of criminal activity. British Journal of criminology , 46 (2), 258-285. Andresen, M. A. (2011). The ambient population and crime analysis. The Professional Geographer , 63 (2), 193-212. Andresen, M. A., & Jenion, G. W. (2010). Ambient populations and the calculation of crime rates and risk. Security journal , 23 , 114-133. Anselin, L. (1990). Spatial dependence and spatial structural instability in applied regression analysis. Journal of Regional science , 30 (2), 185-207. Bernasco, W., & Block, R. (2011). Robberies in Chicago: A Block-Level Analysis of the Influence of Crime Generators, Crime Attractors, and Offender Anchor Points. Journal of Research in Crime and Delinquency , 48 (1), 33-57. https://doi.org/10.1177/0022427810384135 Boessen, A., & Hipp, J. R. (2015). Close‐ups and the scale of ecology: Land uses and the geography of social context and crime. Criminology , 53 (3), 399-426. 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. Boivin, R., & Felson, M. (2018). Crimes by visitors versus crimes by residents: the influence of visitor inflows. Journal of Quantitative Criminology , 34 , 465-480. Brantingham, P., & Brantingham, P. (1995). Criminality of place: Crime generators and crime attractors. Eur. J. on Crim. Pol'y & Rsch. , 3 , 5. Brantingham, P. J., & Brantingham, P. L. (1974). The spatial patterning of burglary. How. J. Penology & Crime Prevention , 14 , 11. Brantingham, P. L., & Brantingham, P. J. (1982). Mobility, notoriety and crime: A study of crime patterns in urban nodal points. Journal of Environmental Systems , 11 (1), 89-99. Brantingham, P. L., & Brantingham, P. J. (1999). A theoretical model of crime hot spot generation. Studies on Crime & Crime Prevention . Brunsdon, C., Corcoran, J., & Higgs, G. (2007). Visualising space and time in crime patterns: A comparison of methods. Computers, Environment and Urban Systems , 31 (1), 52-75. Burgess, E. W. (1925). Can neighborhood work have a scientific basis. In R. E. Park, Burgess. E.W., McKenzie, R.D. (Ed.), The city: Suggestions for investigation of human behaviors in the urban environment (pp. 142-155). University of Chicago Press. Clarke, R. V., & Eck, J. E. (2005). Crime analysis for problem solvers in 60 small steps . US Department of Justice, Office of Community Oriented Policing Services …. Cohen, L. E., & Felson, M. (1979). Social Change and Crime Rate Trends: A Routine Activity Approach. American Sociological Review , 44 (4), 588-608. Crum, J. D., & Ramey, D. M. (2023). Impact of extralegal and community factors on police officers’ decision to book arrests for minor offenses. American Journal of Criminal Justice , 48 (3), 572-601. Felson, M. (2006). Crime and nature . Sage publications. Felson, M., & Boba, R. L. (2010). Crime and everyday life . Sage. Felson, M., & Boivin, R. (2015). Daily crime flows within a city. Crime Science , 4 , 1-10. Felson, M., & Cohen, L. E. (1979). Human-Ecology and Crime - a Routine Activity Approach. Human Ecology , 8 (4), 389-406. https://doi.org/Doi 10.1007/Bf01561001 Felson, M., & Eckert, M. A. (2018). Crime and everyday life: A brief introduction . Sage Publications. Felson, M., & Poulsen, E. (2003). Simple indicators of crime by time of day. International Journal of Forecasting , 19 (4), 595-601. Groff, E. R., & La Vigne, N. G. (2002). Forecasting the future of predictive crime mapping. Crime Prevention Studies , 13 , 29-58. Haleem, M. S., Do Lee, W., Ellison, M., & Bannister, J. (2021). The ‘exposed’population, violent crime in public space and the night-time economy in Manchester, UK. European Journal on Criminal Policy and Research , 27 , 335-352. Hipp, J. R., Bates, C., Lichman, M., & Smyth, P. (2019). Using social media to measure temporal ambient population: Does it help explain local crime rates? Justice Quarterly , 36 (4), 718-748. Hipp, J. R., & Kim, Y.-A. (2019). Explaining the temporal and spatial dimensions of robbery: Differences across measures of the physical and social environment. Journal of Criminal Justice , 60 , 1-12. Johnson, S. D. (2010). A brief history of the analysis of crime concentration. European Journal of Applied Mathematics , 21 (4-5), 349-370. Lafrogne-Joussier, R., & Rollet, V. (2025). Ambient Density and Urban Crime: Evidence from Smartphone Data (April 10, 2025) (Social Science Research Network (SSRN), Issue. I. Elsevier. https://ssrn.com/abstract=4440554 Lan, M., Liu, L., Hernandez, A., Liu, W., Zhou, H., & Wang, Z. (2019). The spillover effect of geotagged tweets as a measure of ambient population for theft crime. sustainability , 11 (23), 6748. Long, D., Liu, L., Xu, M., Feng, J., Chen, J., & He, L. (2021). Ambient population and surveillance cameras: The guardianship role in street robbers' crime location choice. Cities , 115 , 103223. Long, J. S. (1997). Regression models for categorical and limited dependent variables. Advanced quantitative techniques in the social sciences , 7 . Malleson, N., & Andresen, M. A. (2015). The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science , 42 (2), 112-121. Malleson, N., & Andresen, M. A. (2016). Exploring the impact of ambient population measures on London crime hotspots. Journal of Criminal Justice , 46 , 52-63. McDowall, D., & Loftin, C. (2009). Do US city crime rates follow a national trend? The influence of nationwide conditions on local crime patterns. Journal of Quantitative Criminology , 25 , 307-324. Morenoff, J. D., & Sampson, R. J. (1997). Violent crime and the spatial dynamics of neighborhood transition: Chicago, 1970–1990. Social Forces , 76 (1), 31-64. Ratcliffe, J. H. (2004). The hotspot matrix: A framework for the spatio‐temporal targeting of crime reduction. Police practice and research , 5 (1), 5-23. Rossmo, D. K., Lu, Y., & Fang, T. B. (2012). Spatial-temporal crime paths. In Patterns, prevention, and geometry of crime (pp. 16-42). Routledge. Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science , 277 (5328), 918-924. Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas . University of Chicago Press. Townsley, M., Reid, S., Reynald, D., Rynne, J., & Hutchins, B. (2014). Risky facilities: Analysis of crime concentration in high-rise buildings. Trends and Issues in Crime and Criminal Justice (476), 1-7. Tseloni, A., Wittebrood, K., Farrell, G., & Pease, K. (2004). Burglary victimization in England and Wales, the United States and the Netherlands: A cross-national comparative test of routine activities and lifestyle theories. British Journal of criminology , 44 (1), 66-91. Weisburd, D., Groff, E., & Yang, S.-M. (2012). The criminology of place : street segments and our understanding of the crime problem . Oxford University Press. http://dx.doi.org/10.1093/acprof:oso/9780195369083.001.0001 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7634039","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521767617,"identity":"7a97dba7-9ff0-49d2-b911-722a750db495","order_by":0,"name":"Olga Semukhina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYJACxsYGCQYG9gYgswAqxEOUFp7DQKYBgwSxWoCkRDKRWuTbu9MkZ+6wyOOf+f7g5woDmzp+6QbGB2/bcGsxOHN2m+TGMxLFEreTmSXPGKRJSM45wGw4F58Widxtkg/bJBIbbiczSDYYHJYwuJHAJs2LR4v8/LcQLfNvHmb+2WDwH6SF/Tc+LQw3eIEOA2rZcIOZDWjLAbAtzPi0GJzJ3Ww5E6hl45lkM8sGg2TJmTMSmyXnnMPjsPazG2/2ttUlzjt+8PHNhgo7fn6J5IMf3pThcRgWwNhAmvpRMApGwSgYBRgAAKL4UtsfIl+uAAAAAElFTkSuQmCC","orcid":"","institution":"Tarleton State University","correspondingAuthor":true,"prefix":"","firstName":"Olga","middleName":"","lastName":"Semukhina","suffix":""},{"id":521767618,"identity":"4274f172-0b4b-4fc4-bb08-181cdb02fa0d","order_by":1,"name":"Stan Korotchenko","email":"","orcid":"","institution":"Tarleton State University","correspondingAuthor":false,"prefix":"","firstName":"Stan","middleName":"","lastName":"Korotchenko","suffix":""},{"id":521767619,"identity":"69ad7006-eca7-4f7c-8ae0-058bc50d5c7a","order_by":2,"name":"Junghwan Bae","email":"","orcid":"","institution":"Tarleton State University","correspondingAuthor":false,"prefix":"","firstName":"Junghwan","middleName":"","lastName":"Bae","suffix":""},{"id":521767620,"identity":"3a3ef491-8bed-442e-9d9b-7eb8d534ff47","order_by":3,"name":"Christopher Copeland","email":"","orcid":"","institution":"Tarleton State University","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Copeland","suffix":""}],"badges":[],"createdAt":"2025-09-16 22:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7634039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7634039/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92438552,"identity":"5b77f01c-7709-4098-b828-fcfd8acd6c8e","added_by":"auto","created_at":"2025-09-29 17:47:50","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1370699,"visible":true,"origin":"","legend":"","description":"","filename":"Mainmansucript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/35a2e9d2f6bd5077a04bf39e.docx"},{"id":92438550,"identity":"116a0ef3-71c8-4939-8547-7e3f818ad5c6","added_by":"auto","created_at":"2025-09-29 17:47:50","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6011,"visible":true,"origin":"","legend":"","description":"","filename":"e8c40b3cc62740148f6fa6dea90db497.json","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/1741740d12a5c7cd8696628e.json"},{"id":92438554,"identity":"339d95e8-1494-4fb2-9756-4e36820185a7","added_by":"auto","created_at":"2025-09-29 17:47:50","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185332,"visible":true,"origin":"","legend":"","description":"","filename":"e8c40b3cc62740148f6fa6dea90db4971enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/2aee9703b721975d30ea4ef1.xml"},{"id":92439215,"identity":"9e2af9c0-41b9-43e1-b1b1-c99fe6891335","added_by":"auto","created_at":"2025-09-29 17:55:50","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":487715,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/0511cada30a60960b3d9e602.png"},{"id":92439216,"identity":"ca61dc5a-bfb6-4281-9088-ffcc85744de1","added_by":"auto","created_at":"2025-09-29 17:55:50","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":788205,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/b55b509ff34f9711665bada7.png"},{"id":92438548,"identity":"cffdbeee-bf47-461a-ab42-015e1591e2d0","added_by":"auto","created_at":"2025-09-29 17:47:50","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70222,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/b271ac6de16ca25504a17319.png"},{"id":92438549,"identity":"02705d48-3931-4bce-854a-b1fec1a982d8","added_by":"auto","created_at":"2025-09-29 17:47:50","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112260,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/5492d0a49a59cf5aa3b7bdb2.png"},{"id":92438556,"identity":"ab6c7db5-bd09-41cb-9d78-65eada74482d","added_by":"auto","created_at":"2025-09-29 17:47:51","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182901,"visible":true,"origin":"","legend":"","description":"","filename":"e8c40b3cc62740148f6fa6dea90db4971structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/b5e2e725008b3865cef5a91f.xml"},{"id":92438557,"identity":"9e07c800-9cbf-411a-8d73-c39165b1a25b","added_by":"auto","created_at":"2025-09-29 17:47:51","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":189858,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/0f74cb882ee2415662bb7e1a.html"},{"id":92438546,"identity":"c04d1cf4-bac6-422a-aede-1b9ffb7d648d","added_by":"auto","created_at":"2025-09-29 17:47:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":402262,"visible":true,"origin":"","legend":"\u003cp\u003eMaps for Visitor Proportion for Work hour and After-work hour in Arlington, TX in 2019.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/5919ff4d4653a6e13b13e073.png"},{"id":92438547,"identity":"a5172a0d-e7ba-4d95-97ac-c31fbd2dffef","added_by":"auto","created_at":"2025-09-29 17:47:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":533043,"visible":true,"origin":"","legend":"\u003cp\u003eMaps for Crime Incidents in Arlington, TX in 2019.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/3f2625a2c50aa5f7b75a39f2.png"},{"id":93111973,"identity":"fc1461f6-f759-4994-9094-0fef3196db1d","added_by":"auto","created_at":"2025-10-09 07:55:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1515608,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7634039/v1/eccef6f5-2704-4a72-9503-3b3900c758a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDo More Visitors Mean More Crime? Analyzing the Impact of Visitor Patterns on Crime Rates in Urban Areas Using Mobile Device Foot Traffic Data\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDoes having more visitors in a neighborhood mean more crime? Specifically, does an influx of visitors at certain times of the day increase the number of potential offenders and suitable targets, or does it enhance guardianship by adding more people who can help prevent crime? Furthermore, is this effect consistent across different types of crime, or does it vary based on the specific crime being committed? These questions lie at the heart of a growing conversation within criminology, especially in the context of routine activity theory (RAT), which posits that crime occurs when a motivated offender, a suitable target, and the absence of capable guardianship converge (Cohen \u0026amp; Felson, 1979). This study seeks to address these questions by examining the effects of visitor inflows on urban crime rates, focusing on the temporal variations between work hours and after-work hours.\u003c/p\u003e\n\u003cp\u003eUrban crime patterns have long intrigued researchers, particularly in terms of how shifts in population dynamics influence the opportunities for crime. While much of the previous research has concentrated on static residential populations, the dynamic nature of urban environments—especially after work hours—poses new questions about how the influx of visitors affects crime rates. As neighborhoods transition from bustling daytime activities to after-hours leisure and entertainment, the balance between increased crime opportunities and enhanced guardianship becomes more nuanced (Brantingham \u0026amp; Brantingham, 1995; Felson \u0026amp; Boivin, 2015).\u003c/p\u003e\n\u003cp\u003eRAT has evolved to incorporate not only residential populations but also the role of non-residents who travel into neighborhoods for work, shopping, or leisure. Early research, such as Burgess’ (1925) ecological model, noted that individuals often commit crimes in areas where they do not reside, laying the groundwork for the “funnel hypothesis” (Brantingham \u0026amp; Brantingham, 1974; Brantingham \u0026amp; Brantingham, 1982). This hypothesis suggests that daily visitor movements funnel crime risks into specific areas, concentrating crime in neighborhoods with high foot traffic. However, the extent to which these visitor inflows influence different types of crime remains understudied, particularly regarding how these effects vary throughout the day and night.\u003c/p\u003e\n\u003cp\u003eRecent research has expanded on RAT by integrating spatial and temporal dimensions, highlighting how non-residential populations create crime opportunities at certain times (Felson \u0026amp; Boivin, 2015; Long et al., 2021). For instance, while a surge in after-work visitors might increase crime opportunities by bringing more potential offenders and targets into an area, it could also bolster informal guardianship if those visitors are vigilant enough to deter criminal activity (Long et al., 2021). These competing perspectives raise important questions about whether visitor inflows enhance or reduce crime rates and whether these effects are stable across different types of crime, such as aggravated assault, robbery, and motor vehicle theft.\u003c/p\u003e\n\u003cp\u003eHistorically, studies on population dynamics and crime have relied on static census data to estimate the number of people present in an area, leading to potential inaccuracies when analyzing real-time crime risks (Andresen, 2011; Hipp \u0026amp; Kim, 2019). However, recent advancements in mobile tracking technology provide an opportunity to capture ambient populations more accurately. By utilizing mobile device data, this study examines the precise movements of both residents and visitors, offering a more granular understanding of how population shifts impact crime patterns in urban neighborhoods. This method allows researchers to assess real-time population dynamics, thus advancing previous work that primarily relied on census-derived estimates (Malleson \u0026amp; Andresen, 2016).\u003c/p\u003e\n\u003cp\u003eIn addition to filling gaps in the literature on visitor inflows and crime, this study contributes to urban planning and policing strategies. Understanding how after-hours population shifts affect crime can help policymakers design time-specific crime prevention measures, tailored to the changing composition of urban populations throughout the day. By analyzing the relationship between visitor inflows and crime, this research provides a fresh perspective on urban crime dynamics and offers practical insights into the development of targeted crime prevention strategies.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003eThe descriptive statistics for the variables analyzed in this study are presented in Table 1. These statistics offer an overview of the patterns of crime, visitor inflows, and neighborhood characteristics across the 164 census block groups (CBGs) in Arlington, Texas. Table 1 includes variables such as the ratio of visitors during work and after-work hours, crime rates for aggravated assault, robbery, and motor vehicle theft, as well as control variables like concentrated disadvantage, residential instability, and ethnic heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1 about here\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 1. Descriptive Statistics.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eMeans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003cp\u003edeviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eAggravated assault\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e2.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e2.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0-20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eRobbery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0-9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eTheft from motor vehicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e7.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e9.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0-70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eAfter-work hour visitor ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.206-0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eWork hour visitor ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.614-0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eConcentrated disadvantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.07-0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eResidential instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.122-0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eEthnic heterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.06-0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e2.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.096-11.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003e% aged 15 to 34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e56.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e7.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e16.929-62.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eApartment complexes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eCheck-cashing services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0-18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eAlcohol shops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eHotels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eNightclubs/bars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eGas stations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eGrocery stores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote. N(block groups)=164.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf the three crime types, theft from motor vehicles has the highest average count (M = 7.88, SD = 9.43), reflecting common trends in urban property crime where high-traffic areas attract more vehicle-related thefts (Felson, 2006). Aggravated assault (M = 2.36, SD = 2.73) and robbery (M = 1.14, SD = 1.72) occur less frequently on average but exhibit considerable spatial variability across Arlington. These statistics are consistent with findings from other urban crime studies that have noted the uneven distribution of violent crimes across different neighborhood contexts (Weisburd et al., 2012).\u003c/p\u003e\n\u003cp\u003eThe data on visitor ratios highlights the distinct temporal dynamics within Arlington\u0026rsquo;s CBGs. On average, more visitors are present during work hours (M = 0.67, SD = 0.24) compared to after-work hours (M = 0.33, SD = 0.22), which is indicative of typical urban mobility patterns where economic activity draws in large daytime populations (Lan et al., 2019; Malleson \u0026amp; Andresen, 2016). These shifts in population composition are key in understanding how routine activity theory applies to both violent and property crime during different times of the day (Cohen \u0026amp; Felson, 1979).\u003c/p\u003e\n\u003cp\u003eFigures 1 and 2 provide spatial representations of visitor patterns and crime incidents in Arlington. Figure 1 illustrates the variation in visitor proportions during work and after-work hours across different parts of the city, using a color gradient to represent the intensity of visitor concentrations. The geographic shift in visitor patterns from work to after-work hours shows higher daytime activity in central business districts and western industrial areas, while after-work visitor concentrations shift toward southern and northeastern residential and commercial zones.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 1 about here\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial distribution of crime incidents, presented in Figure 2, shows that aggravated assault and robbery are concentrated in the northeastern and southern parts of Arlington, while motor vehicle thefts are more widely distributed. These maps reinforce the findings from routine activity theory and previous spatial crime studies that emphasize the importance of understanding crime attractors and generators (Brantingham \u0026amp; Brantingham, 1995). High-crime areas often align with zones of elevated visitor activity, particularly where commercial or entertainment venues create increased crime opportunities (Felson, 2006).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 2 about here\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe regression analysis employed a spatially lagged negative binomial model to explore the relationship between visitor patterns and crime incidence across Arlington\u0026rsquo;s census block groups (CBGs). This approach addresses both the overdispersion of crime data and the spatial dependence of crime within neighboring areas, aligning with recent spatial criminology research (Bernasco \u0026amp; Block, 2011; Long et al., 2021). As routine activity theory (Cohen \u0026amp; Felson, 1979) emphasizes, the convergence of motivated offenders, suitable targets, and the absence of guardians is critical to understanding crime patterns, and this study extends that framework by considering temporal shifts in visitor patterns and their impact on crime.\u003c/p\u003e\n\u003cp\u003eTable 2 presents the results for after-work hour visitor proportions, showing a positive and significant relationship with violent crimes, specifically aggravated assault and robbery. These findings align with Felson and Boivin (2015), who highlighted that an increase in ambient population, particularly during leisure hours, intensifies the convergence of motivated offenders and suitable targets. In Model 1, a one standard deviation increase in after-work hour visitors is associated with a 19.2% increase in the expected incidence of aggravated assault (b = 1.562, SE = 0.780, p \u0026lt; 0.01). This relationship supports the notion that after-work visitor influxes reduce guardianship capacity, leading to increased opportunities for violent crime (Clarke \u0026amp; Eck, 2005).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2 about here\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Negative Binominal Regression Model Results for After Work Hour Visitor Proportion.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(Model 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(Model 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(Model 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAggravated\u003c/p\u003e\n \u003cp\u003eassault\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eRobbery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eTheft from motor vehicle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eBlock group variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAfter-work hour visitor ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.562**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.386**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.780]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.423]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.607]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eConcentrated disadvantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.193]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.871]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.019]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResidential instability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.882]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.339]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.768]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eEthnic heterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.697]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.371]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.553]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e83.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e38.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-9.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[56.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[75.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[45.53]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e% aged 15 to 34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.209**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.121]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.200]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0968]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eApartment complexes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.186**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.243**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.118**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0525]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0769]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0457]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAlcohol shops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.196]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.322]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.171]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGas stations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.262*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.112]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.149]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0959]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNightclubs/bars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.142]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.190]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.130]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eCheck-cashing services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.101***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0353]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0468]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0311]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eHotels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0590]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0884]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0529]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGrocery stores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.271*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.108]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.152]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0937]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eSpatially lagged predictors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAfter-work hour visitor ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e10.65**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.859*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[3.392]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[5.362]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.725]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eConcentrated disadvantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-9.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.743]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[2.819]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[2.972]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResidential instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-4.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.903]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[3.082]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.478]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eEthnic heterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.0513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.150]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.943]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.515]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e222.8*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e39.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2.604*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[121.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[186.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.992]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e% aged 15 to 34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e272.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.242]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.346]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[99.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eApartment complexes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.0851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.204]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.276]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.195]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAlcohol shops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.255*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.0354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.443]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.640]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.369]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGas stations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.215]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.338]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.166]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNightclubs/bars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.321]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.490]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.286]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eCheck-cashing services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0492]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.111]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0466]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eHotels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.372*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.139]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.221]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.144]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGrocery stores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.233]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.342]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.206]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eSpatially lagged outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAggravated assault\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.062]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eRobbery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.147]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMotor vehicle theft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.013]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-2.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-8.516**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2.708*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.774]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[2.684]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.181]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote. Standard errors in brackets\u003c/p\u003e\n\u003cp\u003e** p\u0026lt;0.01, * p\u0026lt;0.05\u003c/p\u003e\n\u003cp\u003eSimilarly, Model 2 reveals that the ratio of after-work hour visitors is significantly associated with a 46.2% increase in robbery incidents (b = 3.386, SE = 1.423, p \u0026lt; 0.01). This result is consistent with the funneling hypothesis (Brantingham \u0026amp; Brantingham, 1995), which suggests that non-residential activities such as shopping, nightlife, and entertainment can create concentrated crime spots in areas that receive a large influx of visitors during non-work hours. Importantly, this finding further emphasizes the role of commercial establishments, such as check-cashing services and nightclubs, as significant attractors of crime, echoing the conclusions of prior studies on crime attractors (Bernasco \u0026amp; Block, 2011; Felson, 2006).\u003c/p\u003e\n\u003cp\u003eInterestingly, no significant relationship was found between after-work hour visitor ratios and motor vehicle theft (Model 3). This suggests that while after-work hours create more opportunities for violent crimes due to social interactions and leisure activities, they may not have the same effect on property crimes such as vehicle theft. This finding diverges from earlier studies by Hipp and Kim (2019), who found ambient populations to be significant predictors of property crimes. It may be that during after-work hours, increased surveillance or limited vehicle accessibility reduces the opportunity for motor vehicle theft in these areas.\u003c/p\u003e\n\u003cp\u003eIn contrast to the findings for after-work visitors, the relationship between work hour visitor ratios and motor vehicle theft is stronger and statistically significant (Table 3, Model 6). The results indicate a 19.7% increase in motor vehicle theft incidents for each one standard deviation increase in work-hour visitor ratios (b = 7.129, SE = 2.591, p \u0026lt; 0.05). This association supports the theory that higher ambient populations during work hours, particularly in commercial or office districts, create opportunities for theft from unattended vehicles (Andresen, 2011; Malleson \u0026amp; Andresen, 2016). The concentration of visitors in central and western parts of the city, as seen in Figure 1, corresponds with areas of high vehicle traffic, further supporting the routine activity theory\u0026rsquo;s emphasis on target suitability and the absence of capable guardianship during working hours (Cohen \u0026amp; Felson, 1979).\u003c/p\u003e\n\u003cp\u003eTable 3. Negative Binominal Regression Models for Work Hour Visitor Proportion.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(Model 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(Model 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(Model 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAggravated\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eassault\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eRobbery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eTheft from motor vehicle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eBlock group variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eWork hour visitor ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e7.129*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[3.259]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[5.044]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[2.591]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eConcentrated disadvantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.637*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.208]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.911]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.998]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResidential instability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.896]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.364]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.755]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eEthnic heterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.703]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.396]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.544]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e70.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e31.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-15.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[55.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[76.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[44.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e% aged 15 to 34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.217**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.123]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.200]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0946]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eApartment complexes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.190**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.256**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.118*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.053]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0807]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0445]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAlcohol shops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.199]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.335]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.167]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGas stations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.354*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.112]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.151]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.094]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNightclubs/bars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.0192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.146]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.198]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.126]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eCheck-cashing services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0906*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.097*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0353]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0474]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.030]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eHotels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.186*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.062]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0963]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.052]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGrocery stores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.271*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.108]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.152]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.094]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eSpatially lagged predictors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eWork hour visitor ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.0232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.403**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.108]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.156]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0896]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eConcentrated disadvantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-6.354*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[3.584]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[5.584]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[2.800]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResidential instability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-3.872**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.765]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[2.883]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.502]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eEthnic heterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.928]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[3.113]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.625]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2.090*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.153]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.985]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.915]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e% aged 15 to 34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e218.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e7.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e299.6**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[123.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[190.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[100.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eApartment complexes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.245]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.349]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.198]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAlcohol shops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.207]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.283]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.167]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGas stations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.408*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.447]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.668]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.360]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNightclubs/bars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.223]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.373]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.167]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eCheck-cashing services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.325]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.505]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.281]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eHotels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0500]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.118]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0452]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGrocery stores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.351*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.142]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.234]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.139]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eSpatially lagged outcome\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.00772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAggravated assault\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.352]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.197]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0621]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eRobbery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.150]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMotor vehicle theft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[0.0124]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-1.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-7.750**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.597]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[2.628]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e[1.225]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote. Standard errors in brackets.\u003c/p\u003e\n\u003cp\u003e*** p\u0026lt;0.001, ** p\u0026lt;0.01, * p\u0026lt;0.5.\u003c/p\u003e\n\u003cp\u003eHowever, work hour visitor proportions did not exhibit a significant relationship with aggravated assault or robbery (Models 4 and 5), which suggests that opportunities for violent crime are less dependent on the influx of visitors during work hours. This finding resonates with the work of Felson and Poulsen (2003), who found that violent crimes are more likely to occur during after-work leisure periods rather than the structured environments of work hours, where formal guardianship (e.g., security personnel) may be more prevalent.\u003c/p\u003e\n\u003cp\u003eSpatial spillover effects were also observed in the analysis, particularly for violent crimes. As shown in Table 2, the spatially lagged after-work hour visitor ratio is significantly associated with robbery in adjacent CBGs (b = 10.65, SE = 5.362, p \u0026lt; 0.01). This finding underscores the importance of considering the interconnectedness of urban spaces and aligns with the idea that crime in one area can influence nearby regions (Boessen \u0026amp; Hipp, 2015). The funneling effect of visitors into certain neighborhoods not only increases crime risks locally but also creates a ripple effect, expanding crime hotspots into surrounding areas (Brantingham \u0026amp; Brantingham, 1995).\u003c/p\u003e\n\u003cp\u003eMoreover, the spatial lag of motor vehicle theft during work hours demonstrates a similar pattern, where vehicle theft incidents in one block group are influenced by thefts in neighboring CBGs (Table 3, Model 6). This finding supports previous research on the clustering of property crimes in urban areas (Johnson, 2010), suggesting that criminal opportunities extend beyond the immediate vicinity of high-traffic areas and into nearby block groups, reinforcing the need for comprehensive urban planning and policing strategies that account for these spatial dynamics.\u003c/p\u003e\n\u003cp\u003eThe inclusion of control variables in the models highlights the consistent role of neighborhood structural factors in shaping crime patterns. Concentrated disadvantage and residential instability, two key variables grounded in social disorganization theory, were significantly related to aggravated assault and robbery (Sampson, Raudenbush, \u0026amp; Earls, 1997). These findings mirror earlier studies that emphasized the role of socioeconomic conditions in exacerbating crime risks (Morenoff \u0026amp; Sampson, 1997). Additionally, the number of apartment complexes and check-cashing services were significant predictors of crime across several models, consistent with opportunity theories of crime that highlight the role of certain land uses in attracting criminal activity (Bernasco \u0026amp; Block, 2011).\u003c/p\u003e\n\u003cp\u003eOverall, the regression analysis demonstrates that visitor patterns have a differential impact on crime depending on the time of day and the type of crime being examined. After-work hour visitors are more strongly associated with violent crimes, such as aggravated assault and robbery, while work-hour visitors are more likely to influence property crimes, particularly motor vehicle theft. These findings contribute to the growing body of literature on ambient population dynamics and their role in shaping urban crime patterns (Felson \u0026amp; Boivin, 2015; Hipp \u0026amp; Kim, 2019). Additionally, the spatial spillover effects observed in this study highlight the importance of adopting a spatially-aware approach to crime prevention and urban planning.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study reinforces the idea that the size of the ambient population, particularly visitors, can be a strong predictor of both violent and property crimes using more accurate cell phone data that previous literature. Our study’s primary contribution is the insight that different types of ambient populations—those present during working hours versus after working hours—may have varying impacts on property and personal crimes such as aggravated assaults, robberies and motor vehicle thefts.\u003c/p\u003e\n\u003cp\u003eConsistent with the tenets of routine activity theory (RAT), this study demonstrates that higher proportions of after-hours visitors positively impact the occurrence of aggravated assaults and robberies, even when controlling for social disorganization, risky places, and static population density. RAT posits that crime arises when a motivated offender, a suitable target, and a lack of capable guardianship converge. After-work hours increase the availability of potential targets—such as individuals carrying cash or electronics—while capable guardianship (e.g., police or security patrols) may decrease. Additionally, after-hours visitors are more likely to be in the area for entertainment purposes, including alcohol consumption and visits to risky facilities. Alcohol consumption is a significant risk factor for both aggravated assault and robbery, as it impairs judgment and increases vulnerability. Therefore, a higher proportion of after-hours visitors can result in a larger pool of potential victims in the area.\u003c/p\u003e\n\u003cp\u003eMoreover, after-work hours attract predictable crowds to nightlife districts, entertainment venues, and transit hubs. These patterns allow offenders to anticipate victim behaviors and locations, thereby increasing the likelihood of personal crimes, in line with crime pattern theory. Finally, the influx of visitors can weaken social controls and erode social bonds in neighborhoods already struggling with social disorganization, further elevating the likelihood of crimes such as assault and robbery.\u003c/p\u003e\n\u003cp\u003eOur study also found that higher proportions of visitors during work hours increased the probability of motor vehicle theft, whereas a higher proportion of after-hours visitors did not. One plausible explanation for this finding is the duration of time that work-hour visitors spend away from their vehicles while working or attending to other businesses. The longer a vehicle is left parked without supervision, the greater the opportunity for theft.\u003c/p\u003e\n\u003cp\u003eOur study offers several key implications for crime prevention and community safety. First, city planners and urban administrators must anticipate potential increases in assaults and robberies when expanding after-hour venues in mixed-use areas, particularly those serving alcohol. This includes designing these new spaces in alignment with Crime Prevention Through Environmental Design (CPTED) principles. For example, urban designs should minimize hiding spots and enhance visibility and street lighting capacity.\u003c/p\u003e\n\u003cp\u003eFor areas with a higher density of after-hour venues, additional measures should focus on effective guardianship. This may include increasing patrol presence during late hours, as well as implementing security measures by the venues themselves and other space managers. Bars, restaurants, and entertainment venues situated in areas with significant late-night foot traffic could be required to invest in extra security, such as hiring private security personnel or installing surveillance systems, to deter crime and safeguard patrons.\u003c/p\u003e\n\u003cp\u003eFurthermore, parking lots located in areas with significant daytime foot traffic should be required to implement additional security measures to prevent an increase in motor vehicle theft. Since our model found a strong, positive spatial lag for after-hour traffic in relation to robbery and motor vehicle theft, it's crucial to extend these preventive measures to neighboring areas to prevent a spillover effect of crime. Thus, a detailed analysis of foot traffic data—considering factors like the time of day and other relevant characteristics—should be a central element in crime analysis. It is also vital in shaping crime prevention strategies, guiding community development, and issuing licenses to new businesses.\u003c/p\u003e\n\u003ch1\u003e\u003cstrong\u003eAlternative Discussion\u003c/strong\u003e\u003c/h1\u003e\n\u003cp\u003eThis study sought to clarify how time-specific influxes of non-resident visitors shape both violent and property crime in an urban context, using Arlington, Texas, as a case study. By leveraging high-resolution mobile-device location data alongside spatially lagged negative binomial regression models, we identified distinct and temporally contingent relationships between visitor presence and different crime types. Our findings indicate that not all visitors are equal in their influence on crime, nor are all time periods equally criminogenic. After-work hour visitors were positively associated with violent offenses—specifically aggravated assault and robbery—while work-hour visitors were significantly linked to motor vehicle theft. Moreover, spatial lag terms confirmed that these effects do not respect administrative boundaries, diffusing outward to adjacent census block groups. Below, we revisit our theoretical framework, integrate our results with existing scholarship, and outline their implications for crime prevention policy and future research.\u003c/p\u003e\n\u003cp\u003eRoutine Activity Theory (Cohen \u0026amp; Felson, 1979) offers a compelling lens for interpreting these findings. As theorized, crime is most likely to occur when motivated offenders, suitable targets, and the absence of capable guardianship coincide in time and space. Our study contributes to this framework in several critical ways:\u003c/p\u003e\n\u003cp\u003eFirst, our findings highlight the temporal specificity of guardianship. Consistent with Felson and Boivin’s (2015) “daily crime flow” model, the positive association between after-work visitors and violent crimes suggests that evening leisure hours diminish both formal (e.g., private security) and informal (e.g., social cohesion) guardianship, while simultaneously increasing the number of motivated offenders and accessible targets. As nightlife venues attract patrons unfamiliar with neighborhood norms, the potential for confrontational encounters escalates.\u003c/p\u003e\n\u003cp\u003eSecond, our results underscore the need for crime-type differentiation within ambient population research. While many prior studies have aggregated crime into a single outcome (e.g., Hipp \u0026amp; Kim, 2019), our disaggregated approach revealed that work-hour visitor volumes specifically predict motor vehicle theft—a property crime characterized by low confrontation risk and reliance on unsupervised targets. This nuance aligns with Andresen’s (2011) argument that ambient population growth may increase opportunities without proportionally bolstering guardianship.\u003c/p\u003e\n\u003cp\u003eThird, the spatial interdependence of urban places is underscored by our significant spatial lag terms. After-work visitor inflows not only heightened local robbery risk but also spilled over into neighboring areas, validating Brantingham and Brantingham’s (1995) funnel hypothesis. This finding suggests that urban visitor flows radiate criminogenic pressures beyond their immediate locus, complicating neighborhood-level intervention strategies.\u003c/p\u003e\n\u003cp\u003eThis study also offers a methodological contribution by leveraging year-long, GPS-based mobility data to capture the ambient population. Prior research frequently relied on static census data (Boessen \u0026amp; Hipp, 2015) or sporadic geotagged social media posts (Malleson \u0026amp; Andresen, 2015), each limited by demographic skew and temporal coarseness. By distinguishing between work-hour and after-work hour visitor ratios, our study meets calls for next-generation ambient population metrics (Hipp et al., 2019; Lafrogne-Joussier \u0026amp; Rollet, 2025), offering stronger explanatory power and more refined crime-specific insights.\u003c/p\u003e\n\u003cp\u003eOur results carry actionable implications for urban planners, law enforcement, and policymakers. First, targeted policing strategies should prioritize census block groups—and their contiguous neighbors—that experience sharp spikes in after-work visitation, especially those housing alcohol-serving establishments. Directed patrols during peak evening hours may help mitigate the elevated risk of violent crimes.\u003c/p\u003e\n\u003cp\u003eSecond, urban planners and regulatory agencies should integrate crime prevention through environmental design (CPTED) principles into the review of proposed nightlife venues. Enhanced street lighting, strategic CCTV placement, and unobstructed sightlines can deter crime in high-traffic areas (Clarke \u0026amp; Eck, 2005).\u003c/p\u003e\n\u003cp\u003eThird, the strong link between work-hour visitors and vehicle theft points to the need for improved parking lot security in commercial areas. Employers and property owners might consider investing in license plate recognition technologies, gated access systems, and high-visibility security patrols during daytime business hours.\u003c/p\u003e\n\u003cp\u003eFinally, because crime risks spill across administrative boundaries, municipal coordination is essential. Inter-jurisdictional crime prevention programs—particularly those that share visitor flow data—may be better positioned to contain and anticipate shifts in crime hotspots.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, it is based on a single city with a unique suburban-urban composition, potentially limiting generalizability to other contexts such as dense metropolitan cores or rural towns. Second, while smartphone ownership is widespread, certain populations (e.g., the elderly, unhoused, or very young) are underrepresented in GPS-based mobility data. Third, the use of a single pre-pandemic year (2019) provides a stable baseline but precludes insights into how crisis-induced mobility disruptions (e.g., COVID-19) might reshape these dynamics. Fourth, we lacked micro-level data on the purpose of visits (e.g., commuter vs. nightlife patron) and on formal guardianship (e.g., private security deployment), which limits the granularity of mechanism testing.\u003c/p\u003e\n\u003cp\u003eSeveral promising avenues for future inquiry emerge. Multi-city studies could assess whether the visitor-crime relationships documented here generalize to urban centers with different built environments and transit infrastructures. Second, integrating point-of-interest (POI) dwell time data would allow researchers to segment visitor types and isolate the crime implications of specific activities (e.g., work, shopping, leisure). Third, natural experiments, such as the staggered opening of nightlife venues, could facilitate stronger causal inferences. Finally, overlaying guardianship infrastructure—like private security density or camera networks—onto ambient mobility flows could allow for direct tests of interaction effects predicted by RAT and situational crime prevention theory.\u003c/p\u003e\n\u003cp\u003eIn sum, this study affirms that the criminological significance of urban mobility lies not only in how many people move through space but also in when and why they do so. After-work visitors elevate violent crime risk, while work-hour visitors facilitate property crime—particularly motor vehicle theft. These relationships extend across geographic boundaries, creating ripple effects that call for coordinated policy responses. As urban landscapes grow more dynamic and mixed-use, real-time insights into temporal visitor patterns offer a powerful tool for forecasting and preventing crime in an evidence-informed, equitable manner.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eOB - conceptualization, idea, draft writingSK - literature review and discussion draftingJB- data analysis and findings section draftingCC- data preparation and curation, analysis\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eDeclaration of Generative AI and AI-assisted technologies in the writing processDuring the preparation of this work the author(s) used ChatGPT-5 (OpenAI, August 2025 version) to improve readability and grammar. After using this tool, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllison, D. P. (1999). \u003cem\u003eMultiple Regression: A Primer\u003c/em\u003e. California: Pine Forge Press. \u003c/li\u003e\n\u003cli\u003eBoivin, R. \u0026amp; Felson, M. (2018). Crimes by Visitors Versus Crimes by Residents: The Influence of Visitor Inflows. \u003cem\u003eJournal of Quantitative Criminology\u003c/em\u003e. 34:465-480. \u003c/li\u003e\n\u003cli\u003eAndresen, M. A. (2006). Crime measures and the spatial analysis of criminal activity. \u003cem\u003eBritish Journal of criminology\u003c/em\u003e,\u003cem\u003e 46\u003c/em\u003e(2), 258-285. \u003c/li\u003e\n\u003cli\u003eAndresen, M. A. (2011). The ambient population and crime analysis. \u003cem\u003eThe Professional Geographer\u003c/em\u003e,\u003cem\u003e 63\u003c/em\u003e(2), 193-212. \u003c/li\u003e\n\u003cli\u003eAndresen, M. A., \u0026amp; Jenion, G. W. (2010). Ambient populations and the calculation of crime rates and risk. \u003cem\u003eSecurity journal\u003c/em\u003e,\u003cem\u003e 23\u003c/em\u003e, 114-133. \u003c/li\u003e\n\u003cli\u003eAnselin, L. (1990). Spatial dependence and spatial structural instability in applied regression analysis. \u003cem\u003eJournal of Regional science\u003c/em\u003e,\u003cem\u003e 30\u003c/em\u003e(2), 185-207. \u003c/li\u003e\n\u003cli\u003eBernasco, W., \u0026amp; Block, R. (2011). Robberies in Chicago: A Block-Level Analysis of the Influence of Crime Generators, Crime Attractors, and Offender Anchor Points. \u003cem\u003eJournal of Research in Crime and Delinquency\u003c/em\u003e,\u003cem\u003e 48\u003c/em\u003e(1), 33-57. https://doi.org/10.1177/0022427810384135 \u003c/li\u003e\n\u003cli\u003eBoessen, A., \u0026amp; Hipp, J. R. (2015). Close‐ups and the scale of ecology: Land uses and the geography of social context and crime. \u003cem\u003eCriminology\u003c/em\u003e,\u003cem\u003e 53\u003c/em\u003e(3), 399-426. \u003c/li\u003e\n\u003cli\u003eBoivin, R. (2018). Routine activity, population (s) and crime: Spatial heterogeneity and conflicting Propositions about the neighborhood crime-population link. \u003cem\u003eApplied Geography\u003c/em\u003e,\u003cem\u003e 95\u003c/em\u003e, 79-87. \u003c/li\u003e\n\u003cli\u003eBoivin, R., \u0026amp; Felson, M. (2018). Crimes by visitors versus crimes by residents: the influence of visitor inflows. \u003cem\u003eJournal of Quantitative Criminology\u003c/em\u003e,\u003cem\u003e 34\u003c/em\u003e, 465-480. \u003c/li\u003e\n\u003cli\u003eBrantingham, P., \u0026amp; Brantingham, P. (1995). Criminality of place: Crime generators and crime attractors. \u003cem\u003eEur. J. on Crim. Pol\u0026apos;y \u0026amp; Rsch.\u003c/em\u003e,\u003cem\u003e 3\u003c/em\u003e, 5. \u003c/li\u003e\n\u003cli\u003eBrantingham, P. J., \u0026amp; Brantingham, P. L. (1974). The spatial patterning of burglary. \u003cem\u003eHow. J. Penology \u0026amp; Crime Prevention\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e, 11. \u003c/li\u003e\n\u003cli\u003eBrantingham, P. L., \u0026amp; Brantingham, P. J. (1982). Mobility, notoriety and crime: A study of crime patterns in urban nodal points. \u003cem\u003eJournal of Environmental Systems\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(1), 89-99. \u003c/li\u003e\n\u003cli\u003eBrantingham, P. L., \u0026amp; Brantingham, P. J. (1999). A theoretical model of crime hot spot generation. \u003cem\u003eStudies on Crime \u0026amp; Crime Prevention\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eBrunsdon, C., Corcoran, J., \u0026amp; Higgs, G. (2007). Visualising space and time in crime patterns: A comparison of methods. \u003cem\u003eComputers, Environment and Urban Systems\u003c/em\u003e,\u003cem\u003e 31\u003c/em\u003e(1), 52-75. \u003c/li\u003e\n\u003cli\u003eBurgess, E. W. (1925). Can neighborhood work have a scientific basis. In R. E. Park, Burgess. E.W., McKenzie, R.D. (Ed.), \u003cem\u003eThe city: Suggestions for investigation of human behaviors in the urban environment\u003c/em\u003e (pp. 142-155). University of Chicago Press. \u003c/li\u003e\n\u003cli\u003eClarke, R. V., \u0026amp; Eck, J. E. (2005). \u003cem\u003eCrime analysis for problem solvers in 60 small steps\u003c/em\u003e. US Department of Justice, Office of Community Oriented Policing Services \u0026hellip;. \u003c/li\u003e\n\u003cli\u003eCohen, L. E., \u0026amp; Felson, M. (1979). Social Change and Crime Rate Trends: A Routine Activity Approach. \u003cem\u003eAmerican Sociological Review\u003c/em\u003e,\u003cem\u003e 44\u003c/em\u003e(4), 588-608. \u003c/li\u003e\n\u003cli\u003eCrum, J. D., \u0026amp; Ramey, D. M. (2023). Impact of extralegal and community factors on police officers\u0026rsquo; decision to book arrests for minor offenses. \u003cem\u003eAmerican Journal of Criminal Justice\u003c/em\u003e,\u003cem\u003e 48\u003c/em\u003e(3), 572-601. \u003c/li\u003e\n\u003cli\u003eFelson, M. (2006). \u003cem\u003eCrime and nature\u003c/em\u003e. Sage publications. \u003c/li\u003e\n\u003cli\u003eFelson, M., \u0026amp; Boba, R. L. (2010). \u003cem\u003eCrime and everyday life\u003c/em\u003e. Sage. \u003c/li\u003e\n\u003cli\u003eFelson, M., \u0026amp; Boivin, R. (2015). Daily crime flows within a city. \u003cem\u003eCrime Science\u003c/em\u003e,\u003cem\u003e 4\u003c/em\u003e, 1-10. \u003c/li\u003e\n\u003cli\u003eFelson, M., \u0026amp; Cohen, L. E. (1979). Human-Ecology and Crime - a Routine Activity Approach. \u003cem\u003eHuman Ecology\u003c/em\u003e,\u003cem\u003e 8\u003c/em\u003e(4), 389-406. https://doi.org/Doi 10.1007/Bf01561001 \u003c/li\u003e\n\u003cli\u003eFelson, M., \u0026amp; Eckert, M. A. (2018). \u003cem\u003eCrime and everyday life: A brief introduction\u003c/em\u003e. Sage Publications. \u003c/li\u003e\n\u003cli\u003eFelson, M., \u0026amp; Poulsen, E. (2003). Simple indicators of crime by time of day. \u003cem\u003eInternational Journal of Forecasting\u003c/em\u003e,\u003cem\u003e 19\u003c/em\u003e(4), 595-601. \u003c/li\u003e\n\u003cli\u003eGroff, E. R., \u0026amp; La Vigne, N. G. (2002). Forecasting the future of predictive crime mapping. \u003cem\u003eCrime Prevention Studies\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e, 29-58. \u003c/li\u003e\n\u003cli\u003eHaleem, M. S., Do Lee, W., Ellison, M., \u0026amp; Bannister, J. (2021). The \u0026lsquo;exposed\u0026rsquo;population, violent crime in public space and the night-time economy in Manchester, UK. \u003cem\u003eEuropean Journal on Criminal Policy and Research\u003c/em\u003e,\u003cem\u003e 27\u003c/em\u003e, 335-352. \u003c/li\u003e\n\u003cli\u003eHipp, J. R., Bates, C., Lichman, M., \u0026amp; Smyth, P. (2019). Using social media to measure temporal ambient population: Does it help explain local crime rates? \u003cem\u003eJustice Quarterly\u003c/em\u003e,\u003cem\u003e 36\u003c/em\u003e(4), 718-748. \u003c/li\u003e\n\u003cli\u003eHipp, J. R., \u0026amp; Kim, Y.-A. (2019). Explaining the temporal and spatial dimensions of robbery: Differences across measures of the physical and social environment. \u003cem\u003eJournal of Criminal Justice\u003c/em\u003e,\u003cem\u003e 60\u003c/em\u003e, 1-12. \u003c/li\u003e\n\u003cli\u003eJohnson, S. D. (2010). A brief history of the analysis of crime concentration. \u003cem\u003eEuropean Journal of Applied Mathematics\u003c/em\u003e,\u003cem\u003e 21\u003c/em\u003e(4-5), 349-370. \u003c/li\u003e\n\u003cli\u003eLafrogne-Joussier, R., \u0026amp; Rollet, V. (2025). \u003cem\u003eAmbient Density and Urban Crime: Evidence from Smartphone Data (April 10, 2025)\u003c/em\u003e (Social Science Research Network (SSRN), Issue. I. Elsevier. https://ssrn.com/abstract=4440554\u003c/li\u003e\n\u003cli\u003eLan, M., Liu, L., Hernandez, A., Liu, W., Zhou, H., \u0026amp; Wang, Z. (2019). The spillover effect of geotagged tweets as a measure of ambient population for theft crime. \u003cem\u003esustainability\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(23), 6748. \u003c/li\u003e\n\u003cli\u003eLong, D., Liu, L., Xu, M., Feng, J., Chen, J., \u0026amp; He, L. (2021). Ambient population and surveillance cameras: The guardianship role in street robbers\u0026apos; crime location choice. \u003cem\u003eCities\u003c/em\u003e,\u003cem\u003e 115\u003c/em\u003e, 103223. \u003c/li\u003e\n\u003cli\u003eLong, J. S. (1997). Regression models for categorical and limited dependent variables. \u003cem\u003eAdvanced quantitative techniques in the social sciences\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eMalleson, N., \u0026amp; Andresen, M. A. (2015). The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. \u003cem\u003eCartography and Geographic Information Science\u003c/em\u003e,\u003cem\u003e 42\u003c/em\u003e(2), 112-121. \u003c/li\u003e\n\u003cli\u003eMalleson, N., \u0026amp; Andresen, M. A. (2016). Exploring the impact of ambient population measures on London crime hotspots. \u003cem\u003eJournal of Criminal Justice\u003c/em\u003e,\u003cem\u003e 46\u003c/em\u003e, 52-63. \u003c/li\u003e\n\u003cli\u003eMcDowall, D., \u0026amp; Loftin, C. (2009). Do US city crime rates follow a national trend? The influence of nationwide conditions on local crime patterns. \u003cem\u003eJournal of Quantitative Criminology\u003c/em\u003e,\u003cem\u003e 25\u003c/em\u003e, 307-324. \u003c/li\u003e\n\u003cli\u003eMorenoff, J. D., \u0026amp; Sampson, R. J. (1997). Violent crime and the spatial dynamics of neighborhood transition: Chicago, 1970\u0026ndash;1990. \u003cem\u003eSocial Forces\u003c/em\u003e,\u003cem\u003e 76\u003c/em\u003e(1), 31-64. \u003c/li\u003e\n\u003cli\u003eRatcliffe, J. H. (2004). The hotspot matrix: A framework for the spatio‐temporal targeting of crime reduction. \u003cem\u003ePolice practice and research\u003c/em\u003e,\u003cem\u003e 5\u003c/em\u003e(1), 5-23. \u003c/li\u003e\n\u003cli\u003eRossmo, D. K., Lu, Y., \u0026amp; Fang, T. B. (2012). Spatial-temporal crime paths. In \u003cem\u003ePatterns, prevention, and geometry of crime\u003c/em\u003e (pp. 16-42). Routledge. \u003c/li\u003e\n\u003cli\u003eSampson, R. J., Raudenbush, S. W., \u0026amp; Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. \u003cem\u003eScience\u003c/em\u003e,\u003cem\u003e 277\u003c/em\u003e(5328), 918-924. \u003c/li\u003e\n\u003cli\u003eShaw, C. R., \u0026amp; McKay, H. D. (1942). \u003cem\u003eJuvenile delinquency and urban areas\u003c/em\u003e. University of Chicago Press. \u003c/li\u003e\n\u003cli\u003eTownsley, M., Reid, S., Reynald, D., Rynne, J., \u0026amp; Hutchins, B. (2014). Risky facilities: Analysis of crime concentration in high-rise buildings. \u003cem\u003eTrends and Issues in Crime and Criminal Justice\u003c/em\u003e(476), 1-7. \u003c/li\u003e\n\u003cli\u003eTseloni, A., Wittebrood, K., Farrell, G., \u0026amp; Pease, K. (2004). Burglary victimization in England and Wales, the United States and the Netherlands: A cross-national comparative test of routine activities and lifestyle theories. \u003cem\u003eBritish Journal of criminology\u003c/em\u003e,\u003cem\u003e 44\u003c/em\u003e(1), 66-91. \u003c/li\u003e\n\u003cli\u003eWeisburd, D., Groff, E., \u0026amp; Yang, S.-M. (2012). \u003cem\u003eThe criminology of place : street segments and our understanding of the crime problem\u003c/em\u003e. Oxford University Press. http://dx.doi.org/10.1093/acprof:oso/9780195369083.001.0001\u003c/li\u003e\n\u003c/ol\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"temporal visitor patterns, crime incidence, spatial analysis, urban neighborhoods, negative binomial regression, aggravated assault, robbery, motor vehicle theft, routine activity theory, guardianship, ambient population, spatial analysis, urban neighborhoods, aggravated assault, robbery, motor vehicle theft","lastPublishedDoi":"10.21203/rs.3.rs-7634039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7634039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis spatial inquiry addresses a key question in routine activity theory: does an influx of visitors into a neighborhood increase crime opportunities by increasing the number of motivated offenders and suitable targets, or does it enhance guardianship capacity? The study explores these dynamics across different crime types: aggravated assault, robbery, and motor vehicle theft using more accurate cell phone mobility data to estimate work-hour and after work hour visitors in Arlington, TX.\u003c/p\u003e\n\u003cp\u003eResults from spatially lagged negative binomial regression models reveal that higher after-work hour visitor ratios are associated with increased aggravated assault and robbery frequency, likely due to more offenders and targets converging in these areas. Conversely, work hour visitor ratios show a stronger link to motor vehicle theft, suggesting that reduced guardianship during work hours may heighten crime opportunities. The relationships between visitor ratios and crime vary by crime type, indicating that temporal shifts in guardianship and offender-target interactions significantly influence urban crime patterns. These findings provide novel insights into the stability of crime patterns and the role of visitor dynamics, enriching the routine activity theory literature and offering implications for targeted crime prevention strategies in policing and urban planning.\u003c/p\u003e","manuscriptTitle":"Do More Visitors Mean More Crime? Analyzing the Impact of Visitor Patterns on Crime Rates in Urban Areas Using Mobile Device Foot Traffic Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 17:47:46","doi":"10.21203/rs.3.rs-7634039/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed4606a2-ec46-4e52-aac4-63d03dc4c9bd","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-09T07:54:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 17:47:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7634039","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7634039","identity":"rs-7634039","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}