Territorial Dynamics of Medium-Sized Cities: Landscape Fragmentation and Inequality in three Southeastern Mexican Cities

Territorial Dynamics of Medium-Sized Cities: Landscape Fragmentation and Inequality in three Southeastern Mexican Cities | 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 Territorial Dynamics of Medium-Sized Cities: Landscape Fragmentation and Inequality in three Southeastern Mexican Cities Emma Villaseñor, Martha Bonilla, Swany Morteo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8811305/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 Urban expansion in medium-sized cities is reshaping socio-ecological dynamics across Latin America, yet their territorial influence remains understudied. This research analyzes the landscape configuration and socioeconomic conditions surrounding three medium-sized Mexican cities (Xalapa, Oaxaca, and Mérida), using a comparative design with concentric landscape systems (city, Buffer 1, Buffer 2). The results reveal a sharp transition from compact, homogeneous urban cores to highly fragmented peri-urban mosaics dominated by secondary vegetation, grasslands, and agricultural land. Fragmentation remains persistent across the buffer zones, challenging the expectation of a gradual rural–urban gradient. Social variables show that in areas far from the city, where there is less access to basic welfare services, there are urban centers that are offering employment. Taken together, these socio-ecological patterns indicate that medium-sized cities operate as territorial anchors embedded within broader regional systems, in which urban expansion, rural transformation, and uneven development intersect. Understanding these dynamics is crucial for designing territorial policies aligned with sustainable development goals, capable of addressing the effect of landscape fragmentation and fostering more equitable and ecologically coherent forms of metropolitan governance. medium-sized cities socio-ecological systems territorial inequality sustainable development goals Mexico Figures Figure 1 Figure 2 Introduction Urbanization is one of the dominant global megatrends, with far-reaching socio-environmental implications (Slemp et al., 2012 ; UN-Habitat, 2022 ). The process entailing natural or agricultural land converted into impervious built surfaces, alongside a structural transition from primary economic activities toward secondary and tertiary sectors. This process is accelerating worldwide. Urban areas are characterized by higher population density, greater connectivity, and more developed infrastructure, as commonly observed in cities and towns (UN-DESA 2025). Extensive research has documented the environmental impacts of urban expansion, including habitat and biodiversity loss, water scarcity and degradation, and soil contamination (Andersson, 2006; Grimm et al., 2008 ; McDonald et al., 2020 ). Beyond these biophysical effects, land conversion for urban use reshapes the relationships between people and their environments, influencing livelihoods, territorial organization, and socio-ecological dynamics (Arnaiz et al., 2018). Since urban centers exert influence well beyond their administrative boundaries, they configure networks of influence that affect the environmental, economic, and social trajectories of surrounding localities, generating complex spatial patterns (Berdegué et al., 2015 ). By 2050, 68% of the world’s population will reside in urban areas and in Latin America, 45% are cities of less than 500,000 inhabitants (UN-DESA, 2014; Bolay and Kern 2019 ). The rapid expansion of medium-sized cities is leading to profound changes in the landscape and territorial governance. Their future consolidation poses complex challenges related to equitable well-being, environmental protection, and the safeguarding of ecosystem services (Seto et al., 2013 ; Hoffman et al., 2023). These challenges are particularly salient in Latin America, where cities have expanded in diffuse and fragmented ways, displacing or reshaping functions that were formerly concentrated in the urban core (Ortiz-Báez et al., 2021 ). Understanding these spatial patterns is crucial for designing public policies that acknowledge the socio-ecological interdependence between medium-sized cities and their surrounding territories (Berdegué et al., 2015 ; Trejo-Nieto, 2024 ). Different studies show that the distance between localities and nearby cities is a key driver structuring spatial distribution, development opportunities, and exclusion dynamics (Berdegué et al., 2014 ; Ortiz-Baez et al., 2021). It has been documented that proximity to urban centers often provides access to markets, employment, services, and institutional networks (Berdegué et al. 2014 , 2015 ; Ortiz-Baez et al., 2021). Conversely, greater distance is associated with subsistence agriculture, higher socioeconomic marginalization, and reduced opportunities for social mobility, largely due to limited access to educational and formal employment opportunities (Henderson et al., 2001 ; Berdegué & Soloaga, 2018 ; Arnaiz et al., 2018). In Mexico, these spatial patterns intersect with the distribution of Indigenous People, historically concentrated in rural regions of the south and southeast, yet increasingly present in cities due to circular migration processes linking rural and urban spaces (Granados Alcántar & Quezada Ramírez, 2018). Indigenous People are consistently associated with structural barriers to accessing education, health, and adequate nutrition (Singer, 2014). These dynamics suggest that urbanization is not as a linear rural–urban transition but a network of interwoven interactions and bidirectional flows of materials, people, and institutions (Hoffman et al., 2023). This perspective requires cross-disciplinary approaches to address coupled human–nature problems. Examining medium-sized cities through a socio-ecological lens can provide a relevant empirical context for advancing such integrative sustainability research. Although intermediate cities have been defined as those with populations of between 100,000 and 1,000,000 (depending on the source), several authors highlight their role as hubs of social, economic, and cultural interaction, regardless of size. These cities have a multi-scale connective infrastructure, are home to government authorities at different levels, and can present certain economic strengths (Michelini and Davies, 2009 ; Berdegué et al., 2015 ; Trejo-Nieto, 2024 ). In contrast to the highly concentrated dynamics typical of large metropolitan areas, intermediate cities have been identified as potential anchors for more balanced regionally distributed forms of development without such harmful effects on the environment (Llop et al., 2019 ). Despite their potential strategic role, medium-sized cities remain understudied in terms of the socio-environmental impacts of their growth. Research has disproportionately focused on large metropolises, leaving significant knowledge gaps regarding how medium-sized cities shape surrounding landscapes and whether they exhibit characteristics of intermediate cities that can provide advantages over their larger counterparts, such as proximity-based economies, solidarity networks, sustainable mobility, or closer relationships between citizens and local governments (Llop et al., 2019 ; Trejo-Nieto, 2024 ). Addressing these gaps is essential for informing territorial planning that integrates both environmental conservation and regional equity (Maturana et al., 2017 ; Valicelli & Pesci, 2002 ). From a sustainability perspective, these urban–territorial dynamics are directly linked to the achievement of the Sustainable Development Goals (SDGs), particularly those related to inclusive urbanization, territorial equity, and ecosystem integrity. Medium-sized cities occupy a strategic position in this agenda, as they mediate interactions between urban cores and rural hinterlands where social inequalities, land-use pressures, and ecological processes intersect. Understanding how landscape configuration, socioeconomic conditions, and distance to urban centers co-evolve is therefore essential for advancing SDG 11 (Sustainable Cities and Communities) and SDG 10 (Reduced Inequalities), while simultaneously addressing environmental goals such as SDG 6 (Clean Water and Sanitation) and SDG 15 (Life on Land). In this study, we evaluate how three medium-sized Mexican cities—Oaxaca, Xalapa, and Mérida—influence landscape structure and socioeconomic conditions in their surrounding territories. We delineated two concentric buffer zones around each city to analyze the landscape attributes (land-cover diversity, heterogeneity, and fragmentation) and socioeconomic characteristics (population density, Indigenous population, and marginalization) of each system. We aimed to assess how landscape and socioeconomic conditions vary with distance from the city, and how these patterns compare across different urban contexts. Assuming these cities present characteristics of intermediate cities, we hypothesized that landscape configuration and socioeconomic conditions will vary gradually with proximity to the urban core. Thus, areas closest to the city will display higher land-use diversity, landscape fragmentation, population density, and service access, while more distant areas will exhibit greater ecological continuity, lower population density, and higher marginalization (Berdegué et al., 2015 ; Llop et al., 2019 ). Through this cross-disciplinary perspective, our study aims to contribute to advancing integrative knowledge that links biophysical processes, social dynamics, and policy-relevant insights to address sustainability challenges in coupled human–nature systems. Methodology Research setting We selected three medium-sized cities in southeastern Mexico following the classification established by SEDATU, which defines medium-sized cities as those with populations between 100,000 and 1,000,000 inhabitants (SEDATU/CONAPO, 2020 ). These cities, Xalapa, Oaxaca de Juárez, and Mérida, represent distinct socio-environmental and territorial contexts that allow us to examine how medium-sized urban centers influence their surrounding landscapes and socioeconomic conditions. Xalapa is located in the mountainous central region of Veracruz, along the Gulf of Mexico. The rugged topography has resulted in numerous small and dispersed localities. The city covers 122.33 km² and had 488,531 inhabitants in 2020 (INEGI, 2020 ). Together with eight adjacent municipalities, it forms the Xalapa Metropolitan Zone (POTZMX, 2023 ). Population growth accelerated sharply between 1960 and 1980, increasing by more than 300% due primarily to rural-to-urban migration, and continued expanding rapidly through the early 2000s (Benítez et al., 2011 ). The state of Oaxaca, located in southeastern Mexico, features the highest administrative fragmentation in the country, with 570 municipalities and a highly dispersed settlement pattern that includes many localities with fewer than 100 inhabitants. The Oaxaca Metropolitan Zone, including the state capital, Oaxaca de Juárez, comprises 27 municipalities and concentrates the state’s largest share of population and services (POZMO 2025 ). In 2020, the city’s population was 258,913 inhabitants. Urban expansion has occurred through two major pulses: the first in the 1960s, driven by commercial activity and tourism, and the second between 2000 and 2005 (Fig. 1), when the urban footprint expanded further (Madrid 2011 ). Mérida is located in the Yucatán Peninsula. It is a limestone plain characterized by low elevation and a network of subterranean water flows. The city lies 35 km inland from the Gulf of Mexico. In 2020, Mérida had 995,129 inhabitants (INEGI, 2020 ; Fig. 1). The Mérida Metropolitan Zone encompasses nine municipalities and concentrated 52.86% of the state’s 2,320,898 inhabitants in 2020 (Government of the State of Yucatán, 2025 ). Landscape Systems For each case study, we delineated three analytical landscape systems: City (urban polygon), and two concentric zones of urban influence: Buffer 1, and Buffer 2. Within each system, we evaluated spatial landscape characteristics, including land-cover diversity, heterogeneity, and the degree of isolation or connectivity among vegetation and land-use classes (Table 1 ). Buffer distances were determined based on the Euclidean distance between the centroid and the farthest boundary of each city polygon, ensuring that Buffers 1 and 2 represented equivalent baseline distances across cities: 6 km for Xalapa, 7 km for Oaxaca, and 12 km for Mérida. Each system polygon was used to clip the vegetation and land-cover layer from INEGI’s Series VII dataset (INEGI, 2018 ), which was converted to raster format and used as the primary input for the computation of landscape metrics. Spatial data processing was conducted in ArcMap 10.3 (ESRI, 2014 ). Throughout the manuscript, we refer to these three units as landscape systems. Landscape Metrics To characterize spatial patterns and landscape configuration within each system, we computed landscape metrics using Fragstats v.4.2 (Ene & McGarigal 2023 ). The metrics applied at both the class and landscape levels are listed in Table 1 , along with their definitions (Fragstats 2023 ; Arnaiz-Schmitz et al., 2018 , 2023 ). Table 1 Landscape metrics used in the study. Depending on the metric type, analyses were conducted at either the landscape-system level (City, B1, B2) or the land-cover class level (Urban, Grassland, Rainfed Agriculture, Irrigated Agriculture, Secondary Vegetation, Natural Vegetation). Metric / Abbreviation Concept Unit Landscape Property Measured Level of Analysis Largest Patch Index (LPI) Percentage of the landscape occupied by the largest patch of each class; indicator of dominance and landscape homogeneity. 0–100 Area and edge / Composition System, Class Mean Patch Area (AREA_MN) Average area of all patches belonging to a given class. Hectares Area and edge / Composition System, Class Mean Euclidean Nearest-Neighbor Distance (ENN_MN) Mean Euclidean distance to the nearest patch of the same class; quantifies spatial isolation. Meters Aggregation System, Class Contagion Index (CONTAG) Probability that two randomly selected cells belong to the same class; indicator of overall landscape aggregation. 0–100 Aggregation System Shannon Diversity Index (SHDI) Patch diversity weighted by the proportional abundance of each patch type; indicator of landscape heterogeneity. ≥ 0 Diversity System Shannon Evenness Index (SHEI) Proportional abundance of patch types standardized by the logarithm of the number of patch types; indicator of evenness. 0–1 Diversity System Interspersion and Juxtaposition Index (IJI) Degree of intermixing among adjacent patch types; requires ≥ 3 classes. 0–100 Aggregation Class Patch Richness (PR) Number of different patch types present in the landscape. ≥ 1 Diversity Class Socioeconomic Variables Socioeconomic conditions in localities within the three landscape systems (City, Buffer 1, Buffer 2) were characterized using data from INEGI, CONAPO, and CONAGUA. Selected variables included demographic and socioeconomic indicators: population density, number of indigenous language speakers, economically active population, marginalization index, and annual volume of water concessions. To make variables comparable across localities of different sizes, we standardized all values by dividing the total count of each variable by the land area (km²), except for the marginalization index, which is already a standardized composite indicator. Table 2 Socioeconomic variables used for the analysis. Variable Description / Source Unit Level of Analysis Population density Number of inhabitants per unit area Inhabitants per km² System Indigenous-language speakers Population aged 3–130 years speaking an indigenous language (INEGI 2020 ) Speakers per km² System, Landscape Marginalization Index Composite indicator of social exclusion and deprivation based on nine variables across four dimensions (housing, education, services, income) (CONAPO, 2020 ) Standardized index System, Landscape Economically Active Population (EAP) Population aged 12–130 years working, or seeking employment (INEGI, 2020 ) Individuals per km² System, Landscape Annual volume of water concessions Volume of nationally regulated water extraction (REPDA 2020) m³ per year System, Landscape Statistical Analysis To assess differences in spatial patterns among the three landscape systems (City, Buffer 1, and Buffer 2), we conducted an analysis of variance (ANOVA). The Largest Patch Index (LPI) was transformed due to a lack of normality. The ANOVA of class-level metrics was used to evaluate differences in land-cover composition. Pairwise differences were assessed using Tukey’s post hoc test. These analyses were performed in JMP (Carver, 2019 ). To compare total city values with median buffer values, we used non-parametric Kruskal–Wallis tests, given the violations of normality (verified via the Shapiro–Wilk test and visual diagnostics). Dunn’s post hoc test with Bonferroni correction was used to identify significant pairwise differences. All analyses were conducted in RStudio (v. 4.5.1) using the dunn.test, ggplot2, and stats packages. Results Landscape Characterization of Case Studies Figure 2 shows land-cover and vegetation distributions across the three case studies and their corresponding buffer systems. On average, urban cover is the dominant class in city systems (89.30%). Secondary vegetation dominates both Buffer 1 (36.49%) and Buffer 2 (45.83%), followed by agricultural classes (32.42% and 29.02%). Natural vegetation occurs only in Buffer 2 (which is the farthest from the city system core) and is absent from all city systems. Oaxaca is the only city system with irrigated agriculture; Xalapa is the only landscape system where natural vegetation appears within Buffer 1, and Mérida is the only landscape system that contains the class category labeled “non-vegetated surface.” Across all cases, 82% of buffer localities are rural, indicating that these medium-sized cities are surrounded predominantly by rural settlements. CLAVES-CLASE: AGH: Rainfed agriculture (humid conditions), IAG: Irrigated agriculture, RAS: Rainfed agriculture (seasonal), WAT: Water bodies, URB: Urban areas, OAF: Oak Forest, OPF: Oak-pine forest, PIF: Pine forest, POF: Pine-oax forest, MCF: Mountain cloud forest, MGF: Mangrove forest, GRAS: Grassland, TDDF: Tropical dry deciduous forest, NVA: Bare ground/non-vegetated areas, CDV: Coastal dune vegetation, HHV: Halophytic-hydrophilic vegetation, SFV: Secondary forest vegetation, STF: Secondary tropical forest and SMV: Secondary mangrove vegetation. Spatial Patterns of Landscape Systems The spatial patterns of the three landscape systems differ mainly between the city system and the buffer systems, but the spatial patterns are similar between the buffers (Table 3 ). Compared to buffer systems, in the city, the area occupied by the largest patch (LPI) is larger, while the diversity of classes (SHDI) is lower, with these differences being statistically significant. In addition, the average patch size (AREA_MN) in the city is smaller than in the most distant system (buffer 2), and the connectivity between patches (CONTAG) is greater than in the closest system (buffer 1). Taken together, these results indicate that there is greater spatial homogeneity in the city system, mainly associated with the dominance of the urban class, in terms of both patch size and continuity. In contrast, buffer systems showed greater diversity in terms of the size and type of patches they contain. Furthermore, the lack of differences in metrics in both buffers (1 and 2) indicates that, with respect to landscape structure, these units are equivalent. On the other hand, metrics related to equity in coverage distribution (SHEI) and patch isolation (ENN_M) showed no differences between systems. Table 3 Spatial patterns of landscape systems. Values are means and standard deviations. Different letters indicate significant differences (p < 0.05) between landscape systems, according to Tukey’s post hoc test. City Buffer 1 Buffer 2 F-statistic p value Largest Patch Index (LPI) 89.0 (± 7.1) a 37.5 (± 27.3) b 39.1 (± 27.0) b 5.61 0.042 Mean Patch Area (AREA_MN) 208.5 (± 152.9) a 424.9 (± 60.0) ab 691.2 (± 162.2) b 9.86 0.012 Mean Euclidean Nearest-Neighbor Distance (ENN_MN) 539.0 (± 148.1) a 904.9 (± 428.9) a 1312.2 (± 488.1) a 3.03 0.123 Contagion Index (CONTAG) 82.9 (± 10.62) a 61.1 (± 11.62) b 67.5 (± 3.22) ab 4.36 0.067 Shannon Diversity Index (SHDI) 0.4 (± 0.26) a 1.2 (± 0.29) b 1.3 (± 0.19) b 12.48 0.007 Shannon Evenness Index (SHEI) 0.3 (± 0.19) a 0.6 (± 0.24) a 0.6 (± 0.08) a 3.86 0.083 Connectivity by coverage type per system The class metrics show that spatial composition varies between systems depending on the type of coverage studied (Table 4 ). As expected, the spatial pattern of cities is dominated by the urban class: the largest patch index (LPI), the average patch area size (AREA_MN), and the shortest distance to the nearest neighbor (ENN_M) were significantly greater in the city than in the buffers. Furthermore, the only difference between buffers 1 and 2 was observed in the size of the largest urban patch, indicating a gradual variation in the area of this cover. In the buffers, secondary vegetation was the most prominent class and showed differences in distribution compared to the city. The largest patch index, average patch area, and class intercalation index were significantly higher in the buffers, indicating the dominance and high aggregation of patches of this cover and a more heterogeneous mosaic in the buffer systems compared to the city. Additionally, patches of grassland and rainfed agriculture classes had a larger average size in the buffer systems than in the city. It is noteworthy that irrigated agriculture and natural vegetation classes were only found in the buffer system, indicating clear differences in composition between urban systems and their surroundings. Table 4 Landscape metrics by class. Analysis of variance to evaluate changes in the structure of cover classes by system type. Those showing significant differences are in bold. Different superscript letters indicate significant differences (p < 0.05) between landscape systems, according to Tukey's post hoc test. Irrigated agriculture was only found in the buffers, so a t-test was performed, while natural vegetation was only found in buffer 2 and in one sample from buffer 1, so only the averages are reported here. To comply with the assumptions of normality and homoscedasticity, all data were transformed, and these are the values reported. City Buffer 1 Buffer 2 F-statistic p value Urban Patch Richness (PR) 1.06 (± 0.9) a 0.17 (± 1.7) a 0.68 (± 0.3) a 0.77 0.500 Largest Patch Index (LPI) 4.48 (± 0.07) a 1.6 (± 0.9) b -0.1 (± 0.3) c 51.066 0.0002 Mean Patch Area (AREA_MN) 7.71(± 1.1) a 5.09(± 0.8) b 4.76(± 0.3) b 10.30 0.011 Mean Euclidean Nearest-Neighbor Distance (ENN_MN) 5.4 (± 1.1) a 6.3 (± 0.3) ab 7.5(± 0.3) b 8.77 0.023 Interspersion and Juxtaposition Index (IJI) 76.6(± 24.2) a 54.9(± 29.1) a 57.8(± 17.7) a 0.71 0.524 Grassland Patch Richness (PR) 0.16 (± 1.8) a -0.69 (± 0.5) a 0.32 (± 0.5) a 0.67 0.543 Largest Patch Index (LPI) -1.6(± 2.9) a 0.52(± 1.9) a 0.4(± 0.07) a 0.97 0.431 Mean Patch Area (AREA_MN) 1.95(± 1.4) a 5.66(± 1.4) b 5.71(± 0.8) b 8.46 0.017 Mean Euclidean Nearest-Neighbor Distance (ENN_MN) 6.3(± 0.6) a 6.9(± 1.0) a 6.7(± 0.5) a 0.32 0.733 Interspersion and Juxtaposition Index (IJI) 38.8(± 34.2) a 61.4(± 29.4) a 49.4(± 9.4) a 0.54 0.608 Secondary vegetation Patch Richness (PR) 1.14(± 0.9) a -0.8(± 1.0) a 0.33(± 0.2) a 4.62 0.060 Largest Patch Index (LPI) 0.003(± 1.1) a 2.98(± 1.3) b 3.06(± 1.2) b 5.89 0.038 Mean Patch Area (AREA_MN) 2.66(± 0.4) a 6.74(± 1.6) b 7.73(± 1.8) b 9.94 0.012 Mean Euclidean Nearest-Neighbor Distance (ENN_MN) 5.97(± 0.4) a 6.50(± 0.4) a 6.29(± 0.3) a 1.19 0.365 Interspersion and Juxtaposition Index (IJI) 20.22(± 16.7) a 75.71(± 9.1) b 62.19(± 13.0) b 14.07 0.005 Rainfed agriculture Patch Richness (PR) -0.21(± 0.8) a -0.28(± 0.4) a -0.72(± 1.1) a 0.33 0.725 Largest Patch Index (LPI) 0.36(± 0.9) a 1.52(± 1.4) a 1.11(± 1.7) a 0.51 0.620 Mean Patch Area (AREA_MN) 3.25(± 0.2) a 6.00((± 0.7) ab 8.20(± 2.9) b 6.05 0.036 Mean Euclidean Nearest-Neighbor Distance (ENN_MN) 5.96(± 3.7) a 6.69(± 0.6) a 8.8(± 3.5) a 1.48 0.300 Interspersion and Juxtaposition Index (IJI) 19.26 (± 16.9) a 70.05(± 25.8) a 78.8(± 39.5) a 3.68 0.090 Irrigated Agriculture Patch Richness (PR) N/A -0.25(± 0.9) 0.78 (± 0.7) 2.31 0.203 Largest Patch Index (LPI) N/A 1.00(± 1.4) -0.08(± 1.2) 0.93 0.389 Mean Patch Area (AREA_MN) N/A 5.72(± 0.4) 5.82(± 0.9) 0.02 0.881 Mean Euclidean Nearest-Neighbor Distance (ENN_MN) N/A 6.95(± 1.3) 7.56(± 0.4) 0.55 0.496 Interspersion and Juxtaposition Index (IJI) N/A 52.52(± 15.5) 49.25(± 6.8) 0.11 0.755 Natural Vegetation Patch Richness (PR) N/A -0.64 1.62(± 2.5) - - Largest Patch Index (LPI) N/A -2.35 -3.52(± 2.4) - - Mean Patch Area (AREA_MN) N/A 8.6 20.5(± 7.3) - - Mean Euclidean Nearest-Neighbor Distance (ENN_MN) N/A 7.22 12.54(± 3.37) - - Interspersion and Juxtaposition Index (IJI) N/A 95.11 126.26(± 99.9) - - Socioeconomic variables As expected, the city has the highest values for population density, employment, and access to services. However, the differences with respect to the buffers, as well as between the buffers, are not linear. There were no significant differences between systems in the variables of population density or number of indigenous speakers. However, the socioeconomic factors show two patterns: in one, the values are higher in the city, then decrease in B1, and increase in B2. This occurs with the economically active population and the number of indigenous speakers, but the latter variable was not statistically significant (Table 5 ). The other pattern is a gradual decrease among the systems: this pattern is shown by the marginalization index and population density, although only marginalization presents statistically significant differences, specifically between the city and B2 (Table 5 ). Table 5 Analysis of differences in socioeconomic variables by system. Those showing significant differences are presented in bold font. The median is used as a measure of central tendency because it is the measure used by the non-parametric test. The value for the city system is a single, total value. Those for the buffers are medians of the values for all localities. Different letters indicate significant differences (p < 0.05) between landscape systems according to Dunn's post hoc test. City Buffer 1 Buffer 2 X 2 p value Population density 5,583.63 a 1,860.45 a 1,755.01 a 4.12 0.12 Marginalization index 24.44 a 22.44 a 22.22 b 7.38 0.02 Economically active population 2,994.89 a 1,059.61 b 1,840.1 b 12.98 0.001 Indigenous-language speakers 233.98 a 95.62 a 100.97 a 1.04 0.59 Annual volume of water concessions Annual water concessions are highest in Buffer 1, followed by Buffer 2, and lowest in the City. The Kruskal–Wallis results show significant differences (χ² = 187.51, df = 2, p < 0.001), driven by contrasts between urban systems and buffer systems. Landscape Metrics Across Case Studies Landscape metrics at the system level show that differences are greater across the systems (City, B1, B2) than across the landscapes (Oaxaca, Mérida, Xalapa). No significant differences were found among cities for CONTAG, ENN_MN, and LPI, indicating similar aggregation and connectivity patterns. In contrast, NP (number of patches) and SHAPE (patch shape complexity) differ significantly across systems (p = 0.049 and p = 0.021, respectively), with higher fragmentation and morphological complexity in the buffers. AREA_MN is larger in the buffers, indicating the presence of larger patches with increased distance from the urban core (p = 0.009). Landscape composition shows a consistent pattern across the three case studies: 1) more land-cover classes and patch types in buffers than in city systems, and 2) Distinct dominant patch classes by system: urban patches predominated in the city, in Buffer 1, rainfed agriculture and secondary vegetation in Buffer 2. Furthermore, natural vegetation patches are not present in city systems. The total number of land cover classes was highest in Oaxaca, followed by Xalapa, and in all cases, the most marked increase in the number of classes was observed between the city and the first buffer. Socioeconomic Metrics Across Case Studies In Xalapa, significant differences were found only in indigenous-language speakers (χ² = 1.63; p = 0.002), showing the highest values in B1 and declining sharply in B2. The Economically Active Population (EAP) decreases with distance to the city, although not significantly. In Oaxaca, indigenous-language speakers differ significantly among systems (χ² = 8.58; p = 0.01), and EAP shows a marginal trend (p = 0.06), and there were no differences found in population density or marginalization. Mérida showed contrasting behavior compared to the other two cities. The four variables analyzed showed significant differences between systems. As expected, population density is higher in the city than in B1 and B2 (χ² = 7.10; df = 2; p = 0.03). The number of indigenous speakers decreases from the city to B1 but increases considerably in B2 (χ² = 991.10; d.f. = 2; p < 0.001), a pattern that is also observed in the EAP (χ² = 12.99; d.f. = 2; p < 0.001). The marginalization index decreases with distance from the city (χ² = 21.62; d.f. = 2; p < 0.001), with marked differences mainly present between the buffers. Discussion The integrated analysis of landscape structure and socioeconomic conditions across distance gradients from medium-sized cities reveals consistent spatial and social patterns that illuminate the dynamics of urban growth, peri-urban transformation, and territorial inequality. Contrary to the expected presence of a continuous rural–urban gradient, our results show a sharp transition from a highly homogeneous and urban-dominated core to a persistently fragmented peri-urban matrix. This pattern remains stable across both buffer zones. This indicates that fragmentation is not gradually attenuated with distance but instead constitutes a defining feature of the broader urban hinterland. In terms of landscape composition, the dominance of urban land in the city system contrasts with the prevalence of secondary vegetation, grasslands, and agricultural land in the buffer zones, as well as with the exclusive presence of irrigated agriculture and natural vegetation in the most distant areas. These patterns are consistent with the notion of “rural spaces in transition” (Arnaiz et al., 2018) and reflect the fragmentation processes widely documented in Latin America (Maturana et al., 2017 ; Berdegué et al., 2015 ; Seto et al., 2013 ). The greater heterogeneity and patch diversity of the buffers relative to the urban core further supports the interpretation of these zones as complex socio-ecological mosaics shaped by both urban influence and rural persistence. None of the landscape metrics (CONTAG, ENN_MN, LPI, NP, SHAPE) showed significant differences between the three landscapes studied or in the landscape × system interaction, suggesting that the patterns observed respond mainly to the distance gradient to the city and not to regional particularities. Overall, the distribution of land-cover types and their spatial configuration reflect broader processes of metropolization, deconcentration of urban functions, and socio-spatial fragmentation. Areas adjacent to the cities increasingly absorb industrial, commercial, and logistical activities, consistent with patterns observed in intermediate Latin American cities with limited urban planning capacity (Inostroza et al., 2013 ; Maturana et al., 2015). Historically, this pattern is rooted in the agricultural economies that shaped the development trajectories of Xalapa, Oaxaca, and Mérida: from coffee and sugarcane trade in Xalapa, to diversified irrigated agriculture in the Central Valleys of Oaxaca, to the henequen-driven regional economy of Merida. Socioeconomic variables reveal a similarly marked contrast between the urban core and its periphery. The decline in access to education, housing quality, and income with increasing distance from the city center—and the corresponding precarity in more dispersed localities—demonstrate persistent territorial inequality and social fragmentation (Berdegué et al., 2015 ; Aguilar et al., 2025 ). These patterns align with the frameworks of uneven geographical development and urban dualization (Maturana et al., 2015), whereby urban land values and real-estate dynamics concentrate opportunities in central areas while displacing more vulnerable populations toward peripheral locations characterized by lower-quality housing and deficient services. The non-linear patterns of the Economically Active Population (EAP) and the number of indigenous-language speakers (high in the city, low in Buffer 1, and rising again in Buffer 2) suggest that intermediate cities act simultaneously as attractors of labor and nodes of regional intermediation (Trejo-Nieto, 2024 ). The heightened socioeconomic activity observed in distant localities within Buffer 2 indicates the emergence of secondary centers benefiting from “borrowed size” effects, accessing agglomeration advantages linked to larger nearby cities. Teotitlán del Valle in Oaxaca exemplifies this phenomenon, functioning as a dynamic regional pole with a stronger commercial balance than the state capital (Secretaría de Economía, 2025 ; Trejo-Nieto, 2024 ). Patterns of water extraction reinforce these interpretations: the highest intensity of water concessions occurs in Buffer 1, reflecting the relocation of economic activities to peri-urban zones where land is more affordable but still close to the infrastructure (Domínguez-Aguilar, 2011). These findings are characteristic of metropolitan expansion processes shaped by real-estate speculation, business-oriented urban management, and the peripheral restructuring of industry (Andrés López, 2019 ). Although the three case studies exhibit a consistent structure, comprising an urban core surrounded by fragmented peri-urban matrices, their specific configurations reflect distinct historical, geographic, and cultural trajectories. Case study-specific dynamics Xalapa shows a gradual decline in patch size and connectivity of secondary vegetation (primarily pine–oak forests) from the urban core to the outer buffer, which retains a predominantly agro-pastoral character. Socioeconomic indicators display a similar decline in population density, EAP, and service access with distance, consistent with a metropolitan region that has grown steadily but moderately (1% annual growth between 1990 and 2020), and that functions as a service-oriented tertiary center requiring a daily commute from the surrounding municipalities. Meanwhile, peripheral municipalities such as Coatepec, Emiliano Zapata, and Banderilla operate as dormitory towns and regional exchange hubs. In Oaxaca, the landscape reflects the long-standing presence of indigenous settlements surrounding the city, functionally integrated but still lacking full urban infrastructure. Oaxaca displays the highest land-cover richness among the three cases, consistent with its heterogeneous territorial history. Socioeconomic conditions deteriorate with distance, echoing a historically unequal pattern of urbanization that centralized infrastructure and services in the capital. Oaxaca fulfills the role of an intermediate city through its administrative, political, cultural, and economic functions, with regional specialization in services, crafts, and a rapidly expanding mezcal export economy. Nevertheless, the metropolitan region displays marked inequality between rural and urban economies, with persistent deficits in housing, infrastructure, and social services. Mérida, the largest urban agglomeration in southeastern Mexico, exhibits the expected concentration of large urban patches and high population density within the city. However, the sharp drop in EAP and indigenous-language speakers in Buffer 1, followed by a strong increase in Buffer 2, reflects intense socio-territorial segregation linked to exclusive urban policies and a booming real-estate market that pushes lower-income and indigenous populations toward the metropolitan fringe (Aguilar et al., 2025 ). The highest volumes of water concessions are found in Buffer 1 (Kanasín, Umán, Conkal), indicating substantial industrial or high-consumption activity adjacent to the city. Simultaneously, the economic decline of rural areas, driven by soil infertility, market fluctuations, and climatic extremes, fuels migration toward the metropolitan periphery. Merida exemplifies how growth policies that disregard territorial interdependence and cross-locality flows of people, materials, and institutions lead to accelerated socio-environmental deterioration (Hoffman et al., 2023). The metropolitan municipalities of Yucatán also show the highest climate vulnerability, and the uncontrolled expansion of impermeable surfaces, coupled with deregulated hydrosocial cycles, has intensified flooding and aquifer contamination, deepening environmental and social risks. Implications for the SDGs and Territorial Policy The spatial and socioeconomic patterns observed across the three medium-sized cities have direct implications for the advancement of the Sustainable Development Goals (SDGs) and for the design of territorial policies that acknowledge the interdependence between urban centers and their surrounding regions. The coexistence of a highly consolidated urban core with a fragmented and increasingly unequal peri-urban matrix reveals structural challenges for achieving SDG 11 (Sustainable Cities and Communities), particularly in terms of inclusive urbanization, balanced territorial development, and equitable access to services. The persistent socio-spatial fragmentation documented in the buffer zones undermines progress toward SDG 10 (Reduced Inequalities), as population groups, often indigenous and marginalized populations, are pushed toward areas with limited infrastructure, reduced employment opportunities, and lower service provision. Similarly, the expansion of impermeable surfaces, deregulation of water extraction, and concentration of high-consumption economic activities in peri-urban zones directly threaten SDG 6 (Clean Water and Sanitation) and SDG 15 (Life on Land) by compromising aquifer integrity, accelerating landscape degradation, and reducing ecological continuity. Our results underscore the need for territorial governance frameworks that move beyond the administrative boundaries of cities and toward functional territories where ecological processes, economic flows, and social networks operate. Such an approach is essential for implementing integrated, multiscalar policies capable of addressing cross-cutting sustainability challenges rather than isolated sectoral issues. In particular, three policy directions emerge: Strengthening metropolitan and regional coordination actions at the regional and metropolitan levels that prevent localities from becoming dependent on cities, which in turn causes poverty, inequality, and environmental degradation. This includes planning instruments that integrate land-use regulation, political webs, mobility systems, ecosystem conservation, and water management at the scale of the urban region rather than the municipal jurisdiction (Berdegué et al 2015 ). Addressing socio-territorial inequality through differentiated interventions, prioritizing peri-urban and rural localities that experience the greatest vulnerability. Policies that combine social infrastructure, improved accessibility, and mechanisms for local economic diversification are crucial for advancing SDG 10 and ensuring equitable territorial development (Micheliny & Davies 2009; Ortiz-Báez et al., 2021 ). Safeguarding ecological functions within urban and peri-urban mosaics, by protecting natural vegetation remnants and promoting new green areas; regulating land speculation and promoting land-management practices that enhance connectivity and ecosystem services. Such measures contribute to SDG 15 and reinforce the ecological foundations of sustainable urban–rural interactions (Arnaiz et al., 2018). Medium-sized cities have the potential to become territorial anchors that foster equitable development, ecological stewardship, and robust socio-economic systems. However, fulfilling this role requires policy frameworks that acknowledge the complex socio-ecological assemblages revealed by this study. Aligning territorial policy with the SDGs therefore demands an explicit recognition of the city–territory nexus and an integrated governance approach capable of guiding more balanced and sustainable future trajectories. Declarations Author Contribution EV, conceived the research; EV, MB and SM designed the research; EV, MB analyzed the data; SM systematized the data and elaborate the maps; EV, MB wrote and edited the manuscript. Acknowledgement The first author was able to participate in this work thanks to the support granted by SECIHTI in the “Investigadores por Mexico” program. Keith Macmillan revised the English text. Data Availability This study used data obtained from official public databases. The authors compiled, processed, and analyzed these secondary datasets using quantitative analytical methods to address the research questions of the study. No primary data were generated. References Aguilar, A. G., Flores-Espinosa, M., & Hernández, J. (2025). Metropolización, dinámica inmobiliaria y segregación socio-territorial. El caso de Mérida. Yucatán EURE , 51 (153), 1–27. https://doi.org/10.7764/eure.51.153.03 Aguilar-Duarte, Y., Bautista, F., Mendoza, M. E., Frausto, O., Ihl, T., & Delgado, C. (2016). Ivaky: índice de la vulnerabilidad del acuífero kárstico yucateco a la contaminación. Revista Mexicana de Ingeniería Química , 15 (3), 913–933. Andersson, E., Haase, D., Anderson, P., Cortinovis, C., Goodness, J., Kendal, D., Lausch, A., McPhearson, T., Sikorska, D., & Wellmann, T. (2021). What are the traits of a social-ecological system: Towards a framework in support of urban sustainability. NPJ Urban Sustainability , 1 (1), 14. https://doi.org/10.1038/s42949-020-00008-4 Andrés López, G. (2019). El significado de los espacios de actividad económica en la estructura urbana de las ciudades medias españolas. Ciudades , 22 (22), 01–22. https://doi.org/10.24197/ciudades.22.2019.01-22 Arnaiz-Schmitz, C., Aguilera, P. A., Ropero, R. F., & Schmitz, M. F. (2023). Detecting social-ecological resilience thresholds of cultural landscapes along an urban–rural gradient: a methodological approach based on Bayesian Networks. Landscape Ecology , 38 (12), 3589–3604. https://doi.org/10.1007/s10980-023-01732-9 Arnaiz-Schmitz, C., Schmitz, M. F., Herrero-Jáuregui, C., Gutiérrez-Angonese, J. F. D. C., Pineda, F. D., & Montes, C. (2018). Identifying socio-ecological networks in rural-urban gradients: Diagnosis of a changing cultural landscape. Science of the Total Environment , 612 , 625–635. https://doi.org/10.1016/j.scitotenv.2017.08.215 Benítez, G., Pérez-Vázquez, A., Nava-Tablada, M., Equihua, M., & Álvarez-Palaciose, J. L. (2011). Expansión de los asentamientos informales y sus efectos ambientales en la periferia de la Ciudad de Xalapa, Veracruz México. Medio Ambiente y Urbanización , 75 (1), 47–70. Berdegué, J. A., Proctor, F. J., & Cazzuffi, C. (2014). Cities in the Rural Transformation. Working. Paper Series N° 123 Working Group: Development with Territorial Cohesion, Territorial. Cohesion for Development Program Rimisp, Santiago, Chile. Berdegué, J. A., & Soloaga, I. (2018). Small and medium cities and development of Mexican rural areas. World Development , 107 , 277–288. https://doi.org/10.1016/j.worlddev.2018.02.007 Berdegué, J. A., Carriazo, F., Jara, B., Modrego, F., & Soloaga, I. (2015). Cities, territories, and inclusive growth: Unraveling urban–rural linkages in Chile, Colombia, and Mexico. World Development , 73 , 56–71. https://doi.org/10.1016/j.worlddev.2014.12.013 Bolay, J. C., & Kern, A. L. (2019). Intermediate cities. In A. Orum (Ed.), The Wiley Blackwell Encyclopedia of Urban and Regional Studies . John Wiley & Sons Ltd.. 10.1002/9781118568446.eurs0163 Calzada-Infante, L., López-Narbona, A. M., Núñez-Elvira, A., & Orozco-Messana, J. (2020). Assessing the efficiency of sustainable cities using an empirical approach. Sustainability , 12 (7), 2618. https://doi.org/10.3390/su12072618 Carver (2019). Master the concepts and techniques of statistical analysis using JMP(R). Practical Data Analysis with JMP(R), Third Edition, SAS Institute. CONAPO (2020). Índice de marginación por localidad 2020. México: Consejo Nacional de Población, from https://www.datos.gob.mx/dataset/indices_marginacion Domínguez Aguilar, M. (2011). Avances en el estudio de la estructura territorial de la zona metropolitana de Mérida. Yucatán Península , 6 (1), 185–200. Ene, E., & McGarigal, K. (2023). Fragstats. A Spatial Pattern Analysis Program for Categorical Maps , from: https://fragstats.org/index.php ESRI. (2014). ArcMap (Version 10.3) . Environmental Systems Research Institute. Fragstats (2023). Documentación: Métricas de Fragstats, from https://www.fragstats.org GeoComunes, C., Torres-Mazuera, G., & Gómez Godoy, C. (2020). Expansión capitalista y propiedad social en la Península de Yucatán. México, from https://www.ccmss.org.mx/wp-content/uploads/Expansion_capitalista_propiedad_social_ Government of the State of Yucatán (2025). Municipios de Yucatán, from: https://www.yucatan.gob.mx/estado/municipios.php Granados Alcantar, J. A., & Quezada Ramírez, M. F. (2018). Tendencias de la migración interna de la población indígena en México, 1990–2015. Estudios demográficos y urbanos , 33 (2), 327–363. http://dx.doi.org/10.24201/edu.v33i2.1726 Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., & Briggs, J. M. (2008). Global change and the ecology of cities. Science , 319 (5864), 756–760. 10.1126/science.1150195 Henderson, J. V., Shalizi, Z., & Venables, A. J. (2001). Geography and development. Journal of Economic Geography , 1 (1), 81–105. https://doi.org/10.1093/jeg/1.1.81 Hoffmann, E. M., Schareika, N., Dittrich, C., Schlecht, E., Sauer, D., & Buerkert, A. (2023). Rurbanity: A concept for the interdisciplinary study of rural–urban transformation. Sustainability Science , 18 (4), 1739–1753. https://doi.org/10.1007/s11625-023-01331-2 INEGI (2018). Conjunto de datos vectoriales de uso del suelo y vegetación Serie VII. México: Instituto Nacional de Estadística y Geografía, from http://geoportal.conabio.gob.mx/metadatos/doc/html/usv250s7gw.html INEGI (2020). Censo de Población y Vivienda 2020. México: Instituto Nacional de Estadística y Geografía, from https://www.inegi.org.mx/programas/ccpv/2020/ Inostroza, L., Baur, R., & Csaplovics, E. (2013). Urban sprawl and fragmentation in Latin America: A dynamic quantification and characterization of spatial patterns. Journal of environmental management , 115 , 87–97. http://dx.doi.org/10.1016/j.jenvman.2012.11.007 de Janvry, A., & Sadoulet, E. (2001). Income strategies among rural households in Mexico: The role of off-farm activities. World Development , 29 (3), 467–480. https://doi.org/10.1016/S0305-750X(00)00113-3 Llop, J. M., Iglesias, B. M., Vargas, R., & Blanc, F. (2019). Las ciudades intermedias: concepto y dimensiones. Ciudades , 22 , 23–43. https://doi.org/10.24197/ciudades.22.2019.23-43 Madrid, G. (2011). Oaxaca, de ciudad intermedia a metrópoli de Los Valles Centrales. Emergencia de una ciudad-territorio en el sur de México . Universitat Politècnica de Catalunya. PhD Thesis. Emergencia de una ciudad-territorio en el sur de México. Maturana, F., Sposito, M. E. B., Bellet, C., & Henríques, C. (2017). Sistemas urbanos y ciudades medias en Iberoamérica . Impresión gráfica LOM. McDonald, R. I., Mansur, A. V., Ascensão, F., Colbert, M. L., Crossman, K., Elmqvist, T., Gonzalez, A., Güneralp, B., Dagmar Haase, D., Hamann, M., Hille, O., Huang, K., Kahnt, B., Maddox, D., Pacheco, A., Pereira, H. M., Seto, C., Simkin, R., Walsh, B., Werner, A. S., & Ziter, C. (2020). Research gaps in knowledge of the impact of urban growth on biodiversity. Nature Sustainability , 3 (1), 16–24. https://doi.org/10.1038/s41893-019-0436-6 Michelini, J. J., & Davies, C. (2009). Ciudades intermedias y desarrollo territorial:un análisis exploratorio del caso argentino . Documentos de trabajo No.5. Grupo de Estudios sobre Desarrollo Urbano (GEDEUR). UN-Habitat. (2022). World Cities Report 2022 . United Nations Human Settlements Programme. Ortiz-Báez, P., Cabrera-Barona, P., & Bogaert, J. (2021). Characterizing landscape patterns in urban-rural interfaces. Journal of Urban Management , 10 (1), 46–56. https://doi.org/10.1016/j.jum.2021.01.001 POZMO. (2025). Programa de Ordenamiento de la Zona Metropolitana de Oaxaca . SINFRA, Gobierno del Estado de Oaxaca. POTZMX. (2023). Programa de Ordenamiento Territorial de la Zona Metropolitana de Xalapa . Gobierno delEstado de Veracruz. Secretaría de Economía (2025). Perfil económico de Mérida, from https://www.economia.gob.mx/datamexico/es/profile/geo/merida-993101 Secretaría de Economía (2025). Perfil económico de Xalapa, from https://www.economia.gob.mx/datamexico/es/profile/geo/xalapa-993008 SEDATU/CONAPO (2020). Sistema Urbano Nacional 2020. Secretaría de Desarrollo Agrario, Territorial y Urbano / Comisión Nacional de Población, from https://www.datos.gob.mx/dataset/sistema_urbano_nacional Seto, K. C., Sánchez-Rodríguez, R., & Fragkias, M. (2013). The new geography of contemporary urbanization and the environment. Annual Review of Environment and Resources , 37 , 167–194. https://doi.org/10.1146/annurev-environ-100809-125336 Slemp, C., Davenport, M. A., Seekamp, E., Brehm, J. M., Schoonover, J. E., & Williard, K. W. (2012). Growing too fast: Local stakeholders speak out about growth and its consequences for community well-being in the urban–rural interface. Landscape and Urban Planning , 106 (2), 139–148. 10.1016/j.landurbplan.2012.02.017 Singer Sochet, M. (2014). Exclusión o inclusión indígena ? Estudios políticos , 31, pp. 87–106. Trejo-Nieto, A. (2024). Unveiling the intermediate role of Mexico’s mid-sized metropolises. Regional Studies Regional Science , 11 (1), 777–797. https://doi.org/10.1080/21681376.2024.2430540 United Nations. (2014). World Urbanization Prospects: The 2014 Revision . United Nations. United Nations. (2025). World Urbanization Prospects 2025: Summary of Results (Vol. 12). United Nations. UN DESA/POP/2025/TR/NO. UN-Habitat. (2022). World Cities Report 2022 . United Nations Human Settlements Programme (UN-Habitat). Valicelli, L., & Pesci, R. (2002). Las nuevas funciones urbanas: gestión para la ciudad sostenible. CEPAL-Serie Medio ambiente y desarrollo N° 48. https://repositorio.cepal.org/bitstream/handle/11362/5747/S02124_es . pdf?sequence = 1. Villegas-Alzate, J. G. (2022). Consideraciones teórico-metodológicas para el estudio de ciudades intermedias. Jangwa Pana , 22 (1), 1–18. Additional Declarations No competing interests reported. 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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-8811305","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588430004,"identity":"86667ef2-1014-43e2-b895-2f0cd6ff2532","order_by":0,"name":"Emma Villaseñor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYPACCRk+KEuODUwVENbCwwZlGUMYBoStgWtJbCCkhV/sdOIHxjYLHjb24w8fF1TcS++Tbj744YMBQ7TBAexaJGfnbpZgbAM6jCfH2HjGmeLcNpljyZIzDBhyZzZg12JwO3eDBMMZkF9y2KR52xJy2yRyzJh5gFr6cTjM/nbu5h9gLfzPn/8Gaklnk8j/BtbShkOLgXTuNgmGCqAWiQQzZqCWBDaJHDa8tkjczt1mkQDW8sZYmudMgmGbRJox0C8SOP3CD/T+jQ8GdXL8/OkPP/NUJMjLz0h++OFDhU3uBhwhBgYJ2KzHo34UjIJRMApGASEAAA2kSpoPkFiTAAAAAElFTkSuQmCC","orcid":"","institution":"Secretaria de Ciencias, Humanidades Tecnología e Innovación","correspondingAuthor":true,"prefix":"","firstName":"Emma","middleName":"","lastName":"Villaseñor","suffix":""},{"id":588430005,"identity":"5f2902c9-abcb-4727-99bc-1f5d24a5d6b3","order_by":1,"name":"Martha Bonilla","email":"","orcid":"","institution":"Instituto de Ecología","correspondingAuthor":false,"prefix":"","firstName":"Martha","middleName":"","lastName":"Bonilla","suffix":""},{"id":588430006,"identity":"260a4213-44e5-4602-be27-7bceb9077102","order_by":2,"name":"Swany Morteo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Swany","middleName":"","lastName":"Morteo","suffix":""}],"badges":[],"createdAt":"2026-02-07 00:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8811305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8811305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102536444,"identity":"794076e4-e366-48b2-bdc7-e3a08eb0ef06","added_by":"auto","created_at":"2026-02-12 17:28:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eThis image is not available with this version.\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-8811305/v1/27f73056a5439dc704cfe7e6.png"},{"id":102747586,"identity":"68aa3ec6-fbf8-4021-a379-c936bcb50b92","added_by":"auto","created_at":"2026-02-16 09:04:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3197621,"visible":true,"origin":"","legend":"\u003cp\u003eLand use and vegetation coverage of city systems (gray area delimited by the official polygon (blue boundary), buffer 1 (blue boundary), and buffer 2 (red boundary) for each case study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8811305/v1/a30833beee9089ab2a43aa24.png"},{"id":102962198,"identity":"6e74704a-6e6a-4409-8d44-2dba55cc86b2","added_by":"auto","created_at":"2026-02-19 04:05:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4287541,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8811305/v1/ce74c325-bfbb-4039-b0db-c05fb0034b63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Territorial Dynamics of Medium-Sized Cities: Landscape Fragmentation and Inequality in three Southeastern Mexican Cities","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrbanization is one of the dominant global megatrends, with far-reaching socio-environmental implications (Slemp et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; UN-Habitat, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The process entailing natural or agricultural land converted into impervious built surfaces, alongside a structural transition from primary economic activities toward secondary and tertiary sectors. This process is accelerating worldwide. Urban areas are characterized by higher population density, greater connectivity, and more developed infrastructure, as commonly observed in cities and towns (UN-DESA 2025). Extensive research has documented the environmental impacts of urban expansion, including habitat and biodiversity loss, water scarcity and degradation, and soil contamination (Andersson, 2006; Grimm et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; McDonald et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Beyond these biophysical effects, land conversion for urban use reshapes the relationships between people and their environments, influencing livelihoods, territorial organization, and socio-ecological dynamics (Arnaiz et al., 2018). Since urban centers exert influence well beyond their administrative boundaries, they configure networks of influence that affect the environmental, economic, and social trajectories of surrounding localities, generating complex spatial patterns (Berdegué et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy 2050, 68% of the world’s population will reside in urban areas and in Latin America, 45% are cities of less than 500,000 inhabitants (UN-DESA, 2014; Bolay and Kern \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The rapid expansion of medium-sized cities is leading to profound changes in the landscape and territorial governance. Their future consolidation poses complex challenges related to equitable well-being, environmental protection, and the safeguarding of ecosystem services (Seto et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hoffman et al., 2023). These challenges are particularly salient in Latin America, where cities have expanded in diffuse and fragmented ways, displacing or reshaping functions that were formerly concentrated in the urban core (Ortiz-Báez et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Understanding these spatial patterns is crucial for designing public policies that acknowledge the socio-ecological interdependence between medium-sized cities and their surrounding territories (Berdegué et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Trejo-Nieto, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDifferent studies show that the distance between localities and nearby cities is a key driver structuring spatial distribution, development opportunities, and exclusion dynamics (Berdegué et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ortiz-Baez et al., 2021). It has been documented that proximity to urban centers often provides access to markets, employment, services, and institutional networks (Berdegué et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ortiz-Baez et al., 2021). Conversely, greater distance is associated with subsistence agriculture, higher socioeconomic marginalization, and reduced opportunities for social mobility, largely due to limited access to educational and formal employment opportunities (Henderson et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Berdegué \u0026amp; Soloaga, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Arnaiz et al., 2018). In Mexico, these spatial patterns intersect with the distribution of Indigenous People, historically concentrated in rural regions of the south and southeast, yet increasingly present in cities due to circular migration processes linking rural and urban spaces (Granados Alcántar \u0026amp; Quezada Ramírez, 2018). Indigenous People are consistently associated with structural barriers to accessing education, health, and adequate nutrition (Singer, 2014).\u003c/p\u003e \u003cp\u003eThese dynamics suggest that urbanization is not as a linear rural–urban transition but a network of interwoven interactions and bidirectional flows of materials, people, and institutions (Hoffman et al., 2023). This perspective requires cross-disciplinary approaches to address coupled human–nature problems. Examining medium-sized cities through a socio-ecological lens can provide a relevant empirical context for advancing such integrative sustainability research.\u003c/p\u003e \u003cp\u003eAlthough intermediate cities have been defined as those with populations of between 100,000 and 1,000,000 (depending on the source), several authors highlight their role as hubs of social, economic, and cultural interaction, regardless of size. These cities have a multi-scale connective infrastructure, are home to government authorities at different levels, and can present certain economic strengths (Michelini and Davies, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Berdegué et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Trejo-Nieto, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast to the highly concentrated dynamics typical of large metropolitan areas, intermediate cities have been identified as potential anchors for more balanced regionally distributed forms of development without such harmful effects on the environment (Llop et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite their potential strategic role, medium-sized cities remain understudied in terms of the socio-environmental impacts of their growth. Research has disproportionately focused on large metropolises, leaving significant knowledge gaps regarding how medium-sized cities shape surrounding landscapes and whether they exhibit characteristics of intermediate cities that can provide advantages over their larger counterparts, such as proximity-based economies, solidarity networks, sustainable mobility, or closer relationships between citizens and local governments (Llop et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Trejo-Nieto, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Addressing these gaps is essential for informing territorial planning that integrates both environmental conservation and regional equity (Maturana et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Valicelli \u0026amp; Pesci, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a sustainability perspective, these urban–territorial dynamics are directly linked to the achievement of the Sustainable Development Goals (SDGs), particularly those related to inclusive urbanization, territorial equity, and ecosystem integrity. Medium-sized cities occupy a strategic position in this agenda, as they mediate interactions between urban cores and rural hinterlands where social inequalities, land-use pressures, and ecological processes intersect. Understanding how landscape configuration, socioeconomic conditions, and distance to urban centers co-evolve is therefore essential for advancing SDG 11 (Sustainable Cities and Communities) and SDG 10 (Reduced Inequalities), while simultaneously addressing environmental goals such as SDG 6 (Clean Water and Sanitation) and SDG 15 (Life on Land).\u003c/p\u003e \u003cp\u003eIn this study, we evaluate how three medium-sized Mexican cities—Oaxaca, Xalapa, and Mérida—influence landscape structure and socioeconomic conditions in their surrounding territories. We delineated two concentric buffer zones around each city to analyze the landscape attributes (land-cover diversity, heterogeneity, and fragmentation) and socioeconomic characteristics (population density, Indigenous population, and marginalization) of each system. We aimed to assess how landscape and socioeconomic conditions vary with distance from the city, and how these patterns compare across different urban contexts. Assuming these cities present characteristics of intermediate cities, we hypothesized that landscape configuration and socioeconomic conditions will vary gradually with proximity to the urban core. Thus, areas closest to the city will display higher land-use diversity, landscape fragmentation, population density, and service access, while more distant areas will exhibit greater ecological continuity, lower population density, and higher marginalization (Berdegué et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Llop et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Through this cross-disciplinary perspective, our study aims to contribute to advancing integrative knowledge that links biophysical processes, social dynamics, and policy-relevant insights to address sustainability challenges in coupled human–nature systems.\u003c/p\u003e "},{"header":"Methodology","content":"\u003cp\u003eResearch setting\u003c/p\u003e\u003cp\u003eWe selected three medium-sized cities in southeastern Mexico following the classification established by SEDATU, which defines medium-sized cities as those with populations between 100,000 and 1,000,000 inhabitants (SEDATU/CONAPO, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These cities, Xalapa, Oaxaca de Juárez, and Mérida, represent distinct socio-environmental and territorial contexts that allow us to examine how medium-sized urban centers influence their surrounding landscapes and socioeconomic conditions.\u003c/p\u003e\u003cp\u003eXalapa is located in the mountainous central region of Veracruz, along the Gulf of Mexico. The rugged topography has resulted in numerous small and dispersed localities. The city covers 122.33 km² and had 488,531 inhabitants in 2020 (INEGI, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Together with eight adjacent municipalities, it forms the Xalapa Metropolitan Zone (POTZMX, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePopulation growth accelerated sharply between 1960 and 1980, increasing by more than 300% due primarily to rural-to-urban migration, and continued expanding rapidly through the early 2000s (Benítez et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe state of Oaxaca, located in southeastern Mexico, features the highest administrative fragmentation in the country, with 570 municipalities and a highly dispersed settlement pattern that includes many localities with fewer than 100 inhabitants. The Oaxaca Metropolitan Zone, including the state capital, Oaxaca de Juárez, comprises 27 municipalities and concentrates the state’s largest share of population and services (POZMO \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn 2020, the city’s population was 258,913 inhabitants. Urban expansion has occurred through two major pulses: the first in the 1960s, driven by commercial activity and tourism, and the second between 2000 and 2005 (Fig.\u0026nbsp;1), when the urban footprint expanded further (Madrid \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMérida is located in the Yucatán Peninsula. It is a limestone plain characterized by low elevation and a network of subterranean water flows. The city lies 35 km inland from the Gulf of Mexico. In 2020, Mérida had 995,129 inhabitants (INEGI, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fig.\u0026nbsp;1). The Mérida Metropolitan Zone encompasses nine municipalities and concentrated 52.86% of the state’s 2,320,898 inhabitants in 2020 (Government of the State of Yucatán, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLandscape Systems\u003c/p\u003e\u003cp\u003eFor each case study, we delineated three analytical landscape systems: City (urban polygon), and two concentric zones of urban influence: Buffer 1, and Buffer 2. Within each system, we evaluated spatial landscape characteristics, including land-cover diversity, heterogeneity, and the degree of isolation or connectivity among vegetation and land-use classes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBuffer distances were determined based on the Euclidean distance between the centroid and the farthest boundary of each city polygon, ensuring that Buffers 1 and 2 represented equivalent baseline distances across cities: 6 km for Xalapa, 7 km for Oaxaca, and 12 km for Mérida.\u003c/p\u003e\u003cp\u003eEach system polygon was used to clip the vegetation and land-cover layer from INEGI’s Series VII dataset (INEGI, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which was converted to raster format and used as the primary input for the computation of landscape metrics. Spatial data processing was conducted in ArcMap 10.3 (ESRI, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Throughout the manuscript, we refer to these three units as landscape systems.\u003c/p\u003e\u003cp\u003eLandscape Metrics\u003c/p\u003e\u003cp\u003eTo characterize spatial patterns and landscape configuration within each system, we computed landscape metrics using Fragstats v.4.2 (Ene \u0026amp; McGarigal \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The metrics applied at both the class and landscape levels are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, along with their definitions (Fragstats \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Arnaiz-Schmitz et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLandscape metrics used in the study. Depending on the metric type, analyses were conducted at either the landscape-system level (City, B1, B2) or the land-cover class level (Urban, Grassland, Rainfed Agriculture, Irrigated Agriculture, Secondary Vegetation, Natural Vegetation).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eMetric / Abbreviation\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eConcept\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eLandscape Property Measured\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c5\" style=\"text-align: left;\"\u003e \u003cp\u003eLevel of Analysis\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eLargest Patch Index (LPI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003ePercentage of the landscape occupied by the largest patch of each class; indicator of dominance and landscape homogeneity.\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0–100\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eArea and edge / Composition\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem, Class\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eMean Patch Area (AREA_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eAverage area of all patches belonging to a given class.\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eHectares\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eArea and edge / Composition\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem, Class\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eMean Euclidean Nearest-Neighbor Distance (ENN_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eMean Euclidean distance to the nearest patch of the same class; quantifies spatial isolation.\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eMeters\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eAggregation\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem, Class\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eContagion Index (CONTAG)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eProbability that two randomly selected cells belong to the same class; indicator of overall landscape aggregation.\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0–100\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eAggregation\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eShannon Diversity Index (SHDI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003ePatch diversity weighted by the proportional abundance of each patch type; indicator of landscape heterogeneity.\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e≥ 0\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eDiversity\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eShannon Evenness Index (SHEI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eProportional abundance of patch types standardized by the logarithm of the number of patch types; indicator of evenness.\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0–1\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eDiversity\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eInterspersion and Juxtaposition Index (IJI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eDegree of intermixing among adjacent patch types; requires ≥ 3 classes.\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0–100\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eAggregation\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003ePatch Richness (PR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eNumber of different patch types present in the landscape.\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e≥ 1\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eDiversity\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSocioeconomic Variables\u003c/p\u003e\u003cp\u003eSocioeconomic conditions in localities within the three landscape systems (City, Buffer 1, Buffer 2) were characterized using data from INEGI, CONAPO, and CONAGUA. Selected variables included demographic and socioeconomic indicators: population density, number of indigenous language speakers, economically active population, marginalization index, and annual volume of water concessions.\u003c/p\u003e\u003cp\u003eTo make variables comparable across localities of different sizes, we standardized all values by dividing the total count of each variable by the land area (km²), except for the marginalization index, which is already a standardized composite indicator.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocioeconomic variables used for the analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eDescription / Source\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eLevel of Analysis\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003ePopulation density\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eNumber of inhabitants per unit area\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eInhabitants per km²\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eIndigenous-language speakers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003ePopulation aged 3–130 years speaking an indigenous language (INEGI \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eSpeakers per km²\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem, Landscape\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eMarginalization Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eComposite indicator of social exclusion and deprivation based on nine variables across four dimensions (housing, education, services, income) (CONAPO, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eStandardized index\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem, Landscape\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eEconomically Active Population (EAP)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003ePopulation aged 12–130 years working, or seeking employment (INEGI, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eIndividuals per km²\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem, Landscape\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cb\u003eAnnual volume of water concessions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eVolume of nationally regulated water extraction (REPDA 2020)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003em³ per year\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eSystem, Landscape\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eTo assess differences in spatial patterns among the three landscape systems (City, Buffer 1, and Buffer 2), we conducted an analysis of variance (ANOVA). The Largest Patch Index (LPI) was transformed due to a lack of normality. The ANOVA of class-level metrics was used to evaluate differences in land-cover composition. Pairwise differences were assessed using Tukey’s post hoc test. These analyses were performed in JMP (Carver, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo compare total city values with median buffer values, we used non-parametric Kruskal–Wallis tests, given the violations of normality (verified via the Shapiro–Wilk test and visual diagnostics). Dunn’s post hoc test with Bonferroni correction was used to identify significant pairwise differences. All analyses were conducted in RStudio (v. 4.5.1) using the dunn.test, ggplot2, and stats packages.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eLandscape Characterization of Case Studies\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows land-cover and vegetation distributions across the three case studies and their corresponding buffer systems. On average, urban cover is the dominant class in city systems (89.30%). Secondary vegetation dominates both Buffer 1 (36.49%) and Buffer 2 (45.83%), followed by agricultural classes (32.42% and 29.02%). Natural vegetation occurs only in Buffer 2 (which is the farthest from the city system core) and is absent from all city systems. Oaxaca is the only city system with irrigated agriculture; Xalapa is the only landscape system where natural vegetation appears within Buffer 1, and M\u0026eacute;rida is the only landscape system that contains the class category labeled \u0026ldquo;non-vegetated surface.\u0026rdquo;\u003c/p\u003e \u003cp\u003eAcross all cases, 82% of buffer localities are rural, indicating that these medium-sized cities are surrounded predominantly by rural settlements.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCLAVES-CLASE: AGH: Rainfed agriculture (humid conditions), IAG: Irrigated agriculture, RAS: Rainfed agriculture (seasonal), WAT: Water bodies, URB: Urban areas, OAF: Oak Forest, OPF: Oak-pine forest, PIF: Pine forest, POF: Pine-oax forest, MCF: Mountain cloud forest, MGF: Mangrove forest, GRAS: Grassland, TDDF: Tropical dry deciduous forest, NVA: Bare ground/non-vegetated areas, CDV: Coastal dune vegetation, HHV: Halophytic-hydrophilic vegetation, SFV: Secondary forest vegetation, STF: Secondary tropical forest and SMV: Secondary mangrove vegetation.\u003c/p\u003e \u003cp\u003eSpatial Patterns of Landscape Systems\u003c/p\u003e \u003cp\u003eThe spatial patterns of the three landscape systems differ mainly between the city system and the buffer systems, but the spatial patterns are similar between the buffers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared to buffer systems, in the city, the area occupied by the largest patch (LPI) is larger, while the diversity of classes (SHDI) is lower, with these differences being statistically significant. In addition, the average patch size (AREA_MN) in the city is smaller than in the most distant system (buffer 2), and the connectivity between patches (CONTAG) is greater than in the closest system (buffer 1). Taken together, these results indicate that there is greater spatial homogeneity in the city system, mainly associated with the dominance of the urban class, in terms of both patch size and continuity. In contrast, buffer systems showed greater diversity in terms of the size and type of patches they contain. Furthermore, the lack of differences in metrics in both buffers (1 and 2) indicates that, with respect to landscape structure, these units are equivalent. On the other hand, metrics related to equity in coverage distribution (SHEI) and patch isolation (ENN_M) showed no differences between systems.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpatial patterns of landscape systems. Values are means and standard deviations. Different letters indicate significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between landscape systems, according to Tukey\u0026rsquo;s post hoc test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBuffer 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuffer 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest Patch Index (LPI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.0 (\u0026plusmn;\u0026thinsp;7.1) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.5 (\u0026plusmn;\u0026thinsp;27.3) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.1 (\u0026plusmn;\u0026thinsp;27.0) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Patch Area (AREA_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208.5 (\u0026plusmn;\u0026thinsp;152.9) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e424.9 (\u0026plusmn;\u0026thinsp;60.0) \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e691.2 (\u0026plusmn;\u0026thinsp;162.2) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Euclidean Nearest-Neighbor Distance (ENN_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e539.0 (\u0026plusmn;\u0026thinsp;148.1) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e904.9 (\u0026plusmn;\u0026thinsp;428.9) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1312.2 (\u0026plusmn;\u0026thinsp;488.1) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContagion Index (CONTAG)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.9 (\u0026plusmn;\u0026thinsp;10.62) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.1 (\u0026plusmn;\u0026thinsp;11.62) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.5 (\u0026plusmn;\u0026thinsp;3.22) \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.067\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShannon Diversity Index (SHDI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4 (\u0026plusmn;\u0026thinsp;0.26) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (\u0026plusmn;\u0026thinsp;0.29) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3 (\u0026plusmn;\u0026thinsp;0.19) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShannon Evenness Index (SHEI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3 (\u0026plusmn;\u0026thinsp;0.19) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 (\u0026plusmn;\u0026thinsp;0.24) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6 (\u0026plusmn;\u0026thinsp;0.08) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConnectivity by coverage type per system\u003c/p\u003e \u003cp\u003eThe class metrics show that spatial composition varies between systems depending on the type of coverage studied (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As expected, the spatial pattern of cities is dominated by the urban class: the largest patch index (LPI), the average patch area size (AREA_MN), and the shortest distance to the nearest neighbor (ENN_M) were significantly greater in the city than in the buffers. Furthermore, the only difference between buffers 1 and 2 was observed in the size of the largest urban patch, indicating a gradual variation in the area of this cover.\u003c/p\u003e \u003cp\u003eIn the buffers, secondary vegetation was the most prominent class and showed differences in distribution compared to the city. The largest patch index, average patch area, and class intercalation index were significantly higher in the buffers, indicating the dominance and high aggregation of patches of this cover and a more heterogeneous mosaic in the buffer systems compared to the city. Additionally, patches of grassland and rainfed agriculture classes had a larger average size in the buffer systems than in the city. It is noteworthy that irrigated agriculture and natural vegetation classes were only found in the buffer system, indicating clear differences in composition between urban systems and their surroundings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLandscape metrics by class. Analysis of variance to evaluate changes in the structure of cover classes by system type. Those showing significant differences are in bold. Different superscript letters indicate significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between landscape systems, according to Tukey's post hoc test. Irrigated agriculture was only found in the buffers, so a t-test was performed, while natural vegetation was only found in buffer 2 and in one sample from buffer 1, so only the averages are reported here. To comply with the assumptions of normality and homoscedasticity, all data were transformed, and these are the values reported.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBuffer 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuffer 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatch Richness (PR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (\u0026plusmn;\u0026thinsp;0.9) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17 (\u0026plusmn;\u0026thinsp;1.7) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 (\u0026plusmn;\u0026thinsp;0.3) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest Patch Index (LPI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.48 (\u0026plusmn;\u0026thinsp;0.07) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (\u0026plusmn;\u0026thinsp;0.9) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1 (\u0026plusmn;\u0026thinsp;0.3) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Patch Area (AREA_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.71(\u0026plusmn;\u0026thinsp;1.1) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.09(\u0026plusmn;\u0026thinsp;0.8) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.76(\u0026plusmn;\u0026thinsp;0.3) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Euclidean Nearest-Neighbor Distance (ENN_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4 (\u0026plusmn;\u0026thinsp;1.1) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3 (\u0026plusmn;\u0026thinsp;0.3) \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5(\u0026plusmn;\u0026thinsp;0.3) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterspersion and Juxtaposition Index (IJI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.6(\u0026plusmn;\u0026thinsp;24.2) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.9(\u0026plusmn;\u0026thinsp;29.1) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.8(\u0026plusmn;\u0026thinsp;17.7) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrassland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatch Richness (PR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16 (\u0026plusmn;\u0026thinsp;1.8) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.69 (\u0026plusmn;\u0026thinsp;0.5) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 (\u0026plusmn;\u0026thinsp;0.5) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest Patch Index (LPI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.6(\u0026plusmn;\u0026thinsp;2.9) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52(\u0026plusmn;\u0026thinsp;1.9) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4(\u0026plusmn;\u0026thinsp;0.07) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Patch Area (AREA_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95(\u0026plusmn;\u0026thinsp;1.4) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.66(\u0026plusmn;\u0026thinsp;1.4) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.71(\u0026plusmn;\u0026thinsp;0.8) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Euclidean Nearest-Neighbor Distance (ENN_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3(\u0026plusmn;\u0026thinsp;0.6) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9(\u0026plusmn;\u0026thinsp;1.0) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7(\u0026plusmn;\u0026thinsp;0.5) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterspersion and Juxtaposition Index (IJI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.8(\u0026plusmn;\u0026thinsp;34.2) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.4(\u0026plusmn;\u0026thinsp;29.4) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.4(\u0026plusmn;\u0026thinsp;9.4) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecondary vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatch Richness (PR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14(\u0026plusmn;\u0026thinsp;0.9) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.8(\u0026plusmn;\u0026thinsp;1.0) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33(\u0026plusmn;\u0026thinsp;0.2) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest Patch Index (LPI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003(\u0026plusmn;\u0026thinsp;1.1) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98(\u0026plusmn;\u0026thinsp;1.3) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.06(\u0026plusmn;\u0026thinsp;1.2) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Patch Area (AREA_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.66(\u0026plusmn;\u0026thinsp;0.4) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.74(\u0026plusmn;\u0026thinsp;1.6) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.73(\u0026plusmn;\u0026thinsp;1.8) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Euclidean Nearest-Neighbor Distance (ENN_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.97(\u0026plusmn;\u0026thinsp;0.4) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.50(\u0026plusmn;\u0026thinsp;0.4) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.29(\u0026plusmn;\u0026thinsp;0.3) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterspersion and Juxtaposition Index (IJI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.22(\u0026plusmn;\u0026thinsp;16.7) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.71(\u0026plusmn;\u0026thinsp;9.1) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.19(\u0026plusmn;\u0026thinsp;13.0) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRainfed agriculture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatch Richness (PR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21(\u0026plusmn;\u0026thinsp;0.8) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.28(\u0026plusmn;\u0026thinsp;0.4) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.72(\u0026plusmn;\u0026thinsp;1.1) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest Patch Index (LPI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36(\u0026plusmn;\u0026thinsp;0.9) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52(\u0026plusmn;\u0026thinsp;1.4) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11(\u0026plusmn;\u0026thinsp;1.7) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Patch Area (AREA_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.25(\u0026plusmn;\u0026thinsp;0.2) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.00((\u0026plusmn;\u0026thinsp;0.7) \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.20(\u0026plusmn;\u0026thinsp;2.9) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Euclidean Nearest-Neighbor Distance (ENN_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.96(\u0026plusmn;\u0026thinsp;3.7) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.69(\u0026plusmn;\u0026thinsp;0.6) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8(\u0026plusmn;\u0026thinsp;3.5) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterspersion and Juxtaposition Index (IJI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.26 (\u0026plusmn;\u0026thinsp;16.9) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.05(\u0026plusmn;\u0026thinsp;25.8) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.8(\u0026plusmn;\u0026thinsp;39.5) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIrrigated Agriculture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatch Richness (PR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25(\u0026plusmn;\u0026thinsp;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78 (\u0026plusmn;\u0026thinsp;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest Patch Index (LPI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(\u0026plusmn;\u0026thinsp;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08(\u0026plusmn;\u0026thinsp;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Patch Area (AREA_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.72(\u0026plusmn;\u0026thinsp;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.82(\u0026plusmn;\u0026thinsp;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Euclidean Nearest-Neighbor Distance (ENN_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.95(\u0026plusmn;\u0026thinsp;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.56(\u0026plusmn;\u0026thinsp;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterspersion and Juxtaposition Index (IJI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.52(\u0026plusmn;\u0026thinsp;15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.25(\u0026plusmn;\u0026thinsp;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNatural Vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatch Richness (PR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62(\u0026plusmn;\u0026thinsp;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest Patch Index (LPI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.52(\u0026plusmn;\u0026thinsp;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Patch Area (AREA_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.5(\u0026plusmn;\u0026thinsp;7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Euclidean Nearest-Neighbor Distance (ENN_MN)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.54(\u0026plusmn;\u0026thinsp;3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterspersion and Juxtaposition Index (IJI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.26(\u0026plusmn;\u0026thinsp;99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSocioeconomic variables\u003c/p\u003e\u003cp\u003eAs expected, the city has the highest values for population density, employment, and access to services. However, the differences with respect to the buffers, as well as between the buffers, are not linear. There were no significant differences between systems in the variables of population density or number of indigenous speakers. However, the socioeconomic factors show two patterns: in one, the values are higher in the city, then decrease in B1, and increase in B2. This occurs with the economically active population and the number of indigenous speakers, but the latter variable was not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The other pattern is a gradual decrease among the systems: this pattern is shown by the marginalization index and population density, although only marginalization presents statistically significant differences, specifically between the city and B2 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of differences in socioeconomic variables by system. Those showing significant differences are presented in bold font. The median is used as a measure of central tendency because it is the measure used by the non-parametric test. The value for the city system is a single, total value. Those for the buffers are medians of the values for all localities. Different letters indicate significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between landscape systems according to Dunn's post hoc test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBuffer 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuffer 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,583.63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,860.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,755.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarginalization index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.44\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.44\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.22\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomically active population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,994.89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,059.61\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,840.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndigenous-language speakers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233.98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.62\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.97\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnnual volume of water concessions\u003c/p\u003e \u003cp\u003eAnnual water concessions are highest in Buffer 1, followed by Buffer 2, and lowest in the City. The Kruskal\u0026ndash;Wallis results show significant differences (χ\u0026sup2; = 187.51, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), driven by contrasts between urban systems and buffer systems.\u003c/p\u003e \u003cp\u003eLandscape Metrics Across Case Studies\u003c/p\u003e \u003cp\u003eLandscape metrics at the system level show that differences are greater across the systems (City, B1, B2) than across the landscapes (Oaxaca, M\u0026eacute;rida, Xalapa). No significant differences were found among cities for CONTAG, ENN_MN, and LPI, indicating similar aggregation and connectivity patterns. In contrast, NP (number of patches) and SHAPE (patch shape complexity) differ significantly across systems (p\u0026thinsp;=\u0026thinsp;0.049 and p\u0026thinsp;=\u0026thinsp;0.021, respectively), with higher fragmentation and morphological complexity in the buffers. AREA_MN is larger in the buffers, indicating the presence of larger patches with increased distance from the urban core (p\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003eLandscape composition shows a consistent pattern across the three case studies: 1) more land-cover classes and patch types in buffers than in city systems, and 2) Distinct dominant patch classes by system: urban patches predominated in the city, in Buffer 1, rainfed agriculture and secondary vegetation in Buffer 2. Furthermore, natural vegetation patches are not present in city systems. The total number of land cover classes was highest in Oaxaca, followed by Xalapa, and in all cases, the most marked increase in the number of classes was observed between the city and the first buffer.\u003c/p\u003e \u003cp\u003eSocioeconomic Metrics Across Case Studies\u003c/p\u003e \u003cp\u003eIn Xalapa, significant differences were found only in indigenous-language speakers (χ\u0026sup2; = 1.63; p\u0026thinsp;=\u0026thinsp;0.002), showing the highest values in B1 and declining sharply in B2. The Economically Active Population (EAP) decreases with distance to the city, although not significantly. In Oaxaca, indigenous-language speakers differ significantly among systems (χ\u0026sup2; = 8.58; p\u0026thinsp;=\u0026thinsp;0.01), and EAP shows a marginal trend (p\u0026thinsp;=\u0026thinsp;0.06), and there were no differences found in population density or marginalization. M\u0026eacute;rida showed contrasting behavior compared to the other two cities. The four variables analyzed showed significant differences between systems. As expected, population density is higher in the city than in B1 and B2 (χ\u0026sup2; = 7.10; df\u0026thinsp;=\u0026thinsp;2; p\u0026thinsp;=\u0026thinsp;0.03). The number of indigenous speakers decreases from the city to B1 but increases considerably in B2 (χ\u0026sup2; = 991.10; d.f. = 2; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a pattern that is also observed in the EAP (χ\u0026sup2; = 12.99; d.f. = 2; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The marginalization index decreases with distance from the city (χ\u0026sup2; = 21.62; d.f. = 2; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with marked differences mainly present between the buffers.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe integrated analysis of landscape structure and socioeconomic conditions across distance gradients from medium-sized cities reveals consistent spatial and social patterns that illuminate the dynamics of urban growth, peri-urban transformation, and territorial inequality. Contrary to the expected presence of a continuous rural\u0026ndash;urban gradient, our results show a sharp transition from a highly homogeneous and urban-dominated core to a persistently fragmented peri-urban matrix. This pattern remains stable across both buffer zones. This indicates that fragmentation is not gradually attenuated with distance but instead constitutes a defining feature of the broader urban hinterland.\u003c/p\u003e \u003cp\u003eIn terms of landscape composition, the dominance of urban land in the city system contrasts with the prevalence of secondary vegetation, grasslands, and agricultural land in the buffer zones, as well as with the exclusive presence of irrigated agriculture and natural vegetation in the most distant areas. These patterns are consistent with the notion of \u0026ldquo;rural spaces in transition\u0026rdquo; (Arnaiz et al., 2018) and reflect the fragmentation processes widely documented in Latin America (Maturana et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Berdegu\u0026eacute; et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Seto et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The greater heterogeneity and patch diversity of the buffers relative to the urban core further supports the interpretation of these zones as complex socio-ecological mosaics shaped by both urban influence and rural persistence.\u003c/p\u003e \u003cp\u003eNone of the landscape metrics (CONTAG, ENN_MN, LPI, NP, SHAPE) showed significant differences between the three landscapes studied or in the landscape \u0026times; system interaction, suggesting that the patterns observed respond mainly to the distance gradient to the city and not to regional particularities.\u003c/p\u003e \u003cp\u003eOverall, the distribution of land-cover types and their spatial configuration reflect broader processes of metropolization, deconcentration of urban functions, and socio-spatial fragmentation. Areas adjacent to the cities increasingly absorb industrial, commercial, and logistical activities, consistent with patterns observed in intermediate Latin American cities with limited urban planning capacity (Inostroza et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Maturana et al., 2015). Historically, this pattern is rooted in the agricultural economies that shaped the development trajectories of Xalapa, Oaxaca, and M\u0026eacute;rida: from coffee and sugarcane trade in Xalapa, to diversified irrigated agriculture in the Central Valleys of Oaxaca, to the henequen-driven regional economy of Merida.\u003c/p\u003e \u003cp\u003eSocioeconomic variables reveal a similarly marked contrast between the urban core and its periphery. The decline in access to education, housing quality, and income with increasing distance from the city center\u0026mdash;and the corresponding precarity in more dispersed localities\u0026mdash;demonstrate persistent territorial inequality and social fragmentation (Berdegu\u0026eacute; et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Aguilar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These patterns align with the frameworks of uneven geographical development and urban dualization (Maturana et al., 2015), whereby urban land values and real-estate dynamics concentrate opportunities in central areas while displacing more vulnerable populations toward peripheral locations characterized by lower-quality housing and deficient services.\u003c/p\u003e \u003cp\u003eThe non-linear patterns of the Economically Active Population (EAP) and the number of indigenous-language speakers (high in the city, low in Buffer 1, and rising again in Buffer 2) suggest that intermediate cities act simultaneously as attractors of labor and nodes of regional intermediation (Trejo-Nieto, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The heightened socioeconomic activity observed in distant localities within Buffer 2 indicates the emergence of secondary centers benefiting from \u0026ldquo;borrowed size\u0026rdquo; effects, accessing agglomeration advantages linked to larger nearby cities. Teotitl\u0026aacute;n del Valle in Oaxaca exemplifies this phenomenon, functioning as a dynamic regional pole with a stronger commercial balance than the state capital (Secretar\u0026iacute;a de Econom\u0026iacute;a, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Trejo-Nieto, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Patterns of water extraction reinforce these interpretations: the highest intensity of water concessions occurs in Buffer 1, reflecting the relocation of economic activities to peri-urban zones where land is more affordable but still close to the infrastructure (Dom\u0026iacute;nguez-Aguilar, 2011). These findings are characteristic of metropolitan expansion processes shaped by real-estate speculation, business-oriented urban management, and the peripheral restructuring of industry (Andr\u0026eacute;s L\u0026oacute;pez, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the three case studies exhibit a consistent structure, comprising an urban core surrounded by fragmented peri-urban matrices, their specific configurations reflect distinct historical, geographic, and cultural trajectories.\u003c/p\u003e \u003cp\u003eCase study-specific dynamics\u003c/p\u003e \u003cp\u003eXalapa shows a gradual decline in patch size and connectivity of secondary vegetation (primarily pine\u0026ndash;oak forests) from the urban core to the outer buffer, which retains a predominantly agro-pastoral character. Socioeconomic indicators display a similar decline in population density, EAP, and service access with distance, consistent with a metropolitan region that has grown steadily but moderately (1% annual growth between 1990 and 2020), and that functions as a service-oriented tertiary center requiring a daily commute from the surrounding municipalities. Meanwhile, peripheral municipalities such as Coatepec, Emiliano Zapata, and Banderilla operate as dormitory towns and regional exchange hubs.\u003c/p\u003e \u003cp\u003eIn Oaxaca, the landscape reflects the long-standing presence of indigenous settlements surrounding the city, functionally integrated but still lacking full urban infrastructure. Oaxaca displays the highest land-cover richness among the three cases, consistent with its heterogeneous territorial history. Socioeconomic conditions deteriorate with distance, echoing a historically unequal pattern of urbanization that centralized infrastructure and services in the capital. Oaxaca fulfills the role of an intermediate city through its administrative, political, cultural, and economic functions, with regional specialization in services, crafts, and a rapidly expanding mezcal export economy. Nevertheless, the metropolitan region displays marked inequality between rural and urban economies, with persistent deficits in housing, infrastructure, and social services.\u003c/p\u003e \u003cp\u003eM\u0026eacute;rida, the largest urban agglomeration in southeastern Mexico, exhibits the expected concentration of large urban patches and high population density within the city. However, the sharp drop in EAP and indigenous-language speakers in Buffer 1, followed by a strong increase in Buffer 2, reflects intense socio-territorial segregation linked to exclusive urban policies and a booming real-estate market that pushes lower-income and indigenous populations toward the metropolitan fringe (Aguilar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The highest volumes of water concessions are found in Buffer 1 (Kanas\u0026iacute;n, Um\u0026aacute;n, Conkal), indicating substantial industrial or high-consumption activity adjacent to the city. Simultaneously, the economic decline of rural areas, driven by soil infertility, market fluctuations, and climatic extremes, fuels migration toward the metropolitan periphery. Merida exemplifies how growth policies that disregard territorial interdependence and cross-locality flows of people, materials, and institutions lead to accelerated socio-environmental deterioration (Hoffman et al., 2023). The metropolitan municipalities of Yucat\u0026aacute;n also show the highest climate vulnerability, and the uncontrolled expansion of impermeable surfaces, coupled with deregulated hydrosocial cycles, has intensified flooding and aquifer contamination, deepening environmental and social risks.\u003c/p\u003e \u003cp\u003eImplications for the SDGs and Territorial Policy\u003c/p\u003e \u003cp\u003eThe spatial and socioeconomic patterns observed across the three medium-sized cities have direct implications for the advancement of the Sustainable Development Goals (SDGs) and for the design of territorial policies that acknowledge the interdependence between urban centers and their surrounding regions. The coexistence of a highly consolidated urban core with a fragmented and increasingly unequal peri-urban matrix reveals structural challenges for achieving SDG 11 (Sustainable Cities and Communities), particularly in terms of inclusive urbanization, balanced territorial development, and equitable access to services.\u003c/p\u003e \u003cp\u003eThe persistent socio-spatial fragmentation documented in the buffer zones undermines progress toward SDG 10 (Reduced Inequalities), as population groups, often indigenous and marginalized populations, are pushed toward areas with limited infrastructure, reduced employment opportunities, and lower service provision. Similarly, the expansion of impermeable surfaces, deregulation of water extraction, and concentration of high-consumption economic activities in peri-urban zones directly threaten SDG 6 (Clean Water and Sanitation) and SDG 15 (Life on Land) by compromising aquifer integrity, accelerating landscape degradation, and reducing ecological continuity.\u003c/p\u003e \u003cp\u003eOur results underscore the need for territorial governance frameworks that move beyond the administrative boundaries of cities and toward functional territories where ecological processes, economic flows, and social networks operate. Such an approach is essential for implementing integrated, multiscalar policies capable of addressing cross-cutting sustainability challenges rather than isolated sectoral issues. In particular, three policy directions emerge:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStrengthening metropolitan and regional coordination actions at the regional and metropolitan levels that prevent localities from becoming dependent on cities, which in turn causes poverty, inequality, and environmental degradation. This includes planning instruments that integrate land-use regulation, political webs, mobility systems, ecosystem conservation, and water management at the scale of the urban region rather than the municipal jurisdiction (Berdegu\u0026eacute; et al \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAddressing socio-territorial inequality through differentiated interventions, prioritizing peri-urban and rural localities that experience the greatest vulnerability. Policies that combine social infrastructure, improved accessibility, and mechanisms for local economic diversification are crucial for advancing SDG 10 and ensuring equitable territorial development (Micheliny \u0026amp; Davies 2009; Ortiz-B\u0026aacute;ez et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSafeguarding ecological functions within urban and peri-urban mosaics, by protecting natural vegetation remnants and promoting new green areas; regulating land speculation and promoting land-management practices that enhance connectivity and ecosystem services. Such measures contribute to SDG 15 and reinforce the ecological foundations of sustainable urban\u0026ndash;rural interactions (Arnaiz et al., 2018).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMedium-sized cities have the potential to become territorial anchors that foster equitable development, ecological stewardship, and robust socio-economic systems. However, fulfilling this role requires policy frameworks that acknowledge the complex socio-ecological assemblages revealed by this study. Aligning territorial policy with the SDGs therefore demands an explicit recognition of the city\u0026ndash;territory nexus and an integrated governance approach capable of guiding more balanced and sustainable future trajectories.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEV, conceived the research; EV, MB and SM designed the research; EV, MB analyzed the data; SM systematized the data and elaborate the maps; EV, MB wrote and edited the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe first author was able to participate in this work thanks to the support granted by SECIHTI in the \u0026ldquo;Investigadores por Mexico\u0026rdquo; program. Keith Macmillan revised the English text.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study used data obtained from official public databases. The authors compiled, processed, and analyzed these secondary datasets using quantitative analytical methods to address the research questions of the study. No primary data were generated.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAguilar, A. G., Flores-Espinosa, M., \u0026amp; Hern\u0026aacute;ndez, J. (2025). Metropolizaci\u0026oacute;n, din\u0026aacute;mica inmobiliaria y segregaci\u0026oacute;n socio-territorial. El caso de M\u0026eacute;rida. \u003cem\u003eYucat\u0026aacute;n EURE\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(153), 1\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7764/eure.51.153.03\u003c/span\u003e\u003cspan address=\"10.7764/eure.51.153.03\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAguilar-Duarte, Y., Bautista, F., Mendoza, M. E., Frausto, O., Ihl, T., \u0026amp; Delgado, C. (2016). Ivaky: \u0026iacute;ndice de la vulnerabilidad del acu\u0026iacute;fero k\u0026aacute;rstico yucateco a la contaminaci\u0026oacute;n. \u003cem\u003eRevista Mexicana de Ingenier\u0026iacute;a Qu\u0026iacute;mica\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), 913\u0026ndash;933.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersson, E., Haase, D., Anderson, P., Cortinovis, C., Goodness, J., Kendal, D., Lausch, A., McPhearson, T., Sikorska, D., \u0026amp; Wellmann, T. (2021). What are the traits of a social-ecological system: Towards a framework in support of urban sustainability. \u003cem\u003eNPJ Urban Sustainability\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s42949-020-00008-4\u003c/span\u003e\u003cspan address=\"10.1038/s42949-020-00008-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndr\u0026eacute;s L\u0026oacute;pez, G. (2019). El significado de los espacios de actividad econ\u0026oacute;mica en la estructura urbana de las ciudades medias espa\u0026ntilde;olas. \u003cem\u003eCiudades\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(22), 01\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24197/ciudades.22.2019.01-22\u003c/span\u003e\u003cspan address=\"10.24197/ciudades.22.2019.01-22\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnaiz-Schmitz, C., Aguilera, P. A., Ropero, R. F., \u0026amp; Schmitz, M. F. (2023). Detecting social-ecological resilience thresholds of cultural landscapes along an urban\u0026ndash;rural gradient: a methodological approach based on Bayesian Networks. \u003cem\u003eLandscape Ecology\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(12), 3589\u0026ndash;3604. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-023-01732-9\u003c/span\u003e\u003cspan address=\"10.1007/s10980-023-01732-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnaiz-Schmitz, C., Schmitz, M. F., Herrero-J\u0026aacute;uregui, C., Guti\u0026eacute;rrez-Angonese, J. F. D. C., Pineda, F. D., \u0026amp; Montes, C. (2018). Identifying socio-ecological networks in rural-urban gradients: Diagnosis of a changing cultural landscape. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e612\u003c/em\u003e, 625\u0026ndash;635. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2017.08.215\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2017.08.215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen\u0026iacute;tez, G., P\u0026eacute;rez-V\u0026aacute;zquez, A., Nava-Tablada, M., Equihua, M., \u0026amp; \u0026Aacute;lvarez-Palaciose, J. L. (2011). Expansi\u0026oacute;n de los asentamientos informales y sus efectos ambientales en la periferia de la Ciudad de Xalapa, Veracruz M\u0026eacute;xico. \u003cem\u003eMedio Ambiente y Urbanizaci\u0026oacute;n\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(1), 47\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerdegu\u0026eacute;, J. A., Proctor, F. J., \u0026amp; Cazzuffi, C. (2014). Cities in the Rural Transformation. Working.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaper Series N\u0026deg; 123 Working Group: Development with Territorial Cohesion, Territorial.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohesion for Development Program Rimisp, Santiago, Chile.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerdegu\u0026eacute;, J. A., \u0026amp; Soloaga, I. (2018). Small and medium cities and development of Mexican rural areas. \u003cem\u003eWorld Development\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e, 277\u0026ndash;288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.worlddev.2018.02.007\u003c/span\u003e\u003cspan address=\"10.1016/j.worlddev.2018.02.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerdegu\u0026eacute;, J. A., Carriazo, F., Jara, B., Modrego, F., \u0026amp; Soloaga, I. (2015). Cities, territories, and inclusive growth: Unraveling urban\u0026ndash;rural linkages in Chile, Colombia, and Mexico. \u003cem\u003eWorld Development\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e, 56\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.worlddev.2014.12.013\u003c/span\u003e\u003cspan address=\"10.1016/j.worlddev.2014.12.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolay, J. C., \u0026amp; Kern, A. L. (2019). Intermediate cities. In A. Orum (Ed.), \u003cem\u003eThe Wiley Blackwell Encyclopedia of Urban and Regional Studies\u003c/em\u003e. John Wiley \u0026amp; Sons Ltd.. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/9781118568446.eurs0163\u003c/span\u003e\u003cspan address=\"10.1002/9781118568446.eurs0163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalzada-Infante, L., L\u0026oacute;pez-Narbona, A. M., N\u0026uacute;\u0026ntilde;ez-Elvira, A., \u0026amp; Orozco-Messana, J. (2020). Assessing the efficiency of sustainable cities using an empirical approach. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(7), 2618. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su12072618\u003c/span\u003e\u003cspan address=\"10.3390/su12072618\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarver (2019). \u003cem\u003eMaster the concepts and techniques of statistical analysis using JMP(R).\u003c/em\u003e Practical Data Analysis with JMP(R), Third Edition, SAS Institute.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCONAPO (2020). \u0026Iacute;ndice de marginaci\u0026oacute;n por localidad 2020. M\u0026eacute;xico: Consejo Nacional de Poblaci\u0026oacute;n, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.datos.gob.mx/dataset/indices_marginacion\u003c/span\u003e\u003cspan address=\"https://www.datos.gob.mx/dataset/indices_marginacion\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDom\u0026iacute;nguez Aguilar, M. (2011). Avances en el estudio de la estructura territorial de la zona metropolitana de M\u0026eacute;rida. \u003cem\u003eYucat\u0026aacute;n Pen\u0026iacute;nsula\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 185\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEne, E., \u0026amp; McGarigal, K. (2023). Fragstats. \u003cem\u003eA Spatial Pattern Analysis Program for Categorical Maps\u003c/em\u003e, from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fragstats.org/index.php\u003c/span\u003e\u003cspan address=\"https://fragstats.org/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eESRI. (2014). \u003cem\u003eArcMap (Version 10.3)\u003c/em\u003e. Environmental Systems Research Institute.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFragstats (2023). Documentaci\u0026oacute;n: M\u0026eacute;tricas de Fragstats, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fragstats.org\u003c/span\u003e\u003cspan address=\"https://www.fragstats.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeoComunes, C., Torres-Mazuera, G., \u0026amp; G\u0026oacute;mez Godoy, C. (2020). Expansi\u0026oacute;n capitalista y propiedad social en la Pen\u0026iacute;nsula de Yucat\u0026aacute;n. M\u0026eacute;xico, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ccmss.org.mx/wp-content/uploads/Expansion_capitalista_propiedad_social_\u003c/span\u003e\u003cspan address=\"https://www.ccmss.org.mx/wp-content/uploads/Expansion_capitalista_propiedad_social_\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGovernment of the State of Yucat\u0026aacute;n (2025). Municipios de Yucat\u0026aacute;n, from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.yucatan.gob.mx/estado/municipios.php\u003c/span\u003e\u003cspan address=\"https://www.yucatan.gob.mx/estado/municipios.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGranados Alcantar, J. A., \u0026amp; Quezada Ram\u0026iacute;rez, M. F. (2018). Tendencias de la migraci\u0026oacute;n interna de la poblaci\u0026oacute;n ind\u0026iacute;gena en M\u0026eacute;xico, 1990\u0026ndash;2015. \u003cem\u003eEstudios demogr\u0026aacute;ficos y urbanos\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(2), 327\u0026ndash;363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.24201/edu.v33i2.1726\u003c/span\u003e\u003cspan address=\"10.24201/edu.v33i2.1726\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., \u0026amp; Briggs, J. M. (2008). Global change and the ecology of cities. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e319\u003c/em\u003e(5864), 756\u0026ndash;760. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1150195\u003c/span\u003e\u003cspan address=\"10.1126/science.1150195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenderson, J. V., Shalizi, Z., \u0026amp; Venables, A. J. (2001). Geography and development. \u003cem\u003eJournal of Economic Geography\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 81\u0026ndash;105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jeg/1.1.81\u003c/span\u003e\u003cspan address=\"10.1093/jeg/1.1.81\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffmann, E. M., Schareika, N., Dittrich, C., Schlecht, E., Sauer, D., \u0026amp; Buerkert, A. (2023). Rurbanity: A concept for the interdisciplinary study of rural\u0026ndash;urban transformation. \u003cem\u003eSustainability Science\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), 1739\u0026ndash;1753. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11625-023-01331-2\u003c/span\u003e\u003cspan address=\"10.1007/s11625-023-01331-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eINEGI (2018). Conjunto de datos vectoriales de uso del suelo y vegetaci\u0026oacute;n Serie VII. M\u0026eacute;xico: Instituto Nacional de Estad\u0026iacute;stica y Geograf\u0026iacute;a, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geoportal.conabio.gob.mx/metadatos/doc/html/usv250s7gw.html\u003c/span\u003e\u003cspan address=\"http://geoportal.conabio.gob.mx/metadatos/doc/html/usv250s7gw.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eINEGI (2020). Censo de Poblaci\u0026oacute;n y Vivienda 2020. M\u0026eacute;xico: Instituto Nacional de Estad\u0026iacute;stica y Geograf\u0026iacute;a, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.inegi.org.mx/programas/ccpv/2020/\u003c/span\u003e\u003cspan address=\"https://www.inegi.org.mx/programas/ccpv/2020/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInostroza, L., Baur, R., \u0026amp; Csaplovics, E. (2013). Urban sprawl and fragmentation in Latin America: A dynamic quantification and characterization of spatial patterns. \u003cem\u003eJournal of environmental management\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e, 87\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.jenvman.2012.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2012.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Janvry, A., \u0026amp; Sadoulet, E. (2001). Income strategies among rural households in Mexico: The role of off-farm activities. \u003cem\u003eWorld Development\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 467\u0026ndash;480. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0305-750X(00)00113-3\u003c/span\u003e\u003cspan address=\"10.1016/S0305-750X(00)00113-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlop, J. M., Iglesias, B. M., Vargas, R., \u0026amp; Blanc, F. (2019). Las ciudades intermedias: concepto y dimensiones. \u003cem\u003eCiudades\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 23\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24197/ciudades.22.2019.23-43\u003c/span\u003e\u003cspan address=\"10.24197/ciudades.22.2019.23-43\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadrid, G. (2011). \u003cem\u003eOaxaca, de ciudad intermedia a metr\u0026oacute;poli de Los Valles Centrales. Emergencia de una ciudad-territorio en el sur de M\u0026eacute;xico\u003c/em\u003e. Universitat Polit\u0026egrave;cnica de Catalunya. PhD Thesis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmergencia de una ciudad-territorio en el sur de M\u0026eacute;xico.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaturana, F., Sposito, M. E. B., Bellet, C., \u0026amp; Henr\u0026iacute;ques, C. (2017). \u003cem\u003eSistemas urbanos y ciudades medias en Iberoam\u0026eacute;rica\u003c/em\u003e. Impresi\u0026oacute;n gr\u0026aacute;fica LOM.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonald, R. I., Mansur, A. V., Ascens\u0026atilde;o, F., Colbert, M. L., Crossman, K., Elmqvist, T., Gonzalez, A., G\u0026uuml;neralp, B., Dagmar Haase, D., Hamann, M., Hille, O., Huang, K., Kahnt, B., Maddox, D., Pacheco, A., Pereira, H. M., Seto, C., Simkin, R., Walsh, B., Werner, A. S., \u0026amp; Ziter, C. (2020). Research gaps in knowledge of the impact of urban growth on biodiversity. \u003cem\u003eNature Sustainability\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 16\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41893-019-0436-6\u003c/span\u003e\u003cspan address=\"10.1038/s41893-019-0436-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichelini, J. J., \u0026amp; Davies, C. (2009). \u003cem\u003eCiudades intermedias y desarrollo territorial:un an\u0026aacute;lisis exploratorio del caso argentino\u003c/em\u003e. Documentos de trabajo No.5. Grupo de Estudios sobre Desarrollo Urbano (GEDEUR).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUN-Habitat. (2022). \u003cem\u003eWorld Cities Report 2022\u003c/em\u003e. United Nations Human Settlements Programme.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtiz-B\u0026aacute;ez, P., Cabrera-Barona, P., \u0026amp; Bogaert, J. (2021). Characterizing landscape patterns in urban-rural interfaces. \u003cem\u003eJournal of Urban Management\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 46\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jum.2021.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jum.2021.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePOZMO. (2025). \u003cem\u003ePrograma de Ordenamiento de la Zona Metropolitana de Oaxaca\u003c/em\u003e. SINFRA, Gobierno del Estado de Oaxaca.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePOTZMX. (2023). \u003cem\u003ePrograma de Ordenamiento Territorial de la Zona Metropolitana de Xalapa\u003c/em\u003e. Gobierno delEstado de Veracruz.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSecretar\u0026iacute;a de Econom\u0026iacute;a (2025). Perfil econ\u0026oacute;mico de M\u0026eacute;rida, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.economia.gob.mx/datamexico/es/profile/geo/merida-993101\u003c/span\u003e\u003cspan address=\"https://www.economia.gob.mx/datamexico/es/profile/geo/merida-993101\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSecretar\u0026iacute;a de Econom\u0026iacute;a (2025). Perfil econ\u0026oacute;mico de Xalapa, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.economia.gob.mx/datamexico/es/profile/geo/xalapa-993008\u003c/span\u003e\u003cspan address=\"https://www.economia.gob.mx/datamexico/es/profile/geo/xalapa-993008\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSEDATU/CONAPO (2020). Sistema Urbano Nacional 2020. Secretar\u0026iacute;a de Desarrollo Agrario, Territorial y Urbano / Comisi\u0026oacute;n Nacional de Poblaci\u0026oacute;n, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.datos.gob.mx/dataset/sistema_urbano_nacional\u003c/span\u003e\u003cspan address=\"https://www.datos.gob.mx/dataset/sistema_urbano_nacional\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeto, K. C., S\u0026aacute;nchez-Rodr\u0026iacute;guez, R., \u0026amp; Fragkias, M. (2013). The new geography of contemporary urbanization and the environment. \u003cem\u003eAnnual Review of Environment and Resources\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e, 167\u0026ndash;194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-environ-100809-125336\u003c/span\u003e\u003cspan address=\"10.1146/annurev-environ-100809-125336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlemp, C., Davenport, M. A., Seekamp, E., Brehm, J. M., Schoonover, J. E., \u0026amp; Williard, K. W. (2012). Growing too fast: Local stakeholders speak out about growth and its consequences for community well-being in the urban\u0026ndash;rural interface. \u003cem\u003eLandscape and Urban Planning\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e(2), 139\u0026ndash;148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.landurbplan.2012.02.017\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2012.02.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinger Sochet, M. (2014). Exclusi\u0026oacute;n o inclusi\u0026oacute;n ind\u0026iacute;gena\u003cem\u003e? Estudios pol\u0026iacute;ticos\u003c/em\u003e, 31, pp. 87\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrejo-Nieto, A. (2024). Unveiling the intermediate role of Mexico\u0026rsquo;s mid-sized metropolises. \u003cem\u003eRegional Studies Regional Science\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 777\u0026ndash;797. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/21681376.2024.2430540\u003c/span\u003e\u003cspan address=\"10.1080/21681376.2024.2430540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations. (2014). \u003cem\u003eWorld Urbanization Prospects: The 2014 Revision\u003c/em\u003e. United Nations.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations. (2025). \u003cem\u003eWorld Urbanization Prospects 2025: Summary of Results\u003c/em\u003e (Vol. 12). United Nations. UN DESA/POP/2025/TR/NO.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUN-Habitat. (2022). \u003cem\u003eWorld Cities Report 2022\u003c/em\u003e. United Nations Human Settlements Programme (UN-Habitat).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValicelli, L., \u0026amp; Pesci, R. (2002). Las nuevas funciones urbanas: gesti\u0026oacute;n para la ciudad sostenible. CEPAL-Serie Medio ambiente y desarrollo N\u0026deg; 48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://repositorio.cepal.org/bitstream/handle/11362/5747/S02124_es\u003c/span\u003e\u003cspan address=\"https://repositorio.cepal.org/bitstream/handle/11362/5747/S02124_es\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. pdf?sequence\u0026thinsp;=\u0026thinsp;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillegas-Alzate, J. G. (2022). Consideraciones te\u0026oacute;rico-metodol\u0026oacute;gicas para el estudio de ciudades intermedias. \u003cem\u003eJangwa Pana\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\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":"medium-sized cities, socio-ecological systems, territorial inequality, sustainable development goals, Mexico","lastPublishedDoi":"10.21203/rs.3.rs-8811305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8811305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban expansion in medium-sized cities is reshaping socio-ecological dynamics across Latin America, yet their territorial influence remains understudied. This research analyzes the landscape configuration and socioeconomic conditions surrounding three medium-sized Mexican cities (Xalapa, Oaxaca, and M\u0026eacute;rida), using a comparative design with concentric landscape systems (city, Buffer 1, Buffer 2). The results reveal a sharp transition from compact, homogeneous urban cores to highly fragmented peri-urban mosaics dominated by secondary vegetation, grasslands, and agricultural land. Fragmentation remains persistent across the buffer zones, challenging the expectation of a gradual rural\u0026ndash;urban gradient. Social variables show that in areas far from the city, where there is less access to basic welfare services, there are urban centers that are offering employment. Taken together, these socio-ecological patterns indicate that medium-sized cities operate as territorial anchors embedded within broader regional systems, in which urban expansion, rural transformation, and uneven development intersect. Understanding these dynamics is crucial for designing territorial policies aligned with sustainable development goals, capable of addressing the effect of landscape fragmentation and fostering more equitable and ecologically coherent forms of metropolitan governance.\u003c/p\u003e","manuscriptTitle":"Territorial Dynamics of Medium-Sized Cities: Landscape Fragmentation and Inequality in three Southeastern Mexican Cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 17:28:44","doi":"10.21203/rs.3.rs-8811305/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":"cdca4a0e-299b-4507-9634-2865c78ea6f7","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T17:28:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 17:28:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8811305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8811305","identity":"rs-8811305","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}