Exploratory Spatial Data Analysis Research Articles

The urban footprint of rural forced displacement

AbstractThe rapid growth of marginal settlements in the Global South, largely fueled by the resettlement of millions of internally displaced people (IDPs), underscores the urgent need for tailored housing solutions for these vulnerable populations. However, prevailing approaches have often relied on a one-size-fits-all model, overlooking the diverse socio-spatial realities of IDP communities. Drawing on a case study in Medellin, Colombia, where a significant portion of the population consists of forced migrants, this interdisciplinary study merges concepts from human geography and urban theory with computational methods in remote sensing and exploratory spatial data analysis. By integrating socio-spatial theory with quantitative analysis, we challenge the conventional housing paradigm and propose a novel framework for addressing the housing needs of IDPs. Employing a three-phase methodology rooted in Lefebvre’s theoretical framework on the production of space, including participatory mapping, urban morphology characterization, and similarity analysis, we identify distinct patterns within urban IDP settlements and advocate for culturally sensitive housing policies. Our analysis, focusing on Colombia, the country with the largest IDP population globally, reveals the limitations of standardized approaches and highlights the importance of recognizing and accommodating socio-cultural diversity in urban planning. By contesting standardized socio-spatial practices, our research aims not only to promote equality but also to foster recognition and inclusivity within marginalized communities.

Spatial Configuration and Accessibility Assessment of Recreational Resources in Hainan Tropical Rainforest National Park

Recreational resources, fundamental to ecological experiences, are critical in balancing conservation with development. Effective ecotourism planning is especially vital for newly established protected areas such as the Hainan Tropical Rainforest National Park in China’s developing system of natural conservation areas. Targeting Hainan Tropical Rainforest National Park, this study applies nearest neighbor index, kernel density analysis, and exploratory spatial data analysis (ESDA) to study the spatial pattern of 274 recreational resource points. Results indicate a clustered spatial pattern with significant differences in resource density among municipalities. Specifically, 98% of these resources can be reached in 3 h, with an average travel time of 91 min, and cultural resources exhibit greater accessibility than natural resources. Natural resource availability and ethnic culture are major factors of resource distribution and accessibility. This research offers a theoretical basis and practical guidance for optimizing recreational resource allocation and promoting ecotourism in the park, contributing to the ongoing discussion of sustainable tourism development.

Urban Flooding Disaster Risk Assessment Utilizing the MaxEnt Model and Game Theory: A Case Study of Changchun, China

This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks associated with urban flooding disasters using Changchun city as a case study. The validation outcomes pertaining to urban flooding hotspots reveal that 88.66% of the identified flooding sites are situated within areas classified as high-risk and very high-risk. This finding is considered to be more reliable and justifiable when contrasted with the 77.73% assessment results derived from the MaxEnt model. Utilizing the methodology of exploratory spatial data analysis (ESDA), this study applies both global and local spatial autocorrelation to investigate the disparities in the spatial patterns of flood risk within Changchun. This study concludes that urban flooding occurs primarily in the city center of Changchun and shows a significant agglomeration effect. The region is economically developed, with a high concentration of buildings and a high percentage of impervious surfaces. The Receiver Operating Characteristic (ROC) curve demonstrates that the MaxEnt model achieves an accuracy of 90.3%. On this basis, the contribution of each indicator is analyzed and ranked using the MaxEnt model. The primary determinants affecting urban flooding in Changchun are identified as impervious surfaces, population density, drainage density, maximum daily precipitation, and the Normalized Difference Vegetation Index (NDVI), with respective contributions of 20.6%, 18.1%, 13.1%, 9.6%, and 8.5%. This research offers a scientific basis for solving the urban flooding problem in Changchun city, as well as a theoretical reference for early warnings for urban disaster, and is conducive to the realization of sustainable urban development.

Spatio-temporal Situation, Regional Differences, and Dynamic Evolution of the Distribution of Ecological Compensation in the Yellow River Basin

In the context of regional coordinated development and modernization of ecological and environmental governance capacity, the spatial collaborative governance of transboundary river basins has received extensive attention, but the ecological compensation of river basins is faced with the intertwined relationship of rights and responsibilities, and the horizontal ecological compensation mechanism has not yet been perfected. Based on the emergy ecological footprint model, the ecological compensation amount of 90 cities in the Yellow River Basin in 2007-2021 was measured, and the spatiotemporal pattern, regional differences, and dynamic evolution characteristics of distribution were explored by combining exploratory spatial data analysis, Dagum Gini coefficient, kernel density estimation, and spatial Markov chain analysis. The results showed that： ① The spatiotemporal pattern of ecological compensation in the Yellow River Basin was different, the ecological compensation amount decreased slightly in the fluctuation and increased from the northwest to southeast gradient in general, and the high-value areas were concentrated in the areas with a superior ecological background or developed economy, and some upstream cities had given up many development opportunities to maintain the ecological security of the Yellow River Basin, however, failed to obtain reasonable ecological compensation. ② A significant positive spatial agglomeration phenomenon was observed in ecological compensation and the overall spatial distribution trend was "cold in the north and hot in the south." The number of low-payment areas and low-compensation areas gradually decreased, showing a gradually shrinking agglomeration layout from the periphery to the center, whereas the number of high-payment areas and high-compensation areas continued to increase and formed a spatial evolution characteristic of the coexistence of scattered distribution and group distribution. ③ The overall regional differences in ecological compensation expanded. The over-variation density was the main source of the overall differences, and the focus on alleviating the intra-regional and inter-regional differences was located in the downstream areas. ④ A slight multi-level differentiation phenomenon was present in ecological compensation. The influence of different neighbors on the horizontal transfer of ecological compensation was quite different. This spatial spillover effect easily formed a "space club convergence" phenomenon within a certain geographical spatial range, but with the expansion of time, the probability of maintaining the original level of ecological compensation in each city decreased, and mobility gradually increased. Therefore, increasing the investment in ecological compensation in different fields, strengthening the coordinated development between regions, and giving full play to the spatial spillover effect are important ways to solve the current spatial imbalance of ecological compensation in the Yellow River Basin.

SPATIAL ANALYSIS BETWEEN YOUTH AND ADULT EDUCATION AND LABOR MARKET REMUNERATION

This study analyzes the spatial distribution of migration from traditional high school to Youth and Adult Education (YAE) and its relationship with formal work remuneration. For this, the Exploratory Spatial Data Analysis (ESDA) technique was used through global and local analyses. The results of the global analysis indicate a negative spatial autocorrelation between migration from regular secondary education to YAE and formal work remuneration. The local analysis indicated that the Northeast had the highest proportion of municipalities in the cluster with high migration of individuals in the YAE and higher remuneration in the formal market, while the South region had the highest concentration of municipalities with a low proportion of municipalities in the cluster with low migration of individuals in the YAE and high remuneration in the formal job market. Therefore, it is concluded that the low quality of YAE teaching leads individuals to enter the formal job market with a lower pay.

School Closures and the Spatial Ecology of Education Access in 10 U.S. Cities

Public school closures are increasing in frequency, number and size in U.S. cities. This study examines the spatial distribution of public school closures occurring in 10 U.S. cities between 2010 and 2019. I employ Exploratory Spatial Data Analysis (ESDA) techniques to measure the spatial concentration of school closures, or the degree to which school closures cluster. I also develop a measure of spatial accessibility across neighborhoods based on the time it would take to travel to the nearest closed school, in every block group in the 10 study cities. To take into account that traffic, congestion and other factors may play a role, I compute and compare times by car and foot, and compare access based on neighborhood ethnoracial and socioeconomic composition. Findings show that school closures geographically cluster, and neighborhoods with a greater percent of Black residents, whether higher poverty or not, will have longer commute times to the next nearest open school after a school closure. The geographic clustering of closures and the increased commute times to the nearest open school suggest that school deserts may be formed after school closings.

Accessibility and Older and Foreign Populations: Exploring Local Spatial Heterogeneities across Italy

The interplay between accessibility and population change is a relatively new subject in Italian academic research. Along with social and economic factors such as regional economic prosperity, the ease of movement inside and outside an area can play a pivotal role in shaping population dynamics. This study seeks to explore the spatial distribution and spatial relationships of three indicators, including one related to real accessibility (RAI) and two others related, respectively, to the shares of the older population (SOP) and of the foreign population (SFP). An exploratory spatial data analysis is, therefore, conducted at the local level using Italian municipalities as the statistical units for the empirical analysis. Local univariate spatial autocorrelation analysis is used together with a regression analysis based on ordinary least squares (OLS) and geographically weighted regression (GWR) models. The results provide valuable insights into the local heterogeneity that characterizes the distribution of each indicator and the local relationship between them, highlighting the importance of thinking locally in quantitative social sciences.

Analysis of the spatio-temporal distribution of Chinese professional football clubs and influential factors based on empirical evidence of clubs

The frequent relocation of professional football clubs is a phenomenon unique to Chinese professional football, which has long attracted extensive attention from policy makers and scholars. Using data on spatial geography and competitive levels between 1994 and 2021, this research explores the spatial distribution and migration characteristics of top professional football clubs in China, in which GIS spatial analysis and exploratory spatial data analysis (ESDA) were used to analyze the influence of the regional economic, demographic, and developmental levels of the sports industry and institutional supply on the distribution and migration of clubs. The results indicate the following: (1) the overall spatial distribution of professional football clubs in China shows the pattern of “more in the east and less in the west”, and the degree of club concentration gradually increases over time; (2) the migration frequency and downward migration of professional football clubs in China gradually decreased during the period of C League and CSL, and the overall situation tends to be stable; and (3) in the early stages of development of professional leagues, changes in ownership and local policy supply had a significant impact on the distribution and migration of professional clubs. As time progressed, the top-down design of institutional arrangements changed, and the regional economic, demographic, and developmental levels of the sports industry gradually became the essential determinants of the distribution and migration of professional football clubs. In the future, Chinese professional football clubs should focus on rationalizing the advantages of the regional economy, demography, and sports industry as well as reducing their reliance on short-term resources, such as corporate investment and policy benefits.

What are the underlying causes and dynamics of land use conflicts in metropolitan junction areas? A case study of the central Chengdu– Chongqing region in China

Land use conflicts (LUCs), as a spatial manifestation of the conflicts in the human-land relationships, have a profound impact on regional sustainable development. For China’s metropolitan junction areas (MJAs), the existence of “administrative district economies” has made the issue of LUCs more prominent. Based on a case study of the central Chengdu–Chongqing region, we conducted an exploratory spatial data analysis of the evolutionary process of regional LUCs. Furthermore, structural equation modeling was utilized to analyze the dynamic mechanism of LUCs in MJAs, with a particular emphasis on exploring the influences of administrative boundary. The results showed that from 2010 to 2020, LUCs in the central Chengdu–Chongqing region continued to worsen, and the spatial process conflict and spatial structure conflict indices increased by more than 30.0%. The intensification of LUCs in the central Chengdu–Chongqing region from 2010 to 2020 was mainly the result of the deterioration of conflicts in evaluation units with low conflict levels. LUCs in China’s metropolitan areas generally presented a circular gradient distribution, weakening from the core to the periphery, but there were some strong isolated conflict zones in the outer regions. LUCs in China’s MJAs were the result of interactions among multiple factors, e.g., natural environment, socio-economic development, policy and institutional processes, and administrative boundary effects. Administrative boundary affected the flow of socio-economic elements, changing the supply-and-demand competition of stakeholders for land resources, consequently exerting an indirect influence on LUCs. This study advances the theory of the dynamic mechanism of LUCs, and provides theoretical support for the governance of these conflicts in transboundary areas.

Spatiotemporal evolution and influencing factors of urban resilience in the Yellow River Basin, China

The Yellow River Basin of China is a key region that contains myriad interactions between human activities and natural environment. Industrialization and urbanization promote social-economic development, but they also have generated a series of environmental and ecological issues in this basin. Previous researches have evaluated urban resilience at the national, regional, urban agglomeration, city, and prefecture levels, but not at the watershed level. To address this research gap and elevate the Yellow River Basin’s urban resilience level, we constructed an urban resilience evaluation index system from five dimensions: industrial resilience, social resilience, environmental resilience, technological resilience, and organizational resilience. The entropy weight method was used to comprehensively evaluate urban resilience in the Yellow River Basin. The exploratory spatial data analysis method was employed to study the spatiotemporal differences in urban resilience in the Yellow River Basin in 2010, 2015, and 2020. Furthermore, the grey correlation analysis method was utilized to explore the influencing factors of these differences. The results of this study are as follows: (1) the overall level of urban resilience in the Yellow River Basin was relatively low but showed an increasing trend during 2010–2015, and significant spatial distribution differences were observed, with a higher resilience level in the eastern region and a low-medium resilience level in the western region; (2) the differences in urban resilience were noticeable, with industrial resilience and social resilience being relatively highly developed, whereas organizational resilience and environmental resilience were relatively weak; and (3) the correlation ranking of resilience influencing factors was as follows: science and technology level>administrative power>openness>market forces. This research can provide a basis for improving the resilience level of cities in the Yellow River Basin and contribute to the high-quality development of the region.

Unveiling the nexus: exploring the influence of terrorism on energy trade in China and the Belt and Road countries

An examination of the scale of energy trade between China and the countries along the Belt and Road, as well as the terrorist activities in these countries from 2008 to 2019, has been conducted in this study. The spatial and temporal evolution law is revealed through the use of exploratory spatial data analysis. The spatial Durbin model is employed to investigate the impacts and spatial spillover effects of terrorist activities along the Belt and Road on China’s energy trade with these countries. The findings of the study are as follows: Spatial clustering is exhibited by the energy scale of China’s imports and exports with countries along the routes and terrorist activities along the routes, with energy imports concentrated in West Asia and North Africa, energy exports concentrated in Southeast Asia, and terrorist activities concentrated in West Asia and North Africa, South Asia, and Central Asia. A negative effect on the scale of China’s energy imports from the countries along the route is exerted by the terrorism index, and a positive spatial spillover effect is present. The number of terrorist activities and deaths have a negative effect on the scale of China’s energy imports from the countries along the route, but there is no spatial spillover effect. The terrorism index does not affect the scale of energy exports from China to countries along the route, but there is a negative spatial spillover effect. There are differences in the spatial spillover effects of the number of terrorist activities and deaths on the scale of China’s energy exports. The level of economic development, liner transportation convenience, and energy exports dependence of the countries along the route also significantly influence China’s energy trade with these countries.

The Distribution of Public Spending on COVID-19 in Brazil: Exploratory Analysis of State Spatial Data

The Distribution of Public Spending on COVID-19 in Brazil: Exploratory Analysis of State Spatial Data

An integrated urban flooding risk analysis framework leveraging machine learning models: A case study of Xi'an, China

As global climate change intensifies and urbanization progresses, the frequency and extent of urban flooding have been increasing. This trend poses a significant threat to the safety and property of urban residents and severely impedes the advancement of sustainable development goals (SDGs). This study proposed an integrated urban flooding risk analysis framework to provide an essential basis for developing effective adaptation and mitigation measures. The kernel density estimation was adopted to analyze the distribution characteristics of urban flooding hotspots. The maximum entropy (MaxEnt) model was adopted for urban flooding risk probability assessment based on multi-source data. This study conducted an in-depth analysis from three perspectives including spatial analysis, risk planning and model comparison. The MaxEnt model and multiple exploratory spatial data analysis methods were combined to reveal spatial distribution patterns at grid cell scale. The MaxEnt and Zonation models were coupled to generate an urban flooding risk map. Further, six traditional machine learning models were compared with the MaxEnt model. The results indicate that rapid urbanization and intense human activities have significantly increased the number of buildings, leading to loss of previously existing blue and green spaces. These urban characteristics increase the vulnerability of cities to extreme rainfall. Compared to traditional machine learning models, the MaxEnt model has significant advantages. In the field of urban flooding risk, the MaxEnt model demonstrates significant potential for practical application. In summary, the framework empowers urban managers to comprehensively evaluate flooding risk in planning, supporting urban design and risk prevention.

Forest Loss Drivers and Landscape Pressures in a Northern Moroccan Protected Areas’ Network: Introducing a Novel Approach for Conservation Effectiveness Assessment

This study assesses the conservation effectiveness of 21 protected areas (PAs) in Northern Morocco, comprising 3 parks and 18 Sites of Ecological and Biological Interest (SBEIs), against five major landscape pressures (LSPs): deforestation, infrastructure extension, agricultural expansion, fires, and population growth. We propose a novel quantitative methodology using global remote sensing data and exploratory spatial data analysis (ESDA). Data were sourced from Global Forest Change (GFC), Global Land Analysis and Discovery (GLAD), Burned Area Product (MODIS Fire_CCI51), and World Population datasets. The combined impact of the five LSPs was measured using a cumulative effect index (CEI), calculated with the Shannon–Wiener formula at a 1 km2 scale. The CEI was analyzed alongside the distance to the PAs’ network using Moran’s index, identifying four spatial association types: high–high (HH), high–low (HL), low–low (LL), low–high (LH), and non-significant (NS) cells. This analysis defined four zones: inner zone (IZ), potential spillover effect zone (PSEZ), statistically non-significant zone (SNSZ), and non-potential effect zone (NPEZ). Conservation effectiveness was quantified using the conservation ratio (CR), which compared the prevalence of LL versus HL units within IZs and PSEZs. Four disturbance levels (very high, high, medium, and low) were assigned to CR values (0–25%, 25–50%, 50–75%, 75–100%), resulting in sixteen potential conservation effectiveness typologies. Initial findings indicated similar deforestation patterns between protected and unprotected zones, with wildfires causing over half of forest losses within PAs. Conservation effectiveness results categorized the 21 PAs into nine typologies, from high conservation to very high disturbance levels. A significant positive correlation (71%) between CRs in both zones underscored the uniform impact of LSPs, regardless of protection status. However, protected natural area zones in the parks category showed minimal disruption, attributed to their advanced protection status. Finally, we developed a methodological framework for potential application in other regions based on this case study.

Unveiling the Impact of Transportation Infrastructure Construction on Rurality: A Case Study from Guangdong, China

Rurality is an important indicator to reflect the development of villages and reveal internal differences in rural areas. The unbalanced development of transportation infrastructure in the current period of rapid urbanization has become one of the principal elements triggering spatial differences in rurality and changes in rural territorial characteristics. However, there are few studies on the impact of transportation infrastructure accessibility on the multidimensional characteristics of rurality from the perspective of heterogeneity. This paper analyzed the spatio-temporal characteristics of transport accessibility (TA) and the county rurality index (CRI) in Guangdong in 2005, 2010, 2015 and 2020 using an accessibility model and the rurality index and explored the clustering characteristics and interactions of TA and the CRI through exploratory spatial data analysis (ESDA) and geographic weighted regression (GWR) modeling. The findings showed that (1) TA and the CRI in Guangdong were significantly unbalanced in terms of space. The CRI showed a weakening trend in general, forming a distribution pattern of “high in the north and low in the south, high in the west and low in the east”, while TA was on the rise, maintaining a stable pattern of “high in the middle and low in the periphery”. (2) Both TA and the CRI in Guangdong had a Moran’s I value greater than 0.6 during the study period, exhibiting strong spatial agglomeration, while the two showed a significant spatial negative correlation. (3) The influence of TA on the CRI in different dimensions showed dynamic changes in stages, with TA having a higher intensity of effect on society rurality and land rurality in 2005, while on society rurality and industry rurality in 2020. (4) This paper grouped 77 counties in Guangdong into four types of policy zonings—coordination types, lagged types of accessibility, lagged types of rurality and double lagged types—and put forward corresponding development recommendations. The study conducted in this paper contributes to an in-depth understanding of the impact of transportation infrastructure development on the multidimensional characteristics of rurality and provides a basis for policy formulation for coordinated urban–rural development and sustainable rural development.

Assessment of Rural Industry Integration Development, Spatiotemporal Evolution Characteristics, and Regional Disparities in Ethnic Regions: A Case Study of Inner Mongolia Autonomous Region Counties

Ethnic regions in China primarily focus on the development of agricultural and animal husbandry economies, which are relatively underdeveloped. Rural industry integration development (RIID) is considered the foundation and guarantee for ethnic regions to achieve high-quality modernization of agriculture. The purpose of this article is to measure the level of rural industrial integration in ethnic minority areas, analyze the spatial evolution and regional differences, and explore the actual situation of RIID in these regions. The aim is to provide a decision-making basis for local governments to effectively promote the development of rural industrial integration. Based on the improvement of the evaluation index system for rural industrial integration development, this paper takes the counties of the Inner Mongolia Autonomous Region as the research area. Utilizing panel data from the statistical yearbooks of 68 banners and counties in Inner Mongolia from 2011 to 2020, the panel entropy weight TOPSIS method is employed to assess the average level of rural industrial integration in the research area. The ArcGIS natural breakpoint method is employed to classify the level of RIID in county areas. Exploratory Spatial Data Analysis (ESDA) and GeoDa are utilized to analyze the spatial distribution characteristics of RIID. Finally, the Theil index is employed to analyze the regional differences in the level of RIID. The results show the following: (1) The overall level of RIID in ethnic regions is relatively low, with the contributions of the four dimensions in the evaluation index system as follows: integration path > integration foundation > integration sustainability > integration effect. The level of RIID in the study area is as follows: western region > eastern region > central region. (2) Spatially, there are positive correlations and significant spatial clustering in the level of RIID, with the spatial clustering effect of RIID weakening. (3) There are regional differences in the level of RIID, which are expanding. The inter-regional differences are decreasing, while the intra-regional differences are increasing. (4) The construction of agricultural processing facilities, financial investment, financial support, and talent policies are important influencing factors for the current stage of RIID in ethnic regions. Therefore, in the low-level development stage of RIID in ethnic regions, it is necessary to fully utilize the advantages of resource endowment, increase investment in rural infrastructure, and strengthen the guidance of talent flow into rural revitalization construction.

Pooled prevalence, spatial variation and associated factors of HIV testing uptake among multiple sexual partners in Sub Saharan Africa: Spatial and multilevel analysis.

Uptake of HIV testing is vital for the early diagnosis of HIV infection and initiation of treatment, which are used to eliminate the disease's progression and reduce HIV-related mortality. Even if determining HIV testing is imperative to prevent HIV/AIDS among multiple sexual partners who are at higher risk of sexually transmitted infections, most of the countries in Sub Saharan Africa did not fulfil the global targets of UNAIDS. Moreover there is a paucity of literature on spatial variation and factors associated with HIV testing among high-risk groups in SSA. This study aimed to assess the pooled prevalence, spatial variation and determinants of HIV testing uptake among multiple sexual partners in Sub Saharan Africa. The Demographic and Health Surveys data conducted between 2011 and 2021 in 30 Sub-Saharan Africa countries was used to analyze total weighted sample of 56,210 multiple sexual partners. Exploratory spatial data analysis, with countries as the unit of analysis was conducted using ArcGIS V10.7.1 and Sat Scan V 10.1 soft wares. A multilevel binary logistic regression model was used to identify the factors associated with the HIV testing uptake. The Adjusted odds Ratio with a 95% confidence interval was reported to declare the strength of association and their statistical significance. The spatial patterns of HIV testing uptake were found to be non-random. Primary clusters were identified around western and central sub- regions. Multiple sexual partners who were ever married, those attended primary level and above education, those from rich wealth status, aged above 24 years, having good HIV related knowledge, and exposed to media were positive association with HIV testing uptake. However, being male, having working status and living in rural area were negatively associated with HIV testing uptake. At the community-level, multiple sexual partners from communities in Eastern and southern sub regions, countries with upper middle income and countries with the survey year after 2014 were more likely to utilize HIV testing services compared with their counterparts. In this study, the pooled prevalence of the HIV testing uptake among multiple sexual partners was found to be lower than the universal target and showed differences in HIV testing uptake across Sub-Saharan Africa region. Both individual and community-level factors affected HIV testing uptake among multiple sexual partners. Stakeholders should implement interventions to help increase the uptake of HIV testing among those risky groups in this region.

Production factors, regional economic development and agriculture in brazil: A spatial econometrics approach

Agriculture has a great effect on the development process of a region and production land, labor, and technology are determinant factors in its dynamism. This work carries out a spatial analysis for each production factor of Brazilian municipalities in 2006 and 2017 and intends to verify if there are patterns of association and agglomerations. This work is applied research of quantitative and exploratory nature. The Exploratory Analysis of Spatial Data (ESDA) was used to perform spatial analysis for each production factor in Brazilian municipalities in 2006 and 2017 and to check the patterns of association and agglomerations. The main agricultural activities and practice are related to exports as proposed by North. The formation of clusters verified both in the use of the factors of land, labor and technology, and in the value of agricultural production has a polarized role in the Brazilian space, as described by Perroux. This polarization and the formation of clusters expanded at certain points over time, indicating the formation of new links with neighboring municipalities (a la Hirschman). The spatial association pattern was significant in all analyzed variables, with emphasis on the non-physical technology factor, presenting the highest spatial autocorrelation coefficient in both periods analyzed.

Deforestation drivers in northern Morocco: an exploratory spatial data analysis

Formulating effective policies to address or mitigate deforestation requires a comprehensive understanding of the contributing factors. This study examines the drivers of deforestation from 2001 to 2020 in the Tangier-Tetouan-Al Hoceima (TTA) region, a northern Moroccan area distinguished by the country’s highest deforestation rate. Through an extensive review of existing literature and employing Geist and Lambin’s deforestation framework, we identified five key causes: infrastructure extension, agricultural expansion, logging, wildfires as direct causes, and demographic factors as an indirect cause. Data on deforestation and its contributing factors were sourced from diverse databases, including Global Forest Change (GFC), Global Land Analysis and Discovery (GLAD), Burned Area Product (MODIS Fire_CCI51), World Population, Forest Proximate People (FPP), and National Forest Inventory (NFI) datasets. Pixel-level analysis of GFC data indicated that wildfires are the primary driver of deforestation in the region, accounting for 35.2%, followed by agricultural expansion (30.6%), logging (13.2%), and infrastructure extension (10.1%). The remaining 10.9% of losses were attributed to other disturbances, such as illegal extraction, pests, and dieback. Spatial patterns were further analyzed through Exploratory Spatial Data Analysis (ESDA) methods at a 1 km2 gridded scale, revealing strong clustering for all studied factors. Spatial relationships were explored using the bivariate local Moran’s index, which highlighted the highest spatial dependence between deforestation and fires (I = 0.21). Correlations between deforestation and other factors, including agricultural expansion, logging, infrastructure extension, and demographic pressure, were assessed at 0.18, 0.17, 0.08, and 0.05, respectively. Landscape pressures (LSP), encompassing deforestation, agricultural expansion, fires, infrastructure extension, and demographic pressure, were analyzed using the local Geary index, revealing a positive correlation in approximately 59% of spatial units. Last, a composite map of LSP clusters and an explanatory diagram illustrating dominant patterns in the TTA region were generated based on the results from local Geary’s multivariate and local Moran’s univariate tests.

Exploratory analysis of health- related indicators contained in the Sustainable Development Goals for the State of Mexico

The health- related Sustainable Development Goals (SDGs) serve as pivotal benchmarks for gauging the health, well- being, and overall development of a nation’s populace. This study aims to meticulously scrutinize the available SDG indicators for the State of Mexico at the municipal level, employing sophisticated techniques rooted in exploratory spatial data analysis. The primary objective is to comprehensively grasp the interplay between these indicators and their geographic distribution. This analytical approach will unveil discernible patterns encompassing clustering, dispersion, spatial self- correlation, and other geographical trends within the selected indicators. Spatial autocorrelation, notably, plays a pivotal role in discerning similarities or disparities in values and their potential correlation with geographic proximity. This analysis serves to ascertain the existence of significant spatial patterns. To accomplish this, graphical tools like maps and diagrams will be utilized. These visual representations effectively convey geographical information, thereby facilitating the communication of findings. This methodology will be particularly invaluable in pinpointing vulnerable populations. Anticipated outcomes include robust findings that can substantially inform the development agendas and guide the implementation of public health policies within the Mexican entity.