Exploratory Spatial Data Analysis Research Articles

Spatiotemporal Pattern and Spatial Convergence of Land Use Carbon Emission Efficiency in the Pan-Pearl River Delta: Based on the Difference in Land Use Carbon Budget

Research on land use carbon emission efficiency (LUCEE) in the Pan-Pearl River Delta (PPRD) can aid in formulating regional differentiated carbon reduction strategies. In this work, the inversion of carbon emissions using night-time light (NTL) data and the modified Carnegie Ames Stanford Approach (CASA) model were used to measure the net carbon emissions from land use (NCELU). On this basis, the SBM-undesirable model was used to assess the LUCEE. Additionally, the exploratory spatial data analysis (ESDA), Dagum Gini coefficient, and spatial convergence model were further introduced to analyze the spatial correlation, regional differences, and convergence trend of the LUCEE. Findings indicate that: (1) The NCELU showed an increasing fluctuation. During the period of 2006–2020, the NCELU increased from −168.58 million tons to −724.65 million tons. (2) The LUCEE exhibited a three-phase fluctuating downward trend of “decrease–rise–decrease”. The LUCEE first decreased from 0.612 in 2006 to 0.544 in 2008, then gradually increased to 0.632 in 2016, and finally decreased to 0.488 in 2020. Spatially, the LUCEE manifested a distribution characteristic of “high in the north and south, low in the middle”, with distinct spatial clustering features. (3) The overall Gini coefficient in the study period increased from 0.1819 to 0.2461. The primary contributor to the overall difference over the entire sample period was hypervariable density. (4) The PPRD and its various subregions displayed significant features of absolute and conditional β convergence. The speed of regional convergence from fastest to slowest was central > west > east, with the absolute convergence speeds of 0.0505, 0.0360, and 0.0212, respectively. Finally, policy recommendations are proposed to achieve regional carbon neutrality for the PPRD.

Sexual violence against children and adolescents in Paraná State: geospatial analysis and main socioeconomic indicators

ObjectiveChild sexual violence is a multidimensional problem of many contemporary societies, affecting people of all sexes, social stratum and age groups. Offenses involving children and adolescents are more serious, given their total or partial dependence on parents and caregivers. Information on child sexual violence in Brazil is found in raw form and without detail. The objective was to compare the information with social and economic data in the state of Paraná. MethodsThe authors conducted a retrospective study of secondary data from the Notifiable Diseases Information System (SINAN) on cases of sexual violence involving victims aged 0 to 19 years. Results are presented according to notification characteristics. The authors applied exploratory spatial data analysis to assess spatial autocorrelations and investigated relationships by the ordinary least squares regression model. ResultsBetween 2017 and 2021, there were 13,403 reports of child sexual violence in Paraná State, Brazil. Most victims (82.8%) were female and aged between 10 and 14 years. The majority of sexual violence cases (67.8%) occurred in the home environment. The highest rates on a population basis were observed in the North Central and Greater Curitiba regions, mainly in cities with higher population density and with higher rates of other types of violence. ConclusionThe results provide data that can promote a broader understanding of the distribution of sexual violence and the state and associated variations. It is expected to improve the provision of care for victims of child sexual violence and assist in strategic planning to prevent future offenses.

Unveiling the Dynamics of Rural Revitalization: From Disorder to Harmony in China’s Production-Life-Ecology Space

This study utilizes provincial panel data from China spanning the period from 2011 to 2020 to assess the coupled and coordinated development of spatial functions related to production, life, and ecology (PLE) in rural areas. The assessment is based on quantifying the spatial function indices for PLE in China’s rural regions. Additionally, it examines the characteristics of their spatial and temporal evolution, spatial correlation, and driving factors. The findings indicate a modest upward trend in the spatial coupling and coordination levels of these functions across rural China, although a significant proportion of provinces still exhibit a near-disordered decline. Exploratory spatial data analysis reveals a geographical disparity, with higher levels of coupled and coordinated development observed in the eastern regions, lower levels in the west, and noticeable spatial clustering. By employing the spatial Durbin model to investigate the determinants of coupling degrees, we discovered that factors such as regional economic development, urbanization, the urban–rural income gap, financial support for agriculture, science and technology investment level, and agricultural structural adjustments significantly influence the spatial coupling of rural PLE functions. Furthermore, using the geographic detector model, the analysis identifies science and technology investment level, economic development, and financial support for agriculture as key drivers influencing the spatial coupling and coordination of these functions. These findings provide valuable reference points for policies and strategies related to rural management.

A Study on the Spatio-Temporal Evolution Characteristics of Social Development Levels in China

With the increase in regional economic development disparities, a regional coordinated development strategy is put forward that prioritizes human welfare and holistic social progress over a purely materialistic growth model. To address the challenges of balanced regional development, this paper has developed a multidimensional assessment framework of social development encompassing education, healthcare, culture, and social security. Using the entropy weight TOPSIS method, this study measures the social development level across 296 Chinese prefecture-level cities from 1990 to 2020. It explores the spatio-temporal evolution characteristics of China’s social development level through the Dagum Gini coefficient decomposition method and exploratory spatial data analysis. The results indicate that (1) the level of social development in China exhibits a fluctuating upward trend over the time series, showing a phase-wise pattern of decline–rise–rise; (2) there is a clear heterogeneity in the level of social development, with a general hierarchy of Eastern, Northeastern, Western, and Central regions in terms of social development; (3) spatially, China’s social development level has evolved from a patchy distribution in 1990 to a clustered distribution around urban agglomerations by 2020, with pronounced characteristics of spatial imbalance; (4) the level of social development in China displays varying degrees of spatial clustering, with this trend intensifying over time; and (5) over the period 1990–2020, the overall disparity in China’s social development level presents a fluctuating trend, with a notable reduction after an initial increase, and regional disparities following the order of Central, Western, Eastern, and Northeastern regions. This research offers valuable insights for policymakers and scholars seeking to understand and enhance China’s social development landscape.

Spatiotemporal evolution and influencing factors of urban industrial carbon emission efficiency in the Mid-Yangtze River urban agglomeration of China

Carbon emissions from energy consumption caused by industrial production have become an important factor in global warming, and China now has the highest global carbon emissions, with enormous pressure to reduce emissions. The Mid-Yangtze River urban agglomeration (MYRUA), which is an important region for China, also faces the contradiction of balancing environmental protection and economic production. Currently, there is little discussion on low-carbon pollution research in the MYRUA in China. Therefore, this study takes the MYRUA as the research object, uses a slack-based model (SBM) and exploratory spatial data analysis (ESDA) to explore the spatiotemporal evolution of the industrial carbon emission efficiency (ICEE) of the MYRUA in China from 2006 to 2019, and empirically analyses the factors influencing the ICEE based on a spatial econometric model. The results show that (1) in the period 2006–2019, the ICEE in the MYRUA region generally exhibited a “jagged” trend of decreasing, then increasing until finally decreasing again. Spatially, ICEE generally exhibited a dynamic trend of being low throughout, with high-efficiency areas tending to move to the center and low-efficiency areas being scattered. (2) The global Moran's I coefficient for ICEE ranged between 0.002 and 0.213, with the highest Moran's I value and significant spatial correlation occurring in 2006, after which the value gradually decreased. On the local autocorrelation, the ICEE in 2006 exhibited an agglomeration effect and weakened after 2010. (3) In 2019, the level of industrialization (LI) and the level of external openness (LEO) in the MYRUA changed from nonsignificant to significant positive correlations, and the LEO had the largest impact. The energy consumption structure (ECS) was always significantly negatively correlated. The levels of social development (LSD) and environmental regulation (ER) changed from showing a significant positive correlation to showing a nonsignificant correlation. In conclusion, ICEE is affected by interactions of multiple factors. In the future, the ECS of the MYRUA should be continuously optimized and replaced by clean energy. In addition, foreign investment compensates for the lack of production and technology research funds, which can increase investment and promote the digitalization of manufacturing enterprises.

Research on the Decoupling Relationship between Transportation Land and Population Growth: A Case of Guangdong Province in China

The imbalance between the population, transportation, and construction is an important factor affecting coordinated regional development in China. Using transportation land area and population data from 2010, 2015, and 2020 in 121 counties (cities and districts) of Guangdong Province, this study analyzes the spatiotemporal changes in transportation land and population structure. It explores the relationship between transportation land and population size changes using exploratory spatial data analysis (ESDA) and decoupling models. Research suggests the following: (1) From 2010 to 2020, the per capita transportation land in various districts and counties of Guangdong Province showed a hierarchical structure of “low center and high periphery”. Conversely, the proportion of transportation land to construction land displayed a “high center and low periphery” characteristic. (2) During the same period, the total amount of transportation land in the peripheral areas of the Pearl River Delta was higher than that in the PRD (the Pearl River Delta) and the gap widened each year. Meanwhile, the proportion of transportation land to construction land in the peripheral areas of the Pearl River Delta was consistently higher than that in the PRD. (3) From 2010 to 2015, the relationship between population growth and transportation land in Guangdong Province showed a hierarchical pattern, while from 2015 to 2020, the pattern became more complex, and regional imbalance intensified. The decoupling results of PRD showed weak decoupling and expanding negative decoupling from 2010 to 2020. The peripheral areas of the Pearl River Delta are characterized by strong negative decoupling and expanding negative decoupling. Research has shown that although Guangdong Province is one of the most developed provinces in China, the mismatch between transportation land and population size in economically underdeveloped areas has become increasingly severe, with changes in the provincial population distribution pattern. The decoupling phenomenon between the transportation land and population is very obvious, and the policy of achieving regional balanced development through transportation infrastructure construction needs to be reviewed.

Provincial income convergence in Vietnam: spatio-temporal dynamics and conditioning factors

AbstractIn this article, we use a three-step procedure that combines the log t convergence test, Explanatory Spatial Data Analysis, and ordered logit regression to determine the spatio-temporal dynamics and determinants of provincial income clustering in Vietnam during the 2010–2020 period. Our findings are three-fold. First, provincial income clustering in Vietnam follows patterns of club convergence towards multiple equilibria. Seven convergence clubs encompassing 61 provinces are identified. Second, spatial autocorrelation encourages neighboring provinces to converge toward shared income equilibria. High-income clusters are observed in the Northern and Southern Key Economic Regions, while low-income clusters are concentrated in the mountainous areas of Northern Vietnam. Finally, both internal and external factors significantly affect the formation of convergence clubs. Vital internal factors include localities’ initial conditions of physical capital and structural change. Meanwhile, external factors refer to spatial externalities among neighboring provinces. We highlight spatial complementarity in physical capital accumulation and spatial competition in industrial intensification among neighboring provinces.

Coordination analysis of flood-sediment transportation, eco-environment, and socio-economy coupling in the governance of the Yellow River Basin system

The watershed system has a complex game relationship between the benign operation and coordinated development of various elements of flood-sediment transportation, eco-environment, and socio-economy (FES). With the increasing breadth, depth, and intensity of human activities in watersheds, it is urgent to coordinate the FES. The relationship of water–sediment in the Yellow River Basin (YRB) is complex, with a prominent contradiction in water supply and a fragile ecosystem. This research tries to build a comprehensive evaluation model for FES and explore the complex interaction between FES in the YRB from 2000 to 2020. The results demonstrated that (1) the comprehensive flood-sediment transportation index (CFTI) and comprehensive eco-environment index (CEI) presented fluctuating growth. In contrast, the comprehensive socio-economy index (CSI) revealed a linear growth trend. The CFTI of Sanmenxia, CEI of Toudaokuan, and CSI of Ningxia had the highest growth rates, with 36.03%, 6.48%, and 107.5%, respectively. (2) FES's positive and negative effects were alternating, with heterogeneity in both time and space. (3) The coupling coordination degree (CCD) in the YRB indicated an increasing trend, ranging from 0.53 to 0.87, from reluctantly coordinated development to good coordinated development. The lagging subsystem was CFTI (2000–2001 and 2008–2020) and CSI (2002–2007), and the CEI was not lagging. (4) Exploratory Spatial Data Analysis (ESDA) demonstrated significant differences in the CCD of the YRB, and areas with similar CCD within the basin tend to be centrally distributed in space. At the same time, there was negative spatial autocorrelation in coordination. The results provide a scientific theoretical and methodological framework for strategic research on the YRB system's governance, protection, and management.

Research on the spatial allocation of national fitness resources at the street scale—taking Fuzhou city as an example

To explore the spatial allocation of national fitness resources at different spatial scales in Fuzhou city to provide useful references for optimizing and enhancing the spatial allocation of national fitness resources and urban planning. The equity, spatial distribution characteristics, accessibility and supply-demand balance of national fitness resources in Fuzhou city are analysed in depth by using the two-step mobile search method of multiple travel modes, the Gini coefficient, and exploratory spatial data analysis methods. The results show that the overall spatial allocation of national fitness resources is in a balanced state, but there are serious inequities and spatial mismatches in each district (county); the spatial distribution of national fitness resources is characterized by centralized agglomeration and surface dispersion, being dense in the south and sparse in the north. Areas with insufficient resources per capita have an agglomeration-type scattering distribution; areas with sufficient resources per capita have a dispersed patch distribution. Access to national fitness resources and the relationship between their supply and demand are characterized by positive spatial concentration; however, the layout of the national fitness resources planned for the old urban areas urgently needs to be optimized, with the Chating and Antai streets serving as centres, to increase the effective supply. Finally,we suggestions that Top-level design should be strengthened, the communalization of sports public services should be promoted, the service capacity and utilization efficiency of national fitness resources should be enhanced, and the construction of national fitness resources in new urban areas and new industrial agglomerations should be accelerated.

The Spatiotemporal Impact of Digital Economy on High-Quality Agricultural Development: Evidence from China

China’s high-quality economic development is strongly supported by the high-quality development of agriculture, and the digital economy has emerged as a key driver for promoting shared prosperity and high-quality economic development. Against this backdrop, investigating the connection between high-quality agricultural development and the digital economy holds significant importance. This study utilized the entropy-weighted TOPSIS model to evaluate comprehensive evaluation indicators of the two according to panel data from 30 provinces in China between 2011 and 2021. Subsequently, GIS spatial analysis and exploratory spatial data analysis (ESDA) were employed to investigate the spatiotemporal evolution features and spatial correlations. Finally, the spatiotemporal geographically weighted regression (GTWR) model was constructed to examine the spatiotemporal impact of the digital economy on the advancement of high-quality agricultural growth. The results indicate that: (1) from 2011 to 2021, China’s high-quality agricultural development and digital economy both demonstrated a general increasing trend. In terms of spatial distribution, there were significant spatial variations, with a general trend of “Southeast is higher, whereas the Northwest is lower”. The regions with significant value were primarily clustered in the coastal areas in the east and several provincial capitals. (2) Both of the two exhibited significant global spatial self-correlation, and there were also significant spatiotemporal clustering effects in high-quality agricultural growth, gradually forming a high-value cluster centered around Shanghai and a low-value cluster centered around western provinces. (3) The digital economy positively influences the enhancement of high-quality agricultural development, demonstrating notable spatial and temporal heterogeneity. In contrast to the southeastern areas, the influence is more pronounced in the northern and central-western areas.

Modeling regional innovation in Egyptian governorates: Regional knowledge production function approach

Knowledge and innovation have become a significant source of modern regional development. The paper aims to study the spatial association of innovation and model innovation, besides critiquing regional innovation policy, using the regional knowledge production function approach. At the level of the Egyptian governorates, the number of patent applications has been used as a proxy for innovation. The study uses cross‐sectional data for patents in 2018 with lagged time variables for knowledge input factors. The study contributes towards applying the knowledge production function at the level of the Egyptian governorates for the first time. Exploratory spatial data analysis (ESDA) and ordinary least squares (OLS) regression are used, using the knowledge sources and localized characteristics. Our analysis found that research and development activities for academic institutions are associated with innovation outputs. In the context of the Egyptian regional innovation system, the contribution of private research activities is perceived to be negligible, and the innovation outputs are highly associated with the spatial association of the Egyptian governorates.

Determinants and their spatial heterogeneity of carbon emissions in resource-based cities, China

Global climate change associated with increased carbon emissions has become a global concern. Resource-based cities, by estimations, have emerged as major contributors to carbon emissions, accounting for approximately one-third of the national total. This underscores their pivotal role in the pursuit of carbon neutrality goals. Despite this, resource-based cities have long been neglected in current climate change mitigation policy discussions. Accordingly, using exploratory spatial data analysis and Geographical Weighted Regression method, this study investigates the determinants of carbon emissions and their spatial pattern in 113 resource-based cities in China. It can be concluded that: (1) The proportion of carbon emissions from resource-based cities in the national total has shown a marginal increase between 2003 and 2017, and the emissions from these cities have not yet reached their peak. (2) A relatively stable spatial pattern of “northeast high, southwest low” characterizes carbon emissions in resource-based cities, displaying significant spatial autocorrelation. (3) Population size, economic development level, carbon abatement technology, and the proportion of resource-based industries all contribute to the increase in carbon emissions in these cities, with carbon abatement technology playing a predominant role. (4) There is a spatial variation in the strength of the effects of the various influences.

Spatiotemporal variations, influencing factors, and configurational promotion paths of energy efficiency in China’s cities

The promotion of energy efficiency (EE) helps address energy constraints and promote environmental sustainability. This study comprehensively explores the spatiotemporal variations, influencing factors, and configuration promotion paths of EE in 284 Chinese cities during 2003‒2019 using the global super-efficiency minimum distance to strong efficient frontier (G-S-MinDS), exploratory spatial data analysis (ESDA), multiscale geographically weighted regression (MGWR), and fuzzy set qualitative comparative analysis (fsQCA) methods. The findings are: ① China’s cities have an annual average EE of 0.658 with a growth rate of 0.53%, showing considerable promotion potential. ② Industrial structure optimization, population agglomeration, economic development, and increased green coverage contribute positively, while government intervention and openness hinder China’s urban EE. ③ Four configurational promotion paths for enhancing China’s urban EE are identified, where among those paths population density is a core condition, while government intervention is not. This study provides valuable insights into substantially improving urban EE, emphasizing the need for targeted policies to address energy and environmental crises in China.

Alleviating environmental pressure from livestock production: A “livestock-resource environmental carrying capacity” perspective in China

Scientific assessment of the relationships and influencing factors between livestock and resource-environmental carrying capacity (LS-RECC) is essential for alleviating environmental pressure. The crop-livestock balance method was used to measure China’s livestock production potential from 2005 to 2020. The improved coupling coordination degree model (I-CCDM) and exploratory spatial data analysis (ESDA) were used to analyze the spatial-temporal coordinated development characteristics of the LS-RECC. Furthermore, geographically and temporally weighted regression (GTWR) was used to identify the factors influencing the coordination development of the LS-RECC. The findings showed that (1) From 2005 to 2020, the nutrient demand for crop cultivation in China increased, and, the crop system nutrient demand exceeded the livestock nutrient supply in 2007 to 190 ×104 tons, reaching a maximum of 925 ×104 tons in 2019. However, the production potential of livestock in Ningxia, Tibet, Guizhou, Gansu, Yunnan, and Qinghai was severely overloaded. (2) The coupling coordination degree (CCD) of the LS-RECC showed a progressively increasing trend, which indicates that the sustainable development ability of China's livestock industry is constantly increasing. However, compared with that in eastern and central China, the coordinated development level of the LS-RECC in western China was lower, further indicating excessive livestock industry development in western China. (3) Environmental regulation had the strongest effect on the regional coordinated development of the LS-RECC, with obvious spatial differences. The economic development level had a positive effect, but urbanization and Engel's coefficients had the opposite effect. These results provide not only references for reducing environmental pressure on the livestock industry in China but also guidance for the sustainable development of the livestock industry in other large livestock production countries.

Spatial and economic dynamics of bean crops in an important production hub in Brazil

Beans are a traditional grain crop consumed by humans that is of great importance for food security and farmers in terms of income in Brazil. Paraná is one of the largest bean-producing states in the country, but the area needed for its production is being lost, changing the dynamics of bean cultivation, and generating social and economic impacts. The objective of this study was to verify the spatiotemporal and economic dynamics of bean production in Paraná. For this purpose, using mainly data from the 2006 and 2017 agricultural censuses, the location quotient, the Hirschman–Herfindahl Index (HHI), the Williamson coefficient ([Formula: see text]), exploratory spatial data analysis (ESDA), and principal component analysis (PCA) were applied. From the results, it was possible to verify that there was a 13.95% reduction in the number of municipalities that specialized in bean production, from 98 municipalities in 2006 to 86 in 2017. Most specialized municipalities produced black beans or a combination of other colored beans. The HHI revealed that the specialized municipalities accounted for the most income from bean production. The [Formula: see text] indicated that the economic development of the central-eastern and southeastern Paraná mesoregions was related to bean production. Based on the ESDA, it was found a statistically significant and positive presence of spatial concentration in 2006 (Moran’ I = 0.36) and 2017 (Moran’ I = 0.51). Based on the PCA, it was possible to identify two main components that together explained 71.52% of the data variability. The reduction in the planted area, the decrease in the number of specialized municipalities, and the concentration of income from bean production in some municipalities make it difficult to reduce regional disparities in Paraná, and in addition, these factors increase concern about food security; thus, public policies are needed to reduce such impacts.

Integrating Multiscale Geospatial Analysis for Monitoring Crop Growth, Nutrient Distribution, and Hydrological Dynamics in Large-Scale Agricultural Systems

Monitoring crop growth, soil conditions, and hydrological dynamics are imperative for sustainable agriculture and reduced environmental impacts. This interdisciplinary study integrates remote sensing, digital soil mapping, and hydrological data to elucidate intricate connections between these factors in the state of Ohio, USA. Advanced spatiotemporal analysis techniques were applied to key datasets, including the MODIS sensor satellite imagery, USDA crop data, soil datasets, Aster GDEM, and USGS stream gauge measurements. Vegetation indices derived from MODIS characterized crop-specific phenology and productivity patterns. Exploratory spatial data analysis show relationships of vegetation dynamics and soil properties, uncovering links between plant vigor, edaphic fertility, and nutrient distributions. Correlation analysis quantified these relationships and their seasonal evolution. Examination of stream gauge data revealed insights into spatiotemporal relationships of nutrient pollution and stream discharge. By synthesizing diverse geospatial data through cutting-edge data analytics, this work illuminated complex interactions between crop health, soil nutrients, and water quality in Ohio. The methodology and findings provide actionable perspectives to inform sustainable agricultural management and environmental policy. This study demonstrates the significant potential of open geospatial resources when integrated using a robust spatiotemporal framework. Integrating additional measurements and high-resolution data sources through advanced analytics and interactive visualizations could strengthen these insights.

The Spatial Role and Influencing Mechanism of the Digital Economy in Empowering High-Quality Economic Development

Based on the data from 2012 to 2022, this paper comprehensively uses exploratory spatial data analysis, the panel threshold model, and the spatial Durbin model to explore the spatiotemporal evolution characteristics and mechanisms of digital economy and high-quality economic development. The results show that the center of gravity of China’s digital economy development has gradually concentrated in the southeast region, and the level of high-quality economic development has improved rapidly, gradually forming a pattern of radiation driving the development of the central and western regions with the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Pearl River Delta. The digital economy can significantly improve the level of high-quality economic development, but this effect has a lag effect, and it can be extended to the fifth stage. There is a single threshold for high-quality economic development of the digital economy, and the two stages of its development can significantly promote the high-quality development of the economy, but the effect of the latter stage is weakened, showing the nonlinear characteristics of first strong and then weak. The digital economy has a notable positive spatial spillover impact, and its development dividend will raise this province’s and its bordering provinces’ levels of high-quality economic development.

Analysis of the coordinated evolution and driving mechanism of the coupling of tourism economy and urbanization in Southwest China

ABSTRACT The coordinated development of the tourism economy and urbanization is one of the hot issues in the practice and research of regional development. Exploring the complex dynamic relationship between the two can promote new urbanization construction and sustainable regional economic development. This paper uses the coupled coordination degree (CCD) model and exploratory spatial data analysis (ESDA) model to study the spatiotemporal evolution characteristics and driving mechanisms of the coordinated development of Southwest China’s tourism industry and urbanization. The results show that from 2010 to 2020, the comprehensive level of tourism economic development is in an inverted ‘V’ shape, while the complete level of urbanization is on a continuous upward trend. The spatial difference for CCD between the tourism economy and urbanization is remarkable, offering a spatial pattern of the highest in the middle, the second in the east, and the lowest in the west. In addition, the urbanization rate, population density, per capita fiscal revenue, and per capita park green land have significant negative spatial effects on CCD. Per capita budgetary revenue has a positive spatial effect on CCD with a time lag. This study provides a decision-making reference for promoting tourism development and urbanization in southwest regions.

National or Regional Recruitment: “Market Area” of University Cities

The depopulation of eastern Portugal and concentration on the coast has been a longstanding process. Since 1973 new HEIs has been disseminated around the country, and nowadays, every Portuguese region holds a set of HEIs. However, those closer to the coast tend to have a higher demand than those more distant. It is not clear if each one has a kind of restricted market area, such as a spatial monopoly in its neighbouring area or a national recruitment for some of them. This paper is the first exercise of determination of these areas for the Portuguese public network of HEIs. We use a database from the Ministry of Education (DGEEC-Ministry of Education) with information of the pair, place of residence (municipality level) and respective HEI, for enrolled students. Through techniques of Exploratory Spatial Data Analysis (ESDA), we will look for clusters of municipalities that could be market areas for each “university city” considered. We also use fractional regression models that confirm the positive correlation between the chosen university city and the distance to the family home and belonging to the market area. Despite some overlaps, the market areas of the university cities look to be well defined.

Spatial Modeling of the Relationship Between the Social Map and the Level of Educational Development in the Municipalities of the Luján River Basin, Argentina

This study aimed to determine the degree of association between the social map (Synthetic Economic-Housing Index—ISEH) and the level of educational development (Synthetic Educational Index—ISE) modeled for the municipalities of the Lujan River basin. The indices were constructed by applying multivariate analysis methods based on Geographic Information Systems (GIS), and the Exploratory Analysis of Spatial Data determined their links using a spatial decision support system (SDSS). The result facilitated the contrast of the proposed hypothesis. In this sense, a theoretical statement and a methodological tool are built from the cartography. The analysis highlights the importance of contextual variables and their incidence on educational development indicators since the spatial perspective provides the bases to support spatial decision-making at the municipal management level.