Applied Spatial Analysis and Policy最新文献

Under the double pressure of the onset of aging and declining fertility rate, improving the efficiency of spatial allocation of the rural labor force is of great significance in promoting the sustainable development of agriculture. The loess hilly region is a typical ecologically fragile area in China. This paper takes 422 administrative villages in Qin’an County of China’s loess hilly region as the evaluation unit. It combines spatial concentration, spatial interaction, and spatial mismatch index to reveal the characteristics of the relationship between rural population and agricultural production space from a multi-gradient perspective. The results show that: (1) Rural population and agricultural production space show a reverse unbalanced “center-edge” structure, with rural population clustering in the urban area and agricultural production space spreading to the periphery, with a significant unbalanced distribution. (2) Rural population and agricultural production space show diversified relationship characteristics, with significant inconsistency in their spatial concentration, overall low spatial interaction, and obvious spatial mismatch index, which greatly affects the rational utilization of human resources and land resources. (3) The characteristics of the relationship between rural population and agricultural production space are influenced by natural endowment, economic development level, and location conditions, showing a multi-gradient differentiation pattern. Based on the research results, this paper proposes a reasonable path for the coordinated development of rural population and agricultural production space in China’s loess hilly region from a multi-gradient perspective.

The study of the formation and development of urban agglomerations is of great significance, and the connection between cities is the critical foundation for shaping these agglomerations. However, the mechanism behind spatial connection between cities in the formation of urban agglomerations remains unclear. Using the Greater Bay Area (GBA) as a case study, we proposed a Bayesian network framework that integrated the spatial connection index and land use intensity. We constructed a dependency network of land use intensity from the perspective of spatial connection, and summarized the spatiotemporal evolution patterns of urban agglomeration combined with social network analysis methods. The results indicate that: (1) From 1980 to 2020, both land use intensity and spatial connection strength in the GBA have significantly increased, though the characteristics of different cities varied noticeably; (2) The spatial connection center of the urban agglomeration has shifted geographically from Hong Kong and Macao to the Pearl River Delta, and then to the east bank of the Pearl River. Hong Kong, Guangzhou, and Shenzhen are the three core cities in the spatial connection network, each with different development trajectories. (3) A dependency network of changes in land use intensity among cities at different stages from the perspective of spatial connection was constructed, identifying the evolving roles of each city in the development of the urban agglomeration. The study discussed a three-stage development model of urban agglomerations from the perspective of spatial connection, providing a new perspective for exploring the formation mechanism of urban agglomerations.

Rural development initiatives like India’s Shyama Prasad Mukherji Rurban Mission (SPMRM) require efficient methods to identify villages with high socio-economic growth potential. Traditional planning methods, reliant on surveys and expert opinions, are becoming outdated due to the abundance of informative data available online. This paper proposes a novel framework, eRurban, that utilizes machine learning to automate village ranking and analysis for rural development in India. eRurban leverages data from 250,000 gram panchayats (village clusters) to group villages with similar development trajectories through clustering techniques. A key innovation is the introduction of the ClusterRank algorithm, a novel ranking method that utilizes gradient descent to train ranking coefficients for improved accuracy and efficiency. The effectiveness of ClusterRank is demonstrated by its high Spearman correlation coefficient (0.89) when compared to village rankings generated by SPMRM reports. This cost-effective framework offers valuable insights and guidance for rural development planning in India. By automating village ranking and analysis, eRurban addresses limitations of traditional methods and offers a data-driven solution for optimizing resource allocation and promoting sustainable growth in rural areas.

Recently, several big cities have been faced with rapid expansion of the population in China, ultimately leading to unprecedented pressure on urban healthcare facilities. Thus, new healthcare facilities are established to meet residents’ increasing demand for healthcare. Under the current circumstances, an essential task was to evaluate the improvement of these new facilities to residents’ healthcare accessibility. In this study, the rapid development city of Xi’an, China was employed as the case study, and the Gaussian-based 2SFCA method was utilized to measure the spatial distribution of high-level healthcare accessibility by private cars and public transit. The change in accessibility could be obtained by quantifying the difference in accessibility before and after establishment of the new hospitals. Furthermore, the improvement in equity in healthcare accessibility was measured using the Gini coefficient. Finally, the contribution of each new hospital to healthcare accessibility was evaluated from the perspectives of population coverage, average improvement of each bed, and improvement of the Gini coefficient. The results show that the areas with significant improvement are scalloped to the periphery of the study area, especially in the communities near the new hospitals. What’s more, a slight improvement could also be observed in the equity of healthcare accessibility after the construction of the new hospitals. These results could provide guidance for optimizing and allocating healthcare facilities.

This paper seeks to disaggregate the relative effects of key environmental, economic, and social factors, including risk of crime, on food access in Newark, New Jersey. First, we identify environmental risk factors that contribute to the spatial distribution of aggravated assault. We then compare the results of this analysis that generates risk scores for assault in microenvironments to food access maps. The final part of the analysis assesses the relationship of these risk scores to macro level measures of social disorganization in determining their differential impact on food access. The results of this study suggest that high-risk environments for aggravated assaults and the presence of abandoned properties are strongly associated with the distribution of food access. In addition, individuals who are going to bodegas to get food within food deserts are also increasing their risks of becoming victims of aggravated assault. As such, food policy must consider how to mitigate the negative effects of multiple layers of deprivation, including crime, to better eradicate food deserts.

Recent challenges and opportunities necessitate a reexamination of the spatial dynamics of US poverty. In our study, we employed the emerging hot spot analysis to discern spatial and temporal variations in US poverty from 2006 to 2021, across various spatial scales. The primary innovation of this analysis is its capacity to monitor shifts in spatial patterns of poverty over time, providing a dynamic perspective essential for comprehending and addressing non-static processes. We discovered that the majority of US counties exhibit consistent poverty trends, characterized by either persistent or intensifying hot spots (clusters of high poverty) or cold spots (clusters of low poverty), with only a minority experiencing recent changes in poverty trajectories. The choice of spatial scale significantly influences these trends. Our intersectional analysis of poverty with age and race demonstrated that the categories of children and adults have a markedly higher number of counties with persistent and intensifying hot spots compared to the elderly category. Nonetheless, the elderly category has a greater number of new emerging hot spots. Furthermore, our exploration of the interaction between race and poverty dynamics showed that the spatial patterns of emerging poverty hot spots vary among racial groups and do not align with their population distributions. These findings underscore the need for a nuanced and adaptive approach to policy development and implementation.

China's rural revitalization strategy emphasizes the integration of urban and rural development, with the rural living circle serving as a fundamental unit. Public service facilities, especially medical services, are crucial for the well-being of rural residents. Essential for achieving balanced development and improved living standards, these services must ensure equitable access in rural areas, balancing social justice with humanitarian considerations. A comprehensive examination of their availability and interaction with the living circle is key to optimizing these facilities for balanced progress. This study focused on Qin'an County, Gansu, China, where it mapped out living circles based on residents' travel habits and the levels of medical institutions. It analyzed the distribution and accessibility of medical facilities within these circles, investigating their correlation with circle radii. The findings were aimed at enhancing the spatial layout of medical services, yielding three key insights: (1) The spatial layout of rural medical facilities in Qin'an County shows a "core–edge" trend, with most areas by low accessibility, in contrast to the high accessibility in the urban center. (2) A significant negative correlation was identified between the accessibility of medical facilities and the radius of the living circle; in other words, greater accessibility corresponds to a smaller living circle radius. (3) The K-means clustering algorithm proved to be an effective method for optimizing the accessibility of medical facilities, leading to improved access and more balanced distribution across living areas. In China's new stage of development, understanding the complex dynamics of the "people-land" relationship is crucial. The spatiotemporal development pattern, orientation difference characteristics, and spatial optimization layout of rural medical provide guidance for decision making regarding equity and quality improvement of rural public health services.

Wildlife crime has presented a trend of diversity, globality, and severity, which has posed severe threats to biology, ecology, economy, and society. Although researchers in these fields have never stopped fighting against it by proposing prevention and control countermeasures from their disciplinary perspectives, spatial heterogeneity have been largely overlooked in most studies. Here, we use 873 wildlife crime cases in Yunnan, a province with the highest wildlife crime rate in China, to highlight the spatial differences in wildlife crime rate within a province and the reasons behind them. Our results show that the provincial capital, border counties, and tourist cities serve as hotspots for wildlife crime, each characterized by distinct formation mechanisms. Distribution channels, climate and social order, and national culture significantly influence the emergence of these hotspots. Furthermore, the spatial elements of Crime Prevention Through Environmental Design (CPTED) complement the Situational Crime Prevention (SCP) framework and are applied across various scales in the study of wildlife crime management in Yunnan Province. These findings can help us suggest new ideas for preventing wildlife crime from a criminological geography perspective taking into account spatial considerations.

Agglomeration economies are manifested in geographical concentration of firms, as stated by Agglomeration Theory. The objective of this paper was to examine the proximity and concentration of firms in continuous space and in specific directions for the mapping of potential industrial clusters, through an unprecedented approach. The methods used were the indicator semivariogram, anisotropy modeling and kriging interpolation. The geostatistical approach was initially applied in a simulated data with patterns known a priori: random and clusters. Then it was validated with real data of roasted coffee industry in Minas Gerais, Brazil. Each dataset was tested in isotropic and anisotropic scenarios. Geostatistics was sufficient to detect patterns of randomness and spatial dependence, measure the extent of proximity between firms, identify regions with a high level of industrial concentration on the map and estimate an index of concentration at the firm level. The results also advanced the specialized literature by indicating that the anisotropic scenarios, which adjusts the directional bias, better described the reality of the phenomena compared to the isotropic ones. Error measures in anisotropic scenarios were between 7 and 30% more accurate. It was concluded that the direction can be a determinant of the theory of agglomerations and that geostatistics can be useful for mapping potential industry clusters.

This research addresses the critical need for a comprehensive understanding of the spatial dynamics between public schools and residential neighborhoods, both of which significantly influence children’s life outcomes. The study introduces innovative methodologies for examining the spatial relationships between school catchment areas and neighborhoods through geodemographic clustering and spatial congruence measures. By applying these methods to the largest 110 metropolitan areas in the United States, encompassing 24,507 elementary school catchments and 17,608 neighborhoods, the research provides novel insights into how these spatial interactions affect ethnoracial integration and educational diversity. The findings reveal the potential of targeted policy interventions that leverage these spatial relationships to promote equity and reduce segregation in educational settings, thereby offering a valuable framework for policymakers to improve access to opportunities across diverse communities.