Applied Spatial Analysis and Policy最新文献

The global response to the COVID-19 pandemic between January 2020 and late 2021 saw extraordinary measures such as lockdowns and other restrictions being placed on citizens’ movements in many of the world’s major cities. In many of these cities, lockdowns required citizens to stay at home; non-essential business premises were closed, and movement was severely restricted. In this paper, we investigate the effect of these lockdowns and other pandemic response measures on crime counts within the local authorities of England and Wales. Using openly accessible crime records from major police forces in the UK from 2015 to 2023, we discuss the impacts of lockdowns on the incidences of crime. We show that as time passed and citizens’ response to the imposed measures eased, most types of crime gradually returned to pre-pandemic norms whilst others remained below their pre-pandemic levels. Furthermore, our work shows that the effects of pandemic response measures were not uniform across local authorities. We also discuss how the findings of this study contribute to law enforcement initiatives.

The collapse of the socialist regime led to significant changes in migration patterns, garnering considerable attention in geographical research. However, despite the increased interest, many studies on internal migration lack a detailed analysis of its spatial aspects. Spatial autocorrelation methods can reveal spatial patterns, but so far they have not been applied in the detailed research of internal migration in post-socialist countries. The aim of this study is to explore the spatial patterns of internal migration with regard to intra-regional and inter-regional migration processes using selected indicators of spatial autocorrelation (Global Moran’s I, Anselin local Moran’s I and Getis-Ord Gi* statistic) with Slovakia as a case study. A partial goal is to evaluate the benefits of applying these methods in the assessment of internal migration. Local indicators of spatial autocorrelation demonstrated significant differentiation of both intra-regional and inter-regional migration processes. The dominant intra-regional process is the decentralization of the population, which is very intensive in the regions of the largest towns and cities. Inter-regional migration displays spatial polarisation, emphasizing the importance of the location of key economic centres. The methodology employed in this study clearly displays the clusters of municipalities with above-average and below-average values. This approach enables the identification and cartographic interpretation of specific municipalities where migration contributes the most to the spatial redistribution of the population. The study serves as a valuable framework for similar analyses, emphasizing the broader applicability of spatial autocorrelation methods in studying migration patterns.

The nighttime economy is instrumental in driving economic growth, particularly in the post-pandemic era. Nighttime Light (NTL) data is a key source in nighttime economy remote sensing study, with its angular effect directly affecting result accuracy. This study compares the accuracy of identifying nighttime economic agglomerations (NEAs) in Shanghai using Black Marble NTL and POI data at three observation angles: near-nadir, off-nadir, and all-angle. The results indicate that under all three angles, landmark NEAs can be identified fairly well. However, near-nadir demonstrates superior sample library identification accuracy and Theil index performance compared to all-angle and off-nadir. The study reveals that near-nadir observations offer higher accuracy and better suppression of “pseudo-accuracy units”, making them more suitable for studying the nighttime economy. Furthermore, the study analyzes the spatial distribution characteristics of NEAs in Shanghai and finds a distinct “center-periphery” development pattern, suggesting imbalances in overall development. The presence of buildings with scattered high-low distribution and complex urban structures contributes to the variations in NEA identification under different satellite-observed angles. This study provides valuable insights into selecting the appropriate satellite-observed angle for studying NEAs using NTL data. It also explores the potential application of Black Marble NTL data products in socioeconomic remote sensing.

In a period of falling fertility rates and increasing number of various family policies (FPs) that are designed to address this trend, our study explores spatial and temporal variance of such FP measures which could be associated with increased fertility. They have been implemented in a range of countries, but their variance has not yet been systematically mapped. We employ a novel approach, examining gender and economic dimensions of FP measures to develop FP indices scoring 23 European OECD countries throughout the 2000–2021 period and uncover spatial similarities and discrepancies therein. Our findings indicate that degenderizing FP measures are increasingly being adopted across countries, with both genderizing and generous financial measures also enduring. Interestingly, despite an overall decline in fertility rates, FP has become more robust across countries according to the indices. We agree with recent studies that degenderizing FPs are most common in Nordic countries and least so in Central and Eastern Europe. We provide evidence that Hungary and Estonia are exceptions, with different types and more extensive FP. We also found that countries categorised in different contextual clusters sometimes score similarly strongly on genderizing FP (Czechia, Luxembourg, Spain) or weakly on financial FP measures (Slovakia, the UK, Greece). In countries where index scores increased only recently (e.g., Switzerland, Spain), we expect potential increases in fertility rates in the future.

The aim of the current study is to estimate whether spatial data on place features from OpenStreetMap (OSM) produce results similar to those when employing register data to predict future violent crime in public across three Swedish cities of varying sizes. Using violent crime in public as an outcome, four models for each city are produced using a Risk Terrain Modeling approach. One using spatial data on place features from register data and one from OSM, one model with prior violent crime excluded and one with prior crime included. The results show that several place features are significantly associated with violent crime in public independent of using register or OSM data as input. While models using register data seem to produce more accurate and efficient predictions than OSM data for the two smaller cities, the difference for the largest city is negligible indicating that the models provide similar results. As such, OSM place feature data may be of value when predicting the spatial distribution of future violent crime in public and provide results similar to those when using register data, at least when employed in larger compared to smaller cities. Possibilities, limitations, and avenues for future research when using OSM data in place-based criminological research are discussed.

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.