Applied Spatial Analysis and Policy最新文献

As a special product of the evolution of urban-rural regional systems, rural hollowing has become a major constraint to China’s rural revitalization. This paper attempts to construct a theoretical framework of rural multidimensional hollowing from the perspective of population outflow, and to comprehensively assess the level of rural hollowing from the dimensions of population, economy and land. Based on this, this paper describes in detail the characteristics and facts of population outflow and rural hollowing in Longxi County, which is located in Longzhong Loess Hilly Regin. The key factors of rural hollowing are identified in terms of individual characteristics, geographic environment and socio-economic attributes. The main conclusions are as follows: (1) Longxi County has significant spatial differences in population outflow. The proportion of floating population in the household population is as high as 28.6%. The villages with intra-provincial preferences show a “center-periphery” circle distribution pattern, and villages with extra-provincial preferences are mainly located in the boundary area. The rural hollowing level of Longxi is characterized by “economic hollowing (Eh) > population hollowing (Ph) > land hollowing (Lh)”. The hollowing level of each dimension has different spatial distribution characteristics, and the rural hollowing (Rh) level shows a spatial pattern of “high in the east and low in the west, high in the south and low in the north”. (3) The influencing factors of rural hollowing vary significantly and are greatly influenced by the geographical and socio-economic attributes of the villages. When one factor (economic or geographical environment) is dominant, low-intensity intra-provincial mobility may occur. However, when all factors are combined, a high-intensity, multi-directional flow trend emerges within and outside the province. This study contributes theoretical insights into understanding the social changes in rural areas during the rapid urbanization process where population outflows occur, and can also provide inspiration for addressing issues in rural transformation.

Given the unequal distribution of educational opportunities across regions, it is insightful to identify spatial clusters in learning performance and investigate how far the regions are from tapping their full potential of educational resources. This paper explores the spatial aspects of learning performance and estimates the technical efficiency of the primary education system of Indian districts using the spatial stochastic frontier analysis. Specifically, we investigate the effect of spatial associations between districts on primary educational efficiency in achieving better learning outcomes. Also, the study examines the regional patterns in efficiency and learning. We find evidence of spatial clustering of learning. Although the mean technical efficiency is 90 per cent, the findings demonstrate substantial regional disparities in India, with efficiency levels ranging from 68 to 97 per cent. Our results suggest that efficient districts can improve learning outcomes from extra inputs, while better utilization of existing inputs may enhance learning for the relatively inefficient districts. Thus, policy formulation needs to align with the specific shortcomings and needs of the districts.

Illegal parking is a widespread issue in urban environments, leading to significant adverse effects on traffic flow, public safety, and the aesthetics of streetscapes. This paper introduces an innovative methodology for the identification and analysis of illegal parking hotspots. To achieve this, a custom Web-GIS platform is developed that employs crowdsourcing, enabling citizens to report the precise locations and types of parking violations they encounter. The platform collects this data, which is then analyzed using a Spatio-Temporal Density Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm. The methodology was applied in the city of Athens, Greece, with the findings revealing that parking violations are not uniformly distributed but instead show distinct spatial and temporal patterns. Specifically, certain areas and times during the day exhibit considerably higher levels of illegal parking. For instance, commercial areas tend to experience more violations during business hours, while residential areas see more issues during evenings and weekends, mainly due to an increasing number of already parked cars and a lack of organized parking spaces. By providing a data-driven approach to identifying critical areas of illegal parking, city officials can better allocate enforcement resources and develop targeted strategies to mitigate this issue, highlighting the significant implications of the study for urban management and policy-making. Therefore, the main goal of this research is to contribute to the enhancement of urban mobility and the overall quality of life by addressing one of the most persistent challenges in city management.

Equitable and efficient healthcare service is a critical issue for policymakers and planners in public health emergency events. However, current optimization methods of spatial location-allocation for healthcare facilities at community level often overlook the potential requirements stemming from outbreaks of infectious disease, which lead to biases in facility layout. This study proposes a deep network capacitated covering location model to optimize the spatial location-allocation of community healthcare facilities by considering the site suitability of facilities with an emphasis on emergency preparedness and residents’ access on foot. In the experimental analysis in Panyu district, Guangzhou, we establish the site suitability and access evaluation criteria to build a feature cube comprising 18 criteria maps. By labeling positive and negative samples for the deep site evaluation network model training, we input the feature cube to the model to identify suitable candidate sites. Then we evaluate the healthcare facility distribution against residents’ demands to optimize the spatial layout of healthcare facilities considering capacity constraints. The results indicate significant spatial disparities in community healthcare facility access, with the current distribution failing to meet demands. According to our approach, 637 suitable site locations as candidate sites are classified. Moreover, we find that only 51 additional facilities are needed to extend coverage to 91.2% of the population within a 30-minute walk. The proposed model outperforms the analytic hierarchy process by more accurately addressing residents’ actual healthcare needs. From a policy perspective, optimizing healthcare facility location-allocation using the proposed method improves equity and efficiency for residents at a walking scale, while maintaining emergency preparedness.

Rural household quality of living is a fundamental aspect that determines the overall standard of living and well-being experienced by families residing in rural areas. This study comprehensively examines spatial variations in rural household living conditions among 341 administrative blocks located within West Bengal. This study employs the Rural Household Quality of Living Index (RHQoLI) as an analytical tool to assess rural households’ housing, economic and social aspects. The RHQoLI computation using Principal Component Analysis (PCA) involves three indices - Housing Quality Index (HQoLI), Economic Quality Index (EQoLI), and Social Quality Index (SQoLI) - based on twelve indicators. The further analysis employed bivariate and univariate Local Indicators of Spatial Association(LISA) and Moran’s I techniques to examine spatial patterns and heterogeneity. The findings reveal significant regional disparities in the quality of living across various blocks located within West Bengal, with about 115 blocks reflecting low RHQoLI status exhibiting significant clustering patterns at 0.415. Regarding bivariate analysis, SQoLI exhibits a maximum clustering pattern at 0.393, followed by EQoLI and SQoLI. This comprehensive analysis offers insights into variations related to living conditions alongside emerging spatial clustering patterns prevalent within rural West Bengal, facilitating a better understanding of the socioeconomic landscape policymakers can leverage towards creating inclusive development in these areas.

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.