Applied Spatial Analysis and Policy最新文献

The interplay between the digital economy (DE) and the real economy (RE) is crucial for the advancement of “Digital China”. This study employs the modified CRITIC method and the coupled coordination degree model to assess the integration dynamics between the DE and the RE over the period from 2013 to 2020. Employing geospatial analysis, this study analyzes the spatio-temporal characteristics and investigates determinants using geographically and temporally weighted regression (GTWR).This approach introduces the perspective of spatio-temporal heterogeneity, enriching our understanding of the mechanisms underpinning the effective integration between the DE and the RE. The main findings are as follows: (1) Integrated development has consistently improved, although pronounced disparities across provinces and regions persist. Notably, developed areas and the eastern coastal regions exhibit marked superiority. (2) Despite a narrowing of regional integration disparities, significant spatial heterogeneity remains, characterized by a progressive enhancement from west to east and a weak inverted U-shaped pattern in the north-south direction. Spatial agglomeration tendencies have strengthened, particularly with high-high clusters along the eastern coast. (3) The determinants influencing this integration display significant spatio-temporal heterogeneity. Technological innovation, policy environment, and spending power generally show positive influences, whereas openness tends to have a negative impact. Marketization and industrial structure exhibit substantial positive effects, primarily in the southeastern and eastern coastal regions. The importance of environmental dimensions is increasingly evident.

This study utilizes tourists’ digital footprints and travel blog data to construct a dual analytical framework encompassing both behavioral and cognitive dimensions. Employing social network analysis methods, we systematically examine the influence mechanism of colonial imprint attractions on tourism flow network structures, with particular focus on tourists’ spatial behaviors to evaluate the position and attractiveness of colonial imprint attractions in post-colonial urban tourism flows. A multi-label classification method is used to analyze travel blog data and uncover the focal points of tourists’ interests. The purpose of this research is to reveal the impact of colonial imprint attractions on tourism in post-colonial cities and provide insights into how their potential value can be leveraged. Our findings demonstrate that regardless of whether colonial imprint attractions exhibit higher or lower node degree centrality compared to general attractions, their removal significantly weakens the cohesion and connectivity of tourism flow networks in post-colonial cities. Furthermore, we observe that tourists tend to focus more on immediate travel experiences while showing relatively lower perception of the historical values behind these attractions. This research offers a new perspective for further exploration of the role of colonial imprint attractions in the tourism industry and provides ideas on how to enhance these distinctive attractions to contribute to urban tourism development.

High-speed rail (HSR) expansion has led to regional restructuring of industrial and population development. However, little is known about how these effects vary across regions with similar development levels. This study considers 132 cities in Jiangsu and Zhejiang, two developed provinces in eastern China, as examples to comparatively examine the effects of HSR on changes in employed and resident populations over the last decade. The HSR network expansion led to a decreasing ratio of the employed population to the resident population in Jiangsu, whereas an opposite trend was observed in Zhejiang. Large cities in Jiangsu reinforced their attractiveness to both employed and resident populations, and small cities are facing population loss. In contrast, small cities in Zhejiang benefited from HSR development, similar to large cities. These results suggest that regional disparity in Jiangsu amplified the siphoning effect of HSR network development, whereas the relatively balanced development in Zhejiang promoted the optimization effect of HSR network development. This study contributes to understanding the varied effects of HSR development and how they relate to the initial spatial structures of different regions.

With the goal of carbon peak and carbon neutral, increasing carbon emission efficiency has become a critical point in Chinese economic development. Based on Chinese provincial panel data and the DEA model, this paper explores regional protection’s impacts and transmission mechanism on carbon emission efficiency. The heterogeneity of impacts, the interaction of adjacent areas, and the role of carbon emission trading in the impact of regional protection on carbon emission efficiency are further explored. The results show that regional protection can reduce carbon emission efficiency by restraining technical efficiency, allocative efficiency, scale efficiency, and total factor productivity of gross product per unit of carbon emission. In addition, regional protection has a stronger impact on provincial regions with lower carbon emission efficiency, and regional protection in neighboring regions also has a significant negative impact on carbon emission efficiency of the local region. Finally, implementing carbon emission trading significantly reduces the negative impact of regional protection on carbon emission efficiency.

The physical environment influencing crime occurrence has been consistently studied, with a primary focus on felonies. However, while the majority of police efforts are dedicated to preventing and addressing misdemeanors, research on their causes remains relatively insufficient. Furthermore, the factors influencing the occurrence of misdemeanors and felonies may differ. This study aims to analyze the impact of Chicago's physical environmental disorder on the occurrence of misdemeanors and felonies, identifying distinct environmental factors for each crime type. By utilizing Chicago's 311 request and complaint data alongside crime data, the study evaluates the effects of various physical environmental factors—including graffiti, abandoned vehicles, broken streetlights, and illegal liquor sales—on crime occurrence through spatial regression analysis. The findings reveal significant spatial autocorrelation for both crime types, indicating that a disorderly environment in neighboring areas tends to promote crime occurrence. Specifically, trash and abandoned spaces were more strongly associated with misdemeanors, whereas graffiti and illegal liquor shops had a greater influence on felony occurrences. This study suggests community-specific crime prevention and urban planning strategies tailored to each crime type and offers policy recommendations for creating safer communities.

Given the rapid population aging and significant internal migration, it is crucial to examine the spatial heterogeneity of elderly migration to better understand inequalities arising from different motivations. However, relatively few studies have explored the diverse drivers of elderly migration in China. This study focuses on two typical types of elderly migration, migration for offspring caregiving (caregiving-oriented migration) and offsite pension (retirement-oriented migration), and compares them, given their distinct motivations. Using the 2020 China Census and spatial analysis methods, this study compares and analyzes the spatial and temporal patterns and influencing factors of the different types in China’s elderly migration. The results show a markedly unbalanced spatial distribution of elderly migration across China. Although motivated by different reasons, the highest migration ratios are concentrated in eastern provincial capitals and coastal urban agglomerations. At the same time, significant differences emerge in clustering patterns at both intra- and inter-provincial scales. Specifically, intra-provincial elderly migration is dominated by retirement-oriented moves, with a clear predominance in northern and northeastern regions. By contrast, inter-provincial elderly migration is primarily driven by caregiving for offspring, particularly concentrated in coastal areas. Furthermore, we find evidence of spatial spillover effects in both migration types. Results from the spatial lag model suggest that the availability of public services, accessibility of facilities, and quality of living environment are the main factors shaping elderly migration, while socio-economic variables exert comparatively weaker influence.

This study examines the relationship between frequent mental distress (FMD) and fine particulate matter (PM2.5) air pollution. FMD, characterized by 14 or more mentally unhealthy days in a month, Has been increasingly linked to environmental factors, but the impact of air pollution on mental health remains understudied. We have examined total 2,648 contiguous U.S. counties with air pollution data to access whether there is a significant association between frequent mental distress and PM 2.5. We employed hierarchical multivariate regression and complex sample general linear modeling (CSGLM) to deepen understanding of the factors influencing mental distress, with a particular focus on geographic variation. Moreover, we conducted a geographically weighted regression (GWR) to examine the spatially varying association between FMD and PM2.5 after accounting for several well-established confounding factors. This study found that a stronger positive association between FMD and PM2.5 was detected in areas of the southeastern regions, especially in West Virginia, Virginia, and Kentucky. Other confounding factors ranged from positive to negative across the United States, implying some geographic clustering. The study underscored the need for integrated public health approaches to mitigate air pollution and improve mental well-being. Further research should explore biological mechanisms underlying this association and consider regional differences in air quality exposure.

With city development, economic factors, population, and industries continuing to cluster in cities, the layout of cities is becoming increasingly compact. This process leads to the aggravation of air pollution and affects social welfare. Increasing air pollution poses a growing health risk to humans, further affecting income inequality. Therefore, by combining satellite remote sensing data with city maps, this paper constructs a city shape index to describe the compactness of city layout and studies the mechanisms of city shape, air pollution, and income inequality. The study results show that compact cities can help reduce income inequality, but they also increase air pollution. The more serious air pollution in cities leads to greater income inequality. In addition, air pollution leads to a weaker effect of compact city shape on narrowing income inequality. This paper discusses how the compactness of urban layouts shapes social welfare by influencing air pollution and income inequality.

This study examines how public health crises—particularly the COVID-19 pandemic—have reshaped migration governance and spatial mobility across five countries. Focusing on the Netherlands, the United Kingdom, China, Brazil, and Ghana, the study analyzes policy responses and their implications for migration dynamics. We identify recurring and context‑contingent migration patterns across the five studied countries during the pandemic. Our findings suggest that national responses are shaped by factors such as the epidemic trajectory, economic status, healthcare systems, political structures, and urbanization and demographic characteristics. These policy choices, in turn, reconfigured spatial mobility patterns by constraining transnational and internal movements in uneven ways. These interactions are intrinsically linked to each country’s pre-existing migration patterns and policies. This study provides insights into the complex relationships between crisis-driven mobility control and migration governance, underscoring the long-term implications of pandemic responses for vulnerable populations in diverse, globalized societies.

Street advertisements, as an important component of urban landscapes, implicitly convey the economic conditions and social status of different regions through their visual features. This implicit inequality in information not only depends on the type and content of the advertisements but is also directly influenced by their visual features. However, previous studies have mainly focused on analyzing the differences in the content, type, and quantity of advertisements across communities with varying economic levels, while less attention has been given to the disparities in the visual features of advertisements across such communities. To address this issue, this study uses street view images from sample points in Xi’an and applies computer vision techniques to identify and calculate the salience, area, brightness, contrast, and color difference of advertisements. Finally, statistical methods and correlation analysis, combined with housing price data from the sample points, are used to analyze the relationship between advertisement visual features and community housing prices. The study results indicate that: The results of the Tukey Honestly Significant Difference post-hoc test indicate that the salience and area of advertisements are significantly higher in high-price communities compared to low-price communities (p < 0.01). In contrast, the color difference between advertisements and their background is significantly greater in low-price communities than in high-price communities (p < 0.05). The results of the correlation analysis further support these findings, showing a significant positive correlation between advertisement salience, area, and housing prices (p < 0.01).