Applied Spatial Analysis and Policy最新文献

Amid rapid urbanization, promoting low-carbon school commuting is crucial for sustainable urban development, yet the spatial variation and network interdependence of such emissions within cities are poorly understood—hindering refined governance of low-carbon transitions. Focusing on Kaifeng, a city experiencing rapid spatial expansion and restructuring, this study calculates junior high school students’ commuting emissions across 526 residential neighborhoods and constructs a spatially linked emission network using a modified gravity model. Social Network Analysis (SNA) and Quadratic Assignment Procedure (QAP) are then applied to analyze the network’s structural features and influencing factors. The results show that: (1) the overall network demonstrates low density and hierarchy, moderate connectivity, and relatively high efficiency; (2) core neighborhoods demonstrate strong spatial linkages with surrounding areas, acting as both "emitters" and "receivers" due to their pronounced radiating and integrative capacities; (3) while most neighborhoods are interconnected—facilitating fluid network flows—their ability to control adjacent areas is limited, resulting in weaking overall network dominance; and (4) distance is the most significant factor shaping the spatial network, with proximity to schools and public transit stations having a statistically positive influence on commuting-related carbon emission patterns. By developing an intra-urban spatial network of school commuting carbon emissions, this study introduces a novel “relational-structural” framework that moves beyond conventional isolated analysis. We contend that low-carbon governance should evolve from managing individual neighborhoods toward optimizing network linkages through regulating key nodes, shortening commuting distances, and rebalancing the distribution of public transit and schools to achieve systemic emission reductions. Thus, this study offers an innovative "relational-structural" perspective for low-carbon commuting governance, thereby pioneering the construction of a spatial network for school commuting carbon emissions at the small-medium city scale.

Scientific evaluation of the effects of transportation infrastructure on regional market integration is of great practical significance. Based on the policy context of China’s proposal of National Trunk Highway System (NTHS), the paper examines the effects of large-scale highway construction on regional market integration. To achieve a more accurate evaluation and address endogeneity issues, we adopt “city pairs”, which is more in line with the characteristics of transportation infrastructure connectivity and inter-regional market segmentation, as the unit of study to carry out empirical analyze. The estimations suggest that the decline in inter-city traffic time induced by the development of China’s highway network could effectively alleviate market segmentation between them during 1999 to 2017, but this effect varies for cities with different locations and development. The development of China’s highway network can effectively promote the growth of inter-city product trade and the cross-regional mobility of the labor force, suggesting that the current construction of China’s highways can contribute to domestic market integration through these channels. This study also found that China’s provincial administrative boundaries are an influential factor causing inter-city market segmentation, which is consistent with findings in existing literature, highlighting the need to further enhance governmental reforms and eliminate local protectionist behaviors in order to maximize the role of improved highway networks in promoting regional market integration. This study enriches the research in the field of socioeconomic benefits of highway infrastructure development from the perspective of market integration, and provides solid empirical evidence for developing countries and emerging economies to promote domestic market integration through highway network construction.

Human activity in cities increasingly relies on digitally mediated public service delivery, and the integration of artificial intelligence makes government digital governance capacity more central. This study examines the association between local artificial intelligence (AI) development and human activity oriented digital governance in Chinese prefecture level cities. Using a longitudinal panel of 296 cities from 2018 to 2024, we document a positive and robust association between local AI development and digital governance performance. The association is stronger in richer and more populous cities, and in eastern and coastal areas. Mechanism analyses further suggest that digital connectivity and digital talent support stable online service operations and continuous improvement. Overall, the results indicate that AI related development is associated with stronger government digital governance and may contribute to reducing spatial disparities in digital public services.

Spatial patterns of COVID-19 vaccination rates can be used to develop policies to manage the disease as it has transitioned to an ongoing endemic disease. Within this context, there can be different stages of vaccination status. For example, a person can have one or more doses of the COVID-19 vaccine, have completed the initial vaccination regimen of two doses for most forms of the vaccine, or have received booster doses to maintain vaccination benefits over time. We conduct correlation and Gi* hot spot analyses of different vaccination stages using the most recent publicly available county-level data from the CDC to investigate the sensitivity of geospatial patterns to these different definitions. We also conducted a correlation analysis of these stages with the crude COVID-19 death rate and percent of total deaths that were from COVID-19. We find little spatial difference between the vaccination rates for the full population versus only the elderly population. We also find little spatial difference between those who have received at least one dose and those who completed the initial vaccination regimen. However, there are considerable geospatial differences in hot spots and cold spots when comparing those who have received at least one booster dose versus those who have received any dose or have completed the initial regimen. Also, there was variation in both the directions and strengths of the correlations with different stages and the crude COVID-19 death rate. Therefore, ongoing management of COVID-19 should involve analysis of booster doses in addition to overall vaccination rates, as they do not produce the same spatial patterns. Additionally, this study contributes to the literature that analyzes the uptake of bivalent boosters, as well as one of the first to spatially analyze the uptake of boosters for the contiguous United States at a county scale.

The intense shock of urbanization has pushed traditional villages into a widespread survival crisis, yet its internal mechanisms remain a ‘black box’ to be decoded. Based on a sample of 96 traditional villages in Guangzhou, this study innovatively disaggregates built heritage into two core representations: legally protected landmark buildings and the vernacular fabric that constitutes the overall landscape. It employs Structural Equation Modeling (SEM) to quantify the differentiated impact pathways of multiple urbanization shocks on these two components. The research reveals a profound ‘heritage survival paradox’: superior location and economic conditions, the key drivers of urbanization exert the strongest negative impacts on the vernacular fabric (standardized path coefficients β = -0.420 and β = -0.276, respectively). However, these very same factors significantly promote the protection and development of landmark buildings (β = +0.342 and β = +0.324, respectively). This paradox is further corroborated by the impact of demographic shifts: population growth also significantly damages the vernacular fabric (β = -0.194), while having no significant effect on the survival of landmark buildings, revealing their ‘immunity’ to demographic pressure. This study is the first to quantitatively confirm that urbanization plays a ‘selective shaping’ role on built heritage—a process that destroys the whole while catalyzing the individual. The contribution of this research extends beyond mechanistic decoding, providing a quantifiable risk diagnosis framework that offers a scientific basis for shifting from generalized policies to precise, evidence-based conservation strategies.

This study systematically investigates the spatial evolution and driving mechanisms of urban tourism flow networks in Shanghai by integrating complex network analysis, GIS-based spatial modeling, and a negative binomial regression framework, using user-generated travelogue data from online platforms. The findings show that, despite significant disruption caused by the COVID-19 pandemic, Shanghai’s tourism flow network demonstrated strong resilience, exhibiting prominent small-world properties and high clustering coefficients. Its topological structure features a stable core, multiple emerging nodes, and outward spatial diffusion. Traditional landmarks such as the Bund, Lujiazui, and Nanjing Road remain critical hubs. At the same time, emerging and suburban attractions are gaining prominence, signaling an ongoing expansion of tourism activity into peripheral areas. Hierarchical analysis indicates a structural shift from fragmented, loosely connected multi-cluster configurations toward a core-dominated, multi-cluster collaborative system. The network’s integration level has improved, with the Bund maintaining absolute centrality throughout the observation period, as the number of subgroups declined. Centrality metrics highlight persistent spatial concentration alongside dynamic adjustments, with inner-city attractions maintaining high connectivity and consistently strong betweenness centrality. Regression results show that a site’s geographic location, popularity, historical significance, and spatial capacity all positively influence its network centrality. Among these, spatial location and scale effects are especially strong, underscoring the key role of intrinsic attributes in shaping network position. In contrast, transportation accessibility is not statistically significant, likely due to Shanghai’s overall high level of urban connectivity.

Urbanization intensifies aging challenges, positioning cities to address non-communicable diseases like diabetes through age-friendly planning, policies, and health interventions. This study used data from the 2018–2020 CHARLS, along with city characteristics from the China City Statistical Yearbook and Google Earth Engine. Principal component analysis and the entropy method were employed to construct a comprehensive picture of age-friendliness, encompassing five domains: health service and social security, housing conditions, community atmosphere, economic development, and ecological environment. Multilevel logistic regression models were then applied to analyze the relationship between age-friendly environments and the incidence of diabetes among older adults in China. The results indicate that a higher level of community atmosphere was associated with a lower risk of developing diagnosed diabetes within two years. The findings highlight the need to enhance home and community services, integrate smart technologies, and diversify elderly care solutions to mitigate diabetes risk among older adults.

From the global perspective of human–nature sustainable development, ecological well-being performance (EWP) captures the efficiency dimension of sustainability by linking ecological consumption with human well-being. For a rapidly developing country, how to balance development and ecology in the spatial dimension is not only related to spatial coordination and sound policy-making, but also determines the sustainability of growth. However, existing studies often overlook spatial disparities, typological differentiation, and nonlinear determinants. To address these gaps, this study developed a machine learning-based multi-method framework on the Orange visual programming platform, integrating spatiotemporal analysis and interpretable machine learning, with a focus on a typical region of China as the study object. This framework was applied to examine EWP in the middle reaches of the Yangtze River urban agglomeration (MRYRUA) during 2005–2022. The results show an overall improvement but with persistent spatial disparities, notable typological differences among cities, and key drivers dominated by industrial structure and government expenditure. By revealing the disparities behind aggregate progress, this study contributes to more precise estimation results and a clearer understanding of driving mechanisms, while accurately restoring spatial patterns, thereby laying a solid foundation for scientifically formulating multi-scale regional development policies.

Megacity regions are expected to amplify agglomeration economies through a circular cumulative causation process between migration and economic growth, yet the spatial dynamics of this process remains underexplored. This study investigates these dynamics in China’s three largest megacity regions using county-level data from the 2010 and 2020 censuses. Spatial simultaneous equations models are employed to address endogeneity issues. The results reveal variegated regional patterns shaped by polycentricity and institutional contexts. The Yangtze-River-Delta (YRD), a polycentric region with decentralized governance, exhibits strong intercity competitions for population and economic growth. Each 10,000 RMB increase in neighboring areas’ GDP per capita reduced the local net migration rate by 0.008 to 0.010% points between 2010 and 2020. By contrast, the Beijing-Tianjin-Hebei (BTH) region, with a monocentric structure dominated by Beijing’s leverage, shows signs of agglomeration diseconomies. Each 10,000 RMB increase in neighboring regions’ GDP per capita was associated with a 0.006 to 0.010% point increase in the local net migration rate. The Pearl-River-Delta (PRD), marked by low polycentricity and unified provincial governance, exhibits relatively weaker spatial effects. These findings highlight the importance of decentralized governance in fostering agglomeration economies across administrative boundaries and in mitigating core–periphery imbalances within megacity regions.

This study introduces a novel network-based crime risk score, the Street Segment Risk Score (SSRS), designed to enhance crime hotspot predictions on street networks. The SSRS evaluates the risk of individual street segments by incorporating the dynamic spatial influence of nearby urban features on local crime patterns. Our dataset comprises all reported incidents of robbery (n = 2,016) and theft (n = 31,493) from 2015 to 2018 in Chicago’s Central Side (CS). We developed both daily and intraday crime hotspot prediction models that integrate the SSRS and compared their performance—with and without the SSRS—using two graph-based deep learning algorithms, Graph WaveNet (GWNet) and the Spatiotemporal Graph Convolutional Neural Network (STGCN); a traditional deep learning model, Long Short-Term Memory (LSTM); and two baseline methods, Multilayer Perceptron (MLP) and Spatiotemporal Network Kernel Density Estimation (STNetKDE). Results indicate that incorporating the SSRS improves daily robbery hotspot prediction accuracy by up to 5.3% and intraday theft prediction accuracy by as much as 33%. The proposed SSRS demonstrates strong potential to support more precise, street-level security interventions by enhancing daily and intraday crime hotspot predictions.