Applied Spatial Analysis and Policy最新文献

This study explored the external influences of the public sector on the density of environmental nonprofits by examining the government failure and interdependence theories. We used county-level data from the U.S. and spatial models to identify four key drivers that impact the establishment of environmental nonprofits: population heterogeneity, fiscal centralization, government grants, and government expenses. Using a spatial autoregressive model with spatial-autoregressive disturbances, we analyzed the effects of external factors on nonprofit organizations within a local government’s jurisdiction and their density in surrounding areas. Our findings revealed that educational and racial heterogeneity were negatively associated with nonprofit density, supporting the cohesion theory rather than the government failure theory. Fiscal centralization demonstrated a negative correlation with nonprofit density, while government grants and expenses showed positive associations. The study found significant spillover effects: changes in one county’s characteristics influenced nonprofit density in neighboring areas. These findings suggest that the relationship between nonprofits and government can be both complementary and substitutive, depending on the specific characteristics of local jurisdictions. Our results underscored the significance of analyzing spatial patterns and interdependence when studying nonprofits, suggesting that traditional analytical approaches may yield incomplete or biased conclusions. The study contributes to the literature by providing a more nuanced understanding of how environmental nonprofits interact with local governments and each other across jurisdictional boundaries, offering new insights into the complex dynamics of public-nonprofit relationships in environmental sustainability efforts.

The application of geospatial big data to analyze temporal variations in tourist flows and their relationship with fine-grained mobile search engine data merits further exploration. This study explores intraday and inter-day patterns of tourist flows and their correlations with pre-trip online search activities by integrating mobile signaling data and long-term pre-trip search data from multiple platforms. Focusing on Zhongshan Mountain over a three-year period, the research employs the DCC-GARCH method to analyze nonlinear dynamic intraday correlations stemming from different clients (mobile vs. PC), and the TVP-VAR model to examine time-varying impact mechanisms. Key findings reveal: (1) Tourist flow and search volume sequences exhibit a peak-thick-tail distribution with ARCH effects and weekly fluctuations. (2) Pre-trip search fluctuations across devices correlate with tourist volume changes, with mobile-terminal correlations being high and stable, while PC-terminal correlations show positive associations two days in advance, varying seasonally. (3) Weekly fluctuations influence precursor effects, with search indices from 1 to 8 days prior significantly predicting tourist flows, particularly strengthening over 1–4 days. A 1% increase in the mobile search index results in a 0.1%-8% rise in tourist volume across lag periods. By integrating geospatial big data and temporal analysis, this study reveals shifts in pre-travel search behaviors and the spatio-temporal compression effects driven by new technologies in the mobile Internet era. By correlating tourists’ online search activities with their physical movements, the research provides strategies for technology-enabled tourism marketing and destination capacity optimization.

Digital innovation is profoundly reshaping the spatial configuration of innovation networks; however, the diffusion patterns and underlying drivers of inter-city digital innovation in China remain under-explored insufficiently examined. Drawing on digital patent data from 292 cities between 2005 and 2020, this study constructs an inter-city digital innovation network using an improved gravity model and applies complex network analysis together with a TERGM approach for empirical assessment. The findings show that: (1) the network has expanded rapidly, reaching 265 nodes and 3,493 edges, and has formed an “east-dense, west-sparse” spatial structure with heterogeneous characteristics across four major urban agglomerations; (2) trunk diffusion has accelerated, resulting in a multi-centered system led by Beijing and supported by multiple secondary hubs, while emerging nodes such as Kunming and Nanning began to integrate into the trunk after 2010, accompanied by exponential growth in trunk connectivity; and (3) urban economic development and community structure significantly promote diffusion. Moreover, industrial similarity in the digital innovation sector improves cooperative efficiency by a factor of 4.1, whereas similarity in industrial advantage suppresses diffusion by 50.4% per unit due to intensified intra-industry competition. Overall, this study reveals the evolutionary dynamics of China’s inter-city digital innovation network and provides scientific insights for formulating differentiated innovation policies, optimizing resource allocation, and unlocking the potential of digital innovation to support high-quality economic development.

Air pollution is detrimentally associated with many health outcomes, yet its impacts are not equally distributed. Research consistently finds inequalities by ethnicity, area deprivation and age. However, such inequalities are typically investigated separately, potentially underestimating the extent of differential exposures. We aim to investigate inequalities in NOx concentrations across multiple intersecting neighbourhood characteristics in England simultaneously. We do this using the novel Eco-Intersectional Multilevel (EIM) modelling approach, we define analytic “strata” of neighbourhoods based on sociodemographic characteristics. This enables us to quantify NOx concentration inequalities across community types, simultaneously considering area deprivation, ethnicity, education, rurality and age of residents. We find that neighbourhoods belonging to the “most deprived, high proportion minority ethnic, high education, urban and not ageing” stratum had the highest average NOx concentration. This concentration was five times higher than places with the lowest concentration in the mid deprivation, low proportion minority ethnic, high education, rural and ageing stratum. We find clear and striking inequalities by ethnicity. However, we do not find evidence of inequalities by area deprivation that operate independently of community ethnicity, likely due to the strong relationship between ethnicity and deprivation distributions. This study demonstrates the value of taking an intersectional approach to geographical inequalities.

The impact of urban sprawl on house prices is not simply a promotion or suppression, which may be non-linear. This paper discusses the impact of urban sprawl on housing prices using panel data at the prefecture-level city level in China from 2003 to 2020. The research draws the following three conclusions: First, there is a significant U-shaped relationship between urban sprawl and housing prices, and the robustness test conclusions through methods such as instrumental variable are still reliable. Second, the mechanism analysis reveals that urban sprawl suppresses housing prices by increasing building area supply and promotes them by increasing land transfer prices. The net effect shifts from inhibitory to promotional as sprawl intensifies. Finally, urban sprawl affects house prices differently in various types of cities, with first tier cities being more affected. In addition, the promotion effect of urban sprawl on house prices is more pronounced in the eastern region, cities with small population and large area. This paper also combines the phenomenon of excessive speculation in Chinese house prices and finds that compared to before adjustment regulations, urban sprawl has a greater inhibitory effect on house prices after 2010.

While substantial population movements were motivated by COVID health safety concerns, policies and practices following COVID-19 have facilitated transformative flexibility in work and transport, changing the location of economic activities and travel patterns. This study investigates urban residential movements in response to this major urban practice change, using spatial-based multi-year internal migration data in Melbourne. Going beyond previous research, our analysis undertakes a detailed breakdown of socio-economic categories that are more exposed to work practice changes. The purpose is to better understand the effect of new work and transport practices induced by COVID-19 on urban residential mobility, in contrast to the temporary health safety driven population moves. By tracking changing mobility patterns of the targeted occupations benchmarked with the mobility change of all movers, our analysis demonstrates that city-wide workplace re-arrangments have a general feedback effect on urban utility and settlement patterns. When flexible working and commuting become established practices, workers are likely to re-adjust location utility in response to living standards and housing costs. However, their residential change did not target the high-growth and low-density urban zones but focused more on the areas to balance housing affordability and location accessibility/liveability. We demonstrate that the research outputs can assist in appraising the long-term impacts of policy and practice change and population dynamics in urban areas, including the extension of flexible working and commuting arrangements.

As a pivotal component of the Yangtze River Economic Belt, the Middle Yangtze River Urban Agglomeration (MYRUA) experiences continuously growing carbon emissions, making its peaking pathway crucial for achieving China’s national “2030 peak” target. Existing studies often rely on linear models, which inadequately capture the nonlinearity and uncertainty inherent in emission dynamics. Moreover, few have integrated Gaussian Process Regression (GPR) with spatiotemporal analysis at the urban agglomeration scale for forecasting. To address these gaps, this study develops an integrated analytical framework combining spatial analysis and GPR. Utilizing panel data from 31 prefecture-level cities within the MYRUA from 2000 to 2021, we diagnose the spatiotemporal evolution of carbon emissions and conduct scenario-based forecasting. The key findings are: (1) Carbon emissions exhibit a distinct “core-periphery” spatial pattern, forming high-emission clusters centered around Wuhan, Changsha, and Nanchang. (2) The GPR model demonstrates superior predictive accuracy and provides essential uncertainty quantification compared to benchmark models including Artificial Neural Networks, Least Squares Support Vector Machines, and Autoregressive Integrated Moving Average. (3) Scenario forecasts reveal a divergence in peaking pathways among major Chinese urban agglomerations. Developed agglomerations like the Yangtze River Delta and Beijing-Tianjin-Hebei are projected to peak before 2030. In contrast, the MYRUA, along with the Chengdu-Chongqing and Central Plains agglomerations, may peak between 2025 and 2028 under low-growth scenarios but face significant challenges under high-growth scenarios. (4) Assessment using a composite Carbon Peaking Pressure Index identifies the MYRUA as under the highest pressure, attributed to its heavy industrial structure and strong growth inertia. This research provides methodological support and empirical evidence for formulating differentiated carbon peaking strategies for urban agglomerations. We recommend promoting industrial restructuring, energy system decarbonization, and coordinated regional governance to facilitate the emission reduction process.

Over the past two decades, China’s innovation capacity has grown significantly, yet regional innovation inequality has worsened, with existing studies largely overlooking the multi-spatial and temporal dimensions of innovation mobility. This study aims to examine the spatiotemporal patterns of regional innovation mobility in China from 2000 to 2020, focusing on national, regional, and provincial levels. Specifically, we address: (1) the spatiotemporal characteristics of regional innovation mobility, (2) how scale-dependent innovation mobility should be measured, and (3) the underlying causes of regional innovation mobility. Using Global and Local Indicators of Mobility Association (GIMA and LIMAs), we find: (1) innovation mobility is highly sensitive to spatial scale, emphasizing the need for multi-scale analysis, (2) increasing provincial innovation inequality suggests the current innovation development model is unsustainable, and (3) changes in innovation rankings are primarily driven by interregional mobility, which is influenced by geographic proximity, institutional support, and spillover effects from more developed regions. This study extends proximity and spillover theories in innovation geography by conceptualizing regional innovation mobility as a dynamic, spatially dependent process. It contributes to the literature by adopting a multi-scale approach to uncover the spatial mechanisms of innovation mobility and offering new insights into the spatiotemporal dynamics of regional innovation inequality. To mitigate regional innovation disparities and promote balanced development, a multi-faceted policy approach is recommended, focusing on cross-regional knowledge networks, factor mobility reforms, industrial transformation support, and enhanced policy coordination to optimize resource allocation and address the impacts of disruptive technologies.

Urban networks have become important channels for the co-development of urban clusters. However, networks not only enable the sharing of development resources but also transmit potential risks. Accordingly, it is not yet clear how urban shrinkage risks spread through urban networks, which poses a significant challenge to sustainable urban development. The current study introduces the susceptible-infectious-recovered-susceptible (SIRS) epidemic model to discuss the contagious effect of urban shrinkage risks. The urban investment network is built on the basis of the external investment relationships between listed enterprises in Northeast China. The investment network is characterised by a small-world network and a core-periphery structure, and the in- and out-degree centrality of cities is not balanced. Additionally, the results generated by the SIRS epidemic model revealed that the initial source of infections affects the spread rate of the shrinkage risk. The infection rate has the greatest impact on the spread of urban shrinkage risks compared with the recovery rate and immunity loss rate. Further, there is a threshold effect on shrinkage risks, which means that a shrinkage risk originating from anywhere can rapidly propagate throughout the entire region when it reaches a certain threshold.

Under the paradigm of sustainable development, formulating industrial development strategies based on spatial distribution patterns has emerged as a potential pathway to improve air quality. Using balanced panel data from 284 Chinese cities covering the period 2011 to 2022, this paper employs a multi-period difference-in-differences model to empirically examine the impact of innovative industrial clusters on urban air quality. Empirical evidence reveals that: (1) innovative industrial clusters markedly strengthen urban air quality; (2) these clusters contribute to air quality improvement by fostering technological innovation, optimizing the energy structure, and accelerating industrial upgrading; (3) the positive effect of innovative industrial clusters on air quality is more pronounced in cities with stronger economic capacity and fewer institutional barriers to industrial transformation; (4) innovative industrial clusters generate spatial spillover effects, improving air quality both locally and in neighboring regions. This study is among the earlier efforts to highlight the beneficial role of innovative industrial clusters in air quality improvement, offering theoretical insights and policy implications for advancing sustainable development.

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