Applied Spatial Analysis and Policy最新文献

Using Baidu migration big data for China’s Spring Festival travel rush from 2019 to 2024, this study reconstructs pre-, mid-, and post-COVID-19 mobility networks. Exploratory spatial analysis, social network analysis, and an extended gravity model are employed to track the dynamics of inter-city population flows and identify their drivers. Results show that China’s inter-city labor mobility remains relatively stable and continues to concentrate in southeastern city clusters. However, network density has declined and average path length has increased, indicating that flows are becoming more focused on a limited set of cross-regional pairs and that functional differentiation among cities is intensifying. Finally, COVID-19 reshaped the hierarchy of migration determinants: in the post-COVID-19 era, economic factors again dominate labor mobility, while institutional effects recede.

With the progression of global climate change, the rising demand for nuclear energy as a crucial solution has concurrently increased the need for uranium, an essential component of nuclear power. This paper analyzes the global uranium trade through a competitive lens, reviews temporal changes in the competitive landscape, and investigates the determinants of the uranium trade competition. This research examines the dynamics of competition within the global uranium market from 2002 to 2022 by applying complex network theory. Moreover, an extended gravity model was used to analyze the factors influencing uranium import competition. The results show that there is a downward trend in the average intensity of competition among global uranium importers and an upward trend in the closeness of competition. Additionally, the competitive intensity among countries is distributed unevenly, with less than 20 percent of relationships contributing to over 90 percent of the intensity. Besides, the core structure initially led by the United States has gradually transitioned into a more diversified competition network involving European and Asian countries, with China, Germany, and the Netherlands increasingly advancing their positions. Finally, regarding the influencing factors, the Gross Domestic Product, nuclear energy consumption, the capacity of government, and inter-state relations can drive competition among countries, while distance, common official language with other countries, and global production impede competition among countries. Drawing from this analysis, this paper offers targeted policy recommendations designed to assist uranium importing nations in adapting the existing global competitive landscape.

Previous studies have independently investigated the direct effects of economic downturn and internal migration on food insecurity. This study uses a novel integrated approach to analyze both the direct and spatial spillover effects of economic migrants on food insecurity in Pakistan. The economic migration narrative is grounded in the New Economics of Labor Migration (NELM) theory, which frames migration as a strategic household choice aimed at diversifying income streams and mitigating vulnerabilities. District-level household data from the Pakistan Social and Living Standards Measurement 2019–2020 survey were used. We first calculated the Global Moran’s I using the rook contiguity-based spatial weight matrix to assess the presence of spatial autocorrelation in food insecurity. Thereafter, the impacts of economic migration on food insecurity were evaluated using the spatial Durbin model. The results indicate that food insecurity exhibits a significant spatial autocorrelation. The findings from the spatial Durbin model revealed that economic migrants have an insignificant direct effect on food insecurity in their destination districts but show a significant negative association with food insecurity in surrounding areas through spatial spillover effects. This implies that economic migrants do not worsen food security locally but rather improve it in neighboring districts, likely through economic channels such as remittances or by enhancing regional productivity. Therefore, policies should support economic migration with employment opportunities and affordable housing, while also promoting regional economic integration through cross-district infrastructure and formalized remittance channels to leverage these positive spillover effects.

This study investigates whether the corporate sustainability performance (CSP) of companies in emerging markets (EM) influences their share prices and provides value-relevant information. It also examines the spatial interaction effect between share prices using spatial econometric methods to determine how changes in a company's CSP affect the share prices of peer companies within the same country or sector. The analysis covered 18 countries and 2,296 company-years (2013–2020), focusing on companies in the Morgan Stanley Capital International (MSCI) Emerging Markets Index. The findings of this study Moran's I and Geary's C statistics confirmed significant spatial dependence between share prices, validating the use of spatial analysis. The Spatial Durbin Model with fixed effects emerged as the best fit. CSP, excluding governance scores, is value-relevant for investors in EM. Positive and significant spatial interaction effects at the 1% level were observed for all models. Indirect effects were positive for environmental, social, and combined scores (country weight matrix) but negative for governance scores (sector weight matrix).

Understanding tourist behavior at the origin-city level is essential for destination planning and regional tourism management. Most existing studies focus on aggregate intercity mobility volumes, overlooking how tourists from different cities distribute their visits across various attractions. This study addresses this gap by analyzing origin-city-level visitation patterns to three attraction categories (i.e., cultural-historic, natural scenery, and leisure) using weekly mobility data from over 300 Chinese cities to Nanjing during 2018–2019. First, the spatiotemporal heterogeneity of visitation to three attraction categories is analyzed. Second, a data-driven regionalization is conducted based on intercity similarities in attraction visitation. Logistic regression is then employed to examine how city-level characteristics, including socioeconomic, demographic, geographic, and functional attributes, are associated with these visitation patterns. Results reveal pronounced spatial heterogeneity and two dominant visitation regimes covering approximately 92% of cities: one favoring cultural-historic attractions in northern China and Guangdong, and another exhibiting more balanced visitation between cultural-historic and nature-scenery attractions in southern regions. Regression analyses highlight the importance of demographic composition, geographic orientation, urban functions, and digital attention in shaping intercity visitation differences. These findings provide empirical support for behavior-informed tourism planning, enabling policymakers to align destination management and resource allocation with the visitation patterns of different origin cities.

The study aims to analyse the diversity of residential vacancy rates in Poland and identify local factors influencing their formation. Census data from 2021 and a set of economic, demographic, political, and technical-location variables for 378 counties were used. A two-stage research procedure was applied, based on the methods of multiscale geographically weighted regression (MGWR) and k-means + + clustering. MGWR enabled the determination of the spatial variability of the impact of individual predictors, thereby capturing local mechanisms of vacancy formation. Clustering, on the other hand, enables grouping counties according to similar vacancy rates and sets of determinants, providing a basis for creating territorially differentiated interventions. The results indicate that the most critical predictor of vacancy rates is the average year of construction, and the younger the properties in the county, the lower the vacancy rate. Price expectations regarding an increase in property prices also reduce vacancy rates. In contrast, positive net migration has the opposite effect, increasing vacancies in areas with population inflows. Factors such as living costs, local taxes, and security were statistically insignificant. The five clusters of counties identified show different characteristics. The study enables the creation of a more precise housing policy and has international implications.

Determining the most suitable location for a retail store is of great strategic importance, as it is known that choosing the wrong location can lead even the best retailer to failure. Spatial statistical methods play a critical role in analyzing urban settlements, revealing spatial relationships, and strengthening strategic decision-making processes. Objective outputs from spatial analyses enable newly established supermarket chains or small businesses to take more accurate strategic steps, particularly in terms of planning commercial activities and determining regional investment potential. This research proposes a GIS-based decision support framework that integrates multi-source demographic data with advanced spatial econometric approaches and is applicable at different urban scales. In this context, the study aims to determine the most suitable locations for new supermarket branches by integrating demographic data with GIS-based spatial statistical techniques, using the example of Sivas city center (Türkiye), which has characteristics consistent with the concept of emerging urban centers. This methodology incorporates Global Moran’s I, Anselin Local Moran’s I, and Kernel Density Estimation (KDE) spatial statistical methods to measure the overall spatial pattern of market density, identify both clustering areas and statistically significant spatial outliers, and estimate the spatial density of geographically located point data, transforming this density into a continuous surface. Analyses using Global Moran’s I identified clustering at market points in the city. Subsequently, Anselin Local Moran’s I and Kernel Density analyses determined that the clusters were concentrated in central and large neighborhoods. Finally, a Population Density Map was generated for each neighborhood using population data, identifying areas with high population but few markets. All data obtained were overlaid using Overlay analysis to identify potential areas. The findings provide a robust decision support framework for marketing decision-makers, offering an objective guide for identifying high-potential investment areas while minimizing spatial risks. By bridging the gap between spatial econometrics and urban retail planning, this research contributes to the more systematic and sustainable management of commercial land use in developing urban contexts.

The rise of e-commerce and the gig economy, accelerated by COVID-19, has rapidly expanded online-to-offline (O2O) takeaway services and reshaped the spatial distribution of the catering industry in Chinese cities. To examine this process in China, we first analyzed the spatial evolution of the catering industry in Zhuhai before (2019) and after (2023) the pandemic using a hotspot detection model, with particular attention to differences between traditional restaurants and those adopting O2O takeaway services. We find that the traditional core–periphery spatial structure of the catering sector has been disrupted and has evolved into a more non-hierarchical pattern. O2O takeaway restaurants are more diffusely distributed and contribute to the decline of traditional restaurants, a transformation that has been further accelerated by the pandemic. Further analysis using random forest and multi-level geographically weighted regression models suggests that this transformation is primarily driven by behavioral changes among a growing segment of younger, better-educated, digitally proficient, higher-income, and time-constrained consumers, which aligns with the innovation diffusion hypothesis.

China’s innovative Farmland Use Regulation (FUR) system, implemented since 2020, represents a critical effort to curb long-term farmland decline. However, existing literature has yet to empirically evaluate the effectiveness of this complex policy mix and its underlying governance mechanisms. To bridge this gap, this study argues that the effectiveness of the FUR is not uniform but is fundamentally moderated by the underlying Principal-Agent (P-A) dynamics, where policy impacts are contingent on the alignment of incentives between central and local actors. Using a panel dataset from 286 Chinese cities (2019-2022), we employ a two-way fixed-effects model, complemented by instrumental variable strategies and Geo Detector analysis, to assess the policy’s effectiveness. Results suggest that the FUR framework has significantly reduced net farmland loss. More importantly, its success is driven by mechanisms that resolve P-A conflicts by increasing oversight (technological monitoring) and raising non-compliance costs (recultivation enforcement). Conversely, mechanisms reliant on local personnel, where goal conflicts are more acute, prove less effective. Heterogeneity analysis further reveals that the policy is most potent in major agricultural regions where the principal’s and agents’ objectives are more naturally aligned. These findings deepen the understanding of how top-down regulations perform in multi-level governance systems, helping researchers and policymakers anticipate the spatial variance and effectiveness of similar environmental policies.

The booming development of China’s O2O (Online to Offline) e-commerce has attracted intense academic discussion. However, studies on the spatial evolution of O2O e-commerce are rare. Therefore, this study adopted methods such as spatial statistics and geodetector to explore the spatial evolution and determinants of O2O e-commerce. The main conclusions are as follows: (1) China’s O2O e-commerce development has distinct stages: 2012–2014 witnessed a period of rapid growth, during which O2O e-commerce completed swift spatial expansion between cities; 2015–2016 marked a phase of unprecedented expansion, with O2O e-commerce rapidly undergoing spatial diffusion within cities; and 2017–2019 represented a steady development period, during which the O2O e-commerce market pattern has essentially been determined and entered a trajectory of normal development. (2) The overall spatial pattern of O2O e-commerce development has broken the law of China’s economy decreasing from southeast to northwest to a certain extent. Instead, there has been a noticeable rise in central and western core cities, rapidly catching up with eastern coastal cities, resulting in a “multipolar” situation. (3) The spatial expansion of China’s O2O e-commerce exhibits distinct characteristics of mixed diffusion, with early hierarchical diffusion and later contagious diffusion. (4) There has been an increasingly significant polarization phenomenon in the evolution of China’s O2O e-commerce. (5) China’s O2O e-commerce development is influenced primarily by factors such as urban population, traditional commerce, economic scale, informatization level, and transportation. Moreover, the impact of each factor continues to increase with increasing O2O e-commerce development level.