Applied Spatial Analysis and Policy最新文献

This study explores the enduring role of geography in shaping social media behavior. Using Sina Weibo as a case study, the research investigates how geographic factors such as region, distance, place, and urban system hierarchy influence social interactions and content similarity among users. Employing a custom data collection and analysis framework, including web crawlers and the TF-IDF model for content similarity, the study examines 1,000 Weibo users across ten Chinese cities. Results show that geography continues to play a crucial role in online interactions, with regional identity, physical proximity, and place-specific topics significantly affecting user connections and content preferences. Findings indicate that despite technological advancements, geographic factors still shape digital social behaviors, reinforcing the importance of geography even in the online social landscape.

China faces the significant challenge of achieving its 2030 carbon intensity target, and the development of high-end producer services (HPS) is expected to be an important driving force. To better understand the impact mechanism of HPS agglomeration on urban carbon intensity, this study conducts an empirical analysis using panel data from 274 prefecture-level cities from 2006 to 2023. Based on revealing the spatiotemporal evolution characteristics of HPS agglomeration and urban carbon intensity, a spatial econometric model was used to explore the impact of HPS agglomeration on carbon intensity from the perspectives of spatial spillovers and city-size heterogeneity. The empirical results indicate that the average HPS agglomeration and carbon intensity in Chinese cities both gradually decrease, whereas the degree of spatial differentiation continues to expand. Both carbon intensity and HPS agglomeration exhibit significant positive spatial autocorrelation. The results of the spatial Durbin model show that HPS agglomeration contributes to the reduction in carbon intensity at the national level. For every 1% increase in HPS agglomeration, local carbon intensity decreases by 0.048%, but the carbon intensity of neighboring cities increases by 0.013%. In terms of city size, HPS agglomeration can promote a reduction in carbon intensity in large and mega-cities and medium-sized cities, but has no significant effect on small-sized cities. On the basis of these empirical findings, this paper proposes three specific suggestions: policymakers should vigorously increase the speed and scale of HPS development, explore differentiated HPS development paths that match the size of cities, and build an HPS development pattern based on urban agglomeration as the spatial carrier.

As global urbanization shifts from expansive growth to focused regeneration, assessing how various regeneration models impact urban vitality becomes crucial. Historic districts, marked by deep-rooted identities and rising development pressure, demand nuanced regeneration strategies that align preservation with modernization, and serve as testing grounds for these contrasting approaches. However, the comparative efficacy of government-led and market-driven approaches remains underexplored. This study assesses how divergent regeneration models shape urban vitality, using Weibo_Expressed Sentiment (WESI) and Weibo_Check-in density (WCDI) as key indicators. Focusing on Suzhou’s Jianjin Qiao Alley (government-led) and Shiquan Street (market-driven), the research evaluates the spatial-temporal impacts of regeneration. Its mixed-methods framework uses a quasi-experimental Difference-in-Differences (DID) design for robust causal identification, complemented by the machine learning-based Extreme Gradient Boosting (XGBoost) model to handle non-linear prediction and feature analysis. The study draws on geotagged social media check-ins and Points of Interest (POI) data from 2020 to 2024. It quantifies how built-environment elements influence regeneration performance and sentiment expression. Findings reveal a distinct trade-off: (1) the Market-Driven model was superior for improving public perception, causing a significant 0.029% increase in the WESI. (2) In contrast, the Government-Led model excelled at drawing public presence, driving a 0.303% increase in the WCDI, an impact of a much larger magnitude. (3) The predictive XGBoost analysis uncovers a non-monotonic effect where WESI peaks when the catering density index (PCDI) is in the 0.5 to 1.5 range, but turns negative after its value surpasses a threshold of 2. This study challenges conventional regeneration paradigms, uncovering temporal trade-offs between market efficiency and cultural sustainability. By introducing an integrated DID-XGBoost assessment framework, it quantifies the externalities of historic district regeneration, providing a diagnostic tool for optimizing heritage-compatible development.

The equitable distribution of public service facilities is critical to urban livability and social equity. Studies examining the difference in the facility accessibility of communities with different socioeconomic status (SES) in rapidly urbanized China, especially the comparative study based on 15-minute community life circles (15 min-CLC) are rare. Thus, using Nanjing as a case study, this study evaluates the accessibility of public service facilities within the 15 min-CLC of different SES communities based on multi-source data. The findings reveal that there were significant differences in facility accessibility among different SES communities in Nanjing. Communities with lower SES face obvious disadvantages and unfairness in accessing public services, especially health care services, education and transportation. For communities with lower SES, the density of the built environment is a key determinant of facility accessibility. Urban policymakers should appropriately tilt social resources toward vulnerable groups, increase the density and quality of facility construction in communities with low economic status, and promote social equity.

Green roofs have been shown to significantly mitigate urban heat island effects and serve as an important solution for addressing the shortage in urban green spaces. Their capacity to improve the surface thermal environment is influenced by both the morphology of the urban blocks and their landscape patterns. In this study, surface temperatures for four seasons (spring, summer, autumn, and winter) were retrieved, and the morphological types of the central area of Guangzhou were classified using the local climate zone (LCZ) framework. Vector data on green roofs were collected to construct a cooling model and calculate landscape pattern metrics. A geodetector model was employed to analyze the cooling mechanisms of the green roofs. The results indicated that the cooling effect of green roofs is most pronounced in summer, with the highest average cooling intensity of an area reaching 0.63℃. The optimal LCZ type for green roof cooling performance was found to be that of compact low-rise buildings, and compact building areas were identified as more suitable than open building areas for green roof implementation. Furthermore, larger average green roof areas were associated with enhanced cooling effects.

This study explores the spatio-temporal dynamics of Istanbul’s urban mobility by applying a four-stage urban rhythm analysis framework that combines big data analytics with urban theory. Using Istanbul Card data, it reveals how urban rhythms are shaped by social calendars, institutional schedules, and daily practices across different temporal scales (year, season, month, week, day). The findings highlight polyrhythmic nodes—such as the Metrobus corridor and Zincirlikuyu hub—where commuting, leisure, and touristic flows converge, and identify arrhythmias during national holidays and religious festivals. By integrating Lefebvre’s rhythmanalysis and Bakhtin’s chronotope, the study demonstrates how big data can move beyond descriptive analytics to reveal the layered temporalities of urban life. Additionally, the research addresses the Modifiable Temporal Unit Problem (MTUP) by developing a multiscalar methodology that minimizes temporal distortion and enhances the interpretability of rhythm patterns. The results provide actionable insights for adaptive urban planning and transport management, demonstrating how rhythm-based big data analytics can uncover hidden dynamics of urban character and guide more resilient, inclusive mobility strategies.

Equalizing basic public services (BPS) is essential to achieve common prosperity. In the context of new urbanization, there has been less discussion on how urban spatial structure affects the BPS equalization. This study analyzes the theoretical mechanisms by which urban polycentric structure affects the BPS equalization from agglomeration economies and urban amenity perspectives. Based on LandScan data, exploratory spatial data analysis is applied to measure the polycentric structure of urban population, and the BPS equalization within the city is measured using district and county data. The study results show that the urban polycentric structure significantly promotes the BPS equalization. Mechanism tests show that the urban polycentric structure promotes equalizing BPS by strengthening the effect of agglomeration economies and improving urban amenities. Furthermore, the polycentric distribution dominated by a main center, the expansion of the urban scale, and the improvement of transportation facilities are conducive to strengthening the BPS equalization effect in the urban polycentric structure. Moreover, there is an inverted U-shaped nonlinear relationship between the number of urban subcenters and the BPS equalization. A smaller dispersion and a higher degree of balance among urban subcenters will lead to a higher level of BPS equalization. In the future, the government should maintain the leading position of the urban main center and strive to establish a compact, balanced, and appropriate urban polycentric development model.

This study presents an innovative approach that integrates Geographic Information Systems (GIS) and Machine Learning (ML) techniques to optimize school site selection in the Konak and Karabağlar districts of İzmir. The study was conducted using 12 different criteria and 39 alternative sites by integrating the Analytic Hierarchy Process (AHP), Equal Weighting, and Extreme Gradient Boosting (XGBoost) methods with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The alternative sites were analyzed based on the average pixel values of the criteria, and AS4 was identified as the most suitable site. The ML-based method, ML_Rank, significantly contributed to the ranking of alternatives, showing high compatibility with the Equal Weighting and XGBoost methods (ρ = 0.9745 and ρ = 0.8813, respectively). In contrast, AHP exhibited the lowest correlation among the methods, highlighting the superior objectivity and consistency of ML-based approaches in ranking alternatives. The scalable and automated structure of ML_Rank enabled the rapid and reliable analysis of large datasets and complex criteria. Spearman correlation analysis demonstrated that ML-based methods improve decision-making processes by producing objective and consistent results. Thus, the findings reveal that GIS and ML techniques can be effectively utilized in critical planning processes such as school site selection. These methods contribute to sustainable urban planning by supporting the equitable distribution of educational services, enhancing student accessibility, and promoting efficient resource utilization.

Urban planning and policy development increasingly rely on open spatial data and analytical tools to address complex challenges such as rapid urbanization, climate change, and disaster risk. This study systematically reviews and classifies optimization methods applied to open spatial data, aiming to enhance its utility in urban planning and policy contexts. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 77 peer-reviewed articles published between 2015 and 2025 were selected from an initial pool of 1,050 studies sourced from the Scopus database. The findings highlight that institutional datasets—both fully and partially open-access—and crowdsourced platforms, particularly OpenStreetMap (OSM), dominate as primary data sources. QGIS and Python emerge as the most frequently used analytical tools across a diverse range of urban applications. Building on the synthesis of the reviewed literature, this study introduces a five-dimensional optimization framework comprising functional, computational, data connectivity, participatory, and reproducibility dimensions, which collectively enable more adaptive, transparent, and collaborative approaches to urban spatial modeling. The framework offers practical guidance for leveraging open data in evidence-based urban planning and policymaking, ultimately contributing to more sustainable and resilient cities.

The effective utilization of limited urban land is achievable through the implementation of comprehensive zoning plans. Planning decisions must prioritize the allocation of land for its most appropriate uses, grounded in scientific criteria and urban planning principles. This study aims to develop a framework and decision-support platform specifically for the siting of filling stations, which represent a distinct category of urban land use. To inform planning efforts, site selection analyses were conducted using the Analytic Hierarchy Process (AHP), a widely recognized method within the Spatial Multi-Criteria Decision-Making (S-MCDM) framework, integrated with Geographic Information Systems (GIS). The proposed model facilitates the identification of optimal locations for filling stations in accordance with zoning regulations. As a result of the analysis, a total of 3,445 suitable areas were identified within the city of Trabzon, ensuring adequate spatial distribution across all districts. Notably, the findings indicate that 63% of currently operational filling stations are located in areas deemed unsuitable based on the selected criteria and their respective weights within the AHP framework. These results highlight the necessity for evidence-based planning approaches to guide urban land use decisions effectively.