Applied Spatial Analysis and Policy最新文献

This study investigates the spatial patterns of educational differentiation in Guangdong Province and their alignment with objective indicators from a spatial equity perspective. Guangdong, despite its economic development, faces notable internal disparities in education quality. Using 18,296 qualitative evaluations from school administrators (2023), subjective assessments were quantified and spatially analyzed. A Z-Score coordination model was developed to identify Educational Cost-Effective Areas (ECEA), based on 2022 per capita compulsory education expenditure. Results reveal significant funding imbalances between the Pearl River Delta (PRD) core and non-PRD areas, reinforcing inter-regional inequality. ECEAs are concentrated near developed regions but have limited impact in closing broader education gaps. The PRD core generally performs well but contains localized low-value zones, while non-PRD areas show widespread low-quality clusters with internal variation. At the city level, patterns such as center aggregation and boundary effects reflect complex urban resource distribution and spatial equity challenges. The study emphasizes the importance of integrating stakeholders’ subjective experiences with quantitative data. By combining qualitative and quantitative approaches, it identifies spatial imbalances and provides a scientific basis for optimizing educational resource allocation. These findings have significant policy implications for promoting spatial equity and guiding equitable development in rapidly urbanizing regions.

Accurate assessment of agricultural net primary productivity (NPP) is critical for carbon–neutral policies, yet the spatial heterogeneity and long-term dynamics of NPP across diverse ecosystems remain poorly quantified. This study evaluates the spatiotemporal inequality and future trajectories of cropland, forest, grassland, and water-body NPP in Chinese cities (2001–2020) using satellite observations. The results suggest that the economic output of specific agricultural lands and the intensity of agricultural productivity relative to economic output are the main positive and negative drivers of changes in agricultural NPP, respectively. Moreover, different types of NPP inequality show distinct trends, which are predominantly attributed to regional variations in productivity intensity. Multi-model projections suggest overall NPP growth by 2030, yet caution is warranted given threshold effects of human activities and nonlinear climate responses. This study highlights the importance of reducing inequality in NPP and carefully considering multi-model-based policy formulations for carbon neutrality.

Tourism development plays a critical role in enhancing urban competitiveness and driving economic growth. However, research on pedestrian streets, which are important urban tourism destinations, continues to face challenges, such as limited data availability and insufficient attention to the dynamics of tourist sentiment. This study focuses on the Datang pedestrian street in Xi'an, using location-based social media data to analyze the spatiotemporal characteristics of tourist source market and the evolution of tourist sentiment. The results indicated that the sources of tourists evolved from predominantly local and high-GDP city business travelers to a nationwide dispersion, with the center of gravity shifting southeastward. The source structure exhibited three distinct states: discrete distribution, expanded distribution, and contracted distribution. Local tourists primarily engaged in leisure activities, while tourists from other regions emphasized participation. Sentiment analysis showed an annual increase in positive emotions from 2010 to 2017, followed by a decline from 2018 to 2023. The primary factors contributing to this decline include rising tourist numbers paired with diminished experiential quality, shifts in the tourism market alongside rising tourist expectations, external environmental factors, and developmental bottlenecks within the pedestrian street. Socioeconomic and regional demographic factors significantly impact the development of pedestrian streets. This study provides valuable insights for the reproduction of urban pedestrian street, promoting urban tourism and regional economic growth, enhancing urban renewal quality and efficiency, and shaping urban identity.

Investigations on Land Suitability for urban construction (LSUC) remain limited in the mountainous regions of the eastern Mediterranean, despite increasing urbanisation pressures. This study provides a comprehensive assessment of LSUC in the mountain-plateau district of Al-Shiakhbader, Tartous Governorate, Syria, using nine spatial data layers that encompass physical, environmental, and socio-economic factors. The analytic hierarchy process (AHP) was applied within a Geographic Information System (GIS) framework to generate a weighted suitability map. Results reveal that approximately 12% of the study area is very highly suitable for urban construction, predominantly due to favorable terrain slope (average 8°), flat curvature, moderate aspect, and proximity to existing infrastructure such as roads and built-up areas. Conversely, 50% are classified as low to very low suitability, mainly in the north-eastern and south-eastern zones characterized by steep slopes (above 20°), convex curvature, dense vegetation cover, and proximity to geological faults, highlighting critical areas for ecological protection. These findings emphasize the potential for sustainable urban expansion in Al-Shiakhbader, while safeguarding environmentally sensitive zones. We recommend that regional planners prioritize development in highly suitable zones to reduce the risks of landslides and ecological degradation, and implement strict land-use controls in unsuitable areas. This research fills a vital gap in mountainous LSUC studies, offering a replicable GIS-AHP methodology for regional land management and contributing to balanced socio-economic development in mountainous Mediterranean landscapes.

The COVID-19 pandemic has had profound effects on daily life, even long after its initial outbreak. Numerous studies have examined the role of urban open spaces (UOS) in supporting human well-being and urban resilience during and after the pandemic. However, few studies have explored the spatio-temporal shifts in the role of UOS over the long term since the pandemic began. Focusing on South China, a region severely affected by the crisis, we captured the spatial and temporal dynamics of public interactions with and perceptions of UOS in the region before, during, and after the pandemic. Based on systematically processed 232930 geo-tagged Sina Weibo posts posted by residents in 42 UOS, we conducted sentiment analysis and UOS-related cultural ecosystem services (CES) analysis. Our findings revealed a significant decline in UOS visitation level during the pandemic with slight public sentiment fluctuations, underscoring its disruptive impact. Post-pandemic, UOS visitation level rebounded sharply, due to pent-up travel demand and compensatory behaviors. Additionally, public demand for CES tends to diversify, with growing recognition of spiritual and inspirational services. The spatial analysis showed that UOS played a more prominent role in enhancing urban resilience in the Yangtze River Delta urban agglomeration. This research confirms that the pandemic accelerated the spatio-temporal evolution of the role of UOS in South China, highlighting their enhanced importance for crisis response and well-being. It offers actionable insights for urban governance to better prepare for future crises.

In China, intercity consumption exhibits segmented market characteristics shaped by geographical proximity and psychological affinity within the same province. This study constructs an intercity consumption network encompassing 295 prefecture-level cities using 14.4 billion intercity consumption records from UnionPay Business. First, we apply social network analysis to examine the network’s evolution over the sample period. Building on this framework, the Temporal Exponential Random Graph Model (TERGM) is employed to investigate the determinants and formation mechanisms of intercity consumption relationships. The findings reveal that the overall connectivity of China’s consumption network has increased over the sample period, with a consumption structure centered on provincial capitals and municipalities directly under the central government. According to the TERGM results, cities with higher levels of economic development, larger populations, and greater income disparities are more likely to attract intercity consumption flows. Additionally, significant “provincial clustering effects,” “provincial capital effects,” and “geographical effects” are observed. Specifically, cities within the same province are more inclined to establish intercity consumption ties, while provincial capitals and centrally administered municipalities demonstrate a greater capacity to attract consumption. Moreover, geographical proximity, particularly between neighboring cities, fosters stronger intercity consumption linkages. Furthermore, evidence suggests the presence of reciprocity within the intercity consumption network, indicating a mutual reinforcement effect that enhances the overall connectivity of cities. The insights drawn from this study provide valuable reference for understanding and leveraging the advantages of China’s large-scale market.

This paper models road traffic collision counts recorded between 2015 and 2019 in a ward located in the central part of Cape Town in South Africa, using a Bayesian spatio-temporal zero-inflated Negative Binomial approach. The method accounted for the excess zeros present in collision data by separately modeling zero and non-zero collision counts, while also capturing spatial and temporal dependencies through prior distributions. Road-level information was used as fixed-effects covariates, including speed limits, presence of traffic calming measures, traffic signals, road class, number of lanes, whether the intersection is on “Main Road”, and whether a public transport route passes through the intersection. The results reveal that among the covariates included in the selected model, node degree (used as a proxy for traffic flow), the presence of traffic signals, having any major road around the intersection (road class), location along “Main Road”, and the presence of a taxi route at the intersection were all associated with an increase in traffic collision counts at the intersections. The years 2018 and 2019 were associated with higher collision counts compared to the reference year, 2015. For the probability component of the model, the existence of traffic signals at the intersection and location along “Main Road”were both associated with an increase in the chances of at least one collision being observed at the intersection, whereas having any high-speed road around the intersection decreased this chance.

Land-Use and Transport Interaction (LUTI) models have long been regarded as important tools for urban modelling and planning. Despite methodological advances, LUTI models remain underutilised in practice. Practitioners are often reluctant to use complex models that typically demand extensive data requirements, long processing times and call for highly trained and specialised staff. To mitigate these issues and strike a balance between parsimony and comprehensiveness, the current study develops one of the first operational LUTI models in Australia, POPulation and Dwelling Allocation Modelling (POPDAM), achieved via an academic-government collaborative co-design process. POPDAM comprises two integrated modules. The econometric module uses a demand–supply model of dwelling allocation and is grounded in hedonic price and equilibrium theories. The demographic module employs a cohort progression model to produce age-sex scenarios. In contrast to popular micro-level modelling, POPDAM operates at a higher level of spatial granularity allowing simpler implementation but without compromising its practical utility. This meso spatial scale was proven sufficient for policy testing while capturing higher-level economic and demographic trends that may be obscured in individual-based models. This trade-off between simplicity and accuracy yields an optimal computational performance streamlined to mere minutes for scenario execution. Moreover, the co-design project-orientated process bridging academia and government has resulted in a successful implementation of POPDAM alongside an effective transfer of knowledge. The conceptual design of POPDAM provides a level of abstraction that can be easily re-deployed to other situational contexts in Australia and beyond.

Existing research on student mobility in higher education has largely focused on macro-level trends, overlooking micro-level structures that shape access and inequality. This study addresses the gap by reconstructing China’s Independent Admission Program (IAP) field from 2015 to 2018 using school-level student mobility data and complex network analysis, combined with Bourdieu’s field theory and proximity theory from economics. The analysis reveals a distinct core-periphery structure: elite universities attract students nationally with minimal spatial constraint, while eight regional clusters rely heavily on geographic proximity. Regression models show consistent effects of key school status and administrative hierarchy across clusters, but also highlight localized differences in how economic and cultural capital influence student mobility. Path dependency analysis further illustrates how high schools reinforce their comparative advantages by sending students to structurally similar universities over time. While the IAP tends to amplify educational inequality—favoring urban, resource-rich schools—it also plays a compensatory role in certain regional clusters by offering access routes to disadvantaged schools. These findings reveal the dual logic—national and local—underpinning the IAP, and demonstrate the value of complex network methods for uncovering hidden structures and informing policy on educational equity and institutional stratification.

Water scarcity poses a significant challenge to China's sustainable economic and social progress. Despite its critical importance, the impact of the agricultural sector’s production demand on water consumption across regions and sectors through supply chains (known as water footprint) has been largely overlooked, particularly at the city level. This study provides a detailed analysis of the agricultural water footprint at the urban scale, using data from 2012, 2015, and 2017 for 313 Chinese cities. Our findings reveal that agricultural water footprints in Chinese cities exhibit clear spatial heterogeneity, with higher footprints in the central and eastern regions compared to the northwest and southwest. Over time, cities with high water footprints have gradually shifted from north to south. We also identified that hotspot cities are mainly located in the central, eastern, and southern regions of China, such as Beijing, Tianjin, Wuhan, and Nanjing. Based on these insights, we provide policy recommendations aimed at promoting sustainable water resource management at both the sectoral and city levels. These recommendations are intended to mitigate water scarcity and ensure water security in China. To achieve sustainable water resource management, agricultural water footprints must be integrated into urban water management systems. Key policies include optimizing water footprint management in eastern regions through precision irrigation and IoT systems, improving water use efficiency in water-scarce agricultural areas, and enhancing agricultural trade between regions with different water resources. Urban agricultural development should focus on balanced water usage and collaborative governance through cross-city water rights platforms. Industry structure optimization is essential, with a focus on promoting water-efficient technologies and practices. Additionally, encouraging water-saving behaviors through education, subsidies, and policy incentives will help reduce agricultural water footprints and support sustainable water use.