Geographical Analysis最新文献

This paper investigates the interplay between population dynamics and transport connectivity in African municipalities from 1880 to 2020. Using an original historical dataset spanning African countries, we examine how proximity to railways and ports has shaped population dynamics over time. Through the application of Granger causality and Vector Autoregression (VAR) models—accounting for structural breaks and regime changes—we demonstrate that transport infrastructure development has a statistically significant and directional influence on urban population growth. Our findings reveal that city size and growth are not randomly distributed across space; rather, they are systematically linked to historical patterns of transport connectivity. In particular, the largest and fastest-growing cities consistently correspond to strategic nodes at the intersection of railway and maritime networks. These results, reinforced by hierarchical clustering and regime analysis, highlight the enduring impact of colonial infrastructure and institutional path dependence. The study also points to broader implications for contemporary urban planning in the context of containerization and modern global trade flows.

Spatiotemporal clustering occupies an established role in various fields dealing with geospatial analysis, spanning from healthcare analysis to environmental science. One major challenge is application in which cluster assignments are dependent on local densities, meaning that higher-density areas should be treated more strictly for spatial clustering and vice versa. We describe and implement an extended method that covers continuous and adaptive distance rescaling based on kernel density estimates and the orthodromic metric, as well as the distance between time series via dynamic time warping (DTW). In doing so, we provide the wider research community, as well as practitioners, with a way to solve an existing challenge as well as an easy-to-handle and robust open-source software tool. The resulting implementation is highly customizable to suit different application cases, and we verify and test the latter on both an idealized scenario and the recreation of prior work on broadband antibiotics prescriptions in Scotland to demonstrate well-behaved comparative performance. Following this, we apply our approach to fire emissions in Sub-Saharan Africa using data from Earth-observing satellites, and show our implementation's ability to uncover seasonality shifts in carbon emissions of subgroups as a result of time series-driven cluster splits.

Related variety studies in Economic Geography reveal regional diversification mechanisms for regional development, but often overlook geographic fundamentals. By relying on administrative units, the studies may fail to account for spatial continuity and interdependence, which can lead to Modifiable Areal Unit Problems. In this regard, this article introduces an alternative method ‘smoothing the edges’ as proof of concept to strengthen spatial conceptualization. Instead of using administrative units, we construct high-resolution grid cells and define Local Economic Environments (LEEs) around them to calculate economic factors. LEEs capture the conditioning economic context to which each grid cell is exposed. We compare Ordinary Least Squares regression outcomes across three LEE scales, equivalent to NUTS 2, NUTS 3, and municipality levels, and examine how (un)related variety effects behave across scales under the new conceptual framework. We apply two stylized facts from the literature: (Un)Related variety associates with (1) industrial specialization, and with (2) employment growth. A case study with Dutch establishment microdata LISA reveals that effects of (un)related variety are sensitive to scale, particularly in employment growth analysis. These findings highlight the importance of understanding contextual settings, which is critical in informed policy making.

This study analyzes hospital accessibility in Spain at the census section level from 2010 to 2024. Using open data and open-source tools, a replicable geospatial method was developed to estimate travel times and distances to the nearest hospital by car and on foot. Over 500,000 origin–destination pairs were analyzed, combining census centroids, OpenStreetMap hospital data, and road networks. Results show stark territorial disparities, especially in rural and inland areas, where travel times often exceed 30 min and sometimes surpass the “golden hour” critical for emergency care. While over 74% of the population lived within 15 min of a hospital in 2024, around 783,000 people still faced travel times over 1 hour. Accessibility has improved over time, with the share of critically underserved residents falling from 1.91% in 2010 to 1.62% in 2024. This reflects the possible impact of healthcare planning and infrastructure investments. The study provides a scalable, adaptable framework for analyzing access to essential services in other contexts. The resulting open dataset supports further research on spatial healthcare inequalities and informs data-driven health and territorial policy decisions.

Reducing complexity while retaining the maximum amount of information is a key challenge for population analysis. Solving this challenge becomes a necessity when looking at numerous areas over extended periods, which defy manual pattern recognition efforts. This paper introduces Dynamic Time Warping (DTW) as a novel method for population time series clustering, capable of creating distinct, well-separated groups for process-centered population analysis. DTW is benchmarked against a Sequence Analysis model and established typologies based on size, location or density with population data from nearly 3000 towns in Germany for the period 2001 to 2022. The results indicate that DTW consistently outperforms the Sequence Analysis model across various cluster quality measures, producing better-separated typologies of population trajectories. Both models are highly superior to the established typologies. The results highlight the significant advantages of using DTW for clustering continuous time series data, making it well-suited for identifying typologies of municipal population trends.

Large public urban green spaces (LPUGS) provide multiple health and environmental co-benefits by mitigating urban heat, improving air quality and biodiversity, and promoting physical activity, social interactions, and mental wellbeing. There is a lack of accessible, evidence-informed, and internationally validated LPUGS indicators to assist with benchmarking and monitoring progress toward healthy and sustainable cities globally. This study developed and validated internationally applicable spatial indicators of LPUGS availability and accessibility that are directly relevant to health and sustainability outcomes. For 13 cities across 10 middle- to high-income countries, we identified LPUGS ≥ 1 ha by fusing OpenStreetMap and satellite-derived Normalized Difference Vegetation Index data, and estimated residents' access within 500 m pedestrian network distance. We conducted a two-step validation process with local collaborators in each city. Our indicator methods identified LPUGS with greater than 80% accuracy for 12 of the 13 cities, and comparisons against official local reference data for four cities further demonstrated validity. While some open data limitations were identified, the indicators address critical gaps in existing methods by enabling standardized and comparable measurement of LPUGS in diverse cities internationally. Our customizable open-source global indicator tools can inform evidence-based green space planning for urban health and sustainability.

Scales, as survey instruments, are crucial for investigating people's attitudes and behaviors in social science research. Cronbach's alpha is the most frequently used measure of internal consistency reliability for multi-item survey scales. Affected by the spatial context of respondents' locations, the reliability of responses to a scale may vary geographically, whereas classical Cronbach's Alpha assumes that reliability is stationary across all samples being estimated. In this article, a local version of Cronbach's alpha, Geographically Weighted Cronbach's Alpha (GWalpha), is developed to explore how the reliability of a measure varies spatially. Issues concerning the bandwidth, inference, and interpretation are also discussed. We demonstrate the utility of GWalpha on a synthetic dataset with known parameters and an empirical dataset. Results suggest that GWalpha can effectively reveal and measure how spatial context influences survey reliability, which has been largely overlooked in the existing literature.