Geographical Analysis最新文献

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.

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.

While the neighborhood has been shown to contribute to children's mental health, the extent of this contribution is not yet sufficiently clear due to several methodological challenges. We investigate the relative contribution– sign, magnitude, and significance– of six neighborhood characteristics associated with children's mental health when accounting for unobserved heterogeneity, endogeneity, and self-selection in neighborhoods of different socio-economic status. Using a correlated random effects model in space and time and two waves of data extracted from the Lifelines Cohort Study of children between 7 and 15 years of age in northern Netherlands, we analyze both internalizing and externalizing behavior. Besides the importance of the environment for healthy child development, our empirical findings show that the magnitude and significance of neighborhood characteristics increase significantly, especially when we control for endogeneity of maternal family characteristics. Controlling for self-selection again reduces the magnitude slightly, but only for externalizing behavior. Controlling for unobserved child-invariant factors is also found to be significant, but not for unobserved heterogeneity.

The main objective of the study is to analyze the sub-national social and economic convergence in a special spatial setting of Romania, characterized by the double process of national level economic catch up to the European level and the unprecedented deepening of the sub-national, inter-regional inequalities. The main contribution of the article consists in the determination of four multi-dimensional convergence clubs at NUTS 3 level, with specific growth trajectories and characteristics. A secondary contribution is related to the multi-dimensionality of development, revealing the existence of the contradictory processes of economic divergence and social convergence among the Romanian regions.

Major metropolises worldwide are adopting the smart city approach as an aspect of their strategies to address urban challenges. This approach leverages integrated information and communications technology (ICT) to transform urban environments into more efficient and intelligent spaces. A smart pole combines varied services such as CCTV, environmental sensors, and public Wi-Fi into a single infrastructure, collecting data via the Internet of Things (IoT) and transmitting them to an integrated platform for urban management and public service delivery. Given network connectivity and budget constraints, such smart poles must be strategically deployed. However, the absence of specific criteria for establishment causes inefficient installations, contrary to their intended aims to optimize space and resources. This study integrates the optimal deployment of smart poles with an effective design for wireless mesh networking. Specifically, we propose a multiple coverage mesh network problem (MC-MNP) to enhance the placement of smart poles with multi-sensors by improving public service coverage and more efficiently using urban space. The findings of this study offer useful guidelines for optimizing the deployment of multifunctional smart poles and can ultimately deliver a viable implementation methodology for smart cities.