Geographical Analysis最新文献

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.

Spatial econometric models have become very popular in recent years and have started to make their appearance in geospatial studies of health outcomes. Although explicit or implicit causal claims are widely made using these models, this article demonstrates with the use of causal graphs why causal vocabulary should be avoided. The article first introduces spatial causal inference from an epidemiologic perspective and then presents the challenges in the application of these specifications using this framework. Further implications and modeling options are discussed.

Social attributes of neighborhoods, like heritage, and low-level social disorder, are not reflected in official metrics such as deprivation indices. However, research suggests these attributes are important for understanding spatial variations in health and social outcomes. This exploratory study investigated whether recurring themes in local newspaper articles capture meaningful social characteristics that help explain neighborhood health resilience, defined as a dearth of illness after adjusting for deprivation. Topic modeling of geo-referenced texts identified and quantified 55 themes of commonly occurring words in Edinburgh, which capture salient neighborhood attributes. Correlations between the themes and domains of the Scottish Index of Multiple Deprivation (SIMD) were weak, suggesting that newspaper themes captured characteristics beyond those in the SIMD. Reassuringly, expected correlations were observed between crime metrics from newspapers and the SIMD domains. Stepwise regression modeling revealed theoretically plausible themes associated with neighborhood health resilience/vulnerability. Themes on heritage and community sports identity were positively associated with health resilience, whereas low-level social disorder (e.g., littering, antisocial behavior) and “local politics” were negatively associated. This study underscores the potential of using area-based topic modeling of newspaper texts to capture neighborhood aspects neglected in official statistics but could further explain spatial variations in neighborhood health outcomes.

The aim of this study is to investigate in what ways the spatial context matters for the life course trajectory status in terms of vulnerability. In particular, it explores the impacts of spatial contexts aggregated from life course trajectories. It uses a longitudinal micro-dataset, 1990–2019 from Statistics Sweden, to analyze the relationship between geographical context constructed by aggregated life course trajectories, and individual life course trajectories. A latent class analysis (LCA) is employed to identify life courses and examines how these trajectories are influenced by individualized neighborhoods. The findings show that spatial context plays a significant role in shaping individuals' life course trajectories of vulnerability: (1) being in any sort of trajectory of vulnerability, (2) four transitional categories of vulnerability. Residing in a context of Families in distress was associated with higher odds of experiencing vulnerability. Other factors such as country of birth, gender, earlier vulnerability also impact life course trajectory status. This study contributes to answering the question if poor neighborhoods make their residents poorer, posed by Jürgen Friedrichs in the late 1990s. The study goes beyond merely measuring cross-sectional single variable residential segregation patterns as context, offering valuable insights into consequences, supporting planning for geographic equality of opportunity.

This study assessed agreement between food environment measures derived from OpenStreetMap (OSM) data, a commercial dataset, and an administrative dataset (the Canadian Food Environment Dataset, Can-FED) to better understand the suitability of OSM food-related data for food environment research. We calculated Spearman's correlations between continuous retail food environment measures in Can-FED and those derived from OSM and DMTI Spatial. Additionally, using Can-FED as the reference, we assessed the accuracy of categorical food environment variables derived from OSM and DMTI data. OSM consistently reported fewer food retailers than Can-FED, but correlations between density and proportion measures from OSM, DMTI, and Can-FED were moderate to very strong. OSM and DMTI reliably identified areas with low proportions of healthier food retailers and fast-food outlets, though accuracy was lower in areas with higher proportions. In metropolitan areas, where categorized variables from OSM differed from Can-FED, proportions of healthier retailers and fast-food outlets were often underestimated. This study highlights OSM's limitations, such as missing data and error in accurately classifying neighborhood food environments, yet suggests that OSM may be useful for capturing general trends or measuring food environments in low-density areas when higher quality administrative data is not accessible.