Geographical Analysis最新文献

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.

Drawing on data from the Eurobarometer Survey, this study explores the distribution of stigma and discrimination towards LGBTI communities at the sub-national level. There has been increased attention at global and pan-European levels around LGBTI rights mostly drawing on national-level measurements. However, there is limited research or understanding of the complex and pervasive problem of stigma and discrimination towards LGBTI groups at regional levels. Yet, it is widely noted that regional disparities exist across demographic characteristics; thus, national-level data may not be suitable for planning and policy making. We utilized two questions from the Eurobarometer as a proxy for levels of stigma and discrimination against LGBTI communities. We drew on novel Small Area Estimation (SAE) methods to produce the first reliable estimates and analysis for sub-national areas across Europe. The findings widen our understanding of differences around stigma and discrimination towards LGBTI communities both between and within nation states, emphasizing how regional-level analysis is necessary to develop targeted policies and interventions. Our findings demonstrate that programming and policy based on only national data should be utilized with caution. We argue that novel methods, such as SAE, can be utilized to support more effective data-driven decision making.

This article introduces a time-series clustering approach for classifying, visualizing, and exploring neighborhood dynamics. We illustrate the method for the case of racial and ethnic dynamics of neighborhoods in 64 U.S. metropolitan areas from 1990 to 2020. We establish typologies of continuous attribute trajectories for the share of Black, White, and Hispanic populations at the census tract level and explore generalizability versus specificity tradeoffs when varying the cluster analysis scale. Our results affirm a consistent decline in White population shares in neighborhoods across most metropolitan areas, accompanied by varied increases in Black and Hispanic populations. We also highlight the importance of metropolitan context in shaping neighborhood trends. While all cities show a trend towards increased diversity, the specific patterns and rates of change vary considerably, highlighting the unique demographic dynamics at play in each metropolitan area. The time-series clustering approach offers some advantages over previously used methods for visualizing and classifying longitudinal neighborhood dynamics like sequence analysis or growth change modeling in that it clusters the full continuous time series and does assume a pre-determined functional form.

OSMnx is a Python package for downloading, modeling, analyzing, and visualizing urban networks and any other geospatial features from OpenStreetMap data. A large and growing body of literature uses it to conduct scientific studies across the disciplines of geography, urban planning, transport engineering, computer science, and others. The OSMnx project has recently developed and implemented many new features, modeling capabilities, and analytical methods. The package now encompasses substantially more functionality than was previously documented in the literature. This article introduces OSMnx's modern capabilities, usage, and design—in addition to the scientific theory and logic underlying them. It shares lessons learned in geospatial software development and reflects on open science's implications for urban modeling and analysis.

Spatially and temporally varying coefficient (STVC) models are attracting attention as a flexible tool to explore the spatio-temporal patterns in regression coefficients. However, these models often struggle with balancing the computational efficiency, flexibility, and interpretability of the coefficients. This study develops a fast and flexible STVC model to address this challenge. To enhance flexibility and interpretability, we assume multiple processes in each varying coefficient, including purely spatial, purely temporal, and spatio-temporal interaction processes with or without time cyclicity. We combine a pre-conditioning method with a model selection procedure, inspired by reluctant interaction modeling, to estimate the strength of each process in each coefficient in a computationally efficient manner, while removing redundant processes as necessary. Monte Carlo experiments demonstrate that the proposed method outperforms alternatives in terms of coefficient estimation accuracy and computational efficiency. We then apply the proposed method to a crime analysis. The result confirms that the proposed method provides reasonable estimates. The STVC model is implemented in the R package spmoran.