Geographical Analysis最新文献

Understanding settlement patterns and their spatial distribution is essential for various fields, including geography, demography, and sociology. This paper explores a novel approach to assess the distance between settlement patterns of two populations with a specific focus on territorial variation inequalities. Building upon the conceptual framework of the Lorenz curve, we introduce new indices and graphical representations that permit us to emphasize territorial differences in settlement between two populations. By emphasizing the distance between settlement patterns, this methodology captures variations and inequalities in territorial distribution. Rather than simply characterizing settlement patterns as concentrated or dispersed, this approach considers the extent to which populations are spatially separated or integrated. To test this new approach, three foreign reference communities were examined (Egyptians, Chinese, and Romanians) known in the literature for their markedly different settlement patterns in Italy. We identified three different patterns for these populations that highlight the importance of considering local variations and spatial interactions in the study of settlement patterns. The results obtained seem to agree with the theory of settlements of foreign populations in Italy, albeit with additional geographical information. Through this research, we aim to provide a new methodology for measuring the distance between the settlement patterns of two different populations, filling some gaps in traditional methods.

Robbery and break-and-enter (BE) crimes require investigations into how these contrasting crimes co-occur. Utilizing robbery and BE data from the City of Toronto in Canada, this study analyzed the mean and area-specific crime trends, their risk factors, and the shared and crime-specific risk and hotspot areas. Results suggest an increase in robbery (0.23, 95% credible interval (CI): 0.17–0.29) and BE (0.08, 95% CI: 0.04–0.12) crimes during 2021–2022, revealing the most prominent area-specific trends in northwest and northeastern Toronto. The findings suggest that spatially lagged variables can offer deeper insights into complex spatial interactions of real-life factors that influence crime. Robberies were positively associated with the household and dwellings indicator (2021 Ontario Marginalization Index) but not its spatial lag, while BE crimes had no direct association with it but showed a positive association with its spatial lag. Neighborhoods in northwestern, northeastern, and southcentral parts of Toronto were hotspots of robberies, while southcentral and northwestern parts were at elevated risk due to BE. The findings demonstrate the complexities associated with the co-occurrence of multiple crime types and highlight the need for more unified and integrated theories to contextualize neighborhood effects of crime determinants and their impact on crimes.

Nearly 38 million people in the United States live in poverty. The Census Bureau's official poverty measure significantly undercounts poverty as it solely focuses on a minimum food diet and fails to account for the geographic variations in living costs. This article offers a geographically adaptive framework for combining multidimensional poverty indicators and modeling the locally adjusted costs of food, housing and utilities, healthcare, childcare, transportation, taxes, and other necessities to assess poverty and geographic inequality across neighborhoods and population sub-groups. We employ a co-design approach for developing the poverty assessment framework and evaluating the results with end users to ensure that communities can build trust and a sense of ownership that enhances the usability and actionability of poverty data. The datasets, quantitative frameworks, and algorithms were woven into an interactive geospatial dashboard toolkit for seamlessly integrating, cleaning, standardizing, and visually communicating the poverty metrics with a broad range of users. Results from this paper advance spatial data analyses and reproducible spatial model-building methods that enable researchers to gain higher resolution, context-specific, and geographically dynamic knowledge of poverty and inequalities.

This study addresses the location of emergency rescue vehicle bases in the western region of the city of Rio de Janeiro. The research aims to serve the greatest number of people within WHO parameters, which stipulates a maximum waiting time of 10 min. Various scenarios are explored, varying the number of rescue vehicles from 10 to 50 and the number of bases from 10 to 22. The study reveals that having an adequate number of rescue vehicles is crucial before determining the number and locations of the bases. Even in optimized scenarios with 5 or 8 bases, population coverage still falls short of the ideal, especially for 10-min response times. However, increasing the number of bases, up to a maximum of 22, shows significant improvements in population coverage, especially when aiming for a 10-min response time. Suggestions for future research include calculating the exact number of rescue vehicles needed to optimize the service and considering cost implications. This study highlights the importance of enhancing the emergency response system to meet the needs of the population.

This study introduces an agent-based model (ABM) pedestrian simulation tool to assess the risk of close contact (6 feet) in dynamic indoor environments, specifically in urban settings with diverse social activities and spatial structures. Our approach uses machine learning-based sensitivity analysis (SA) to identify factors impacting the number of individual contacts, such as individual stay time and area. In addition, we conducted an in-depth quantitative analysis to evaluate how specific factors, such as the strategic placement of obstacles, dwell time, and stay time near the entrances, mitigate the number of contacts. This analysis provides valuable insights for developing practical guidelines to curb contact risks in indoor environments. Lastly, we share the model, validation methods, and associated data as an open-source Python library, complete with comprehensive documentation. This aims at fostering collaborative research and enables the application of our model across various scenarios, contributing to the development of spatially explicit models. Such efforts enhance the understanding of contact risks in urban indoor settings and promote joint research efforts, thus advancing the field through shared knowledge and tools.

Comparing spatial data sets is a ubiquitous task in data analysis, however the presence of spatial autocorrelation means that standard estimates of variance will be wrong and tend to over-estimate the statistical significance of correlations and other observations. While there are a number of existing approaches to this problem, none are ideal, requiring detailed analytical calculations, which are hard to generalize or detailed modeling of the data generating process, which may not be straightforward. In this work we propose an approach based on permuting or resampling at fixed spatial autocorrelation, measured by Moran's I, in order to generate a null model that accounts for spatial dependence. Testing on real and synthetic data, we find that, as long as the spatial autocorrelation is not too strong, this approach works just as well as if we knew the data generating process exactly and allows us to compute P-values with the correct Type-I error rate.

Place-based spatial accessibility quantifies the distribution of access to goods and services across space. The Two-Step Floating Catchment Area (2SFCA) family of methods have become a default tool for spatial accessibility analysis in part due to their intuitive approach and interpretability. This family of methods relies on calculating catchment areas around supply locations to estimate the area and population that may utilize them. However, these “catchment areas” are generally defined by origin-destination matrices of travel-time, giving us point-to-point distances and not polygons with actual area. This means that population geographies (census tracts, blocks, etc.) are binarily included or excluded, with no room for partial inclusion. When using nongranular data, which is often the case due to data privacy restrictions, this has the potential to cause significant errors in accessibility measurements. In this article, we propose Areal 2SFCA: a new approach that considers the area of overlap between travel-time polygons and population geographies. We demonstrate the effectiveness of the Areal 2SFCA method using a case study that compares the Enhanced Two-Step Floating Catchment Area (E2SFCA) and Areal E2SFCA for the state of Illinois in the USA using multiple population granularities.

Multiple fabric assessment (MFA) is a computer-aided procedure designed for identifying and characterizing urban fabric types (morphotypes) from a street-based perspective. Nonetheless, the original MFA presents some limitations: it relies on surface-based descriptors, conceived as proxy variables for the pedestrian perspective in urban form analysis, rather than direct sight-based measurements. It also uses building footprint classes as proxies for building types. The spatial statistics on the street network concentrate on patterns of over- and under-represented values, which often results in a limited number of morphotypes. Furthermore, the morphotypes are typically valid only for a specific study area. This article presents the latest methodological advancements in MFA overcoming these four limitations. Its implementation over the eight largest French metropolitan areas successfully distinguishes approximately 20 distinct place-specific morphotypes, which are further aggregated into a comprehensive multi-level nested taxonomy. The new MFA procedure allows a nationwide comparative analysis of contemporary urban forms, laying the groundwork for a comprehensive understanding of morphologically regionalized metropolitan areas. Through detailed algorithmic improvements and nationwide implementation, integrating traditional urban morphology with streetscape analysis, MFA provides insights into the analogies and differences of the urban fabric in contemporary metropolitan areas, enabling interoperability with other domains of urban research.

Public school closures are increasing in frequency, number and size in U.S. cities. This study examines the spatial distribution of public school closures occurring in 10 U.S. cities between 2010 and 2019. I employ Exploratory Spatial Data Analysis (ESDA) techniques to measure the spatial concentration of school closures, or the degree to which school closures cluster. I also develop a measure of spatial accessibility across neighborhoods based on the time it would take to travel to the nearest closed school, in every block group in the 10 study cities. To take into account that traffic, congestion and other factors may play a role, I compute and compare times by car and foot, and compare access based on neighborhood ethnoracial and socioeconomic composition. Findings show that school closures geographically cluster, and neighborhoods with a greater percent of Black residents, whether higher poverty or not, will have longer commute times to the next nearest open school after a school closure. The geographic clustering of closures and the increased commute times to the nearest open school suggest that school deserts may be formed after school closings.