Geographical Analysis最新文献

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.

This article presents a pedagogic review and explanation of a core idea in location theory. Central to the analysis is the von Thünen model, a cornerstone of agricultural land use theory. The model is adapted to non-uniform transport surfaces, enabling an exploration of how improved transport corridors, such as roads and canals, alter economic landscapes. The article explores the influence of enhanced transport corridors within agricultural growing regions and provides a conceptual framework for understanding the impacts of improved transport infrastructure. By examining incremental changes of varying effectiveness, we shed light on the multifaceted effects of these corridors. Using a spatial-price equilibrium model, findings reveal and quantify how these improvements contribute to increased supply, price moderation, and cost reduction. Networks that provide full connectivity with large reductions in transport cost have the greatest effect, as might be expected, but there are subtle spatial zones displaying differential impacts.

A common issue faced by spatial analysts is that of multiple testing. When hypotheses are tested at multiple points in time or space, care must often be taken to avoid results containing too many false positives. There are many ways to address this outcome, and these are reviewed in this article. We begin with a review of some of the basic, longstanding approaches to multiple testing. This is followed by a summary of the more recent objective of controlling the false discovery rate and the effects of spatial autocorrelation on it. The number of true null hypotheses is an important quantity, and some approaches to its estimation are reviewed. In the literature on spatial analysis, there have been several newer approaches to multiple testing, and these are also reviewed. These include some recent methods outside of the literature in geography, but they have potential applicability for many of the problems addressed by geographers, especially since they focus upon the discovery of clusters. The article includes an illustration and closes with some ideas for taking further steps in treating multiple hypotheses in the context of methods commonly used in geographical analysis.

This year is the 50th anniversary of Besag's classic auto-models publication, a cornerstone in the development of modern-day spatial statistics/econometrics. Besag struggled for nearly two decades to make his conceptualization collectively successful across a wide suite of random variables. But only his auto-normal, and to a lesser degree his auto-logistic/binomial, were workable. Others, like his auto-Poisson, were effectively failures, whereas still others, such as potentials like an auto-Weibull, defied even awkward mathematical incorporations of spatial lag terms. Besag circumvented this impediment by introducing an auto-normal random effects components (within a Bayesian estimation context), building upon his single total success. This article describes an alternative approach, partly paralleling his reformulation while avoiding inserting spatial lag terms directly into probability density/mass functions, implanting spatial autocorrelation into cumulative distributions functions (CDFs), instead, via a spatially autocorrelated uniform distribution. The already existing probability integral transform and quantile function mathematical statistics theorems enable this mechanism to spatialize any random variable, with these new ones labeled sui-models.

This article describes a new spatial optimization model, the Multiple Gradual Maximal Covering Location Problem (MG-MCLP). This model is useful when coverage from multiple facilities or sensors is necessary to consider a demand to be covered, and when the quality of that coverage varies with the number of located facilities within the service distance, and the distance from the demand itself. The motivating example for this model uses a coupled GIS and optimization framework to determine the optimal locations for acoustic sensors—typically used in police applications for gunshot detection—in Tuscaloosa, AL. The results identify the optimal facility locations for allocating multiple facilities, at different locations, to cover multiple demands and evaluate those optimal locations with distance-decay. Solving the MG-MCLP over a range of values allows for comparing the performance of varying numbers of available resources, which could be used by public safety operations to demonstrate the number of resources that would be required to meet policy goals. The results illustrate the flexibility in designing alternative spatial allocation strategies and provide a tractable covering model that is solved with standard linear programming and GIS software, which in turn can improve spatial data analysis across many operational contexts.

Mete, M. O., & Yomralioglu, T. (2023) A hybrid approach for mass valuation of residential properties through geographic information systems and machine learning integration. Geographical Analysis, 55(4), 535–559.

The funding statement for this article was missing. The below funding statement has been added to the article:

“Funding for the research project was received from Scientific Research Projects Coordination Unit of Istanbul Technical University under grant MDK-2021-43080.”

We apologize for this error.