Geographical Analysis最新文献

Reproducible research becomes even more imperative as we build the evidence base on SARS-CoV-2 epidemiology, diagnosis, prevention, and treatment. In his study, Paez assessed the reproducibility of COVID-19 research during the pandemic, using a case study of population density. He found that most articles that assess the relationship of population density and COVID-19 outcomes do not publicly share data and code, except for a few, including our paper, which he stated “illustrates the importance of good reproducibility practices”. Paez recreated our analysis using our code and data from the perspective of spatial analysis, and his new model came to a different conclusion. The disparity between our and Paez’s findings, as well as other existing literature on the topic, give greater impetus to the need for further research. As there has been near exponential growth of COVID-19 research across a wide range of scientific disciplines, reproducible science is a vital component to produce reliable, rigorous, and robust evidence on COVID-19, which will be essential to inform clinical practice and policy in order to effectively eliminate the pandemic.

This study discusses the importance of balancing spatial and non-spatial variation in spatial regression modeling. Unlike spatially varying coefficients (SVC) modeling, which is popular in spatial statistics, non-spatially varying coefficients (NVC) modeling has largely been unexplored in spatial fields. Nevertheless, as we will explain, consideration of non-spatial variation is needed not only to improve model accuracy but also to reduce spurious correlation among varying coefficients, which is a major problem in SVC modeling. We consider a Moran eigenvector approach modeling spatially and non-spatially varying coefficients (S&NVC). A Monte Carlo simulation experiment comparing our S&NVC model with existing SVC models suggests both modeling accuracy and computational efficiency for our approach. Beyond that, somewhat surprisingly, our approach identifies true and spurious correlations among coefficients nearly perfectly, even when usual SVC models suffer from severe spurious correlations. It implies that S&NVC model should be used even when the analysis purpose is modeling SVCs. Finally, our S&NVC model is employed to analyze a residential land price data set. Its results suggest existence of both spatial and non-spatial variation in regression coefficients in practice. The S&NVC model is now implemented in the R package spmoran.

Urban polycentricity has become a key concept in urban and regional studies and is increasingly adopted as an organizational framework for conducting empirical research. Within this literature, polycentric urban regions are commonly defined as territories that have multiple, proximately located (sub)centers and are characterized by balanced urban development. However, analytical-operational frameworks to identify and classify PURs are often ad hoc efforts to answer a specific research question and underlying work is often shelved rather than shared and/or made accessible. As a result, challenges associated with generalizability, reproducibility, and replicability clearly loom large in the urban polycentricity literature. Against this backdrop, this article describes the discrepancy between a rich debate on polycentricity and the paucity of tools enabling the disambiguation and reproducibility of results claimed by various authors around this polysemic concept. We present an online and open tool—PURban—that brings together the major analytical-operational frameworks and data sets in urban polycentricity research and allows parametrizing key operational choices. To illustrate the tool, we demonstrate how it facilitates the identification, mapping and analysis of degrees of morphological polycentricity in European urban systems. We conclude by reflecting on how this tool can act as a catalyst for future research on urban polycentricity.

Online retailing and multi-/omni-channel shopping are gaining in importance. However, there is a significant lack of research focused on incorporating online shopping into models of spatial shopping behavior. The present study aims (1) to construct a store choice model which includes both physical and online stores as well as the opportunity for omni-channel shopping, and (2) to identify the main drivers of spatial shopping behavior given the availability of both channels. Based on a representative survey, this study employs a revealed-preference approach toward store choice and expenditures in furniture retailing. The statistical analysis is performed using a hurdle model approach, with the expenditures of individual consumers at (online or physical) furniture stores serving as the dependent variable. Results show that channel choice (online vs. offline) is mainly influenced by psychographic characteristics, place of residence, and age of the consumers. Store choice and expenditures are primarily explained by store features such as assortment size, omni-channel integration, and accessibility. This study demonstrates that e-shopping can be integrated into a store choice model and that both the modeling approach and the subsequent findings are of significance for retail companies and spatial planning.

Spatial interaction models can simulate commuting levels and evaluate the effort required for commuting optimization, providing more valuable information than a linear program. We take advantage of a large data set in Shanghai and exploit the fact that these data can be split by a key socioeconomic stratifier (education). By simulating the effect of shorter trip length, we estimate the extent to which commuting is relatively organized or excessive. More important, however, is that characteristics of the study region promote somewhat different results compared to US cities: well-educated cohorts use their higher-income to dominate central locations with higher access and amenity. The findings are consistent with the mathematical expectation from prior work, in that trip length reduction represents a decrease in entropy (i.e., an increase in organization). The potential for such improvement varies by educational level, and generally is higher for well-educated workers and lower for poorly educated workers.

A recent review noted important differences in the results of the local Moran's statistic depending on the inference method. These differences had significant practical implications. In closing, the authors speculated the differences may be due to local spatial heterogeneity. In this article, we propose that different null hypotheses, not heteroskedasticity, generate these differences. To test this, we examine the null hypotheses implicit in common statistical significance tests of local Moran’s . We design an experiment to assess the impact of local heterogeneity on tests conducted under the two most common null hypotheses. In this experiment, we analyze the relationship between measures of local variance, such as the local spatial heteroskedasticity (LOSH) statistic, and components of the local Moran’s statistic. We run this experiment with controlled synthetic heteroskedastic data and with uncontrolled real-world data with varying degrees and patterns of local heteroskedasticity. We show that, in both situations, estimates that use the same null are extremely similar, regardless of estimation method. In contrast, all estimates (regardless of the null) are moderately affected by spatial heteroskedasticity. Ultimately, this article demonstrates that there are important conceptual and computational differences about null hypothesis in local testing frameworks, and these differences can have significant practical implications. Therefore, researchers must be aware as to how their choices may shape the observed spatial patterns.

Geographic access to food retailers has long been considered an important determinant of food-related behaviors. Despite methodological improvements in assessing food environments, their associations with food behaviors have remained inconsistent. We argue that one possible reason for these inconsistencies is the lack of information about how an individual’s time use dynamics play out in space. To this point, few studies on the combined effects of food geography and time use on food behaviors exist, and methods to achieve such analyses have been underdeveloped. In this study, we propose a novel application of multi-channel sequence analysis (MCSA) to identify joint patterns of time use and food-related geographic contexts. We explore how those spatiotemporal patterns are associated with individuals’ food shopping and food-related household chores. This analytical workflow is demonstrated using time use diaries and GPS trajectories collected in Toronto in 2019. This test case identifies spatiotemporal patterns with distinctive characteristics of disaggregated time use and spatial exposure to food retail and finds associations between these distinct space-time patterns and participation in food-related activities. This application of MCSA affords a promising novel approach for food environment researchers to perform nuanced assessments of the sequenced spatiotemporal contexts in which food-related behaviors occur.

Inter-individual interactions are one of the key factors driving patterns of wildlife movement; however, methods for capturing and analyzing inter-individual interactions from wildlife tracking data remain limited. Extracting contacts from wildlife tracking data is a challenge owing to the complex spatial and temporal patterns and the volume of tracking data sets. Knowledge of the time and location of contacts are crucial to understanding the spatiotemporal patterns of contacts and how they relate to the environment, individual behavior, and social structure. In this article we introduce a new suite of functions in the wildlifeDI R package for automating contact analysis, summaries, and outputs (e.g., visualizations) from studies tracking many individuals simultaneously, building upon the existing methods for studying interactive behavior between dyads already present within the package. The package has applications to study contact and interaction for the study of animal behavior, social networks, and disease transmission. We demonstrate two applications of contact analysis using the wildlifeDI package: female white-tailed deer (Odocoileus virginianus) contacts and contacts between hunters and male white-tailed deer. The wildlifeDI package represents a new set of advanced, reproducible analyses to identify and study contacts and interactions in wildlife tracking studies. We designed the analyses and outputs to integrate into existing R analysis workflows to facilitate adoption of the package into a wide variety of wildlife tracking studies.

In a classical linear regression model (CLRM), the magnitude of disturbances is characterized by σ². When individual observations are aggregated into regions, the modifiable areal unit problem (MAUP) appears. The presence of the MAUP brings significant challenges to estimating σ², as the traditional ordinary least square estimator at the individual level, s², becomes downward biased at the aggregate level. Based on the information available before and after the aggregation process, three estimators of σ² at the aggregate level are proposed in this study: the trace estimator, the harmonic estimator, and the arithmetic estimator. Endorsed by Monte–Carlo simulations, these estimators provide significantly better estimates than directly borrowing s² at the aggregate level, but each achieves a different trade-off between the availability of required information and the accuracy of estimates. The findings provide a solid foundation for inferential statistics, such as constructing confidence intervals and performing hypothesis testing for CLRMs at the aggregate level.

The recent growth of geographic data science (GDS) fuelled by increasingly available open data and open source tools has influenced urban sciences across a multitude of fields. Yet there is limited application in urban morphology—a science of urban form. Although quantitative approaches to morphological research are finding momentum, existing tools for such analyses have limited scope and are predominantly implemented as plug-ins for standalone geographic information system software. This inherently restricts transparency and reproducibility of research. Simultaneously, the Python ecosystem for GDS is maturing to the point of fully supporting highly specialized morphological analysis. In this paper, we use the open source Python ecosystem in a workflow to illustrate its capabilities in a case study assessing the evolution of urban patterns over six historical periods on a sample of 42 locations. Results show a trajectory of change in the scale and structure of urban form from pre-industrial development to contemporary neighborhoods, with a peak of highest deviation during the post-World War II era of modernism, confirming previous findings. The wholly reproducible method is encapsulated in computational notebooks, illustrating how modern GDS can be applied to urban morphology research to promote open, collaborative, and transparent science, independent of proprietary or otherwise limited software.