Applied Geography最新文献

This paper leverages store location data and analysis software from retail industry vendors to analyze the unique geographies of electronic commerce (e-commerce) offerings among major grocery chains in the United States. Join-Count statistics are calculated to identify patterns of clustering among offerings, and a spatial probit model is implemented to regress various trade area characteristics on a given grocery banner's decision to offer some form of e-commerce (pickup, delivery, or both) or no offering at a given store location. A regional case study of the Southeastern United States demonstrates how model results can shift based on scale, pointing to unique regional dynamics that deviate from broader national trends. This analysis carries implications for location planning and competitive defense strategies in the grocery industry, and also establishes avenues for future research.

Fine particulate matter (PM_2.5) is a major health and environmental concern, with significant spatiotemporal dynamics in urban areas. Low-cost air quality sensor (LCS) networks offer a paradigm-changing opportunity to acquire high spatiotemporal resolution data, revealing the urban pollution landscape with sufficient detail for effective policymaking and health assessment. This study advances geospatial air quality research by using classic and spatial Markov chains to analyze the seasonality and intra-daily variations of PM_2.5 using LCS data. Results highlight distinctive PM_2.5 seasonality, with the “Good” state predominating in summer and being least common in winter. Midday is the peak period for the “Good” state, while mornings and nights have poorer conditions, suggesting a need for stricter pollution control during evening traffic rush hours. Notably, the impact of temporal scale on spatial Markov analysis is substantial, showing a broader range of air pollution states, increased stability, and reduced variation between time intervals compared to daily assessments. Site-level analysis reveals that rural sites are more likely to maintain “Good” state and less likely to transition out of it. Overall, this study highlights the effectiveness of high spatiotemporal resolution data and demonstrates the capacity of Markov chains to reveal nuances in phenomena such as air pollution.

Urban traffic crashes represent a significant challenge affecting public safety and urban mobility worldwide. This study introduces a novel application of Sparse Non-negative Matrix Factorization with spatial constraints to analyze spatio-temporal patterns of traffic crashes at a city level. Using comprehensive crash data from Denver and Manhattan during 2020, we developed and validated a model capable of capturing distinct temporal dynamics and spatial distributions of traffic crashes. Unlike traditional methods, our approach integrates sparsity and spatial constraints, enhancing the model's ability to handle the inherent sparsity and geographical dependencies found in urban traffic data. The results demonstrate the model's effectiveness in identifying high-risk areas and times, providing actionable insights that can inform urban planning and targeted safety interventions. The study underscores the potential of advanced data-driven techniques in urban traffic analysis and contributes to the broader efforts of improving traffic safety through informed decision-making and policy development.

The cultural ecosystem services (CESs) -related studies for decades have centered on the visual connotations of human perceptions rather than incorporating the other sensory experiences. This paper works to narrow the research gaps through scrutinizing the CESs of Chinese Classical Gardens from a multisensory perspective. We first demonstrate a qualitative phenomenological approach to conceptualizing the CES typologies of Chinese Classical Gardens, and then propose a novel deep learning approach to measuring their CESs based on online reviews from the lens of five senses. Following, the inter-relationships among the CESs are examined using co-occurrence network analysis. Results show that the CESs typologies of Chinese Classical Gardens include 7 main categories and 21 minor classes. Among them, the visual perception based CESs make up the highest proportion, but a noteworthy proportion of hearing, touch and taste based CESs is also observed. Additionally, the visual perception based CESs and auditory perception based CESs generally present higher centrality within the networked typology. Based on the discoveries, we finally discuss implications for landscape management. This paper foregrounds the effectiveness and feasibility of scrutinizing CESs from a multisensory perspective, and adds fuels to unpack the full spectrum of CESs for the geographical community.

Partisan segregation in the United States is often interpreted as evidence of limited social interaction among out-partisans, or partisan homophily. In this paper, I draw on 2020 US presidential election results and data on the pairwise density of social ties between the populations of 22,537 zip code tabulation areas (ZCTA) to examine how different areas are socially connected to politically similar others. Using the local Moran index, I first identify clusters of ZCTAs where there is evidence of partisan homophily or heterophily. In a series of multinomial logistic regressions, I then also examine differences in the probability of each cluster across different settlement types and regions, and across areas with differences in the relative connectedness and geographic distance to others. I find that partisan homophily is the norm across areas, broadly tracking partisan segregation along the urban-rural continuum. However, the populations of Democratic-leaning areas, which are most likely to be in cities and suburbs, are on average likely to have more of their co-partisan social ties in relatively distant areas when compared to the populations of Republican-leaning areas. This highlights the prospect of partisan differences in the role of non-local context in local political outcomes.

This paper explores the emerging paradigm of alternative food networks (AFN), with a focus on AMAP (Association pour le Maintien de l’Agriculture Paysanne) in France as a case study. Delving into the multifaceted concept of spatial proximity within such networks, this paper explores three hypotheses drawn from literature on the spatiality of AFN, analyzing both ‘local’ distances variability and the characteristics of agricultural and urban contexts where such local-based AFN take place. Methodologically, the study leverages an original, manually compiled database at a national scale encompassing AMAP producers and distribution points. Specific analytical protocols are developed combining traditional geographical approaches with machine learning techniques. Key findings reveal the influence of both population density and agricultural land availability on the distances between producers and selling locations. Moreover, the study discerns that the nature of products and their processing levels significantly shapes 'local' distances. Additionally, the paper offers insights into distinctive features of the morphological landscape associated with AMAP producers. These findings may serve as a catalyst for future inquiries into the spatial dynamics and potential spatial configurations of alternative food networks.

The forestland expansion is the joint efforts by various behavioral actors, and among them governments and farmers play a more important role in it. However, their afforestation behaviors haven't be together investigated. Examining Beijing metropolitan area, this research distinguishes between government-led (GA) and farmer-led (FA) afforestation on arable land, and analyzes their spatial characteristics and the influencing factors using Ordinary Least Squares and Geographically Weighted Regression models. Our findings show: (1) Spatial distributions of GA and FA are non-random and systematically organized. GA dominates both in the areas close to the central city and at the outermost edge of the metropolitan area. FA leads in intermediate zones between GA dominant areas. (2) Both GA and FA are rational strategies for land allocation. GA in areas close to the central city is regarded as a trade-off between public benefits of afforestation and potential loss of fiscal revenue, while GA at the metropolitan area's outermost edge prioritizes ecological gains and lower land costs. FA, however, is driven by maximizing household welfare amidst the challenges brought by rural exodus and a rapid aging society. The findings deepen the existing investigations and provide a guidance for spatially organizing GA and synthetically regulating FA.

In 2021, Charles Lee defined disproportionate environmental and public health impacts and offered an approach for environmental justice (EJ) leaders to identify disproportionate impacts. However, Lee's model has not yet been compared to current mapping models, nor has it been applied to the highly disadvantaged landscape of Arkansas. In this study, we extend Lee's model and offer a complementary theory of disadvantage to identify, characterize, and describe disproportionate impacts in Arkansas. We ask: What communities are most disproportionately impacted? What is the spatial distribution of risk to environmental burdens and population vulnerabilities? How do these burdens and vulnerabilities cluster together? How might the theory of disadvantage assist in disproportionate impacts research? We deploy a descriptive, mixed-methods design using data from national mapping tools. Findings reveal regions of Arkansas that are disproportionately impacted. However, no region is devoid of high risk. Minority populations are the most impacted groups, and populations living in disproportionately impacted communities tend to suffer from specific burdens and vulnerabilities that erode well-being. The theory of disadvantage is found to be useful for disproportionate impacts research. These results offer EJ leaders a new lens to view and design mapping tools and they clarify intervention points.

Urban green turn has gained global momentum, with cities worldwide actively developing urban green amenities (UGAs). While considerable attention has been given to how expanding UGAs impact residential dynamics, the role of space in shaping these relationships has been underexplored. Employing global and local regression models, we investigate the associations between UGA characteristics, property prices and sales transactions, using data from 443 neighborhoods in Wuhan, China. Our findings reveal that at the city-wide level, larger UGAs are associated with gentrification trends marked by rising property prices and population shifts. High-quality UGAs are linked to higher risks of gentrification but do not significantly indicate direct displacement. Meanwhile, substantial bodies of water are associated with mitigating the effects of increased property prices and population shifts. At the local scale, these relationships show spatial heterogeneity, particularly between old inner-city areas and traditional industrial zones. Additionally, we analyze the roles of the state and real estate developers in influencing UGA development and the associated social ramifications in China. This study enhances our understanding of UGAs and their impacts on residential dynamics in a non-Western context, enriching the discourse on the social implications of urban green movements and ecological initiatives.

Worldwide, the COVID-19 pandemic has been influenced by a combination of environmental and sociodemographic drivers. To date, population studies have overwhelmingly focused on the impact of societal factors. In New Mexico, the rate of COVID-19 infection and mortality varied significantly among the state's geographically dispersed, and racially and ethnically diverse populations who are exposed to unique environmental contaminants related to resource extraction industries (e.g. fracking, mining, oil and gas exploration). By looking at local patterns of COVID-19 disease severity, we sought to uncover the spatially varying factors underlying the pandemic. We further explored the compounding role of potential long-term exposures to various environmental contaminants on COVID-19 mortality prior to widespread applications of vaccinations. To illustrate the spatial heterogeneity of these complex associations, we leveraged multiple modeling approaches to account for spatial non-stationarity in model terms. Multiscale geographically weighted regression (MGWR) results indicate that increased potential exposure to fugitive mine waste is significantly associated with COVID-19 mortality in areas of the state where socioeconomically disadvantaged populations were among the hardest hit in the early months of the pandemic. This relationship is paradoxically reversed in global models, which fail to account for spatial relationships between variables. This work contributes both to environmental health sciences and the growing body of literature exploring the implications of spatial nonstationarity in health research.