GEOGRAPHICAL ANALYSIS地理分析
GEOGRAPHICAL ANALYSIS(英文缩写 GEOGR ANAL),ISSN 0016-7363,eISSN 1538-4632,中文译名:地理分析 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 3.566 | Q2 |
| 2022 | 3.600 | Q2 |
| 2023 | 3.300 | Q1 |
| 2024 | 4.300 | Q1 |
| 2025 | 2.400 | Q2 |
GEOGRAPHICAL ANALYSIS 最新收录文献
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1. Exploring Space-Time Interactions in Fatal Opioid Overdoses.
PMID:日期:2025-07-19This study investigates fatal opioid overdoses in Riverside County, California, between January 2020 and March 2023, employing advanced spatial-temporal analysis methods to uncover significant clusters and their underlying contexts. By integrating global and local Knox tests, the research identifies both broad trends and specific hotspots of fatal overdoses. The findings reveal substantial spatial disparities, with higher overdose rates in rural areas and neighborhoods characterized by lower socioeconomic status and larger Hispanic populations. Despite a lower overall overdose risk among Hispanics, their neighborhoods exhibit a higher occurrence of fatal overdoses, highlighting complex interactions between individual and environmental factors. These insights underscore the need for targeted, contextually informed public health interventions and policies to effectively address the opioid crisis.
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2. Absolute Space or Relational Space, Which Governs Spatiotemporally Extended Effects in Disease Dispersion?
PMID:日期:2025-04-29Prevailing disease models typically focus on in-situ effects, that is, the health risks of a location are affected by the transmission-driving factors of the same location. The ex-situ effects, in contrast, extend health risks from neighboring locations and earlier days to focal locations and current dates. These effects could be critical but have not received much attention. This study investigates the extended effects in absolute space and relational space. We examine whether the effects exist, whether they differ between the two spaces, and whether they vary with the order of neighbors and the number of prior dates in both spaces. Results show that extended effects are generally present. Mild effects are identified in absolute space, while greater effects are observed in relational space. The effects vary slightly with the neighbor order in absolute space, but considerably in relational space where the second-order neighbors exert the most prominent effects. In both spaces, the effects diminish at the third-order and last for up to three days. These findings advocate multiple spatializations that offer an in-depth understanding of disease dispersion in specific and dynamic geographic phenomena at large.
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3. An Examination of the Stochastic Distribution of Spatial Accessibility to Intensive Care Unit Beds during the COVID-19 Pandemic: A Case Study of the Greater Houston Area of Texas.
PMID:日期:2022-07-09Sufficient and reliable health care access is necessary for people to be able to maintain good health. Hence, investigating the uncertainty embedded in the temporal changes of inputs would be beneficial for understanding their impact on spatial accessibility. However, previous studies are limited to implementing only the uncertainty of mobility, while health care resource availability is a significant concern during the coronavirus disease (COVID-19) pandemic. Our study examined the stochastic distribution of spatial accessibility under the uncertainties underlying the availability of intensive care unit (ICU) beds and ease of mobility in the Greater Houston area of Texas. Based on the randomized supply and mobility from their historical changes, we employed Monte Carlo simulation to measure ICU bed accessibility with an enhanced two-step floating catchment area (E2SFCA) method. We then conducted hierarchical clustering to classify regions of adequate (sufficient and reliable) accessibility and inadequate (insufficient and unreliable) accessibility. Lastly, we investigated the relationship between the accessibility measures and the case fatality ratio of COVID-19. As result, locations of sufficient access also had reliable accessibility; downtown and outer counties, respectively, had adequate and inadequate accessibility. We also raised the possibility that inadequate health care accessibility may cause higher COVID-19 fatality ratios.
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4. Effects of Vaccination and the Spatio-Temporal Diffusion of Covid-19 Incidence in Turkey.
PMID:日期:2022-06-04This study assesses the spatio-temporal impact of vaccination efforts on Covid-19 incidence growth in Turkey. Incorporating geographical features of SARS-CoV-2 transmission, we adopt a spatial Susceptible-Infected-Recovered (SIR) model that serves as a guide of our empirical specification. Using provincial weekly panel data, we estimate a dynamic spatial autoregressive (SAR) model to elucidate the short- and the long-run impact of vaccination on Covid-19 incidence growth after controlling for temporal and spatio-temporal diffusion, testing capacity, social distancing behavior and unobserved space-varying confounders. Results show that vaccination growth reduces Covid-19 incidence growth rate directly and indirectly by creating a positive externality over space. The significant association between vaccination and Covid-19 incidence is robust to a host of spatial weight matrix specifications. Conspicuous spatial and temporal diffusion effects of Covid-19 incidence growth were found across all specifications: the former being a severer threat to the containment of the pandemic than the latter.
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5. The Spatial Association of Demographic and Population Health Characteristics with COVID-19 Prevalence Across Districts in India.
PMID:日期:2022-05-30In less-developed countries, the lack of granular data limits the researcher's ability to study the spatial interaction of different factors on the COVID-19 pandemic. This study designs a novel database to examine the spatial effects of demographic and population health factors on COVID-19 prevalence across 640 districts in India. The goal is to provide a robust understanding of how spatial associations and the interconnections between places influence disease spread. In addition to the linear Ordinary Least Square regression model, three spatial regression models-Spatial Lag Model, Spatial Error Model, and Geographically Weighted Regression are employed to study and compare the variables explanatory power in shaping geographic variations in the COVID-19 prevalence. We found that the local GWR model is more robust and effective at predicting spatial relationships. The findings indicate that among the demographic factors, a high share of the population living in slums is positively associated with a higher incidence of COVID-19 across districts. The spatial variations in COVID-19 deaths were explained by obesity and high blood sugar, indicating a strong association between pre-existing health conditions and COVID-19 fatalities. The study brings forth the critical factors that expose the poor and vulnerable populations to severe public health risks and highlight the application of geographical analysis vis-a-vis spatial regression models to help explain those associations.
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6. Making Space in Geographical Analysis.
PMID:日期:2022-03-23In this commentary we reflect on the potential and power of geographical analysis, as a set of methods, theoretical approaches, and perspectives, to increase our understanding of how space and place matter for . We emphasize key aspects of the field, including accessibility, urban change, and spatial interaction and behavior, providing a high-level research agenda that indicates a variety of gaps and routes for future research that will not only lead to more equitable and aware solutions to local and global challenges, but also innovative and novel research methods, concepts, and data. We close with a set of representation and inclusion challenges to our discipline, researchers, and publication outlets.
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7. Reproducibility of Research During COVID-19: Examining the Case of Population Density and the Basic Reproductive Rate from the Perspective of Spatial Analysis.
7. COVID-19疫情期间研究的可重复性：从空间分析角度考察人口密度与基本再生率案例PMID:日期:2021-11-18The emergence of the novel SARS-CoV-2 coronavirus and the global COVID-19 pandemic in 2019 led to explosive growth in scientific research. Alas, much of the research in the literature lacks conditions to be reproducible, and recent publications on the association between population density and the basic reproductive number of SARS-CoV-2 are no exception. Relatively few papers share code and data sufficiently, which hinders not only verification but additional experimentation. In this article, an example of reproducible research shows the potential of spatial analysis for epidemiology research during COVID-19. Transparency and openness means that independent researchers can, with only modest efforts, verify findings and use different approaches as appropriate. Given the high stakes of the situation, it is essential that scientific findings, on which good policy depends, are as robust as possible; as the empirical example shows, reproducibility is one of the keys to ensure this.
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8. Reproducible Science Is Vital for a Stronger Evidence Base During the COVID-19 Pandemic.
PMID:日期:2021-11-16Reproducible 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.
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9. Neighborhood Blighted Property Removal and 311 Calls for Non-Emergency Services: A Test of a Marker of Social Control.
PMID:日期:2021-03-15Many studies have demonstrated that collective efficacy is associated with positive health outcomes, lower crime, and violence in urban communities, and residents' emotional connection to their community. Remediation of blighted properties has been theoretically linked to increases in collective efficacy. The purpose of this study was to examine the impact of blighted property remediation on city non-emergency 311 calls for public incivilities and deterioration, as potential markers of collective efficacy. We used a quasi-experimental design to test whether 311 calls for service changed around remediated vacant lots in New Orleans, Louisiana, United States, many of which were left vacant after Hurricane Katrina in 2005. In six city neighborhoods eligible for blighted property remediation as part of a city program, 203 treated vacant lots were matched 1:3 without replacement to control lots that were eligible for but did not receive treatment. This yielded a total of 812 vacant lots partitioned within 48 months, or 38,976 lot-months. Controls were in the same New Orleans neighborhoods as their matched treatment lots but were at least 250 feet away to minimize contamination. Overall difference-in-differences models detected postintervention declines in calls related to dumping and garbage, and slight but mostly non-significant changes in calls between intervention and control lots in all but calls for dumping and vehicles. Blighted property remediation may have an impact on dumping and garbage, which is important. Despite being geographically specific, low-cost and longitudinal, the nature of 311 calls and structural and historic factors at play in both the concentration of vacant properties in communities and residents' willingness to call must be considered. Further analyses of changes in 311 data and additional qualitative inquiry are warranted to more fully determine the utility of these data.
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10. Future Developments in Geographical Agent-Based Models: Challenges and Opportunities.
PMID:日期:2020-12-04Despite reaching a point of acceptance as a research tool across the geographical and social sciences, there remain significant methodological challenges for agent-based models. These include recognizing and simulating emergent phenomena, agent representation, construction of behavioral rules, and calibration and validation. While advances in individual-level data and computing power have opened up new research avenues, they have also brought with them a new set of challenges. This article reviews some of the challenges that the field has faced, the opportunities available to advance the state-of-the-art, and the outlook for the field over the next decade. We argue that although agent-based models continue to have enormous promise as a means of developing dynamic spatial simulations, the field needs to fully embrace the potential offered by approaches from machine learning to allow us to fully broaden and deepen our understanding of geographical systems.