INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE国际地理信息科学杂志
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE(英文缩写 INT J GEOGR INF SCI),ISSN 1365-8816,eISSN 1362-3087,中文译名:国际地理信息科学杂志 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：2025-10-01 至 2026-09-30，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 | 5.152 | Q1 |
| 2022 | 5.700 | Q1 |
| 2023 | 4.300 | Q1 |
| 2024 | 5.100 | Q1 |
| 2025 | 5.900 | Q1 |
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 最新收录文献
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1. Pharos: a mobile GIS application for network assessment and ground-truth mapping to support mHealth study design in low-resource settings.
PMID:日期:2026-03-17Mobile health (mHealth) is a promising tool for improving healthcare access, particularly in low-resource settings. However, limited mobile accessibility and poor connectivity remain significant barriers to implementing mHealth interventions in these regions. To address these challenges and support the development and scalability of mHealth studies, we developed Pharos, a mobile GIS application designed to assess network coverage and facilitate ground-truth mapping. Pharos autonomously measures spatiotemporal variations in mobile network signal strength and enables precise mapping of critical landmarks and environmental features. We deployed Pharos in a four-county region along the shores of Lake Victoria in Western Kenya as part of a preparatory phase for a large-scale mHealth study focused on schistosomiasis control. Over six months and 10,000 km, Pharos collected high- resolution data on network performance and landmark locations, generating a comprehensive dataset that links network availability with environmental features. These results provide essential insights for planning and implementing mHealth interventions in low-resource settings, with potential applications in infectious disease surveillance and other global health initiatives.
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2. Beyond absolute space: modeling disease dispersion and reactive actions from a multi-spatialization perspective.
PMID:日期:2025-09-25Dynamic geographical phenomena, such as the transmission of communicable diseases, are inherently complex processes. Concerns have arisen in the GIScience community that the prevailing absolute spatialization is insufficient to capture the complexity. This study investigates health risks in the frame of multiple spatializations: relational space (home and workplaces), relative space (serviceplaces), and mental space (perception). First, we estimate health risks in terms of the presence of influenza-like illness symptoms in relational space and relative space. Second, we estimate the probability of taking reactive actions based on health threats perceived in mental space. A two-layer Bayesian network model and the SHAP model are used to support the intended study. Findings reaffirm the pivotal role of relational space in disease transmission. Relative space is found to impose substantial health risks that exceeded those of relational space, yet the risks varied across serviceplace types. Perceived health threats in mental space effectively motivated reactive actions. The multi-spatialization frame enables the representation of health risks at the nexus of proximity, relations, relative contacts, and perception, and can be extended to many geographical phenomena both theoretically and empirically.
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3. Deconstructing rurality to better "place" health data.
PMID:日期:2025-03-31Rural-urban classification schemes are frequently used in ecological studies of population health. However, the algorithms used to produce these classifications as well as their underlying assumptions may not match their intended use in health research. Here, we focus on the spatial distribution of features of the physical environment that are related to health - such as healthcare - to examine the extent to which eight classification schemes capture the heterogeneous context of rural places. We further explore how well rural-urban classifications distinguish between different types of rural places by comparing rural Tribal reservations with other rural areas in the American southwest. Because health services and infrastructure are often distributed through state and federal programs to underserved populations in rural areas, this approach speaks to the broader political implications in how rural communities are defined and represented. Results indicate that rural-urban classifications do not adequately reflect heterogeneous contexts within and across rural places. We advocate for more appropriate population health models that explain contextual differences in the relationship between health and place.
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4. SpaCE: a spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome.
PMID:日期:2025-01-28Understanding the relationship between risk factors, geospatial patterns, and disease outcomes is essential in health geography research. These relationships can inform the implementation of healthcare and public health strategies to improve health outcomes. To accurately uncover such complex relationships, it is necessary to have a predictive model capable of integrating both health variables and spatial information to forecast health outcomes, along with a tool to interpret and reveal the patterns identified by this model. We developed a Spatial Counterfactual Explainable Deep Learning model (SpaCE), comprising a spatially explicit health outcome predictor and a prototype-guided counterfactual explanation. The SpaCE model unifies geospatial and health variables to improve predictions and generates hypothetical examples with minimal changes but opposite outcomes. Using these counterfactuals, SpaCE assesses the impact of each variable in different spatial contexts. We evaluated the model for predicting cardiac arrest survival outcomes. With a 0.682 AUCROC score, the SpaCE exceeds baseline models by 10.2%. Further analysis also reveals that the geospatial context significantly affects how various risk factors affect the survival outcomes of patients. Overall, the SpaCE model significantly improves predictive accuracy and explainability. It provides targeted interventions at both individual and geographic levels, and the cardiac arrest case study shows its high adaptability to various disease scenarios.
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5. Exploring Human Mobility: A Time-Informed Approach to Pattern Mining and Sequence Similarity.
PMID:日期:2024-11-21The surge in the availability of spatial big data has sparked increased interest in researching human mobility patterns. Despite this, discovering human mobility patterns from such spatial big data and assessing the similarity between patterns remains a formidable challenge. This study introduces two novel methods: the Time-Informed pattern mining (TiPam) method for frequent pattern mining and a Time-Aware Longest Common Subsequence (T-LCS) algorithm for assessing similarity between time-conscious sequences. Leveraging these innovative algorithms, our research introduces an analytical framework for analyzing human mobility patterns at both individual and aggregated levels. As a case study, this proposed workflow is applied to examine the daily mobility patterns of voluntary mobile phone users in Kampala, Uganda. The 135 participants are found in four distinct groups labeled with distinct mobility properties for users in each group: "stay-at-home," "unoccupied," "education-oriented," and "work-oriented." The results effectively showcase the efficiency of the framework and the novel techniques employed. The framework's versatility extends to human mobility studies with other forms of data and across various research fields.
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6. A research agenda for GIScience in a time of disruptions.
PMID:日期:2024-09-29Social issues, AI, and climate change are just a few of the disruptive focuses impacting science. The field of GIScience is well positioned to respond to accelerating disruptions due to the interdisciplinary nature of the field and the ability of GIScience approaches to be used in support of decision-making. This manuscript aims to start a conversation that will establish a research agenda for GIScience in an age of disruptions. We outline three guiding principles: (1) focusing on the relevance and real-world impact of research, (2) adopting systems-based thinking and contextual approaches and (3) emphasizing inclusive practices. We then outline prioritized research areas organized by what topics are important focal areas (Data and Infrastructure, Artificial Intelligence, and Causality and Generalizability), and what approaches to science we should be attentive to (Impactful Open Science, Collaborative and Convergent Science, and through Diverse Participation and Partnerships). We conclude with a call to increase impact by balancing slow science with practical and policy-oriented research. We also recognize that while broad adoption of spatial approaches is a signal of GIScience's success, we should continue to work together to advance core knowledge centered on spatial thinking and approaches.
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7. Visit probability and accessibility within space-time prism of activity program.
PMID:日期:2024-07-17Space-time prism (STP) is widely applied to measure individuals' ability to reach opportunities given the resource limitations. The majority of STP models treat prism-based accessibility as binary measures, wherein all locations within a prism are assumed equally accessible while others are deemed inaccessible. Although a few STP models examined heterogeneous interiors by modeling visit probabilities within the STPs, they primarily focused on trip-level analysis and did not explore the application for accessibility measurement. This study proposes a model framework based on multi-state supernetworks for constructing and estimating the probabilistic STPs of daily activity programs. The estimation is implemented with latent class models to account for individual heterogeneities in travel and activity participation. Based on the probabilistic STPs, we suggest a space-time accessibility measurement incorporating visit probabilities. We validate the model framework using mobility trajectory data collected in the Netherlands and demonstrate that the visit probability model can effectively capture the probabilistic STP interiors and the proposed accessibility measurement can provide a comprehensive evaluation of accessibility in the presence of activity chains.
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8. Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI.
PMID:日期:2024-06-20Cartographic map generalization involves complex rules, and a full automation has still not been achieved, despite many efforts over the past few decades. Pioneering studies show that some map generalization tasks can be partially automated by deep neural networks (DNNs). However, DNNs are still used as black-box models in previous studies. We argue that integrating explainable AI (XAI) into a DL-based map generalization process can give more insights to develop and refine the DNNs by understanding what cartographic knowledge exactly is learned. Following an XAI framework for an empirical case study, visual analytics and quantitative experiments were applied to explain the importance of input features regarding the prediction of a pre-trained ResU-Net model. This experimental case study finds that the XAI-based visualization results can easily be interpreted by human experts. With the proposed XAI workflow, we further find that the DNN pays more attention to the building boundaries than the interior parts of the buildings. We thus suggest that boundary intersection over union is a better evaluation metric than commonly used intersection over union in qualifying raster-based map generalization results. Overall, this study shows the necessity and feasibility of integrating XAI as part of future DL-based map generalization development frameworks.
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9. An Evaluation of Geo-located Twitter Data for Measuring Human Migration.
PMID:日期:2022-06-15This study evaluates the spatial patterns of flows generated from geo-located Twitter data to measure human migration. Using geo-located tweets continuously collected in the U.S. from 2013 to 2015, we identified Twitter users who migrated per changes in county-of-residence every two years and compared the Twitter-estimated county-to-county migration flows with the ones from the U.S. Internal Revenue Service (IRS). To evaluate the spatial patterns of Twitter migration flows when representing the IRS counterparts, we developed a normalized difference representation index to visualize and identify those counties of over-/under-representations in the Twitter estimates. Further, we applied a multidimensional spatial scan statistic approach based on a Poisson process model to detect pairs of origin and destination regions where the over-/under-representativeness occurred. The results suggest that Twitter migration flows tend to under-represent the IRS estimates in regions with a large population and over-represent them in metropolitan regions adjacent to tourist attractions. This study demonstrated that geo-located Twitter data could be a sound statistical proxy for measuring human migration. Given that the spatial patterns of Twitter-estimated migration flows vary significantly across the geographic space, related studies will benefit from our approach by identifying those regions where data calibration is necessary.
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10. A grammar for interpreting geo-analytical questions as concept transformations.
PMID:日期:2022-05-30Geographic Question Answering (GeoQA) systems can automatically answer questions phrased in natural language. Potentially this may enable data analysts to make use of geographic information without requiring any GIS skills. However, going beyond the retrieval of existing geographic facts on particular places remains a challenge. Current systems usually cannot handle geo-analytical questions that require GIS analysis procedures to arrive at answers. To enable , GeoQA systems need to interpret questions in terms of a transformation that can be implemented in a GIS workflow. To this end, we propose a novel approach to question parsing that interprets questions in terms of core concepts of spatial information and their functional roles in context-free grammar. The core concepts help model spatial information in questions independently from implementation formats, and their functional roles indicate how concepts are transformed and used in a workflow. Using our parser, geo-analytical questions can be converted into expressions of concept transformations corresponding to abstract GIS workflows. We developed our approach on a corpus of 309 GIS-related questions and tested it on an independent source of 134 test questions including workflows. The evaluation results show high precision and recall on a gold standard of concept transformations.