International Journal of Geographical Information Science最新文献

Deconstructing rurality to better "place" health data. 解构农村以更好地“放置”健康数据。

IF 5.1 1区地球科学

International Journal of Geographical Information Science Pub Date : 2025-03-31 DOI: 10.1080/13658816.2025.2482718

Daniel Beene, Yan Lin, Joseph H Hoover, Xun Shi

{"title":"Deconstructing rurality to better \"place\" health data.","authors":"Daniel Beene, Yan Lin, Joseph H Hoover, Xun Shi","doi":"10.1080/13658816.2025.2482718","DOIUrl":"10.1080/13658816.2025.2482718","url":null,"abstract":"Rural-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.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12435940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145075061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SpaCE: a spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome. 空间：用于预测院外心脏骤停生存结果的空间反事实可解释深度学习模型。

IF 5.1 1区地球科学

International Journal of Geographical Information Science Pub Date : 2025-01-28 DOI: 10.1080/13658816.2024.2443757

Jielu Zhang, Lan Mu, Donglan Zhang, Zhuo Chen, Janani Rajbhandari-Thapa, José A Pagán, Yan Li, Gengchen Mai, Zhongliang Zhou

{"title":"SpaCE: a spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome.","authors":"Jielu Zhang, Lan Mu, Donglan Zhang, Zhuo Chen, Janani Rajbhandari-Thapa, José A Pagán, Yan Li, Gengchen Mai, Zhongliang Zhou","doi":"10.1080/13658816.2024.2443757","DOIUrl":"https://doi.org/10.1080/13658816.2024.2443757","url":null,"abstract":"Understanding 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.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12377555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144953919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A research agenda for GIScience in a time of disruptions. 混乱时期gisscience的研究议程。

IF 5.1 1区地球科学

International Journal of Geographical Information Science Pub Date : 2025-01-01 Epub Date: 2024-09-29 DOI: 10.1080/13658816.2024.2405191

Trisalyn Nelson, Amy E Frazier, Peter Kedron, Somayeh Dodge, Bo Zhao, Michael Goodchild, Alan Murray, Sarah Battersby, Lauren Bennett, Justine I Blanford, Carmen Cabrera-Arnau, Christophe Claramunt, Rachel Franklin, Joseph Holler, Caglar Koylu, Angela Lee, Steven Manson, Grant McKenzie, Harvey Miller, Taylor Oshan, Sergio Rey, Francisco Rowe, Seda Şalap-Ayça, Eric Shook, Seth Spielman, Wenfei Xu, John Wilson

{"title":"A research agenda for GIScience in a time of disruptions.","authors":"Trisalyn Nelson, Amy E Frazier, Peter Kedron, Somayeh Dodge, Bo Zhao, Michael Goodchild, Alan Murray, Sarah Battersby, Lauren Bennett, Justine I Blanford, Carmen Cabrera-Arnau, Christophe Claramunt, Rachel Franklin, Joseph Holler, Caglar Koylu, Angela Lee, Steven Manson, Grant McKenzie, Harvey Miller, Taylor Oshan, Sergio Rey, Francisco Rowe, Seda Şalap-Ayça, Eric Shook, Seth Spielman, Wenfei Xu, John Wilson","doi":"10.1080/13658816.2024.2405191","DOIUrl":"10.1080/13658816.2024.2405191","url":null,"abstract":"Social 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.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"39 1","pages":"1-24"},"PeriodicalIF":5.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12347541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring Human Mobility: A Time-Informed Approach to Pattern Mining and Sequence Similarity. 探索人类移动性：一种基于时间信息的模式挖掘和序列相似性方法。

IF 4.3 1区地球科学

International Journal of Geographical Information Science Pub Date : 2025-01-01 Epub Date: 2024-11-21 DOI: 10.1080/13658816.2024.2427258

Hao Yang, X Angela Yao, Christopher C Whalen, Noah Kiwanuka

{"title":"Exploring Human Mobility: A Time-Informed Approach to Pattern Mining and Sequence Similarity.","authors":"Hao Yang, X Angela Yao, Christopher C Whalen, Noah Kiwanuka","doi":"10.1080/13658816.2024.2427258","DOIUrl":"https://doi.org/10.1080/13658816.2024.2427258","url":null,"abstract":"The 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.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"39 3","pages":"627-651"},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11906185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143648472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GPU-accelerated parallel all-pair shortest path routing within stochastic road networks 随机道路网络中的 GPU 加速并行全对最短路径路由选择

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2024-09-01 DOI: 10.1080/13658816.2024.2394651

Wenwu Tang, Tianyang Chen, Marc P. Armstrong

引用次数: 0

Collective flow-evolutionary patterns reveal the mesoscopic structure between snapshots of spatial network 集体流演变模式揭示了空间网络快照之间的中观结构

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2024-09-01 DOI: 10.1080/13658816.2024.2395953

Zhongfu Ma, Di Zhu

引用次数: 0

Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi’s domain adaptability 用于图像分析的地理空间基础模型：评估和增强 NASA-IBM Prithvi 的领域适应性

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2024-08-30 DOI: 10.1080/13658816.2024.2397441

Chia-Yu Hsu, Wenwen Li, Sizhe Wang

引用次数: 0

Translating street view imagery to correct perspectives to enhance bikeability and walkability studies 将街景图像转换为正确的视角，以加强自行车可骑性和步行可行性研究

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2024-08-27 DOI: 10.1080/13658816.2024.2391969

Koichi Ito, Matias Quintana, Xianjing Han, Roger Zimmermann, Filip Biljecki

引用次数: 0

A multi-view ensemble machine learning approach for 3D modeling using geological and geophysical data 利用地质和地球物理数据进行三维建模的多视角集合机器学习方法

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2024-08-22 DOI: 10.1080/13658816.2024.2394228

Deping Chu, Jinming Fu, Bo Wan, Hong Li, Lulan Li, Fang Fang, Shengwen Li, Shengyong Pan, Shunping Zhou

引用次数: 0

A backfitting maximum likelihood estimator for hierarchical and geographically weighted regression modelling, with a case study of house prices in Beijing 用于分层和地理加权回归建模的反拟合最大似然估计器，以北京房价为例进行研究

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2024-08-21 DOI: 10.1080/13658816.2024.2391412

Yigong Hu, Richard Harris, Richard Timmerman, Binbin Lu

引用次数: 0