Pub Date : 2023-10-10DOI: 10.1080/13658816.2023.2264921
Chanwoo Jin, Sohyun Park, Hui Jeong Ha, Jinhyung Lee, Junghwan Kim, Johan Hutchenreuther, Atsushi Nara
AbstractHuman mobility analytics using artificial intelligence (AI) has gained significant attention with advancements in computational power and the availability of high-resolution spatial data. However, the application of deep learning in social sciences and human geography remains limited, primarily due to concerns with model explainability. In this study, we employ an explainable GeoAI approach called geographically localized interpretable model-agnostic explanation (GLIME) to explore human mobility patterns over large spatial and temporal extents. Specifically, we develop a two-layered long short-term memory (LSTM) model capable of predicting individual-level residential mobility patterns across the United States from 2012 to 2019. We leverage GLIME to provide geographical perspectives and interpret deep neural networks at the state level. The results reveal that GLIME enables spatially explicit interpretations of local impacts attributed to different variables. Our findings underscore the significance of considering path dependency in residential mobility dynamics. While the prediction of complex human spatial decision-making processes still presents challenges, this research demonstrates the utility of deep neural networks and explainable GeoAI to support human dynamics understanding. It sets the stage for further finely tuned investigations in the future, promising deep insights into intricate mobility phenomena.Keywords: Explainable GeoAImodel-agnostic explanationlong short-term memory (LSTM)trajectory predictionresidential mobility AcknowledgementsAny opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data, codes, and instructions that support the findings of this study are available on figshare at https://doi.org/10.6084/m9.figshare.21543549.v1Notes1 We have 75 variables in total as categorical variables including state and housing type are input as dummy variables into the models.Additional informationFundingThis research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant No. RS-2022-00165821) and the Faculty of Social Science at Western University. This work was also supported in part by the National Science Foundation under Grant No. 2031407.Notes on contributorsChanwoo JinChanwoo Jin is an assistant professor in the Department of Humanities and Social Sciences at Northwest Missouri State University. He holds a PhD in Geography at the University of California, Santa Barbara/San Diego State University (Joint Doctoral Program). His main research interests include big spatiotemporal data analysis, Geospatial Artificial Intelligence (GeoAI), human mobility and urban dynamics.Sohyun ParkSohyun Park is an assistant professo
{"title":"Predicting households’ residential mobility trajectories with geographically localized interpretable model-agnostic explanation (GLIME)","authors":"Chanwoo Jin, Sohyun Park, Hui Jeong Ha, Jinhyung Lee, Junghwan Kim, Johan Hutchenreuther, Atsushi Nara","doi":"10.1080/13658816.2023.2264921","DOIUrl":"https://doi.org/10.1080/13658816.2023.2264921","url":null,"abstract":"AbstractHuman mobility analytics using artificial intelligence (AI) has gained significant attention with advancements in computational power and the availability of high-resolution spatial data. However, the application of deep learning in social sciences and human geography remains limited, primarily due to concerns with model explainability. In this study, we employ an explainable GeoAI approach called geographically localized interpretable model-agnostic explanation (GLIME) to explore human mobility patterns over large spatial and temporal extents. Specifically, we develop a two-layered long short-term memory (LSTM) model capable of predicting individual-level residential mobility patterns across the United States from 2012 to 2019. We leverage GLIME to provide geographical perspectives and interpret deep neural networks at the state level. The results reveal that GLIME enables spatially explicit interpretations of local impacts attributed to different variables. Our findings underscore the significance of considering path dependency in residential mobility dynamics. While the prediction of complex human spatial decision-making processes still presents challenges, this research demonstrates the utility of deep neural networks and explainable GeoAI to support human dynamics understanding. It sets the stage for further finely tuned investigations in the future, promising deep insights into intricate mobility phenomena.Keywords: Explainable GeoAImodel-agnostic explanationlong short-term memory (LSTM)trajectory predictionresidential mobility AcknowledgementsAny opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data, codes, and instructions that support the findings of this study are available on figshare at https://doi.org/10.6084/m9.figshare.21543549.v1Notes1 We have 75 variables in total as categorical variables including state and housing type are input as dummy variables into the models.Additional informationFundingThis research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant No. RS-2022-00165821) and the Faculty of Social Science at Western University. This work was also supported in part by the National Science Foundation under Grant No. 2031407.Notes on contributorsChanwoo JinChanwoo Jin is an assistant professor in the Department of Humanities and Social Sciences at Northwest Missouri State University. He holds a PhD in Geography at the University of California, Santa Barbara/San Diego State University (Joint Doctoral Program). His main research interests include big spatiotemporal data analysis, Geospatial Artificial Intelligence (GeoAI), human mobility and urban dynamics.Sohyun ParkSohyun Park is an assistant professo","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136357441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1080/13658816.2023.2266495
Yingjie Hu, Gengchen Mai, Chris Cundy, Kristy Choi, Ni Lao, Wei Liu, Gaurish Lakhanpal, Ryan Zhenqi Zhou, Kenneth Joseph
Social media messages posted by people during natural disasters often contain important location descriptions, such as the locations of victims. Recent research has shown that many of these location descriptions go beyond simple place names, such as city names and street names, and are difficult to extract using typical named entity recognition (NER) tools. While advanced machine learning models could be trained, they require large labeled training datasets that can be time-consuming and labor-intensive to create. In this work, we propose a method that fuses geo-knowledge of location descriptions and a Generative Pre-trained Transformer (GPT) model, such as ChatGPT and GPT-4. The result is a geo-knowledge-guided GPT model that can accurately extract location descriptions from disaster-related social media messages. Also, only 22 training examples encoding geo-knowledge are used in our method. We conduct experiments to compare this method with nine alternative approaches on a dataset of tweets from Hurricane Harvey. Our method demonstrates an over 40% improvement over typically used NER approaches. The experiment results also show that geo-knowledge is indispensable for guiding the behavior of GPT models. The extracted location descriptions can help disaster responders reach victims more quickly and may even save lives.
{"title":"Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages","authors":"Yingjie Hu, Gengchen Mai, Chris Cundy, Kristy Choi, Ni Lao, Wei Liu, Gaurish Lakhanpal, Ryan Zhenqi Zhou, Kenneth Joseph","doi":"10.1080/13658816.2023.2266495","DOIUrl":"https://doi.org/10.1080/13658816.2023.2266495","url":null,"abstract":"Social media messages posted by people during natural disasters often contain important location descriptions, such as the locations of victims. Recent research has shown that many of these location descriptions go beyond simple place names, such as city names and street names, and are difficult to extract using typical named entity recognition (NER) tools. While advanced machine learning models could be trained, they require large labeled training datasets that can be time-consuming and labor-intensive to create. In this work, we propose a method that fuses geo-knowledge of location descriptions and a Generative Pre-trained Transformer (GPT) model, such as ChatGPT and GPT-4. The result is a geo-knowledge-guided GPT model that can accurately extract location descriptions from disaster-related social media messages. Also, only 22 training examples encoding geo-knowledge are used in our method. We conduct experiments to compare this method with nine alternative approaches on a dataset of tweets from Hurricane Harvey. Our method demonstrates an over 40% improvement over typically used NER approaches. The experiment results also show that geo-knowledge is indispensable for guiding the behavior of GPT models. The extracted location descriptions can help disaster responders reach victims more quickly and may even save lives.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135043452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1080/13658816.2023.2262550
Jinmeng Rao, Song Gao, Sijia Zhu
AbstractThe prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users. Such trajectory big data bring new opportunities to human mobility research but also raise public concerns with regard to location privacy. In this work, we present the Conditional Adversarial Trajectory Synthesis (CATS), a deep-learning-based GeoAI methodological framework for privacy-preserving trajectory data generation and publication. CATS applies K-anonymity to the underlying spatiotemporal distributions of human movements, which provides a distributional-level strong privacy guarantee. By leveraging conditional adversarial training on K-anonymized human mobility matrices, trajectory global context learning using the attention-based mechanism, and recurrent bipartite graph matching of adjacent trajectory points, CATS is able to reconstruct trajectory topology from conditionally sampled locations and generate high-quality individual-level synthetic trajectory data, which can serve as supplements or alternatives to raw data for privacy-preserving trajectory data publication. The experiment results on over 90k GPS trajectories show that our method has a better performance in privacy preservation, spatiotemporal characteristic preservation, and downstream utility compared with baseline methods, which brings new insights into privacy-preserving human mobility research using generative AI techniques and explores data ethics issues in GIScience.Keywords: Geoprivacygenerative adversarial networkhuman mobilityGeoAIsynthetic data generation AcknowledgmentThe authors acknowledge the funding support provided by the American Family Insurance Data Science Institute Funding Initiative at the University of Wisconsin-Madison. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funder(s).Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data and codes that support the findings of this study are available at the following link on figshare: https://doi.org/10.6084/m9.figshare.20760970. It is worth noting that due to the non-disclosure agreement with the data provider, we are not releasing the original individual-level GPS trajectory data but sharing the k-anonymized aggregated human mobility data used in our experiments.Additional informationNotes on contributorsJinmeng RaoJinmeng Rao is a research scientist at Mineral Earth Sciences. He received his PhD degree from the Department of Geography, University of Wisconsin-Madison. His research interests include GeoAI, Privacy-Preserving AI, and Location Privacy.Song GaoSong Gao is an associate professor in GIScience at the Department of Geography, University of Wisconsin-Madison. He holds a PhD in Geography at the University of California, Santa Barbara. His main research intere
{"title":"CATS: Conditional Adversarial Trajectory Synthesis for privacy-preserving trajectory data publication using deep learning approaches","authors":"Jinmeng Rao, Song Gao, Sijia Zhu","doi":"10.1080/13658816.2023.2262550","DOIUrl":"https://doi.org/10.1080/13658816.2023.2262550","url":null,"abstract":"AbstractThe prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users. Such trajectory big data bring new opportunities to human mobility research but also raise public concerns with regard to location privacy. In this work, we present the Conditional Adversarial Trajectory Synthesis (CATS), a deep-learning-based GeoAI methodological framework for privacy-preserving trajectory data generation and publication. CATS applies K-anonymity to the underlying spatiotemporal distributions of human movements, which provides a distributional-level strong privacy guarantee. By leveraging conditional adversarial training on K-anonymized human mobility matrices, trajectory global context learning using the attention-based mechanism, and recurrent bipartite graph matching of adjacent trajectory points, CATS is able to reconstruct trajectory topology from conditionally sampled locations and generate high-quality individual-level synthetic trajectory data, which can serve as supplements or alternatives to raw data for privacy-preserving trajectory data publication. The experiment results on over 90k GPS trajectories show that our method has a better performance in privacy preservation, spatiotemporal characteristic preservation, and downstream utility compared with baseline methods, which brings new insights into privacy-preserving human mobility research using generative AI techniques and explores data ethics issues in GIScience.Keywords: Geoprivacygenerative adversarial networkhuman mobilityGeoAIsynthetic data generation AcknowledgmentThe authors acknowledge the funding support provided by the American Family Insurance Data Science Institute Funding Initiative at the University of Wisconsin-Madison. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funder(s).Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data and codes that support the findings of this study are available at the following link on figshare: https://doi.org/10.6084/m9.figshare.20760970. It is worth noting that due to the non-disclosure agreement with the data provider, we are not releasing the original individual-level GPS trajectory data but sharing the k-anonymized aggregated human mobility data used in our experiments.Additional informationNotes on contributorsJinmeng RaoJinmeng Rao is a research scientist at Mineral Earth Sciences. He received his PhD degree from the Department of Geography, University of Wisconsin-Madison. His research interests include GeoAI, Privacy-Preserving AI, and Location Privacy.Song GaoSong Gao is an associate professor in GIScience at the Department of Geography, University of Wisconsin-Madison. He holds a PhD in Geography at the University of California, Santa Barbara. His main research intere","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135142073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1080/13658816.2023.2266497
Hang Zhang, Guanpeng Dong, Jinfeng Wang, Tong-Lin Zhang, Xiaoyu Meng, Dongyang Yang, Yong Liu, Binbin Lu
The Geographical Detector Model (GDM) is a popular statistical toolkit for geographical attribution analysis. Despite the striking resemblance of the q-statistic in GDM to the R-squared in linear regression models, their explicit connection has not yet been established. This study proves that the q-statistic reduces into the R-squared under a linear regression framework. Under linear regression and moderate-to-strong spatial autocorrelation, Monte Carlo simulation results show that the GDM tends to underestimate the importance of variables. In addition, an almost perfect power law relationship is present between the percentage bias and the degree of the spatial autocorrelations, indicating the presence of fast uplifting bias in response to increasing levels of spatial autocorrelations. We propose an integrated approach for variable importance quantification by bringing together the spatial econometrics model and the game theory based-Shapley value method. By applying our proposed methodology to a case study of land desertification in African, it is found human activity tends to affect land desertification both directly and indirectly. However, such effects appear to be underestimated or undistinguished in the classic GDM.
{"title":"Understanding and extending the geographical detector model under a linear regression framework","authors":"Hang Zhang, Guanpeng Dong, Jinfeng Wang, Tong-Lin Zhang, Xiaoyu Meng, Dongyang Yang, Yong Liu, Binbin Lu","doi":"10.1080/13658816.2023.2266497","DOIUrl":"https://doi.org/10.1080/13658816.2023.2266497","url":null,"abstract":"The Geographical Detector Model (GDM) is a popular statistical toolkit for geographical attribution analysis. Despite the striking resemblance of the q-statistic in GDM to the R-squared in linear regression models, their explicit connection has not yet been established. This study proves that the q-statistic reduces into the R-squared under a linear regression framework. Under linear regression and moderate-to-strong spatial autocorrelation, Monte Carlo simulation results show that the GDM tends to underestimate the importance of variables. In addition, an almost perfect power law relationship is present between the percentage bias and the degree of the spatial autocorrelations, indicating the presence of fast uplifting bias in response to increasing levels of spatial autocorrelations. We propose an integrated approach for variable importance quantification by bringing together the spatial econometrics model and the game theory based-Shapley value method. By applying our proposed methodology to a case study of land desertification in African, it is found human activity tends to affect land desertification both directly and indirectly. However, such effects appear to be underestimated or undistinguished in the classic GDM.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135141958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1080/13658816.2023.2266824
Rui Ma, Jiayu Chen, Chendi Yang, Xin Li
AbstractSemantic 3D city models have been widely used in computer graphics, geomatics, planning, construction, and urban simulation. While traditional geometric models are used only for visualization purposes, semantic 3D city models contain abundant detailed information, such as location, classification, and functional aspects. Such semantics can facilitate a better interpretation of the built environment by computers. However, the current semantic 3D city models are mostly specific to particular city object types and features, with unclear spatial semantics, which limits their broader applications. This study, therefore, proposes a novel framework called OSMsc, where OSM refers to OpenStreetMap and sc refers to semantic city. The OSMsc framework considers OSM as the primary data source to construct city objects within the specified study area, construct semantic connectors, enrich spatial semantics, and generate the CityJSON-formatted model. The case studies demonstrate that semantic 3D city models constructed by OSMsc are free from geometric and semantic errors, applicable to any city worldwide, and have potential for urban studies, such as urban morphology and urban microclimate analysis.Keywords: Semantic 3D city modelspatial semanticsCityJSONOpenStreetMap Authors’ contributionsRui Ma: conceptualization, data collection, coding design, analysis, manuscript writing and subsequent revisions. Jiayu Chen: conceptualization, manuscript review and subsequent revisions. Chendi Yang: data acquisition and visualization. Xin Li: project administration, conceptualization, manuscript writing, reviewing, and revisions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe source code for OSMsc is available at GitHub (https://github.com/ruirzma/osmsc) and the Semantic 3D City Models (S3CMs) of 25 cities in the US and Europe are available at Figshare (https://doi.org/10.6084/m9.figshare.21779507.v2).Additional informationNotes on contributorsRui MaRui Ma is a PhD candidate in the Department of Architecture and Civil Engineering, City University of Hong Kong. His research interests include urban energy modeling, GIS spatial analysis and semantic city modeling.Jiayu ChenJiayu Chen is an Associate Professor in the Department of Construction Management at Tsinghua University. His research focuses on human-centric intelligent construction systems, human-machine collaboration, and urban building digital modeling.Chendi YangChendi Yang is a PhD candidate in the Department of Architecture and Civil Engineering, City University of Hong Kong. Her main research interests include the built environment, spatial analysis, human behavior and urban analytics.Xin LiXin Li is an Associate Professor of Urban Planning at the Department of Architecture and Civil Engineering, City University of Hong Kong. Her research uses economic theories and statistical and GIS tools to study a wide range of urban issues,
{"title":"OSMsc: a framework for semantic 3D city modeling using OpenStreetMap","authors":"Rui Ma, Jiayu Chen, Chendi Yang, Xin Li","doi":"10.1080/13658816.2023.2266824","DOIUrl":"https://doi.org/10.1080/13658816.2023.2266824","url":null,"abstract":"AbstractSemantic 3D city models have been widely used in computer graphics, geomatics, planning, construction, and urban simulation. While traditional geometric models are used only for visualization purposes, semantic 3D city models contain abundant detailed information, such as location, classification, and functional aspects. Such semantics can facilitate a better interpretation of the built environment by computers. However, the current semantic 3D city models are mostly specific to particular city object types and features, with unclear spatial semantics, which limits their broader applications. This study, therefore, proposes a novel framework called OSMsc, where OSM refers to OpenStreetMap and sc refers to semantic city. The OSMsc framework considers OSM as the primary data source to construct city objects within the specified study area, construct semantic connectors, enrich spatial semantics, and generate the CityJSON-formatted model. The case studies demonstrate that semantic 3D city models constructed by OSMsc are free from geometric and semantic errors, applicable to any city worldwide, and have potential for urban studies, such as urban morphology and urban microclimate analysis.Keywords: Semantic 3D city modelspatial semanticsCityJSONOpenStreetMap Authors’ contributionsRui Ma: conceptualization, data collection, coding design, analysis, manuscript writing and subsequent revisions. Jiayu Chen: conceptualization, manuscript review and subsequent revisions. Chendi Yang: data acquisition and visualization. Xin Li: project administration, conceptualization, manuscript writing, reviewing, and revisions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe source code for OSMsc is available at GitHub (https://github.com/ruirzma/osmsc) and the Semantic 3D City Models (S3CMs) of 25 cities in the US and Europe are available at Figshare (https://doi.org/10.6084/m9.figshare.21779507.v2).Additional informationNotes on contributorsRui MaRui Ma is a PhD candidate in the Department of Architecture and Civil Engineering, City University of Hong Kong. His research interests include urban energy modeling, GIS spatial analysis and semantic city modeling.Jiayu ChenJiayu Chen is an Associate Professor in the Department of Construction Management at Tsinghua University. His research focuses on human-centric intelligent construction systems, human-machine collaboration, and urban building digital modeling.Chendi YangChendi Yang is a PhD candidate in the Department of Architecture and Civil Engineering, City University of Hong Kong. Her main research interests include the built environment, spatial analysis, human behavior and urban analytics.Xin LiXin Li is an Associate Professor of Urban Planning at the Department of Architecture and Civil Engineering, City University of Hong Kong. Her research uses economic theories and statistical and GIS tools to study a wide range of urban issues, ","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135141826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AbstractFor urban growth modeling, assessment metrics derived from cell-by-cell comparisons are mainly related to the size of the study area and the urban growth rate. Non-urban areas always occupy an important part of the city to which cellular automata (CA) models do not contribute much, so the simulation accuracy is often exaggerated when this part is included. To enable comparing simulation results across models, regions, and time, we developed an improved equivalent area-based assessment (EQASS) method using cell-by-cell comparison metrics. As against existing assessment methods, EQASS is computed by including the same area of urban and suburban areas (i.e., equivalent areas). EQASS was tested in three Chinese coastal cities using a heuristic CA model and two spatial statistical CA models to simulate urban growth. The results show that EQASS can exclude correct rejections that are not attributable to CA models; these correct rejections have a significant impact on the model assessment. The improved assessment can better evaluate the performance of CA models across regions and over time than the conventional assessment method that accounts for the full study area. This study extends the simulation assessment method and provides a good solution for selecting the best CA model from many candidate models.Keywords: Model assessmentcellular automatabuffer analysisurban growthaccuracy comparison Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe software, codes and input datasets involved in this study are available at https://doi.org/10.6084/m9.figshare.21203147.Additional informationFundingSupported by the National Natural Science Foundation of China (42071371) and the National Key R&D Program of China (2018YFB0505400).Notes on contributorsChen GaoChen Gao received the M.S. degree in marine sciences from Shanghai Ocean University, Shanghai, China, in 2021. She is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Yongjiu FengYongjiu Feng received the Ph.D. degree in geomatics from Tongji University, Shanghai, China, in 2009. He is currently a Professor and Associate Dean with the College of Surveying and Geo-Informatics, Tongji University. His research interests include spatial modeling, synthetic aperture radar, and radar detection of the moon and deep space.Mengrong XiMengrong Xi received the B.E. degree in geomatics engineering from Tongji University, Shanghai, China, in 2022. He is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Rong WangRong Wang received the M.S. degree in marine sciences from Shanghai Ocean University, Shanghai, China, in 2022. She is currently working toward the Ph.D. degree in artificial intelligence with Tongji University, Shanghai, China.Pengshuo LiPengshuo Li received the B.E. degree in geomatics engineering from Tongj
{"title":"An improved assessment method for urban growth simulations across models, regions, and time","authors":"Chen Gao, Yongjiu Feng, Mengrong Xi, Rong Wang, Pengshuo Li, Xiaoyan Tang, Xiaohua Tong","doi":"10.1080/13658816.2023.2264942","DOIUrl":"https://doi.org/10.1080/13658816.2023.2264942","url":null,"abstract":"AbstractFor urban growth modeling, assessment metrics derived from cell-by-cell comparisons are mainly related to the size of the study area and the urban growth rate. Non-urban areas always occupy an important part of the city to which cellular automata (CA) models do not contribute much, so the simulation accuracy is often exaggerated when this part is included. To enable comparing simulation results across models, regions, and time, we developed an improved equivalent area-based assessment (EQASS) method using cell-by-cell comparison metrics. As against existing assessment methods, EQASS is computed by including the same area of urban and suburban areas (i.e., equivalent areas). EQASS was tested in three Chinese coastal cities using a heuristic CA model and two spatial statistical CA models to simulate urban growth. The results show that EQASS can exclude correct rejections that are not attributable to CA models; these correct rejections have a significant impact on the model assessment. The improved assessment can better evaluate the performance of CA models across regions and over time than the conventional assessment method that accounts for the full study area. This study extends the simulation assessment method and provides a good solution for selecting the best CA model from many candidate models.Keywords: Model assessmentcellular automatabuffer analysisurban growthaccuracy comparison Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe software, codes and input datasets involved in this study are available at https://doi.org/10.6084/m9.figshare.21203147.Additional informationFundingSupported by the National Natural Science Foundation of China (42071371) and the National Key R&D Program of China (2018YFB0505400).Notes on contributorsChen GaoChen Gao received the M.S. degree in marine sciences from Shanghai Ocean University, Shanghai, China, in 2021. She is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Yongjiu FengYongjiu Feng received the Ph.D. degree in geomatics from Tongji University, Shanghai, China, in 2009. He is currently a Professor and Associate Dean with the College of Surveying and Geo-Informatics, Tongji University. His research interests include spatial modeling, synthetic aperture radar, and radar detection of the moon and deep space.Mengrong XiMengrong Xi received the B.E. degree in geomatics engineering from Tongji University, Shanghai, China, in 2022. He is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Rong WangRong Wang received the M.S. degree in marine sciences from Shanghai Ocean University, Shanghai, China, in 2022. She is currently working toward the Ph.D. degree in artificial intelligence with Tongji University, Shanghai, China.Pengshuo LiPengshuo Li received the B.E. degree in geomatics engineering from Tongj","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-03DOI: 10.1080/13658816.2023.2254382
Stefano De Sabbata, Pengyuan Liu
Geodemographic classifications are exceptional tools for geographic analysis, business and policy-making, providing an overview of the socio-demographic structure of a region by creating an unsupervised, bottom-up classification of its areas based on a large set of variables. Classic approaches can require time-consuming preprocessing of input variables and are frequently a-spatial processes. In this study, we present a groundbreaking, systematic investigation of the use of graph neural networks for spatial geodemographic classification. Using Greater London as a case study, we compare a range of graph autoencoder designs with the official London Output Area Classification and baseline classifications developed using spatial fuzzy c-means. The results show that our framework based on a Node Attributes-focused Graph AutoEncoder (NAGAE) can perform similarly to classic approaches on class homogeneity metrics while providing higher spatial clustering. We conclude by discussing the current limitations of the proposed framework and its potential to develop into a new paradigm for creating a range of geodemographic classifications, from simple, local ones to complex classifications able to incorporate a range of spatial relationships into the process.
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Pub Date : 2023-09-26DOI: 10.1080/13658816.2023.2257262
Yao Yao, Qia Zhu, Zijin Guo, Weiming Huang, Yatao Zhang, Xiaoqin Yan, Anning Dong, Zhangwei Jiang, Hong Liu, Qingfeng Guan
AbstractRapid land-use change detection (LUCD) is pivotal for refined urban planning and management. In this paper, we investigate LUCD through learning embeddings of points of interest (POIs) from multiple temporalities. There are several prominent challenges: (1) the co-occurrence problem of multi-temporal POIs, (2) the heterogeneity of POI categorization, and (3) The lack of human-crafted labels. Therefore, multi-temporal POIs need to be aligned in the embedding space for effective LUCD. This study proposes a multi-temporal POI embedding (MT-POI2Vec) technique for LUCD in a fully unsupervised manner. In MT-POI2Vec, we first utilize random walks in POI networks to capture their single-period co-occurrence patterns; then, we leverage manifold learning to capture (1) single-period categorical semantics of POIs to enforce semantically similar POI embedding to be close and (2) cross-period categorical semantics to align multi-temporal POI embedding in a unified embedding space. We conducted experiments in Shenzhen, China, which demonstrates that the proposed method is effective. Compared with several baseline models, MT-POI2Vec can better align multi-temporal POIs and thus achieve higher performance in LUCD. In addition, our model can effectively identify areas with unchanged land use and land use changes in residential and industrial areas at a fine scale.Keywords: Land-use changeembedding space alignmentpoints of interestPOI embedding AcknowledgementsWe would like to acknowledge the comments and insights from the editors and three anonymous reviewers that helped lift the quality of the article.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementWe share the codes and the sub-sampled data of the study at https://doi.org/10.6084/m9.figshare.24081699.Additional informationFundingThis work was supported by the National Key Research and Development Program of China [2019YFB2102903], the National Natural Science Foundation of China [41801306, 42101421 and 42171466]; the “CUG Scholar” Scientific Research Funds at China University of Geosciences (Wuhan) [2022034], a grant from Alibaba Innovative Research Project [20228670], a Guangdong-Hong Kong-Macau Joint Laboratory Program [2020B1212030009], and a grant from State Key Laboratory of Resources and Environmental Information System. W.H. acknowledges the financial support from the Knut and Alice Wallenberg Foundation.Notes on contributorsYao YaoYao Yao is a professor at China University of Geosciences (Wuhan), a researcher from the Center for Spatial Information Science at the University of Tokyo, and a visiting scholar at Alibaba Group. His research interests are geospatial big data mining, analysis, and computational urban science.Qia ZhuQia Zhu is a graduate student at China University of Geosciences (Wuhan). His research interests are spatial representation learning and urban land use change detection.Zijin GuoZijin Guo is a graduate
{"title":"Unsupervised land-use change detection using multi-temporal POI embedding","authors":"Yao Yao, Qia Zhu, Zijin Guo, Weiming Huang, Yatao Zhang, Xiaoqin Yan, Anning Dong, Zhangwei Jiang, Hong Liu, Qingfeng Guan","doi":"10.1080/13658816.2023.2257262","DOIUrl":"https://doi.org/10.1080/13658816.2023.2257262","url":null,"abstract":"AbstractRapid land-use change detection (LUCD) is pivotal for refined urban planning and management. In this paper, we investigate LUCD through learning embeddings of points of interest (POIs) from multiple temporalities. There are several prominent challenges: (1) the co-occurrence problem of multi-temporal POIs, (2) the heterogeneity of POI categorization, and (3) The lack of human-crafted labels. Therefore, multi-temporal POIs need to be aligned in the embedding space for effective LUCD. This study proposes a multi-temporal POI embedding (MT-POI2Vec) technique for LUCD in a fully unsupervised manner. In MT-POI2Vec, we first utilize random walks in POI networks to capture their single-period co-occurrence patterns; then, we leverage manifold learning to capture (1) single-period categorical semantics of POIs to enforce semantically similar POI embedding to be close and (2) cross-period categorical semantics to align multi-temporal POI embedding in a unified embedding space. We conducted experiments in Shenzhen, China, which demonstrates that the proposed method is effective. Compared with several baseline models, MT-POI2Vec can better align multi-temporal POIs and thus achieve higher performance in LUCD. In addition, our model can effectively identify areas with unchanged land use and land use changes in residential and industrial areas at a fine scale.Keywords: Land-use changeembedding space alignmentpoints of interestPOI embedding AcknowledgementsWe would like to acknowledge the comments and insights from the editors and three anonymous reviewers that helped lift the quality of the article.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementWe share the codes and the sub-sampled data of the study at https://doi.org/10.6084/m9.figshare.24081699.Additional informationFundingThis work was supported by the National Key Research and Development Program of China [2019YFB2102903], the National Natural Science Foundation of China [41801306, 42101421 and 42171466]; the “CUG Scholar” Scientific Research Funds at China University of Geosciences (Wuhan) [2022034], a grant from Alibaba Innovative Research Project [20228670], a Guangdong-Hong Kong-Macau Joint Laboratory Program [2020B1212030009], and a grant from State Key Laboratory of Resources and Environmental Information System. W.H. acknowledges the financial support from the Knut and Alice Wallenberg Foundation.Notes on contributorsYao YaoYao Yao is a professor at China University of Geosciences (Wuhan), a researcher from the Center for Spatial Information Science at the University of Tokyo, and a visiting scholar at Alibaba Group. His research interests are geospatial big data mining, analysis, and computational urban science.Qia ZhuQia Zhu is a graduate student at China University of Geosciences (Wuhan). His research interests are spatial representation learning and urban land use change detection.Zijin GuoZijin Guo is a graduate","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134887204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AbstractInvestigating the relationship between built environment factors and roadway safety is crucial for preventing road traffic accidents. Although studies have analyzed traffic-related built environment factors based on pre-determined zonal units, conclusive evidence regarding the relationship between streetscape features and traffic accidents at a fine-grained road segment level is still lacking. With the widespread availability of large-scale street view images, automatically analyzing urban built environments on a large scale is possible. Therefore, the aim of this study was to investigate the relationship between streetscape features and traffic accidents at a fine-grained road segment level using street view images. Specifically, we employed semantic image segmentation to extract streetscape elements from urban street view images, and then created traffic crash-related variables, including the street-level built environment variables, traffic variables, land-use indices, and proximity characteristics, at the road-segment level. Finally, we adopted a classification-then-regression strategy to model the number of traffic crashes while considering the zero-inflated and spatial heterogeneity issues. Our findings suggest that streetscape features can effectively reflect built-environment characteristics at the road-segment level. Moreover, a comparison of our proposed modeling method with existing models demonstrates its superior performance. The results provide insight into the development of effective planning strategies to improve traffic safety.Keywords: Traffic crashesstreet view imagesstreetscape featuresgeographically weighted Poisson regression AcknowledgmentsWe are grateful to Prof. May Yuan, Prof. Christophe Claramunt, and the anonymous referees for their valuable comments and suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe sample data and codes that support the findings of this study are available on ‘figshare.com’ with the identifier at the permanent link: https://doi.org/10.6084/m9.figshare.21384024.v1Additional informationFundingThis work was supported by the National Natural Science Foundation of China [41971406, 42271470, 42001340]; Guangdong Basic and Applied Basic Research Foundation [2022A1515011586]; State Key Laboratory of Geo-Information Engineering [No. SKLGIE2021-M-4-1]; and the China Scholarship Council (CSC) during a visit by Sheng Hu to National University of Singapore.Notes on contributorsSheng HuSheng Hu is a Postdoctoral Scholar at the Beidou Research Institute, South China Normal University. He is also a Distinguished Associated Research Fellow at South China Normal University. His research interests include geospatial artificial intelligence and geospatial data science.Hanfa XingHanfa Xing is a Professor for Geoinformatics at South China Normal University. He is also an Associated Dean of the Beidou Research Institute, Sout
{"title":"Uncovering the association between traffic crashes and street-level built-environment features using street view images","authors":"Sheng Hu, Hanfa Xing, Wei Luo, Liang Wu, Yongyang Xu, Weiming Huang, Wenkai Liu, Tianqi Li","doi":"10.1080/13658816.2023.2254362","DOIUrl":"https://doi.org/10.1080/13658816.2023.2254362","url":null,"abstract":"AbstractInvestigating the relationship between built environment factors and roadway safety is crucial for preventing road traffic accidents. Although studies have analyzed traffic-related built environment factors based on pre-determined zonal units, conclusive evidence regarding the relationship between streetscape features and traffic accidents at a fine-grained road segment level is still lacking. With the widespread availability of large-scale street view images, automatically analyzing urban built environments on a large scale is possible. Therefore, the aim of this study was to investigate the relationship between streetscape features and traffic accidents at a fine-grained road segment level using street view images. Specifically, we employed semantic image segmentation to extract streetscape elements from urban street view images, and then created traffic crash-related variables, including the street-level built environment variables, traffic variables, land-use indices, and proximity characteristics, at the road-segment level. Finally, we adopted a classification-then-regression strategy to model the number of traffic crashes while considering the zero-inflated and spatial heterogeneity issues. Our findings suggest that streetscape features can effectively reflect built-environment characteristics at the road-segment level. Moreover, a comparison of our proposed modeling method with existing models demonstrates its superior performance. The results provide insight into the development of effective planning strategies to improve traffic safety.Keywords: Traffic crashesstreet view imagesstreetscape featuresgeographically weighted Poisson regression AcknowledgmentsWe are grateful to Prof. May Yuan, Prof. Christophe Claramunt, and the anonymous referees for their valuable comments and suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe sample data and codes that support the findings of this study are available on ‘figshare.com’ with the identifier at the permanent link: https://doi.org/10.6084/m9.figshare.21384024.v1Additional informationFundingThis work was supported by the National Natural Science Foundation of China [41971406, 42271470, 42001340]; Guangdong Basic and Applied Basic Research Foundation [2022A1515011586]; State Key Laboratory of Geo-Information Engineering [No. SKLGIE2021-M-4-1]; and the China Scholarship Council (CSC) during a visit by Sheng Hu to National University of Singapore.Notes on contributorsSheng HuSheng Hu is a Postdoctoral Scholar at the Beidou Research Institute, South China Normal University. He is also a Distinguished Associated Research Fellow at South China Normal University. His research interests include geospatial artificial intelligence and geospatial data science.Hanfa XingHanfa Xing is a Professor for Geoinformatics at South China Normal University. He is also an Associated Dean of the Beidou Research Institute, Sout","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135436958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing intervisibility analysis methods suffer from computational inefficiency due to redundant sampling points. To address this issue, we propose a new approximate method called line-of-sight (LoS) zoning, which leverages continuous terrain relief to identify potentially obscuring zones (POZ) of LoS. By limiting the sampling range to a much smaller POZ, the number of sampling points is significantly reduced. The optimal sampling interval of 6 is determined by striking a balance between computational efficiency and accuracy. Through experiments in both mountainous and plain areas, regardless of the height range and resolution conditions, we demonstrate the high efficiency of the LoS zoning method, especially in scenarios with a high proportion of visible LoS. To account for potential visibility errors caused by sharp peaks in the terrain, we conducted experiments under fixed time intervals to assess the calculation quality of different methods. The results show that in mountainous and plain areas, the improvement in detection rate compared to the hopping strategy method is around 4–6 times in most scenarios. This significant performance enhancement highlights the superiority of the LoS zoning method, and shows great promise in terrain avoidance, path planning in the military, and detection of dangerous targets.
{"title":"A line-of-sight zoning method for intervisibility computation by considering terrain relief","authors":"Zengjie Wang, Xiaoyu Niu, Zhenxia Liu, Wen Luo, Zhaoyuan Yu, Jiyi Zhang, Linwang Yuan","doi":"10.1080/13658816.2023.2254825","DOIUrl":"https://doi.org/10.1080/13658816.2023.2254825","url":null,"abstract":"Existing intervisibility analysis methods suffer from computational inefficiency due to redundant sampling points. To address this issue, we propose a new approximate method called line-of-sight (LoS) zoning, which leverages continuous terrain relief to identify potentially obscuring zones (POZ) of LoS. By limiting the sampling range to a much smaller POZ, the number of sampling points is significantly reduced. The optimal sampling interval of 6 is determined by striking a balance between computational efficiency and accuracy. Through experiments in both mountainous and plain areas, regardless of the height range and resolution conditions, we demonstrate the high efficiency of the LoS zoning method, especially in scenarios with a high proportion of visible LoS. To account for potential visibility errors caused by sharp peaks in the terrain, we conducted experiments under fixed time intervals to assess the calculation quality of different methods. The results show that in mountainous and plain areas, the improvement in detection rate compared to the hopping strategy method is around 4–6 times in most scenarios. This significant performance enhancement highlights the superiority of the LoS zoning method, and shows great promise in terrain avoidance, path planning in the military, and detection of dangerous targets.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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