International Journal of Geographical Information Science最新文献

{"title":"Extending regionalization algorithms to explore spatial process heterogeneity","authors":"Hao Guo, Andre Python, Yu Liu","doi":"10.1080/13658816.2023.2266493","DOIUrl":"https://doi.org/10.1080/13658816.2023.2266493","url":null,"abstract":"AbstractIn spatial regression models, spatial heterogeneity may be considered with either continuous or discrete specifications. The latter is related to delineation of spatially connected regions with homogeneous relationships between variables (spatial regimes). Although various regionalization algorithms have been proposed and studied in the field of spatial analytics, methods to optimize spatial regimes have been largely unexplored. In this paper, we propose two new algorithms for spatial regime delineation, two-stage K-Models and Regional-K-Models. We also extend the classic Automatic Zoning Procedure to a spatial regression context. The proposed algorithms are applied to a series of synthetic datasets and two real-world datasets. Results indicate that all three algorithms achieve superior or comparable performance to existing approaches, while the two-stage K-Models algorithm largely outperforms existing approaches on model fitting, region reconstruction and coefficient estimation. Our work enriches the spatial analytics toolbox to explore spatial heterogeneous processes.Keywords: Regionalizationspatial heterogeneityspatial regimespatial regression NotesAcknowledgmentsThe authors thank members of Spatial Analysis Group, Spatio-temporal Social Sensing Lab for helpful discussion. The constructive comments from anonymous reviewers are gratefully acknowledged.Data and codes availability statementThe data and codes that support the findings of this study are available at https://github.com/Nithouson/regreg.Disclosure statementThe authors declare that they have no conflict of interest.Notes1 For example, the population in each region is required to be as similar as possible or above a predefined value (see Duque et al. (Citation2012), Folch and Spielman (Citation2014), Wei et al. (Citation2021)).2 Note that the optimization of spatial regimes differs from Openshaw (Citation1978), where spatial units are aggregated into areas, and each area is treated as an observation in a global regression model.3 Note that in EquationEquation 1(1) L(R)=∑j=1p∑1≤i1<i2≤nI[ui1,ui2∈Rj]||xi1−xi2||2,(1) , the number of considered unit pairs in the sum is ∑j=1M(|Rj|2), which is smaller if |Rj|(j=1,…,M) are close to each other. Hence the objective function might favor solutions whose regions have similar numbers of units.4 Throughout the paper, we describe the case of lattice data (spatial data on areal units). Our approach is also applicable to point observation data after building adjacency (with k-nearest neighbors (KNN) or Delaunay triangulation, for example).5 This usually happens when min_obs is close to n/p, where p is the number of regions. Given min_obs≪n/p, this issue does not cause problems, as observed in our experiments.6 If a region with inadequate units has two or more neighboring regions, we select the neighbor which minimizes the total SSR after the merge.7 When min_obs is too large or K is too small (close to p), exceptions may occur that the number of","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135859064","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}

{"title":"Graph based embedding learning of trajectory data for transportation mode recognition by fusing sequence and dependency relations","authors":"Wenhao Yu, Guanwen Wang","doi":"10.1080/13658816.2023.2268668","DOIUrl":"https://doi.org/10.1080/13658816.2023.2268668","url":null,"abstract":"AbstractAs an important task in spatial data mining, trajectory transportation mode recognition can reflect various individual behaviors and traveling patterns in urban space. As trajectory is essentially a sequence, many scholars use the sequence inference models to mine the information in trajectory data. However, such methods often ignored the spatial correlation between trajectory points and implemented the evaluation based only on representative feature statistics selected in the trajectory data preprocessing stage, thus have difficulties in acquiring high-order traveling pattern features. In this study, we propose a novel ensemble recognition method for representing trajectory data with the graph structure based on sequence and dependency relations. This method integrates the sequence of trajectory points and the correlation between characteristic points of a travel path into a fused graph convolutional network to obtain semantic feature information at multiple levels. We validate our proposed method with experiments on the trajectory benchmark dataset from the Microsoft GeoLife project. The results demonstrated that our proposed graph network outperforms other baseline methods in the transportation mode recognition task of trajectories. This method can help to discover the movement patterns of urban residents, and further provide effective assistance for the management of cities.Keywords: Trajectory datagraph convolution networktransportation mode recognitionfeature extractionfeature fusion AcknowledgmentsThe authors are grateful to the associate editor, Urska Demsar, and the anonymous referees for their valuable comments and suggestions. The project was supported by the National Natural Science Foundation of China (42371446 and 42071442) and by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No.CUG170640). This research was also supported by Meituan.Author contributionsWenhao Yu: Conceptualization, methodology, formal analysis, validation, writing—original draft preparation, writing—review and editing, supervision, project administration, funding acquisition; Guanwen Wang: Methodology, validation, formal analysis, investigation, writing—original draft preparation, writing—review and editing, visualization. All authors have read and agreed to the published version of the manuscript.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 with a DOI at (https://doi.org/10.6084/m9.figshare.21608310).Additional informationNotes on contributorsWenhao YuWenhao Yu received the B.S. and Ph.D. degrees in Geoinformatics from the Wuhan University, Wuhan, China, in 2010 and 2015, respectively. He is a professor at China University of Geosciences, Wuhan, China (CUG). His research interests include spatial data mining, map generalization, and deep learning.Guanwen ","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136211026","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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"A graph neural network framework for spatial geodemographic classification","authors":"Stefano De Sabbata, Pengyuan Liu","doi":"10.1080/13658816.2023.2254382","DOIUrl":"https://doi.org/10.1080/13658816.2023.2254382","url":null,"abstract":"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.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135739388","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}

{"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}