Pub Date : 2023-11-14DOI: 10.1080/15230406.2023.2264756
Pengbo Li, Haowen Yan, Xiaomin Lu
ABSTRACTPattern recognition of linear feature sets, such as river networks, road networks, and contour clusters, is essential in cartography and geographic information science. Previous studies have investigated many methods to identify the patterns of linear feature sets; the key to each of these studies is to generate a reasonable and computable representation for each set. However, most existing methods are only designed for a specific task or data type and cannot provide a general solution for formalizing linear feature sets owing to their complex geometric characteristics, spatial relations and distributions. In addition, some methods require human involvement to specify characteristics, choose parameters, and determine the weights of different measures. To reduce human intervention and improve adaptability to various feature types, this paper proposes a novel deep learning architecture for learning the representations of linear feature sets. The presented model accepts vector data directly without extra data conversion and feature extraction. After generating local, neighborhood, and global representations of inputs, the representations are aggregated accordingly to perform pattern recognition tasks, including classification and segmentation. In the experiments, building groups classification and road interchanges segmentation achieved accuracies of 98% and 89%, respectively, indicating the model’s effectiveness and adaptability.KEYWORDS: Linear feature setpattern recognitiondeep learningbuilding group classificationroad interchange detection AcknowledgmentsThe authors sincerely thank the editors and the anonymous reviewers for their valuable feedback and insightful comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data and code that support the findings of this study are available with the identifier at the public link (https://doi.org/10.6084/m9.figshare.21789881).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [41930101, 42161066], Gansu Provincial Department of Education: The “Innovation Star” Project of Excellent Postgraduates [2023CXZX-506] and the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, No. [KF-2022-07-015].
{"title":"MultiLineStringNet: a deep neural network for linear feature set recognition","authors":"Pengbo Li, Haowen Yan, Xiaomin Lu","doi":"10.1080/15230406.2023.2264756","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264756","url":null,"abstract":"ABSTRACTPattern recognition of linear feature sets, such as river networks, road networks, and contour clusters, is essential in cartography and geographic information science. Previous studies have investigated many methods to identify the patterns of linear feature sets; the key to each of these studies is to generate a reasonable and computable representation for each set. However, most existing methods are only designed for a specific task or data type and cannot provide a general solution for formalizing linear feature sets owing to their complex geometric characteristics, spatial relations and distributions. In addition, some methods require human involvement to specify characteristics, choose parameters, and determine the weights of different measures. To reduce human intervention and improve adaptability to various feature types, this paper proposes a novel deep learning architecture for learning the representations of linear feature sets. The presented model accepts vector data directly without extra data conversion and feature extraction. After generating local, neighborhood, and global representations of inputs, the representations are aggregated accordingly to perform pattern recognition tasks, including classification and segmentation. In the experiments, building groups classification and road interchanges segmentation achieved accuracies of 98% and 89%, respectively, indicating the model’s effectiveness and adaptability.KEYWORDS: Linear feature setpattern recognitiondeep learningbuilding group classificationroad interchange detection AcknowledgmentsThe authors sincerely thank the editors and the anonymous reviewers for their valuable feedback and insightful comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data and code that support the findings of this study are available with the identifier at the public link (https://doi.org/10.6084/m9.figshare.21789881).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [41930101, 42161066], Gansu Provincial Department of Education: The “Innovation Star” Project of Excellent Postgraduates [2023CXZX-506] and the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, No. [KF-2022-07-015].","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"31 27","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-07DOI: 10.1080/15230406.2023.2271379
Matthew H. Ruther
ABSTRACTThis paper investigates how ancillary geographic data – in particular, information on land or assessor parcels – might be used to improve estimates for small area populations and population characteristics. It seeks to determine whether parcel land use codes can be used to reliably replicate population and housing distributions within small (subcounty) areas and whether other parcel attributes – in addition to land use – exhibit any explanatory power in replicating population and housing characteristics within these same places. The basis for this paper was Professor Barbara Buttenfield’s service on the Census Scientific Advisory Committee, in which her working group explored the utility of administrative source data as an alternative or complement to federal survey data. This analysis highlights some of the benefits and complications of the incorporation of parcel data into geodemographic estimation, and the findings demonstrate that such a use is problematic but encouraging.KEYWORDS: Parcelneighborhoodpopulationestimationcadastralsmall-area Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available in the Github UofLKSDC/CAGIS2022 repository available at https://zenodo.org/badge/latestdoi/515252659. Elements of these data were derived from the following resources available in the public domain: National Historical Geographic Information System [https://www.nhgis.org].
{"title":"Estimating neighborhood-level population characteristics from parcel data","authors":"Matthew H. Ruther","doi":"10.1080/15230406.2023.2271379","DOIUrl":"https://doi.org/10.1080/15230406.2023.2271379","url":null,"abstract":"ABSTRACTThis paper investigates how ancillary geographic data – in particular, information on land or assessor parcels – might be used to improve estimates for small area populations and population characteristics. It seeks to determine whether parcel land use codes can be used to reliably replicate population and housing distributions within small (subcounty) areas and whether other parcel attributes – in addition to land use – exhibit any explanatory power in replicating population and housing characteristics within these same places. The basis for this paper was Professor Barbara Buttenfield’s service on the Census Scientific Advisory Committee, in which her working group explored the utility of administrative source data as an alternative or complement to federal survey data. This analysis highlights some of the benefits and complications of the incorporation of parcel data into geodemographic estimation, and the findings demonstrate that such a use is problematic but encouraging.KEYWORDS: Parcelneighborhoodpopulationestimationcadastralsmall-area Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available in the Github UofLKSDC/CAGIS2022 repository available at https://zenodo.org/badge/latestdoi/515252659. Elements of these data were derived from the following resources available in the public domain: National Historical Geographic Information System [https://www.nhgis.org].","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"2 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-07DOI: 10.1080/15230406.2023.2272660
Zhixin Yu, Chen Zhou, Manchun Li
ABSTRACTThis study presents an asynchronous parallel strategy coordinating central processing unit (CPU) and graphic processing unit (GPU) to accelerate neighborhood operation (NO). Specifically, we propose a data partitioning method called multi-anchor task queuing and a task scheduling method called bi-direction task scheduling, which can support CPU and GPU to find the responsible data blocks rapidly and concurrently handle their tasks via a bi-direction merge. Moreover, we optimize the organization of threads distributed among the CPU and GPU. Experimental results show that when a 1.7 GB raster dataset is processed, the speedup ratio achieved by the proposed parallel algorithm reaches 29.63, which is 19% and 18% higher than those of the GPU and standard asynchronous parallel algorithm, respectively. Additionally, the load balance index is below 0.085, which is significantly better than the value achieved by a conventional algorithm. Thus, the strategy achieves a higher speedup ratio and more adaptable load balance, thereby accelerating the NO more efficiently. Further, the impacts of the data volume, computational intensity, organization mode of the GPU threads, and granularity of the GPU stream on the parallel efficiency are evaluated and discussed. We also test the efficiency of four other common NOs with our strategy.KEYWORDS: Geographical raster dataneighborhood operationparallel computingCPU and GPUload balance AcknowledgmentsThe authors sincerely thank the anonymous reviewers and editors for their valuable feedback and constructive comments, which greatly contribute to improving this paper.Disclosure statementNo potential conflict of interest was reported by the author(s).CRediT authorship contribution statementZhixin Yu: Conceptualization, Methodology, Software, Visualization, Writing – original draft.Chen Zhou: Conceptualization, Data Curation, Supervision, Validation, Writing – review & editing.Manchun Li: Supervision, Writing – review & editing.Data availability statementThe computer code and sample dataset that support the findings of this study are available at https://www.doi.org/10.17605/OSF.IO/AG3QC. The code was developed using C++. A CPU with multiple cores and a CUDA-enabled GPU are necessary. It is recommended to run the code on OpenMP 2.0, CUDA 11.2 and GDAL 3.2.0 or later.Additional informationFundingThis work was supported by the National Natural Science Foundation of China [grant numbers 42271414 and 41901318].
{"title":"A parallel strategy to accelerate neighborhood operation for raster data coordinating CPU and GPU","authors":"Zhixin Yu, Chen Zhou, Manchun Li","doi":"10.1080/15230406.2023.2272660","DOIUrl":"https://doi.org/10.1080/15230406.2023.2272660","url":null,"abstract":"ABSTRACTThis study presents an asynchronous parallel strategy coordinating central processing unit (CPU) and graphic processing unit (GPU) to accelerate neighborhood operation (NO). Specifically, we propose a data partitioning method called multi-anchor task queuing and a task scheduling method called bi-direction task scheduling, which can support CPU and GPU to find the responsible data blocks rapidly and concurrently handle their tasks via a bi-direction merge. Moreover, we optimize the organization of threads distributed among the CPU and GPU. Experimental results show that when a 1.7 GB raster dataset is processed, the speedup ratio achieved by the proposed parallel algorithm reaches 29.63, which is 19% and 18% higher than those of the GPU and standard asynchronous parallel algorithm, respectively. Additionally, the load balance index is below 0.085, which is significantly better than the value achieved by a conventional algorithm. Thus, the strategy achieves a higher speedup ratio and more adaptable load balance, thereby accelerating the NO more efficiently. Further, the impacts of the data volume, computational intensity, organization mode of the GPU threads, and granularity of the GPU stream on the parallel efficiency are evaluated and discussed. We also test the efficiency of four other common NOs with our strategy.KEYWORDS: Geographical raster dataneighborhood operationparallel computingCPU and GPUload balance AcknowledgmentsThe authors sincerely thank the anonymous reviewers and editors for their valuable feedback and constructive comments, which greatly contribute to improving this paper.Disclosure statementNo potential conflict of interest was reported by the author(s).CRediT authorship contribution statementZhixin Yu: Conceptualization, Methodology, Software, Visualization, Writing – original draft.Chen Zhou: Conceptualization, Data Curation, Supervision, Validation, Writing – review & editing.Manchun Li: Supervision, Writing – review & editing.Data availability statementThe computer code and sample dataset that support the findings of this study are available at https://www.doi.org/10.17605/OSF.IO/AG3QC. The code was developed using C++. A CPU with multiple cores and a CUDA-enabled GPU are necessary. It is recommended to run the code on OpenMP 2.0, CUDA 11.2 and GDAL 3.2.0 or later.Additional informationFundingThis work was supported by the National Natural Science Foundation of China [grant numbers 42271414 and 41901318].","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"5 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135480037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-02DOI: 10.1080/15230406.2023.2264754
Elok Surya Pratiwi, Su-Min Shen, Junun Sartohadi
There has been a limited amount of research focused on the design of landslide maps, which are considered as one of the potential means to communicate disaster risks to the public. Hence, this article aims to conduct a systematic review of the process involved in creating landslide maps specifically for the purpose of disaster risk communication with non-expert users. While this topic is still under-studied, it has gained increasing coverage in the peer-reviewed literature over the past five years. The review examines the variations in the process of creating landslide maps, considering aspects such as planning, mapping techniques, presentation, and dissemination. However, there are several areas that require improvement, including diversifying the types of maps, considering the role and involvement of users, developing more user-friendly designs, and reducing reliance on experts during the dissemination process. The findings of this review provide valuable insights into the current limitations in establishing these maps and offer guidance for future research in this field.
{"title":"Progress and challenges in designing landslide maps for disaster risk communication: a systematic review","authors":"Elok Surya Pratiwi, Su-Min Shen, Junun Sartohadi","doi":"10.1080/15230406.2023.2264754","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264754","url":null,"abstract":"There has been a limited amount of research focused on the design of landslide maps, which are considered as one of the potential means to communicate disaster risks to the public. Hence, this article aims to conduct a systematic review of the process involved in creating landslide maps specifically for the purpose of disaster risk communication with non-expert users. While this topic is still under-studied, it has gained increasing coverage in the peer-reviewed literature over the past five years. The review examines the variations in the process of creating landslide maps, considering aspects such as planning, mapping techniques, presentation, and dissemination. However, there are several areas that require improvement, including diversifying the types of maps, considering the role and involvement of users, developing more user-friendly designs, and reducing reliance on experts during the dissemination process. The findings of this review provide valuable insights into the current limitations in establishing these maps and offer guidance for future research in this field.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"1 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135935857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-02DOI: 10.1080/15230406.2023.2264755
Barry J Kronenfeld, Kwang il (Jason) Yoo
ABSTRACTEpidemiological maps on COVID-19 dashboards were critical to disseminating information during the pandemic, but dashboard creators faced difficulties avoiding common misinterpretation pitfalls that result from varying population density. Furthermore, most dashboards did not include animated maps despite their intuitive visual analogy to the temporal unfolding of events. This study explores the effectiveness of population cartograms as a basis for animated maps showing the progression of a pandemic. The ability to recall locations of peak case rates per population was compared for subjects receiving animated maps and cartograms overlaid with proportional symbols and choropleth colors representing case counts and rates per population, respectively. Results confirm that map readers often confuse case counts with rates on standard proportional symbol maps and fail to notice small, densely populated enumeration units on standard choropleth maps. Population cartograms reduced these common visual biases for both map forms, but map readers were unable to track rates per population on proportional symbol cartograms even with prior instruction. Although animations of standard choropleth maps and colored proportional symbol cartograms were most preferred by subjects, choropleth cartograms are recommended for consideration by dashboard creators as they effectively communicate case rate trends while avoiding visual biases associated with other map types.KEYWORDS: COVID-19map dashboardscartogramsanimationepidemiology AcknowledgmentsWe would like to express our gratitude for the editors’ and anonymous reviewers’ timely and constructive feedback.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementAnimations used in the human subjects experiment described in this study are openly available on the first author’s website at https://castle.eiu.edu/~bjkronenfeld/projects/cagis23animations/.Additional informationFundingFunding for the human subjects experiment was provided by an Eastern Illinois University Student Impact Grant.
{"title":"Effectiveness of animated choropleth and proportional symbol cartograms for epidemiological dashboards","authors":"Barry J Kronenfeld, Kwang il (Jason) Yoo","doi":"10.1080/15230406.2023.2264755","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264755","url":null,"abstract":"ABSTRACTEpidemiological maps on COVID-19 dashboards were critical to disseminating information during the pandemic, but dashboard creators faced difficulties avoiding common misinterpretation pitfalls that result from varying population density. Furthermore, most dashboards did not include animated maps despite their intuitive visual analogy to the temporal unfolding of events. This study explores the effectiveness of population cartograms as a basis for animated maps showing the progression of a pandemic. The ability to recall locations of peak case rates per population was compared for subjects receiving animated maps and cartograms overlaid with proportional symbols and choropleth colors representing case counts and rates per population, respectively. Results confirm that map readers often confuse case counts with rates on standard proportional symbol maps and fail to notice small, densely populated enumeration units on standard choropleth maps. Population cartograms reduced these common visual biases for both map forms, but map readers were unable to track rates per population on proportional symbol cartograms even with prior instruction. Although animations of standard choropleth maps and colored proportional symbol cartograms were most preferred by subjects, choropleth cartograms are recommended for consideration by dashboard creators as they effectively communicate case rate trends while avoiding visual biases associated with other map types.KEYWORDS: COVID-19map dashboardscartogramsanimationepidemiology AcknowledgmentsWe would like to express our gratitude for the editors’ and anonymous reviewers’ timely and constructive feedback.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementAnimations used in the human subjects experiment described in this study are openly available on the first author’s website at https://castle.eiu.edu/~bjkronenfeld/projects/cagis23animations/.Additional informationFundingFunding for the human subjects experiment was provided by an Eastern Illinois University Student Impact Grant.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"38 2‐3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-02DOI: 10.1080/15230406.2023.2268502
Fabian Kuster, Ian T. Ruginski, S.I. Fabrikant
The globally increasing frequency of flood events highlights the importance of effective flood risk communication. The influence of the viewing perspective of mapped flood events on human risk perception has not yet been a research focus of the geovisualization community. This empirical study aims to fill this gap by investigating how the viewing perspective of flood risk maps, that is, 2D orthographic vs. 2.5D oblique views, influence human flood risk perception and decision-making. Results on how viewing perspective might influence measured risk perception are in line with prior inconclusive research on the utility and usability of adding a third viewing dimension on static maps. Unlike prior research would have suggested, we find that the individual risk attitude of our participants had no direct influence on their risk ratings in the context of this study. With additional empirical evidence on how static 2D and oblique 2.5D hazard maps might influence the public’s risk perception and decision-making, we hope to further inform policy and decision makers on the critical importance of well-designed cartographic displays for effective and efficient hazard and risk communication. We also provide an open-source code repository for making reproducible experiments with our static maps.
{"title":"How does your viewing perspective matter for decision-making with flood risk maps?*","authors":"Fabian Kuster, Ian T. Ruginski, S.I. Fabrikant","doi":"10.1080/15230406.2023.2268502","DOIUrl":"https://doi.org/10.1080/15230406.2023.2268502","url":null,"abstract":"The globally increasing frequency of flood events highlights the importance of effective flood risk communication. The influence of the viewing perspective of mapped flood events on human risk perception has not yet been a research focus of the geovisualization community. This empirical study aims to fill this gap by investigating how the viewing perspective of flood risk maps, that is, 2D orthographic vs. 2.5D oblique views, influence human flood risk perception and decision-making. Results on how viewing perspective might influence measured risk perception are in line with prior inconclusive research on the utility and usability of adding a third viewing dimension on static maps. Unlike prior research would have suggested, we find that the individual risk attitude of our participants had no direct influence on their risk ratings in the context of this study. With additional empirical evidence on how static 2D and oblique 2.5D hazard maps might influence the public’s risk perception and decision-making, we hope to further inform policy and decision makers on the critical importance of well-designed cartographic displays for effective and efficient hazard and risk communication. We also provide an open-source code repository for making reproducible experiments with our static maps.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"102 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136017961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-10-31DOI: 10.1080/15230406.2023.2267967
Yue Lin
ABSTRACTSpatial point mapping is a useful practice in exploratory point pattern analysis, but it poses significant privacy risks as the identity of individuals may be revealed from the maps. Geomasking methods have been developed to mitigate the risks by displacing spatial points before mapping. However, many of these methods rely on a weak privacy notion called spatial k-anonymity, which is insufficient to withstand the growing amount of spatial data (e.g. land use) that adversaries can use as side information to infer the actual locations of individuals. We proposes a method called geo-indistinguishable masking to address this issue by relying on a strong privacy notion called geo-indistinguishability. This notion ensures consistent levels of privacy protection regardless of any side information. The method consists of two steps. The first step involves creating a masking area for each spatial point to include a set of candidate locations to which the point can be relocated. In the second step, we formulate an optimization model to ensure the masked locations satisfy geo-indistinguishability while minimizing the distance displaced. Computational experiments on a synthetic dataset demonstrate that our proposed method is both efficient and effective in providing strong privacy protection while preserving the spatial point patterns.KEYWORDS: Differential privacygeo-indistinguishabilitygeomaskinggeoprivacyspatial anonymization Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data and code that support the findings of this study are available on Figshare at https://doi.org/10.6084/m9.figshare.23632443.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2023.2267967.Notes1. https://www.gurobi.com/.2. https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer.3. https://www.coin-or.org/.
{"title":"Geo-indistinguishable masking: enhancing privacy protection in spatial point mapping","authors":"Yue Lin","doi":"10.1080/15230406.2023.2267967","DOIUrl":"https://doi.org/10.1080/15230406.2023.2267967","url":null,"abstract":"ABSTRACTSpatial point mapping is a useful practice in exploratory point pattern analysis, but it poses significant privacy risks as the identity of individuals may be revealed from the maps. Geomasking methods have been developed to mitigate the risks by displacing spatial points before mapping. However, many of these methods rely on a weak privacy notion called spatial k-anonymity, which is insufficient to withstand the growing amount of spatial data (e.g. land use) that adversaries can use as side information to infer the actual locations of individuals. We proposes a method called geo-indistinguishable masking to address this issue by relying on a strong privacy notion called geo-indistinguishability. This notion ensures consistent levels of privacy protection regardless of any side information. The method consists of two steps. The first step involves creating a masking area for each spatial point to include a set of candidate locations to which the point can be relocated. In the second step, we formulate an optimization model to ensure the masked locations satisfy geo-indistinguishability while minimizing the distance displaced. Computational experiments on a synthetic dataset demonstrate that our proposed method is both efficient and effective in providing strong privacy protection while preserving the spatial point patterns.KEYWORDS: Differential privacygeo-indistinguishabilitygeomaskinggeoprivacyspatial anonymization Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data and code that support the findings of this study are available on Figshare at https://doi.org/10.6084/m9.figshare.23632443.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2023.2267967.Notes1. https://www.gurobi.com/.2. https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer.3. https://www.coin-or.org/.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-10-31DOI: 10.1080/15230406.2023.2264747
Harrison Cole, Anthony Robinson
ABSTRACTMaps are frequently employed in the natural hazard mitigation planning (NHMP) process for analyze a community’s vulnerability to hazards and illustrating the character of potential hazards. But because the encoded information of these maps relies on visual access, blind or low-vision (B/LV) people who want to contribute to their community’s NHMP efforts are therefore effectively excluded from any stage of the process involving maps. In response, we investigate tactile flood maps as an accessible tool for NHMP. Our study proposes a workflow for creating thematic tactile flood maps using existing resources, methods, and design conventions, and then evaluates those thematic tactile maps to understand user confidence amongst B/LV users. This work contributes a proposed configuration of tactile mapping resources to be used for NHMP and evaluates user confidence while using those resources. Results suggest that B/LV users respond positively to using tactile maps in a high-stakes context such as NHMP, and that tactile maps can expand the number and diversity of people who are able to contribute to NHMP. To maximize contributions, we recommend that future tactile map research invest a greater amount of attention to developing resources for B/LV people to create, edit, and distribute maps themselves.KEYWORDS: Usabilityuser experienceblindlow visiondisabilitydisasterhazard AcknowledgmentsThe authors would like to thank Michelle McManus, Naomi Rosenberg, Deborah Klein, Ken Quinn, and Tim Prestby for their essential contributions to this project.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementParticipants in this study did not agree for their data to be shared publicly, so supporting data is not available.Supplementary dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2023.2264747
{"title":"Thematic tactile maps for accessible flood mitigation planning: design and evaluation","authors":"Harrison Cole, Anthony Robinson","doi":"10.1080/15230406.2023.2264747","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264747","url":null,"abstract":"ABSTRACTMaps are frequently employed in the natural hazard mitigation planning (NHMP) process for analyze a community’s vulnerability to hazards and illustrating the character of potential hazards. But because the encoded information of these maps relies on visual access, blind or low-vision (B/LV) people who want to contribute to their community’s NHMP efforts are therefore effectively excluded from any stage of the process involving maps. In response, we investigate tactile flood maps as an accessible tool for NHMP. Our study proposes a workflow for creating thematic tactile flood maps using existing resources, methods, and design conventions, and then evaluates those thematic tactile maps to understand user confidence amongst B/LV users. This work contributes a proposed configuration of tactile mapping resources to be used for NHMP and evaluates user confidence while using those resources. Results suggest that B/LV users respond positively to using tactile maps in a high-stakes context such as NHMP, and that tactile maps can expand the number and diversity of people who are able to contribute to NHMP. To maximize contributions, we recommend that future tactile map research invest a greater amount of attention to developing resources for B/LV people to create, edit, and distribute maps themselves.KEYWORDS: Usabilityuser experienceblindlow visiondisabilitydisasterhazard AcknowledgmentsThe authors would like to thank Michelle McManus, Naomi Rosenberg, Deborah Klein, Ken Quinn, and Tim Prestby for their essential contributions to this project.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementParticipants in this study did not agree for their data to be shared publicly, so supporting data is not available.Supplementary dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2023.2264747","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"2006 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-10-26DOI: 10.1080/15230406.2023.2267418
Gunjan Barua, Thomas Pingel, Theodore Lim
{"title":"Urban thermal map design considerations: color, shading, and resolution","authors":"Gunjan Barua, Thomas Pingel, Theodore Lim","doi":"10.1080/15230406.2023.2267418","DOIUrl":"https://doi.org/10.1080/15230406.2023.2267418","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134908187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-10-26DOI: 10.1080/15230406.2023.2264758
May Yuan
{"title":"Multiple representations in geospatial databases, the brain’s spatial cells, and deep learning algorithms","authors":"May Yuan","doi":"10.1080/15230406.2023.2264758","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264758","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"28 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134909124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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