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RelatedMaterial
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update: {"uri"=>["", "https://github.com/cybergis/real_time_heat_exposure_with_LBSMD"], "uri_type"=>["", "URL"], "citation"=>["", "Real time heat exposure with LBSMD - GitHub page\r\n"], "datacite_list"=>["", "IsSupplementedBy"]}
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2024-05-30T16:26:45Z
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RelatedMaterial
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update: {"link"=>["https://www.tandfonline.com/doi/full/10.1080/13658816.2024.2343063", "https://doi.org/10.1080/13658816.2024.2343063"], "uri"=>["", "10.1080/13658816.2024.2343063"], "uri_type"=>["", "DOI"], "citation"=>["Lyu, F., Zhou, L., Park, J., Baig, F., & Wang, S. (2024). Mapping dynamic human sentiments of heat exposure with location-based social media data. International Journal of Geographical Information Science, 1-24.", "Lyu, F., Zhou, L., Park, J., Baig, F., & Wang, S. (2024). Mapping dynamic human sentiments of heat exposure with location-based social media data. International Journal of Geographical Information Science, 1–24. https://doi.org/10.1080/13658816.2024.2343063"], "datacite_list"=>["", "IsSupplementTo"]}
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2024-05-30T16:26:45Z
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Dataset
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update: {"description"=>["This dataset contains all the datasets used in the study conducted for the research publication titled \"Mapping dynamic human sentiments of heat exposure with location-based social media data\". This paper develops a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate heat exposure maps from human sentiments on social media.\r\n\r\n## What’s inside - A quick explanation of the components of the zip file\r\n* US folder includes the shapefile corresponding to the United State with County as spatial unit\u2028* Census_tract folder includes the shapefile corresponding to the Cook County with census tract as spatial unit\r\n* data/data.txt includes instruction to retrieve the sample data either from Keeling or figshare\r\n* geo/data20000.txt is the heat dictionary created in this paper, please refer to the corresponding publication to see the data creation process\r\n\r\nJupyter notebook and code attached to this publication can be found at: https://github.com/cybergis/real_time_heat_exposure_with_LBSMD", "This dataset contains all the datasets used in the study conducted for the research publication titled \"Mapping dynamic human sentiments of heat exposure with location-based social media data\". This paper develops a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate heat exposure maps from human sentiments on social media.\r\n\r\n## What’s inside - A quick explanation of the components of the zip file\r\n* US folder includes the shapefile corresponding to the United State with County as spatial unit\u2028\r\n* Census_tract folder includes the shapefile corresponding to the Cook County with census tract as spatial unit\r\n* data/data.txt includes instruction to retrieve the sample data either from Keeling or figshare\r\n* geo/data20000.txt is the heat dictionary created in this paper, please refer to the corresponding publication to see the data creation process\r\n\r\nJupyter notebook and code attached to this publication can be found at: https://github.com/cybergis/real_time_heat_exposure_with_LBSMD"]}
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2024-05-30T16:23:09Z
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RelatedMaterial
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update: {"uri"=>[nil, ""], "uri_type"=>[nil, ""], "datacite_list"=>[nil, ""], "note"=>[nil, ""]}
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2024-05-30T15:50:25Z
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RelatedMaterial
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update: {"uri"=>[nil, ""], "uri_type"=>[nil, ""], "datacite_list"=>[nil, ""], "note"=>[nil, ""]}
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2024-05-30T15:50:25Z
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Dataset
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update: {"version_comment"=>[nil, ""], "subject"=>[nil, "Physical Sciences"]}
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2024-05-30T15:50:25Z
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