Mining point-of-interest data from social networks for urban land use classification and disaggregation
Over the last few years, much online volunteered geographic information (VGI) has emerged and has been increasingly analyzed to understand places and cities, as well as human mobility and activity. However, there are concerns about the quality and usability of such VGI. In this study, we demonstrate...
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| Format: | Article |
| Language: | en_US |
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Elsevier
2015
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| Online Access: | http://hdl.handle.net/1721.1/98005 https://orcid.org/0000-0003-0600-3803 https://orcid.org/0000-0002-3483-5132 https://orcid.org/0000-0001-5457-9909 |
| _version_ | 1826218030026194944 |
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| author | Jiang, Shan Alves, Ana Rodrigues, Filipe Pereira, Francisco C. Ferreira, Joseph, Jr. |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Jiang, Shan Alves, Ana Rodrigues, Filipe Pereira, Francisco C. Ferreira, Joseph, Jr. |
| author_sort | Jiang, Shan |
| collection | MIT |
| description | Over the last few years, much online volunteered geographic information (VGI) has emerged and has been increasingly analyzed to understand places and cities, as well as human mobility and activity. However, there are concerns about the quality and usability of such VGI. In this study, we demonstrate a complete process that comprises the collection, unification, classification and validation of a type of VGI—online point-of-interest (POI) data—and develop methods to utilize such POI data to estimate disaggregated land use (i.e., employment size by category) at a very high spatial resolution (census block level) using part of the Boston metropolitan area as an example. With recent advances in activity-based land use, transportation, and environment (LUTE) models, such disaggregated land use data become important to allow LUTE models to analyze and simulate a person’s choices of work location and activity destinations and to understand policy impacts on future cities. These data can also be used as alternatives to explore economic activities at the local level, especially as government-published census-based disaggregated employment data have become less available in the recent decade. Our new approach provides opportunities for cities to estimate land use at high resolution with low cost by utilizing VGI while ensuring its quality with a certain accuracy threshold. The automatic classification of POI can also be utilized for other types of analyses on cities. |
| first_indexed | 2024-09-23T17:12:43Z |
| format | Article |
| id | mit-1721.1/98005 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T17:12:43Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/980052022-09-30T00:27:12Z Mining point-of-interest data from social networks for urban land use classification and disaggregation Jiang, Shan Alves, Ana Rodrigues, Filipe Pereira, Francisco C. Ferreira, Joseph, Jr. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Department of Urban Studies and Planning Jiang, Shan Ferreira, Joseph, Jr. Pereira, Francisco C. Over the last few years, much online volunteered geographic information (VGI) has emerged and has been increasingly analyzed to understand places and cities, as well as human mobility and activity. However, there are concerns about the quality and usability of such VGI. In this study, we demonstrate a complete process that comprises the collection, unification, classification and validation of a type of VGI—online point-of-interest (POI) data—and develop methods to utilize such POI data to estimate disaggregated land use (i.e., employment size by category) at a very high spatial resolution (census block level) using part of the Boston metropolitan area as an example. With recent advances in activity-based land use, transportation, and environment (LUTE) models, such disaggregated land use data become important to allow LUTE models to analyze and simulate a person’s choices of work location and activity destinations and to understand policy impacts on future cities. These data can also be used as alternatives to explore economic activities at the local level, especially as government-published census-based disaggregated employment data have become less available in the recent decade. Our new approach provides opportunities for cities to estimate land use at high resolution with low cost by utilizing VGI while ensuring its quality with a certain accuracy threshold. The automatic classification of POI can also be utilized for other types of analyses on cities. Singapore-MIT Alliance for Research and Technology (Singapore. National Research Foundation) Fundacao para a Ciencia e a Tecnologia (MIT-Portugal Program) Fundacao para a Ciencia e a Tecnologia (Grant PTDC/ECM-TRA/1898/2012) 2015-08-03T12:06:17Z 2015-08-03T12:06:17Z 2015-01 Article http://purl.org/eprint/type/JournalArticle 01989715 http://hdl.handle.net/1721.1/98005 Jiang, Shan, Ana Alves, Filipe Rodrigues, Joseph Ferreira, and Francisco C. Pereira. “Mining Point-of-Interest Data from Social Networks for Urban Land Use Classification and Disaggregation.” Computers, Environment and Urban Systems (January 2015). © 2014 Elsevier Ltd. https://orcid.org/0000-0003-0600-3803 https://orcid.org/0000-0002-3483-5132 https://orcid.org/0000-0001-5457-9909 en_US http://dx.doi.org/10.1016/j.compenvurbsys.2014.12.001 Computers, Environment and Urban Systems Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Elsevier Open Access |
| spellingShingle | Jiang, Shan Alves, Ana Rodrigues, Filipe Pereira, Francisco C. Ferreira, Joseph, Jr. Mining point-of-interest data from social networks for urban land use classification and disaggregation |
| title | Mining point-of-interest data from social networks for urban land use classification and disaggregation |
| title_full | Mining point-of-interest data from social networks for urban land use classification and disaggregation |
| title_fullStr | Mining point-of-interest data from social networks for urban land use classification and disaggregation |
| title_full_unstemmed | Mining point-of-interest data from social networks for urban land use classification and disaggregation |
| title_short | Mining point-of-interest data from social networks for urban land use classification and disaggregation |
| title_sort | mining point of interest data from social networks for urban land use classification and disaggregation |
| url | http://hdl.handle.net/1721.1/98005 https://orcid.org/0000-0003-0600-3803 https://orcid.org/0000-0002-3483-5132 https://orcid.org/0000-0001-5457-9909 |
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