Using OpenStreetMap point-of-interest data to model urban change-A feasibility study
User-generated content is a valuable resource for capturing all aspects of our environment and lives, and dedicated Volunteered Geographic Information (VGI) efforts such as OpenStreetMap (OSM) have revolutionized spatial data collection. While OSM data is widely used, considerably little attention h...
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| Veröffentlicht in: | PloS one 2019-02, Vol.14 (2), p.e0212606-e0212606 |
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| creator | Zhang, Liming Pfoser, Dieter |
| description | User-generated content is a valuable resource for capturing all aspects of our environment and lives, and dedicated Volunteered Geographic Information (VGI) efforts such as OpenStreetMap (OSM) have revolutionized spatial data collection. While OSM data is widely used, considerably little attention has been paid to the quality of its Point-of-interest (POI) component. This work studies the accuracy, coverage, and trend worthiness of POI data. We assess the accuracy and coverage using another VGI source that utilizes editorial control. OSM data is compared to Foursquare data by using a combination of label similarity and positional proximity. Using the example of coffee shop POIs in Manhattan we also assess the trend worthiness of OSM data. A series of spatio-temporal statistical models are tested to compare change in the number of coffee shops to home prices in certain areas. This work overall shows that, although not perfect, OSM POI data and specifically its temporal aspect (changeset) can be used to drive urban science research and to study urban change. |
| doi_str_mv | 10.1371/journal.pone.0212606 |
| format | Article |
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While OSM data is widely used, considerably little attention has been paid to the quality of its Point-of-interest (POI) component. This work studies the accuracy, coverage, and trend worthiness of POI data. We assess the accuracy and coverage using another VGI source that utilizes editorial control. OSM data is compared to Foursquare data by using a combination of label similarity and positional proximity. Using the example of coffee shop POIs in Manhattan we also assess the trend worthiness of OSM data. A series of spatio-temporal statistical models are tested to compare change in the number of coffee shops to home prices in certain areas. This work overall shows that, although not perfect, OSM POI data and specifically its temporal aspect (changeset) can be used to drive urban science research and to study urban change.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0212606</identifier><identifier>PMID: 30802251</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Analysis ; Biology and Life Sciences ; Coffee ; Coffee (Beverage) ; Coffeehouses ; Criminal statistics ; Data collection ; Data Curation ; Digital mapping ; Earth Sciences ; Ecology and Environmental Sciences ; Feasibility studies ; Geographic Information Systems ; Geography ; Geospatial data ; Humans ; Mathematical models ; Navigation systems ; Normal distribution ; Open systems ; Physical Sciences ; Prices ; Restaurants ; Seasonal variations ; Smart cities ; Social Sciences ; Spatial data ; Statistical analysis ; Statistical models ; Trends ; Urban Renewal ; User generated content</subject><ispartof>PloS one, 2019-02, Vol.14 (2), p.e0212606-e0212606</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Zhang, Pfoser. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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While OSM data is widely used, considerably little attention has been paid to the quality of its Point-of-interest (POI) component. This work studies the accuracy, coverage, and trend worthiness of POI data. We assess the accuracy and coverage using another VGI source that utilizes editorial control. OSM data is compared to Foursquare data by using a combination of label similarity and positional proximity. Using the example of coffee shop POIs in Manhattan we also assess the trend worthiness of OSM data. A series of spatio-temporal statistical models are tested to compare change in the number of coffee shops to home prices in certain areas. 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dedicated Volunteered Geographic Information (VGI) efforts such as OpenStreetMap (OSM) have revolutionized spatial data collection. While OSM data is widely used, considerably little attention has been paid to the quality of its Point-of-interest (POI) component. This work studies the accuracy, coverage, and trend worthiness of POI data. We assess the accuracy and coverage using another VGI source that utilizes editorial control. OSM data is compared to Foursquare data by using a combination of label similarity and positional proximity. Using the example of coffee shop POIs in Manhattan we also assess the trend worthiness of OSM data. A series of spatio-temporal statistical models are tested to compare change in the number of coffee shops to home prices in certain areas. 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| subjects | Accuracy Analysis Biology and Life Sciences Coffee Coffee (Beverage) Coffeehouses Criminal statistics Data collection Data Curation Digital mapping Earth Sciences Ecology and Environmental Sciences Feasibility studies Geographic Information Systems Geography Geospatial data Humans Mathematical models Navigation systems Normal distribution Open systems Physical Sciences Prices Restaurants Seasonal variations Smart cities Social Sciences Spatial data Statistical analysis Statistical models Trends Urban Renewal User generated content |
| title | Using OpenStreetMap point-of-interest data to model urban change-A feasibility study |
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