Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning
| العنوان: | Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning |
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| المؤلفون: | Elizabeth Resor, Sarah Williams, Sveta Milusheva, Guadalupe Bedoya, Robert Marty, Arianna Legovini |
| المصدر: | PLoS ONE, Vol 16, Iss 2, p e0244317 (2021) PLoS ONE |
| بيانات النشر: | Public Library of Science (PLoS), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Geographic information system, Computer science, Social Sciences, Transportation, Crash, computer.software_genre, Machine Learning, Urban Environments, Resource (project management), Sociology, Medicine and Health Sciences, Public and Occupational Health, City Planning, Multidisciplinary, Applied Mathematics, Simulation and Modeling, Accidents, Traffic, Social Communication, Transportation Infrastructure, Terrestrial Environments, Social Networks, Motorcycles, Physical Sciences, Engineering and Technology, Medicine, Safety, Network Analysis, Algorithms, Research Article, Computer and Information Sciences, Science, Twitter, Research and Analysis Methods, Machine learning, Civil Engineering, Machine Learning Algorithms, Artificial Intelligence, Urban planning, Humans, Social media, business.industry, Ecology and Environmental Sciences, Traffic Safety, Kenya, Communications, Roads, Geolocation, Artificial intelligence, business, Geoparsing, Social Media, computer, Mathematics |
| الوصف: | With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. We test our hypothesis by creating road traffic crash location data, which is scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over five and young adults. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012–2020 and cluster them into 22,872 unique crashes during this period. For a subset of crashes reported on Twitter, a motorcycle delivery service was dispatched in real-time to verify the crash and its location; the results show 92% accuracy. To our knowledge this is the first geolocated dataset of crashes for the city and allowed us to produce the first crash map for Nairobi. Using a spatial clustering algorithm, we are able to locate portions of the road network ( |
| اللغة: | English |
| تدمد: | 1932-6203 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3be9a814e0b5cd15e74eb1fa72a9cdfc https://doaj.org/article/9293cbdffc66410a8f5fe50cc1a79e4c |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....3be9a814e0b5cd15e74eb1fa72a9cdfc |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19326203 |
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