{"doc_desc":{"idno":"DDI_KEN_2012-2023_RTC_v01_M","producers":[{"name":"Development Data Group","abbr":"DECDG","affiliation":"World Bank","role":"Documentation of the DDI"}],"prod_date":"2024-05-23"},"study_desc":{"title_statement":{"idno":"KEN_2012-2023_RTC_v01_M","title":"Road Traffic Crashes 2012-2023","sub_title":"Derived from Crowdsourced Reports from Ma3Route","alternate_title":"RTC 2012-23"},"authoring_entity":[{"name":"Sveta Milusheva","affiliation":"World Bank"}],"production_statement":{"funding_agencies":[{"name":"World Bank","abbr":"","role":""}]},"distribution_statement":{"contact":[{"name":"Sveta Milusheva","affiliation":"WBG","email":"smilusheva@worldbank.org","uri":""},{"name":"Guadalupe Bedoya","affiliation":"WBG","email":"gbedoya@worldbank.org","uri":""},{"name":"Robert Marty","affiliation":"WBG","email":"rmarty@worldbank.org","uri":""},{"name":"Amy Dolinger","affiliation":"WBG","email":"adolinger@worldbank.org","uri":""},{"name":"Arianna Legovini","affiliation":"WBG","email":"alegovini@worldbank.org","uri":""}]},"version_statement":{"version":"The datasets contain the time and location of road traffic crashes in Kenya (primarily Nairobi); crash information is derived from crowdsourced reports from @Ma3Route. Ma3Route is a mobile\/web\/SMS platform that crowdsources transport data and provides users with information on traffic, road traffic crash (RTC), matatu directions and driving reports. Users post RTC or traffic information to Ma3Route, where Ma3Route then publishes the post on Twitter. Tweets from @Ma3Route were queried using the Twitter API (tweets were no longer queried once Twitter rebranded to X).","version_date":"2024-05-20"},"study_info":{"abstract":"This project geolocated the location of road traffic crashes based on crowdsourced reports of crashes from Ma3Route, a mobile\/web\/SMS platform that crowdsources transport data","coll_dates":[{"start":"2012-08-01","end":"2023-07-12","cycle":"1"}],"nation":[{"name":"Kenya","abbreviation":"KEN"}],"geog_coverage":"Primarily Nairobi, Kenya","analysis_unit":"Road traffic crashes","data_kind":"Observation data\/ratings [obs]"},"method":{"data_collection":{"sampling_procedure":"All tweets from @Ma3Route from August 2012 to July 2023","coll_mode":["Internet [int]"]}},"data_access":{"dataset_use":{"contact":[{"name":"Sveta Milusheva","affiliation":"WBG","email":"smilusheva@worldbank.org","uri":""},{"name":"Guadalupe Bedoya","affiliation":"WBG","email":"gbedoya@worldbank.org","uri":""},{"name":"Robert Marty","affiliation":"WBG","email":"rmarty@worldbank.org","uri":""},{"name":"Amy Dolinger","affiliation":"WBG","email":"adolinger@worldbank.org","uri":""},{"name":"Arianna Legovini","affiliation":"WBG","email":"alegovini@worldbank.org","uri":""}],"cit_req":"Milusheva S, Marty R, Bedoya G, Williams S, Resor E, et al. (2021) \u201cApplying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.\u201d PLOS ONE 16(2): e0244317. https:\/\/doi.org\/10.1371\/journal.pone.0244317","disclaimer":"The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses."}}},"schematype":"survey","tags":[{"tag":"DOI"}]}