DDI_KEN_2017-2018_MLCRM_v01_M_WB Development Economics Data Group 2021-03-24 NADA Version 01 (March 2021) Manually Labelled Crash Reports from Ma3Route 2017-2018 MLCRM 2017-2018 KEN_2017-2018_MLCRM_v01_M World Bank Sveta Milusheva Robert Marty Guadalupe Bedoya Sarah Williams Elizabeth Resor Arianna Legovini NADA Robert Marty An associated ''Tweet IDs From Ma3Route 2012-2020" dataset is available at: https://microdata.worldbank.org/index.php/catalog/3820 - v2.1: Edited, anonymous dataset for public distribution. The purpose of the Tweet IDs and Manually Labelled Crash Reports from Ma3Route 2017-2018 project is identify tweets from the @Ma3Route twitter handles that report road traffic crash reports. Using the Twitter API, tweets were scraped from Ma3Route, which is a mobile/web/SMS platform that crowdsources transport data and provides users with information on on road traffic crash reports as well as traffic, matatu directions, and driving reports. Kenya Kenya (primarily Nairobi) Road traffic crash reports Tweets reporting road traffic crash reports, scraped from twitter handle @Ma3Route Observation data/ratings [obs] Other [oth] Using the Twitter API, tweets were scraped from the twitter handle @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 were obtained in order to identify tweets that reported RTC. Tweets from May 2012 to July 2020 were scraped and a "truth dataset", of tweets manually coded to determine if they reported crash reports and the location of the reported crashes, was generated. Additional information on the data is provided in additional documents found under the 'Documentation' tab. Users of the data agree to keep confidential all data contained in these datasets and to make no attempt to identify, trace or contact any individual whose data is included in these datasets. Use of the dataset must be acknowledged using a citation which would include: - the Identification of the Primary Investigator - the title of the survey (including country, acronym and year of implementation) - the survey reference number - the source and date of download Example, World Bank Group. Kenya - Manually Labelled Crash Reports from Ma3Route (MLCRM) 2017-2018. Ref. KEN_2017-2018_MLCRM_v01_M. Dataset downloaded from [url] on [date]. Licensed access 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. twitter_truth Using the Twitter API, tweets were scrapped from the twitter handle @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 were obtained in order to identify tweets that reported RTC. This dataset includes a manually labelled dataset of a subset of tweets indicating which tweets report a crash and the location of crashes. 9479 9 Unique ID Unique ID Unique ID Unique ID Unique ID Unique ID 9479 1 9479 Tweet ID Tweet ID Tweet ID Tweet ID Tweet ID Tweet ID 7724 Time Date/Time (EAT) Time Date/Time (EAT) Time Date/Time (EAT) Time Date/Time (EAT) Time Date/Time (EAT) Time Date/Time (EAT) 9479 1814496118000 1848681624000 Tweet reports crash Tweet reports crash Tweet reports crash Tweet reports crash Tweet reports crash Tweet reports crash 9479 1 No 1 Yes Latitude of crash Latitude of crash Latitude of crash Latitude of crash Latitude of crash Latitude of crash 4193 5286 -4.06 1.257 Longitude of crash Longitude of crash Longitude of crash Longitude of crash Longitude of crash Longitude of crash 4191 5288 34.146 40.171 Crash ID (from coder 1) Crash ID (from coder 1) Crash ID (from coder 1) Crash ID (from coder 1) Crash ID (from coder 1) Crash ID (from coder 1) 3796 5683 907 41898 Crash ID (from coder 2) Crash ID (from coder 2) Crash ID (from coder 2) Crash ID (from coder 2) Crash ID (from coder 2) Crash ID (from coder 2) 3674 5805 909 47227 Landmark used to geocode Landmark used to geocode Landmark used to geocode Landmark used to geocode Landmark used to geocode Landmark used to geocode 4210