Open AccessAssessing the Topological Consistency of Crowdsourced OpenStreetMap Data
Author(s) -
Sukhjit Singh Sehra
Publication year - 2014
Publication title -
human computation
Language(s) - English
Resource type - Journals
ISSN - 2330-8001
DOI - 10.15346/hc.v1i2.15
Subject(s) - crowdsourcing , volunteered geographic information , data consistency , consistency (knowledge bases) , computer science , data quality , citizen science , data collection , data science , quality (philosophy) , data mining , quality assurance , information retrieval , world wide web , database , artificial intelligence , mathematics , statistics , engineering , metric (unit) , philosophy , operations management , botany , external quality assessment , epistemology , biology
OpenStreetMap is world leader in collecting map data contributed by users, called crowdsourcing. But we have little knowledge about the people who collect it, their skills, knowledge or patterns of data collection. Also OpenStreetMap has loose coordination and no top-down quality assurance processes. This makes map data more vulnerable to errors and incomplete. To make the map data navigable, it must not have errors. The current proposal has been conducted to identify errors OpenStreetMap data. Small area of Punjab has been taken as test data for finding inconsistencies. It has been concluded that data contains lots of such errors and is not mature enough to be commercial purposes.