{'fraction_non_alphanumeric': 0.017015706806282723, 'fraction_numerical': 0.0, 'mean_word_length': 5.071428571428571, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Assessing the Topological Consistency of Crowdsourced OpenStreetMap Data

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.