{'fraction_non_alphanumeric': 0.01366120218579235, 'fraction_numerical': 0.0, 'mean_word_length': 5.426900584795321, '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}}
CityWatch: the personalized crime prevention assistant

Motivated by rising levels of crime against property and findings in criminology research, we are developing CityWatch - the first mobile application that supports crime prevention behavior at community level. CityWatch leverages data on past crime incidents, which are sourced both from trustworthy sources, like the national census and the insurance industry, and from its users through crowd-sourcing. It applies machine learning algorithms to analyze the past incidents together with further data characterizing the living areas and learns common patterns of crime. These patterns are then leveraged in a general forecasting component, as well as in generating personalized risk profiles and crime prevention tips for registered users based on their account information. The results are visualized in an interactive map, where users can analyze past crime in their neighborhood and view predictions of future crime. Users can report a new crime and opt to receive notifications about new incidents in their proximity or area of residence.