Sharing city's operational data with public is a recent trend that can help in identifying deficiencies and bringing improvements in city operations. Analyzing such kinds of data can have a strong impact on problem solving as it connects public and private sectors with their city. Los Angeles (LA) is a good example of the ongoing index on the US open datasets. Since last several years, LA authorities worked hard to engage with its residents by launching several datasets, these datasets were represented by dashboards showing the progress of the city's performance and services. Any resident can review and assess progress of the city authorities in many aspects (e.g. building information, safety, water, streets conditions). Since open datasets can provide us with the ability to analyze and discover their values, we decided to analyze a dataset on crime statistics in Los Angeles. In this paper, we present a comprehensive analysis for the LA crime data from 2010 to present. This dataset was created by the Los Angeles Police Department (LAPD) and it is updated on a regular basis. The dataset contains approximately 1.5 million records, where each record represents a crime incident. We analyze multiple features including the activity of crimes (i.e. number of crimes) in terms of year, month, weekdays, time of the day, area, victim sex, victim age and victim descent. In addition, we analyze the reporting period of a crime incident by calculating the average reporting days (i.e. number of days the victim took to report a crime incident) in terms of multiple factors. Our analysis uncovers the unique characteristics and insights of safety measures and crime prevention in the city.

Additional Metadata
Keywords Crime statistics, Data analysis, Dataset, Open government
Persistent URL dx.doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.174
Conference 15th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
Citation
Ibrahim, R. (Rami), & Shafiq, M.O. (2018). On the measurement and analysis of safety in a large city. In Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017 (pp. 1068–1075). doi:10.1109/DASC-PICom-DataCom-CyberSciTec.2017.174