Exploring crime patterns in Mexico City

• Main Authors: C. A. Piña-García, Leticia Ramírez-Ramírez
• Format: Article
• Language: English
• Published: SpringerOpen 2019-07-01
• Series: Journal of Big Data
• Subjects: Big Data; Crime patterns; Mexico City; Predictive model; Twitter; Google Trends
• Online Access: http://link.springer.com/article/10.1186/s40537-019-0228-x

Abstract Introduction Life in the city generates data on human behavior in many different ways. Measuring human behavior in terms of criminal offenses plays a critical role on identifying the most common crime type in each urban area. A primary concern for the population of the largest cities in the world is to avoid specific city zones that could show a significant risk. This case of study systematically explores different sources of data including social media related to crime reports. In addition, this research seeks to examine and predict the most frequent crimes in Mexico City. Case description This case study was conducted in the form of an exploratory research. One of the parties involved is the Mexico City Police Department which released the approximate locations and categories of all crime reports from January 2013 to September 2016. We analyze the impact of crime in Mexico City based on 13 official crime categories: Robbery passerby, Theft of motor vehicle, Robbery of business property, Card fraud, Homicide, Domestic burglary, Robbery on public transportation, Rape, Firearm injuries, Robbery in subway, Robbery on taxi, Robbery to carrier, and Robbery to deliver person. We compare and analyze how people report a crime through the traditional system and using social media. Discussion and evaluation This research uses a quantitative case study approach to investigate, how our predictive model is able to forecast the total number of reported crimes in the following week based on its previous weekly aggregated observations and Google Trends series. Similarly, this case study seeks to determine whether Twitter performs correctly as a “social crime sensor” in terms of detecting certain areas and boroughs that are more likely to show criminal behavior. Conclusions In this study we used a linear predictive model in order to evaluate the performance of Google Trends in predicting crime rates based on weekly analysis. In addition, Twitter showed a suitable performance to discover the spatial distribution of crime frequency in Mexico City. Finally, this study provides an important opportunity to develop and encourage tailored strategies to tackle crime.