Crimes are a common societal concern impacting quality of life and economic growth.
Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have
often increased, leading safety and security agencies with the need to apply novel approaches and
advanced systems to better predict and prevent occurrences. The use of geospatial technologies,
combined with data mining and machine learning techniques allows for significant advances in the
criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018,
was georeferenced and treated using spatial analysis methods, which allowed the identification of
spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time
pattern mining. Using lasso regression analysis, significance for crime variables were found, with
random forest and decision tree supporting the important variable selection. Lastly, tweets related to
insecurity were collected and topic modeling and sentiment analysis was performed. Together, these
methods assist interpretation of patterns, prediction and ultimately, performance of both police and
planning professionals
