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Criminal Networks. October 3, 2019. Criminal Networks vs Social Networks. Criminal networks have many hidden edges Edges are intentionally hidden Leaders of criminal networks Not well connected Not easily detected Leaders of social networks Very well connected Easily accessible.
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Criminal Networks October 3, 2019
Criminal Networks vs Social Networks • Criminal networks have many hidden edges • Edges are intentionally hidden • Leaders of criminal networks • Not well connected • Not easily detected • Leaders of social networks • Very well connected • Easily accessible
Detecting communities in criminal networks • Edges intentionally hidden to minimize impact of arrests • This affects the quality of community detection • Our goal is to add the edges which we believe to be hidden • This new network is called “Augmented” • This will help us with our community detection
Caviar Dataset • Drug smuggling Montreal based gang • Phone call based network • No arrests made till the end • Only confiscated shipments, but without arresting anyone • Dataset consists of 11 snapshots • Each snapshot represents 2 months • All the snapshots together show us the network evolution over 2 years
Communities in Caviar Network • Four communities • Hashish smugglers • Cocaine smuggler • Transport support • Money laundering Money laundering Cocaine smugglers Hashish smugglers Transport
Caviar 8th snapshot Node 87 gains importance
Caviar 11th snapshot Node 3 is no longer trusted
Italian Drug Smuggling Network • A call and meetings based network of an Italy based, but international smuggling organization. • Network evolves for 1.5 years • Knowledge of the roles of most nodes • Roles: trafficker, support, retailer, buyer • Strict, hierarchical, organized structure among nodes • 40 nodes and 65 edges in Chalonero, 35 nodes and 58 edges in Stupor Mundi
Chalonero Dataset • Cocaine smuggling network • From South America to Italy • Law enforcement had access to their phone calls • Helped them determine the function of each member of the gang
What do we do with this information • How does crime affect the day to day life of a normal person? • How can we intervene crime, to disrupt crime with the limited amount of resources that we have?
City of Chicago - Data used Datasets: • Police crime data (2002-2017) • IUCR (Illinois Uniform Crime Reporting) code • Census data • Shapefile: geographic information about community areas
Predicting Future Monthly Crimes Conclusion • Number of crimes varies between months • Best predictions are made when predicting number of crimes in the given month/season from historical data for the same month/season
Crime rate • Crime rate is measured as crime per capita • So total number of crimes / total population of district • To make comparison between districts fair • Since districts with large populations will naturally have more crimes than districts with smaller population • Neighborhood crime rate (in future slides) • Total crime of all neighboring districts / total populations of all neighboring districts
Yearly Crime Rate Predictions F1 -> Home Ownership Rate F2 -> Education Level F3 -> Poverty Rate F4 -> Neighborhood Crime Rate F5 -> Community Crime Rate F4, and F5 are based on the previous year
Which features to use for crime predictions? F1 -> Home Ownership Rate F2 -> Education Level F3 -> Poverty Rate F4 -> Last years Neighborhood ……..Crime Rate F5 -> Last years Community ……..Crime Rate
P-values • Helps us determine the significance of our results • Value between 0 and 1 • Small p-value • Less than or equal to 0.05 • Indicates strong evidence against the null hypothesis • The smaller the p-value is for one of our features • The more important that feature is • Note: p-values for our features are constant regardless of how we normalize our features
Applying Intervention to Crime • Our resources are limited • If not we could just deploy infinite police men into the city, and eliminate crime • Given our resource limitation • Where should we deploy our additional police • Which district will give us the most decrease of the overall crime in Chicago
Applying intervention • For the year 2016, we decreased crime “applied intervention” to each district, one at a time • All measurements are made in crime per capita • We saw how this impacted the predicted crime rate, for the whole city of chicago for 2017 • We compared this prediction, to the prediction made with no intervention • Districts that caused the most drop in 2017 city crime, were considered the most influential
Applying intervention continued • Applying intervention to a district, doesn’t just impact that district’s crime rate • It also impacts the crime rates of its neighboring districts • Districts that share a border with our intervened districts
Applying intervention continued • If a district isn’t intervend, and its not a neighbor of any district that has been intervened • Due to the simplicity of our simulations • This districts expected crime rate should remain unchanged • As if no interventions were applied to any districts at all
Green -> most influential Light blue -> second most influential Orange -> third most influential Dark blue -> least influential Most and Least Influential Districts
Intervention Ripple Effect • In our study we only look at the next year after intervention is applied • In reality this intervention will have an effect on crime for many years to come, and it will reach district that are far away, not just the direct neighboring districts • The further the district, the less affected they will be • Eventually all districts will be affected by the intervention • First year: neighbors are effected • Second year: neighbors of neighbors are effected • ….
Conclusion on Interventions • Some districts are much more influential in reducing crimes than other • Most influential district • D62 • Crime reduction multiplier of about 2.74 • Least influential district • D74 • Crime reduction multiplier of about 1.38 • D62 is 2x as influential as D74
