Crime Prediction Using Multiple-ANFIS Architecture and Spatiotemporal Data

TL;DR

This paper presents a novel multi-ANFIS architecture utilizing spatiotemporal data to predict crime occurrences in Dhaka, aiding law enforcement in resource allocation and crime prevention.

Contribution

It introduces a new multi-ANFIS framework combining spatiotemporal data for crime prediction, enhancing decision-making capabilities for law enforcement.

Findings

Effective crime prediction model developed

Improved resource allocation for law enforcement

Potential reduction in crime rates

Abstract

Statistical values alone cannot bring the whole scenario of crime occurrences in the city of Dhaka. We need a better way to use these statistical values to predict crime occurrences and make the city a safer place to live. Proper decision-making for the future is key in reducing the rate of criminal offenses in an area or a city. If the law enforcement bodies can allocate their resources efficiently for the future, the rate of crime in Dhaka can be brought down to a minimum. In this work, we have made an initiative to provide an effective tool with which law enforcement officials and detectives can predict crime occurrences ahead of time and take better decisions easily and quickly. We have used several Fuzzy Inference Systems (FIS) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to predict the type of crime that is highly likely to occur at a certain place and time.