Abstract

This study explores how machine learning and weather data can be used for the prediction of crime more accurately in the city of Seattle. Predictive policing is a method in law enforcement that uses data and computer algorithms to forecast the locations that crimes are likely to happen. Although many studies focused on using past crime data alone, this research also includes weather conditions like temperature, rainfall, and humidity, which may influence when and where crimes occur. Several machine learning models, including Random Forest (RF) and Support Vector Machines (SVM), were used to classify areas of the city into high-risk and low-risk zones. These models were trained using a large dataset that included historical crime reports and weather records. To make the results easier to understand and apply in the real world, GIS tools and heatmaps were used to show crime hotspots and patterns across different parts of the city. The results showed that weather does have an impact on crime rates, especially for crimes like assault or theft. The models performed better when weather data was included, which shows that environmental factors should be part of future crime prediction systems. However, some challenges such as potential bias in historical crime data and the complexity of some machine learning models were seen, which can be hard to explain to non-technical users. Overall, this study shows that combining machine learning, environmental factors, and spatial mapping can create stronger and more useful crime prediction tools. This approach can help police departments plan patrols more effectively and use their resources in smarter and fairer ways.

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ayman Ibrahim

Campus

RIT Dubai

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