This repository contains a comprehensive analysis of crime trends in Omaha, Nebraska. The project aims to identify patterns in criminal activity, focusing on temporal and spatial distributions, and to provide insights that can inform effective policies and public safety measures.
- Introduction
- Research Questions
- Approach
- Dataset
- Required Packages
- Plots and Tables
- Exploratory Data Analysis (EDA)
- Conclusions
- References
As a resident of Nebraska, I am deeply invested in addressing one of our community's most significant issues: crime. This project, "Analyzing Crime Trends in Omaha: A Data Exploration," delves into the complex landscape of criminal activity in Omaha to identify patterns and critical areas of concern. By using data analysis, the goal is to shape effective policies and bolster public safety measures.
- Is there a statistically significant difference in the mean reported hours of crime between weekends and weekdays?
- What are the spatial distributions of crime across different districts in Omaha?
- What are the temporal patterns of crime occurrences by hour of the day?
The project follows a structured approach:
- Data preparation and cleaning
- Exploratory data analysis
- Statistical hypothesis testing
- Visualization of findings
The analysis uses crime data from the Omaha Police Department, including details on incident types, locations, and times. The datasets are cleaned, preprocessed, and analyzed to understand crime trends.
The project utilizes several Python packages:
pandasfor data manipulationnumpyfor numerical operationsmatplotlibandseabornfor visualizationscipyfor statistical analysisfoliumfor geographic data visualization
The project includes various visualizations:
- Histograms
- Scatter plots
- Heat maps
- Time series plots
- Geographic maps
Tables present statistical metrics and hypothesis test results.
EDA reveals valuable insights into crime patterns in Omaha:
- Distribution of Incident Types: Types and frequencies of reported crimes.
- Spatial Distribution: Geographic analysis of crime locations.
- Temporal Patterns: Variation in crime reporting by hour and day.
The analysis indicates significant differences in crime reporting times between weekends and weekdays, highlighting the importance of considering temporal factors in crime analysis and public safety efforts. Incorporating additional variables and employing advanced spatial analysis could offer a richer understanding of crime dynamics.