📊

Fatal Police Shootings Data Analysis

Completed 2024 Comprehensive Data Analysis Pipeline with Statistical Testing and Visualization

This project is a comprehensive data analysis study of fatal interactions between law enforcement and citizens in the USA. The analysis covers data from 2015 to 2024, examining fatal police shootings, law enforcement deaths, demographic patterns, geographic distributions, temporal trends, and statistical relationships. The project uses multiple datasets from Washington Post and other sources to provide insights into police-civilian interactions, mental health factors, racial disparities, and law enforcement agency involvement. It includes statistical hypothesis testing (t-tests, ANOVA, chi-square), linear regression for temporal trends, geographic visualization on USA maps, and comprehensive cross-analysis of multiple variables.

Data Science Python Development Statistical Analysis Data Visualization Geographic Analysis Social Science Public Policy

Overview

This project is a comprehensive data analysis study of fatal interactions between law enforcement and citizens in the USA. The analysis covers data from 2015 to 2024, examining fatal police shootings, law enforcement deaths, demographic patterns, geographic distributions, temporal trends, and statistical relationships. The project uses multiple datasets from Washington Post and other sources to provide insights into police-civilian interactions, mental health factors, racial disparities, and law enforcement agency involvement. It includes statistical hypothesis testing (t-tests, ANOVA, chi-square), linear regression for temporal trends, geographic visualization on USA maps, and comprehensive cross-analysis of multiple variables.

Key Features

Comprehensive analysis of fatal police shootings (2015-2024)

Geographic visualization on USA maps with GeoPandas

Statistical hypothesis testing (t-tests, ANOVA, chi-square)

Linear regression for temporal trends

Demographic analysis (age, gender, race) with statistical tests

Mental health factor analysis and cross-analysis

Law enforcement agency analysis and rankings

Temporal analysis (monthly trends, seasonal patterns)

State and county-level death count analysis

Comparative analysis of law enforcer vs. civilian deaths

pages.portfolio.projects.fatal_police_shootings_analysis.features.10

Technical Highlights

Analyzed multiple datasets from Washington Post and other sources

Created geographic visualizations on USA maps showing event locations

Performed comprehensive statistical hypothesis testing

Identified temporal trends using linear regression

Analyzed demographic patterns with proper statistical methods

Examined mental health factors and their relationships

Challenges and Solutions

Multiple Data Sources

Integrated data from multiple sources with different formats using standardized loading and cleaning procedures

Missing Data

Carefully analyzed and handled missing values in critical columns like oricodes and demographic data

Geographic Data

Converted location data to geographic coordinates using GeoPandas and Shapely

Statistical Testing Assumptions

Verified assumptions (normality, independence) before performing statistical tests

Large Dataset Processing

Optimized Pandas operations and used chunk processing for efficient handling of large datasets

Temporal Analysis

Implemented time series aggregation, linear regression, and temporal visualizations for trend analysis

Technologies

Data Processing

Pandas NumPy xlrd

Visualization

Matplotlib Seaborn GeoPandas WordCloud

Statistical Analysis

SciPy Hypothesis Testing Regression Analysis

Geographic

Shapely GeoPandas

Data

Python Jupyter Notebook

Project Information

Status
Completed
Year
2024
Architecture
Comprehensive Data Analysis Pipeline with Statistical Testing and Visualization
Category
Data Science