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Crime Analysis and Prediction

Overview

This project aims to provide comprehensive analysis and insights into crime patterns and trends using various techniques such as map-based analysis, month-based analysis, age-based analysis, crime prediction, crime sentiment analysis, and a model for resource allocation, crime prediction, sentiment analysis for social media comments for finding probable criminal activities.

System Architecture

Untitled - Frame 1

Features

Map-Based Analysis

  • Utilizes maps to visualize crime hotspots, aiding in understanding geographic patterns of criminal activities.
  • Provides detailed views of crime distribution across different regions, enabling targeted interventions and resource allocation.

Month-Based Analysis

  • Analyzes crime data based on months to identify seasonal trends and variations in criminal activities.
  • Helps in understanding temporal patterns and planning preventive measures accordingly.

Age-Based Analysis

  • Focuses on age demographics to study crime trends for women and sensitive age groups like the elderly and children.
  • Provides insights into vulnerable age categories and potential areas for social interventions.

Crime Predictor

  • Utilizes machine learning models to predict potential crime occurrences based on historical data and relevant factors.
  • Aids law enforcement agencies in proactive planning and resource allocation.

Crime Sentiment Analysis

  • Analyzes public sentiment related to crime through social media and other sources.
  • Provides a sentiment score to gauge public perception and concerns regarding safety and security.

Resource Allocation Model

  • A model for finding the nearest police station containing needed resources for the user-reported crime.
  • Maximizes efficiency in resource utilization and response to varying crime scenarios.

Criminal prediction Model

  • A model for predicting the most probable criminals based on the previous data for criminals including the time range, the locations of the crimes, and the crime type.

Tech stack

  • Javascript: Integrating Google Maps API
  • Python: Machine learning models
  • Html/CSS: Website design
  • Flask: Prediction model deployment
  • Firebase: Realtime data storage and deployment
  • Streamlit: Other models deployment
  • Huggingface: Other models deployment
  • AWS Amplify: Website deployment

Installation

  1. Make Sure you have Python 3.x and Node.js installed in your system installed on your system.

  2. Clone the repository:

    git clone https://github.com/CID123456/crime-analysis-project.git

  3. Install dependencies in backend:

    cd backend
pip install -r requirements.txt
python app.py

  4. Install live-server

    npm install live-server

  5. Run in root directory (containing the index.html):

    live-server

Demo video

Demo video link - https://youtu.be/gC80tkqQN64