Geospatial-crime-analysis-and-prediction

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Geospatial-crime-analysis-and-prediction

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Crime and Criminal Analysis System integrating geospatial, temporal, and demographic analytics for predictive modeling of criminal activities. It employs machine learning for optimizing police resource allocation and incorporates real-time social media scraping for proactive crime detection.

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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](https://github.com/CID123456/crime-analysis-and-prediction/assets/164514449/8c50df37-7adf-432b-b1ee-23a6bf6360d8)

## 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](https://www.python.org/downloads/) and [Node.js](https://nodejs.org/en/download) installed in your system installed on your system.
2. Clone the repository:

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

3. Install dependencies in backend:

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

4. Install live-server
   
   ```bash
   npm install live-server

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

```bash
    
    live-server
    
## Demo video

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

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