Smart Crime Analytics

To develop a comprehensive tool that integrates hotspot detection, pattern recognition, performance management, and predictive modeling, we’ll build a modular architecture, each serving a specialized function. The modules will interact with both the frontend and backend to offer real-time insights to users.

1. Project Structure

We’ll structure the project like this:

crime-analysis-tool/
├── backend/
│   ├── app.py
│   ├── config.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── hotspot_detection.py
│   │   ├── pattern_recognition.py
│   │   ├── performance_management.py
│   │   └── predictive_modeling.py
│   ├── services/
│   │   ├── __init__.py
│   │   └── data_service.py
│   ├── utils/
│   │   ├── __init__.py
│   │   └── database.py
│   └── requirements.txt
├── frontend/
│   ├── public/
│   │   └── index.html
│   ├── src/
│   │   ├── components/
│   │   │   ├── HotspotMap.js
│   │   │   ├── PatternChart.js
│   │   │   ├── PerformanceDashboard.js
│   │   │   └── PredictionMap.js
│   │   ├── services/
│   │   │   └── api.js
│   │   ├── App.js
│   │   └── index.js
│   ├── package.json
│   └── README.md
├── data/
│   └── crime_data.csv
├── .gitignore
└── README.md

2. Backend Code (Flask)

First, we’ll set up the Flask backend for the application. Each module will have a corresponding Python file to handle its functionality.

2.1 app.py – Flask Main App

This will serve as the API layer for the tool, handling requests and calling the appropriate functions from different modules.

pythonCopy codefrom flask import Flask, jsonify, request
from models.hotspot_detection import get_hotspots
from models.pattern_recognition import get_patterns
from models.performance_management import get_performance_metrics
from models.predictive_modeling import predict_crime

app = Flask(__name__)

# Route for hotspot detection
@app.route('/api/hotspots', methods=['POST'])
def hotspots():
    data = request.json  # Expecting crime data (with location and time)
    hotspots = get_hotspots(data)
    return jsonify(hotspots)

# Route for pattern recognition
@app.route('/api/patterns', methods=['POST'])
def patterns():
    data = request.json  # Expecting historical crime data
    patterns = get_patterns(data)
    return jsonify(patterns)

# Route for performance management
@app.route('/api/performance', methods=['GET'])
def performance():
    metrics = get_performance_metrics()
    return jsonify(metrics)

# Route for predictive modeling
@app.route('/api/predict', methods=['POST'])
def predict():
    data = request.json  # Expecting historical and auxiliary data (socio-economic, weather, etc.)
    predictions = predict_crime(data)
    return jsonify(predictions)

if __name__ == '__main__':
    app.run(debug=True)

2.2 hotspot_detection.py

This module will use Kernel Density Estimation (KDE) or Getis-Ord Gi (Hotspot Analysis)* for detecting hotspots.

pythonCopy codeimport numpy as np
import geopandas as gpd
from sklearn.neighbors import KernelDensity

def get_hotspots(crime_data):
    """
    Hotspot detection using Kernel Density Estimation (KDE).
    """
    # Assuming crime_data is a list of dicts with 'latitude' and 'longitude'
    coordinates = np.array([[c['latitude'], c['longitude']] for c in crime_data])
    
    kde = KernelDensity(bandwidth=0.01).fit(coordinates)  # Adjust bandwidth as needed
    density = kde.score_samples(coordinates)

    # Add density to each crime data point and return results
    for i, crime in enumerate(crime_data):
        crime['density'] = density[i]
    
    return crime_data

2.3 pattern_recognition.py

For pattern recognition, we’ll use clustering algorithms like K-Means or DBSCAN to identify recurring crime patterns.

pythonCopy codeimport pandas as pd
from sklearn.cluster import DBSCAN

def get_patterns(crime_data):
    """
    Pattern recognition using DBSCAN clustering.
    """
    df = pd.DataFrame(crime_data)
    
    # Perform clustering on latitude and longitude
    clustering = DBSCAN(eps=0.01, min_samples=5).fit(df[['latitude', 'longitude']])
    df['cluster'] = clustering.labels_
    
    # Group by clusters and return the patterns
    patterns = df.groupby('cluster').apply(lambda x: x.to_dict(orient='records')).to_dict()
    return patterns

2.4 performance_management.py

This module will compute performance metrics, such as response times and clearance rates.

pythonCopy codeimport pandas as pd

def get_performance_metrics():
    """
    Placeholder for performance management logic, such as response time analysis.
    """
    # Dummy data for now
    metrics = {
        'average_response_time': 7.2,  # In minutes
        'clearance_rate': 80.5,  # Percent
        'patrol_efficiency': 92.1  # Percent
    }
    
    return metrics

2.5 predictive_modeling.py

This module will use machine learning (e.g., Random Forest or ARIMA) for predicting future crimes.

pythonCopy codefrom sklearn.ensemble import RandomForestRegressor
import pandas as pd

def predict_crime(crime_data):
    """
    Predictive modeling using a simple RandomForestRegressor.
    """
    df = pd.DataFrame(crime_data)
    
    # Features could be time, location, weather, etc.
    X = df[['latitude', 'longitude', 'time_of_day', 'day_of_week', 'weather_conditions']]
    y = df['crime_occurred']  # Assuming binary classification (crime/no crime)
    
    # Train a Random Forest model (for demo purposes, train/test split is skipped)
    model = RandomForestRegressor()
    model.fit(X, y)
    
    predictions = model.predict(X)
    df['predicted_risk'] = predictions
    
    return df[['latitude', 'longitude', 'predicted_risk']].to_dict(orient='records')

3. Frontend Code (React.js)

We’ll create a simple React.js frontend that calls the backend API to fetch and display the data. For now, I’ll just create the structure with a basic map and chart component.

3.1 App.js

The main React component that manages the frontend logic.

jsxCopy codeimport React, { useState, useEffect } from 'react';
import Map from './components/Map';
import Charts from './components/Charts';

function App() {
  const [hotspots, setHotspots] = useState([]);
  const [patterns, setPatterns] = useState([]);
  const [performance, setPerformance] = useState({});
  const [predictions, setPredictions] = useState([]);

  useEffect(() => {
    // Fetch hotspots
    fetch('/api/hotspots', { method: 'POST', body: JSON.stringify({}) })
      .then(response => response.json())
      .then(data => setHotspots(data));

    // Fetch patterns
    fetch('/api/patterns', { method: 'POST', body: JSON.stringify({}) })
      .then(response => response.json())
      .then(data => setPatterns(data));

    // Fetch performance
    fetch('/api/performance')
      .then(response => response.json())
      .then(data => setPerformance(data));

    // Fetch predictions
    fetch('/api/predict', { method: 'POST', body: JSON.stringify({}) })
      .then(response => response.json())
      .then(data => setPredictions(data));
  }, []);

  return (
    <div className="App">
      <h1>Crime Analysis Tool</h1>
      <Map hotspots={hotspots} predictions={predictions} />
      <Charts performance={performance} patterns={patterns} />
    </div>
  );
}

export default App;

3.2 Map.js

This component will use Leaflet.js or Mapbox to render maps.

jsxCopy codeimport React from 'react';
import { MapContainer, TileLayer, CircleMarker } from 'react-leaflet';

const Map = ({ hotspots, predictions }) => {
  return (
    <MapContainer center={[51.505, -0.09]} zoom={13} style={{ height: '400px', width: '100%' }}>
      <TileLayer
        url="https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png"
      />
      {hotspots.map((spot, index) => (
        <CircleMarker
          key={index}
          center={[spot.latitude, spot.longitude]}
          radius={spot.density * 10}
          color="red"
        />
      ))}
    </MapContainer>
  );
};

export default Map;

3.3 Charts.js

This component will render charts using Chart.js.

jsxCopy codeimport React from 'react';
import { Line } from 'react-chartjs-2';

const Charts = ({ performance, patterns }) => {
  const data = {
    labels: ['Metric 1', 'Metric 2', 'Metric 3'],
    datasets: [{
      label: 'Performance Metrics',
      data: [performance.average_response_time, performance.clearance_rate, performance.patrol_efficiency],
      backgroundColor: 'rgba(75,192,192,0.4)',
      borderColor: 'rgba(75,192,192,1)',
      borderWidth: 1
    }]
  };

  return (
    <div>
      <h2>Performance Metrics</h2>
      <Line data={data} />
    </div>
  );
};

export default Charts;