Crime Map

Overview

After recently moving into the Newham district in London, I wanted to see which areas had the highest crime rates. Therefore, this dashboard deep-dives into the crime rates and statistics.

Key Features

Interactive maps showing crime hotspots
Temporal analysis of crime patterns
Breakdown by crime types and districts
Demographic correlation analysis
Trend forecasting and seasonal patterns

Technologies Used

Python for data processing and analysis
Tableau for interactive visualizations
UK Police API for data collection
Pandas for data manipulation
GeoPandas for spatial analysis

Data Collection Process


import requests
import pandas as pd

# API Configuration
base_url = "https://data.police.uk/api"
endpoint = "/crimes-street/all-crime"

# Define Area Parameters
params = {
    "poly": "51.507222,-0.127647:51.522420,-0.102827:51.517814,-0.076428:51.487366,-0.068504:51.477458,-0.096115:51.498466,-0.119421",
    "date": "2021-01",
    "polyname": "london",
    "page": 1
}

# Fetch Crime Data
dfs = []
while True:
    response = requests.get(f"{base_url}{endpoint}", params=params)
    
    # Check response status
    if response.status_code != 200:
        print(f"Request failed with status code {response.status_code}")
        break

    # Process data
    data = response.json()
    if not data:
        break

    dfs.append(pd.json_normalize(data))
    params["page"] += 1

# Save Collected Data
all_data = pd.concat(dfs)
all_data.to_csv("police-data-uk.csv", index=False)

# Optional: Display Data Summary
print(f"Total crime records collected: {len(all_data)}")
print(f"Unique crime categories: {all_data['category'].nunique()}")