Crime Data Analysis & Heatmaps With Fireducks: Visualizing Crime Trends With Fireducks, Ai & Data Science

INTRODUCTION:-
With the increase in the rate of crimes, there is raising concern among the citizens and the authorities. Understanding crime patterns can help law enforcement agencies allocate resources efficiently and enhance public safety. This blog gives detailed information on how AI and data science can be used to analyze the data about crimes and create an interactive heatmap to visualize the high crime places. Using Python, FireDucks, GeoPandas, and visualization tools such as Folium and Plotly, we can take an unprocessed dataset containing crime and convert the unprocessed dataset into an informative crime heatmap.
PROBLEM STATEMENT:-
Crime data is often scattered across multiple sources, making it difficult to analyze the trends effectively. By developing an AI-powered crime heatmap, we can provide valuable insights for policymakers, researchers, and public safety organizations.
SOLUTION/ APPROACH:-
Step 1: Collecting Crime Data (India)
In India, crime data is reported by agencies like the National Crime Records Bureau (NCRB) and local police departments. Some reliable sources for data collection include
NCRB Reports: https://ncrb.gov.in/
Open Government Data (OGD) Platform India: https://data.gov.in/
State Police Portals (Delhi, Maharashtra, Tamil Nadu, etc.)
For this project, we will use NCRB’s city-wise crime data, which includes:
Crime type (Murder, Theft, Assault, Cybercrime, etc.)
Location (State, City, Latitude, Longitude)
Time (Year, Month)
Step 2: Data Preprocessing & Cleaning (India Crime Data)
We will preprocess the NCRB data to ensure consistency using FireDucks.
import fireducks as fd
# Load dataset (Modify path based on the source)
df = fd.read_csv("ncrb_crime_data.csv")
# Convert timestamp
df["Year"] = fd.to_datetime(df["Year"], format="%Y")
# Remove missing values
df = df.dropna(subset=["Latitude", "Longitude"])
# Display the cleaned dataset
df.head()
Step 3: Exploratory Data Analysis (EDA) - India Crime Patterns
Key Insights from Indian Crime Data:
Crime hotspots by city (Delhi, Mumbai, Bangalore, etc.)
Types of crimes increasing over time (e.g., Cybercrime, Domestic Violence)
Seasonal trends (Festivals, Elections, etc.)
Top 5 Most Crime-Prone Cities in India
top_cities = df["City"].value_counts().head(5)
print(top_cities)
Crime Trends Over Time (2000-2023)
import matplotlib.pyplot as plt
df.groupby("Year")["Crime Type"].count().plot(kind="line", figsize=(10,5), marker="o", linestyle="-")
plt.title("Crime Trends in India (2000-2023)")
plt.xlabel("Year")
plt.ylabel("Number of Crimes")
plt.show()
Step 4: Creating an Interactive Crime Heat Map for India
Using Folium to create a heatmap overlaying major Indian cities.
import folium
from folium.plugins import HeatMap
# Create base map centered at India
india_map = folium.Map(location=[20.5937, 78.9629], zoom_start=5)
# Convert data to list format
heat_data = df[["Latitude", "Longitude"]].values.tolist()
# Add HeatMap layer
HeatMap(heat_data, radius=10).add_to(india_map)
# Save and display map
india_map.save("india_crime_heatmap.html")
india_map
This heatmap will highlight crime-dense areas in Indian states and cities.
TOOLS AND TECHNOLOGIES USED:-
TOOL | PURPOSE |
Python | Data processing & analysis |
FireDucks | Data manipulation |
GeoPandas | Geospatial analysis |
Folium | Interactive heatmap visualization |
Plotly | Interactive charts & EDA |
Jupyter Notebook | Development environment |
BENCHMARKING RESULTS:-
Using a dataset of 100,000 crime reports, we evaluated:
TASK | TIME TAKEN |
Data Cleaning (Pandas) | ~3s |
Crime Trend Analysis (Matplotlib) | ~2s |
Heatmap Generation (Folium) | ~5s |
CONCLUSION:-
Crime data analysis using AI and visualization tools helps law enforcement and city planners make data-driven decisions. Our interactive heatmap can:
Identify high-crime zones dynamically.
Show temporal crime trends for better resource allocation.
Help in crime prevention strategies using historical patterns.
📌 FAQ: Crime Data Analysis & Heatmap with FireDucks:-
- Where does the crime dataset come from?
The dataset is sourced from: -
NCRB (National Crime Records Bureau) → [https://ncrb.gov.in/](https://ncrb.gov.in/)
Open Government Data (OGD) India → [https://data.gov.in/](https://data.gov.in/)
- How does FireDucks improve data processing?
FireDucks is optimized for handling large-scale datasets. It offers:
Faster operations than Pandas
Efficient memory management
Optimized geospatial data handling
- Can this project scale to large datasets?
Yes! The project can handle large datasets by:
Using FireDucks (better memory efficiency)
Processing data in batches
Using Folium & Plotly for interactive visualizations
For real-world applications, integrating BigQuery, Spark, or a database like PostgreSQL can help further.
- How can law enforcement use this heatmap?
Identify crime hotspots
Allocate police resources efficiently
Track seasonal crime trends
Use AI to predict future crimes
- Can I contribute to this project?
Yes! To contribute: -
Fork the GitHub repository- https://github.com/angela479/crime-data-analysis
Improve crime datasets, visualizations, and performance
Submit a pull request
By integrating machine learning, future work could predict crime occurrences based on past data trends.
GitHub Repository Link: https://github.com/angela479/crime-data-analysis
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