📂 Download the full dataset and dashboard files here
This project involved analyzing real-world crime data from the Los Angeles Police Department (LAPD) to identify spatial and temporal crime patterns, victim profiles, and weapon usage trends. 📂 Download the full dataset and dashboard files here
Goal: Deliver data-driven insights for crime prevention strategies and resource allocation through advanced visualization and exploratory data analysis.
- Which neighborhoods experience the highest crime rates?
- What are the most common crime types, and when do they peak?
- Are there patterns in crime based on time of day or day of week?
- Which age/gender groups are most targeted?
- How do weapons and crime premises vary by incident?
| Stack | Tools Used |
|---|---|
| Data Cleaning | Python (Pandas), Excel |
| Visualization | Tableau |
| Statistical Exploration | Seaborn, Matplotlib |
| Geo-Mapping | Tableau Maps, Heatmaps |
- 📍 Source: LAPD Crime Dataset (public)
- 🗂️ Fields included:
Crime Date,Time,Location,Premise,Weapon Used,Victim Age,Victim Gender,Crime Category
- Identified high-crime divisions such as Central, Wilshire, and Hollywood using interactive heatmaps and neighborhood-level filters.
- Crimes peak on Friday afternoons and evenings
- Created time series charts to visualize weekly & daily patterns
- Spotted reporting delays by comparing incident vs report times
- Visualized age and gender distributions across crime types
- Found specific targeting of youth (11–20 years) in certain areas
- Guns, knives, and strong-arm tactics were most common
- Top crime types included burglary, vehicle theft, and assault
- Created comparative visualizations of weapon vs premise type
Built using Tableau:
- 🌆 Crime Hotspot Map – Interactive map by LAPD division
- 🕒 Crime Over Time – Weekly & daily pattern visualization
- 👥 Victim Demographics – Age and gender breakdown
- 🔫 Weapon & Crime Type Analysis – Understand risk factors by premise
- Friday evenings are the most dangerous times across all divisions
- Central LA shows consistently high crime volume
- Crimes involving weapons are more common on streets and alleys
- Victims in the 11–30 age group are disproportionately impacted
- Reporting delays often lead to loss of investigative efficiency
- Increase patrol presence in Wilshire, Central, and Hollywood divisions during evening hours
- Launch youth-targeted crime prevention campaigns in vulnerable areas
- Investigate delay patterns and improve real-time incident reporting systems
- Allocate more surveillance in high weapon-use zones (e.g., parking lots, public spaces)
- Law enforcement officers: Better resource planning & patrol optimization
- City planners: Informed decisions on urban safety measures
- Community outreach teams: Targeted awareness campaigns
- Data scientists: Foundation for predictive crime modeling
- Integrate with real-time incident APIs
- Build predictive models to anticipate future crime hotspots
- Combine with socioeconomic data for deeper community impact analysis
- Create a public safety dashboard for residents and policymakers