Denver Crime Dashboard
Quick Links: Tableau Dashboard
Table of Contents:
Introduction:
This Tableau dashboard project provided an in-depth analysis of crime trends across Denver in 2021 and 2022, aiming to identify areas where law enforcement may need to improve its resource allocation and strategic focus. By exploring neighborhood-specific crime trends, types of offenses, and peak crime times/days, the dashboard offers insights into how the city might enhance safety measures.
Goal: The project focused on three main objectives:
Understand year-over-year changes in overall crime rates, as well as specific categories like violent and non-violent crimes.
Analyze the spatial distribution of crimes by neighborhood and identify hotspots.
Identify time-based patterns, such as peak hours or days, to help optimize patrol scheduling and resource deployment.
Key Questions:
Which neighborhoods in Denver saw the highest increases in crime rates from 2021 to 2022?
What times of day are associated with the highest crime frequencies, and are there noticeable trends across weekdays?
Which types of crimes have increased the most, and how might this information guide preventative measures?
Process:
Tools: Tableau to create the visualizations and Kaggle as the primary data source for Denver's crime statistics.
Methodology: Inspired by other dashboards and the perspective of a police officer or city planner, I carefully considered which data points and visualizations would provide the most actionable insights. My process involved creating key performance indicators (KPIs) for total, violent, and non-violent crimes, as well as crime statistics split by daytime and nighttime to help pinpoint when additional resources might be needed. Additionally:
Maps show neighborhood crime densities, enabling a geographic understanding of high-crime areas.
Horizontal bar charts visualize differences in crime types between 2021 and 2022, highlighting both percentage and numerical changes.
Time-based filters help users compare patterns across months, days, and hours.
Customizable sorting options allow crime data to be viewed by district, with year-to-year comparisons or filtered by percentage change.
Results:
The dashboard uncovered a general increase in crime rates in 2022, with notable spikes during weekday daytime hours, especially from 9 a.m. to 2 p.m. Certain neighborhoods, such as DIA and University Hills, exhibited the sharpest increases, particularly in auto-related offenses. I shared this dashboard with a colleague at my current workplace, which led to further collaboration on Tableau projects in a professional setting, highlighting the impact of visualization in decision-making.
Specific findings include:
Total crime rate increase in Denver from 2021 to 2022, with a significant rise in non-violent offenses such as auto thefts and property crimes.
DIA and University Hills neighborhoods emerged as high-risk areas, especially for auto-related crimes, suggesting a potential need for targeted policing or community programs.
Daytime hours (11 a.m. to 3 p.m.) showed increased crime rates, especially on weekdays, pointing to an opportunity for increased visibility and patrols during these hours.
Insights:
Neighborhoods with Increased Crime Rates: DIA and University Hills should be priority areas for additional policing resources, especially focused on reducing auto-related crimes, which saw the most significant increases.
Adjusting Patrol Schedules: With peak crime times identified during midday hours on weekdays, a shift in patrol scheduling could enhance police visibility during these periods.
Community Engagement: The rise in auto-related offenses in specific neighborhoods suggests the need for a community-focused approach to prevention, potentially involving public education campaigns around auto theft prevention.
Learnings:
This project was a valuable exercise in learning and applying advanced Tableau functionalities. I developed expertise in designing intuitive and interactive dashboards tailored to specific user needs. Key takeaways include:
Enhanced Tableau Proficiency: I gained hands-on experience in using Tableau’s capabilities to manipulate and visualize large datasets, which improved my ability to create insightful dashboards.
Data Analysis and Interpretation Skills: This project strengthened my ability to interpret data in a way that highlights actionable insights, allowing for a deeper understanding of data-driven decision-making processes.
Real-World Application and Collaboration: Presenting this dashboard led to an opportunity to assist colleagues with their Tableau work, underscoring the importance of effective data visualization in professional contexts.