Emergency Response Modeling
Developing spatial analysis tools that help teams study response coverage, travel patterns, and planning scenarios.
Technologies: Python, GIS, Spatial Analysis, Data Preparation
View DetailsCurrent & Recent Projects
Work across public-sector analytics, GIS, application development, and data integration. Descriptions stay intentionally general to protect internal data and processes.
Featured Data Science Project · Completed · September 2026
Under Pressure: Housing Affordability and Displacement Vulnerability
An exploratory study of housing affordability pressure and displacement vulnerability across Mecklenburg County census tracts and Charlotte neighborhoods.
Final Research Question
Which housing, demographic, and economic characteristics are associated with rent burden across Mecklenburg County census tracts, and what patterns of neighborhood displacement vulnerability appear in City of Charlotte data?
How the scope changed
The project started with a broader question about displacement and gentrification. I narrowed it to rent burden and the City's separate vulnerability classification because those are the outcomes the available data can support.
Completed analysis
I cleaned and merged public data, compared housing and demographic characteristics across census tracts, examined their associations with rent burden, and reviewed neighborhood-level City indicators separately.
Data sources
The study uses the 2024 American Community Survey five-year estimates and two City of Charlotte geographic data services.
What the results mean
The analysis highlights affordability patterns worth investigating. It does not prove displacement, estimate individual risk, or identify neighborhoods that have gentrified.
PythonPandasCensus APIArcGIS REST APIData VisualizationStatistical Analysis
Featured Data Science Project · Completed · October 2026
Where Will Charlotte Crashes Cluster Next Month?
A machine-learning study that forecasts monthly reported crash counts across 500-meter grid cells and identifies areas that may deserve closer transportation-safety review.
Research Question
Can past crash patterns predict which 500-meter areas of Charlotte will have higher reported crash counts next month, and do separate morning and afternoon forecasts improve the hotspot picture?
Approach
I prepared spatial and time-based features, used chronological training, validation, and test periods, and compared two machine-learning models against a simple baseline.
All-day results
The top 5% of predicted grid cells captured 1,604 of 3,864 reported December crashes, or 41.5% of the total.
Time-of-day extension
The morning model captured 38.4% of reported crashes, while the afternoon model captured 42.6% within each period's top-ranked cells.
Responsible use
The forecasts rank areas for further review. They do not predict individual crashes or replace engineering analysis, field observations, or community knowledge.
41.5%All-day crashes captured
38.4%Morning crashes captured
42.6%Afternoon crashes captured
PythonPandasScikit-learnSpatial AnalysisTime-based ValidationData Visualization
More Current Work
Additional projects and areas of focus
Risk-Informed Planning
Exploring clear, explainable ways to combine location-based information with operational planning and human review.
Technologies: GIS, Data Architecture, Process Design
View DetailsGrant Research Tool
Building a tool that helps teams search, organize, and review funding opportunities more efficiently.
Technologies: Flask, Python, Search, UI/UX
View DetailsMapbook Automation
Creating repeatable workflows for producing consistent maps and planning materials as source data changes.
Technologies: Python, GIS, Automation, Documentation
View DetailsInternal Request Routing
Designing a simpler way for staff to find the right process, resource, or team for common internal requests.
Technologies: Workflow Design, Application Development, UI/UX
View DetailsDepartment Mobile App Concept
Planning a mobile-friendly resource hub that brings guidance, references, and common workflows into one place.
Technologies: Product Design, Mobile UX, Internal Tools
View DetailsSnowflake Data Integration & Analytics
Supporting reliable data workflows so teams can use consistent information for reporting, analysis, and decision-making.
General Focus:
- Preparing and validating data for analytics
- Supporting repeatable pipelines and quality checks
- Documenting datasets and business definitions
- Collaborating with technical and operational teams
Technologies: Snowflake, SQL, Python, Data Integration
View DetailsWant to see earlier work?
My past projects page includes student, internship, and application-development work.
View Past Projects