Washington DC Parks and Social Dynamics
ArcGIS Pro | ArcGIS Web| Python | SQL | Machine Learning | Regression Modeling | Clustering Algorithms | Economic Forecasting | Data Visualization | Tableau | Statistical Analysis | Geospatial Data Analysis | Public Policy Analysis | Business Strategy Development | A/B Testing | Data Mining
Project Description
This project showcases the comprehensive use of ArcGIS to create an interactive and data-driven exploration of Washington DC's parks, tree canopy, and urban environment. By leveraging multiple datasets from Open Data DC, the U.S. Census Bureau, and custom CSV files, I designed a dynamic Story Map that provides insights into the city's natural resources, crime patterns, and socio-economic dynamics. The project integrates statistical analysis, spatial data analysis, and predictive modeling to examine the interplay between Washington DC's green spaces and public health, safety, and community well-being.
The Story Map offers a visually engaging, data-rich platform that enables users to explore the impact of green spaces on urban dynamics, including their correlation with crime rates, population trends, and educational outcomes. It highlights the significance of Washington DC's tree canopy and national parks in shaping the urban ecosystem, providing policy recommendations that align urban planning with environmental sustainability.
Project Skills
ArcGIS Pro & Online: Created interactive maps that visualize tree canopy, crime incidents, and park accessibility across Washington DC. Utilized tools like Intersect, Merge, and Attribute Table for detailed spatial analysis.
Data Science: Applied clustering, regression, and machine learning models to identify trends and relationships between environmental data and socio-economic factors.
Data Integration: Combined datasets from multiple sources, including the Urban Tree Canopy 2020, Crime Incidents 2023, and National Parks, into a cohesive narrative.
Statistical Analysis: Conducted statistical analysis to uncover correlations between parks, education, and crime using regression analysis, clustering techniques, and NLP for analyzing policy language in relation to urban dynamics.
Economic Impact: Evaluated the economic contributions of green spaces to public health, crime reduction, and community cohesion, offering insights for urban policy development.
Business Strategy: Developed strategic recommendations on park funding, safety enhancements, and public engagement to support urban sustainability and community well-being.
Project Demostrates
This project demonstrates my ability to integrate environmental policy with economic decision-making, highlighting a valuable intersection of data science, urban analytics, and public policy. Through advanced GIS tools, I provided actionable insights that promote economic growth and community improvement. By leveraging neural networks and machine learning techniques, I forecasted the long-term impact of green spaces on crime rates, health outcomes, and educational achievement, showcasing my capacity for forward-looking analyses. My ability to synthesize large datasets and create interactive visualizations empowered city planners and policymakers to make data-driven decisions on urban development. Additionally, this project emphasizes the economic contributions of Washington DC’s green infrastructure, demonstrating how parks and green spaces reduce crime, improve public health, and enhance educational outcomes. By conducting a thorough cost-benefit analysis, I supported sustainable urban planning strategies that align environmental and economic goals. This work reflects my proficiency in spatial analysis, urban planning, and economic policy, showcasing the human capital I provide to drive impactful business and public sector outcomes.