Planning for resilience to natural hazards🌐

This story map highlights the intersection of wildfire vulnerability, natural hazards, social vulnerability, and community resilience. It serves as a comprehensive guide for understanding and addressing the complex challenges posed by wildfires. Key focus areas include:

• Natural Vulnerability: Explores how environmental conditions like drought, precipitation, and fuel availability exacerbate wildfire hazards.
• Social Vulnerability: Examines the socio-economic and demographic factors that influence a community’s ability to prepare for, respond to, and recover from wildfires.
• Community Resilience: Highlights strategies for strengthening community capacity to withstand and recover from wildfire events, including preparedness planning, infrastructure improvements, and equitable resource allocation.

Analyzing Parking Spaces in LA🌐

This project examines the distribution and utilization of parking spaces in Los Angeles, utilizing data analysis to provide insights into parking availability and usage patterns. Key highlights include:

Data Collection: Gathered comprehensive data on parking spaces across various neighborhoods in Los Angeles, including information on parking meters, public parking lots, and residential parking zones. Usage Patterns: Analyzed occupancy rates, peak usage times, and the impact of parking availability on traffic congestion in different areas. Recommendations: Proposed strategies for optimizing parking space allocation, improving signage, and implementing dynamic pricing to enhance parking efficiency and reduce congestion.

Train Delay Time Prediction for NJ Transit🌐

This report presents a comprehensive analysis of transit delay prediction, focusing on the development and evaluation of models to forecast delays in public transportation systems. Key components of the study include:

Data Analysis: Preprocessing transit data and identifying key delay factors like weather and time of day. Predictive Modeling: Development and evaluation of machine learning models to forecast delays. Insights: Actionable recommendations to enhance transit efficiency and reduce delays.

Predicting Bikeshare Demand in Jersey City🌐

This project explores Jersey City's bike share program, leveraging data to understand its usage and impact. Key highlights include:

Usage Patterns: Analysis of trip frequency, duration, and popular routes. Program Impact: Assessment of the bike share program’s role in promoting sustainable transportation, reducing traffic congestion, and enhancing connectivity in urban areas. Recommendations: Insights for optimizing station placement, improving user experience, and increasing accessibility for underrepresented groups.

Risk Predictions of Armed Robbery Incidents in Chicago🌐

This report provides a comprehensive assessment of risks in Chicago, leveraging data to identify vulnerabilities and propose actionable strategies. Key components include:

Risk Identification: Mapping areas at risk for hazards such as flooding, crime, and infrastructure vulnerabilities. Urban Resilience: Analyzing the city's preparedness and response strategies to enhance resilience against identified threats. Actionable Insights: Recommendations for improving urban planning, emergency response, and resource allocation to mitigate risks effectively.

Improving Zillow's Housing Price Predictions in Philadelphia🌐

This report presents a focused evaluation of housing price prediction challenges in Philadelphia, leveraging local data to enhance Zillow's predictive model. Key components include:

Local Intelligence Integration: Incorporating neighborhood characteristics such as household income, housing condition, and proximity to amenities to improve prediction accuracy. Model Analysis:Applying OLS regression to explore relationships between variables and assess model performance, with findings indicating significant errors and limitations in capturing spatial patterns. Actionable Insights: Proposing advanced modeling techniques, additional demographic data, and spatial regression methods to refine predictions and address geographic disparities.