LA Traffic Analysis using ArcGIS + R

This project performs a geospatial analysis of traffic crashes in Los Angeles County using ArcGIS + R (sf, tidyverse, tidycensus, tigris, tidygeocoder). The objective is to geocode crash locations, aggregate crash severity by census block groups, and analyze how socio-demographic factors correlate with crash risk.

The workflow includes:

Part 1: Geocoding vehicle crash data by constructing full street intersection addresses and converting them into spatial coordinates using the ArcGIS geocoder.

Part 2: Converting both bike and vehicle crash datasets into spatial features, filtering crashes from 2014–2018, and aggregating injuries & fatalities per census block group.

Part 3: Integrating American Community Survey (ACS) socio-demographic data (e.g., income, poverty rate, racial composition, education level) to identify statistical relationships and spatial disparities in crash burden using correlation analysis.

The result is a data-driven spatial risk model that highlights which neighborhoods experience the highest transportation vulnerability, creating a foundation for equity-based transportation planning and policy recommendations.