Investigated spatial patterns within New York City where drivers experience difficulty finding parking. Utilized R and a Google Maps API to create data visualizations and implement Spatial Autocorrelation, Clustering, and Kriging to geospatially visualize and elicit insight on parking difficulty in New York City. Tested the Null Hypothesis that high parking times are randomly distributed using Global Moran's I and Geary's C. Employed universal kriging to predict the time taken to find parking in New York City
Enclosed files:
- Dataset: Searching_for_parking_NA.csv
- Zip file (dataset): Searching_for_parking_NA.csv.zip
- R Markdown: Spatial Trends in NYC Parking Traffic.rmd
- Knitted Rmd: Spatial Trends in NYC Parking Traffic.html
- Project Report: Project Report.docx
- PowerPoint Presentation: Presentation.pptx