Houston Demolition: Gentrification

In this project, we used demolition data as well as average income per person to extrapolate the importance of demolitions in gentrification. This project was done using Python, Excel, R, and R-shiny. We used Python, along with GeoPy and the Google Maps V3 API to find the latitude, longitude, and zip codes of the addresses of demolition sites. Once these values were added to the data, it was sorted and filtered in Excel to allow for easy data reading in R. In R, the data was then turned into a time series analysis when combined with the income data in order to be able to predict the demolitions per zip code in the near future, as well as DIANA clustering to find similarities in zip codes. Along with that, the location data was put into a map using R-shiny and Leaflet, where it was then categorized by demolition type and zip code for easier visualization and use by even those inexperienced in Data science. By combining visual and technical data science methods, we hope to provide accessible and useful data analysis on the relation between demolitions and gentrification.

Built With

• excel
• python
• r
• r-shiny