Statistic Basics and Linear Regression

Background This post explores some of the basic concepts of statistics. I mostly explore these concepts using linear regression. This is a reproducible example if you have R Studio just make sure you have installed the correct packages. library(DataExplorer) ## Warning: package ‘DataExplorer’ was built under R version 4.1.2 library(tidyverse) ## Warning: package ‘tidyverse’ was built under R version 4.1.2 ## Warning: package ‘ggplot2’ was built under R version 4.

Sim CCS City

Background I extended the work of (Wu and Plantinga 2003) using R and certain equation modififications to investigate how the addition of a light rail line would influence a city. Cities have housed most of the human race since 2004 and as a result influence most of the individual, commercial, and industrial energy and GHG patterns. However, city designs, have placed amenities in isolated suburban pockets, the abundance of roads have encouraged sprawl, and zoning laws have discouraged density.

Monocentric City Modeling

I extended the work of (Wu and Plantinga 2003) using R and certain equation modifications to investigate how the addition of a light rail line would influence a city. Cities have housed most of the human race since 2004 and as a result influence most of the individual, commercial, and industrial energy and GHG patterns. However, city designs, have placed amenities in isolated suburban pockets, the abundance of roads have encouraged sprawl, and zoning laws have discouraged density.

Austin Land Use

I compiled the land use of Austin by zoning and land use data. Furthermore, number of parking spaces and the square footage of those parking spaces by land use (single family homes, apartments and commercial buildings). Furthermore, I compared that square footage with the square footage actually in the buildings and the land area of the city of Austin. And then I used the building data I compiled to determine how much rooftop space Austin has and how much solar that could hold.