All posts filed under: Urban Planning

Bakersfield Site Analysis

In January 2016, I was given the opportunity by Mr. Frank Tripicchio to work on a site analysis project on a property in Bakersfield, CA. The objective was to create recommendations on best uses for the property through demographic and economic analysis. To help me with this report, I brought on Oliver Yang, who worked with PKF Hospitality Consulting. The Project The site is located on the Southwestern corner of the intersection between Highway 178 and Comanche Dr., about 5 miles away from downtown Bakersfield. It is an undeveloped, five-acre parcel with two residential communities in the immediate area. I conducted spatial analysis with data from the U.S. Census Bureau and the City of Bakersfield to visualize the current building and zoning conditions in the area as well as the demographic distributions. We assessed community needs by looking at current services, businesses, and retail in the area and found a gap in the supply. As the site is fairly remote, there is a lack of businesses servicing the area. There are around 3000 residential rooftops in the surrounding …

GIS Specialization – Fundamentals of GIS

On February 22, 2016, I started the GIS Specialization Course with UC Davis through Coursera. Today, I completed the first course in the series. As I already have a couple years of GIS experience, the first course Fundamentals of GIS was more like a review of the basics. At the same time, I definitely learned new skills such as map package sharing and creating bookmarks. To complete the first course, I needed to create a map for the final assignment. The original data is at the precinct level. I had to aggregate the voting data for Proposition 37 and total votes to the county level. This is the map I created: I must say, this process took longer than I expected. I am definitely a bit rusty with the map-making. My spatial analysis skills are also rusty. At first, I used a different geoprocessing tool. Instead of directly using a spatial join with the intersect method, I took the long way around using Intersect, Merge, and then Dissolve. However, this presented issues because the data became more …

k-Means Cluster Analysis – Machine Learning

Machine Learning Data Analysis This is the last lesson of the fourth course of my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera. If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? For this assignment, the goal is to run a k-Means Cluster Analysis using my variables: Urban Population, Urban Population Growth, GDP Growth, Population Growth, Employment Rate, and Energy Use per Capita in 2007. Here, GDP per Capita in 2007 is used as the validation variable. I am trying to identify if there are clusters of characteristics that associate with certain values of GDP per Capita based on national data from 2007. As before, the data is split into 70% training data and 30% test data. However, the k-means cluster analysis will only be run on the training data set. The Elbow Curve Graph shows that 2, 3, and 4 clusters could be interpreted, though it is …

Lasso Regression – Machine Learning

Machine Learning Data Analysis This is the third lesson of the fourth course of my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera. If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? For this assignment, the goal is to run a Lasso Regression that identifies the impact of each of my explanatory variables: Urban Population, Urban Population Growth, GDP Growth, Population Growth, Employment Rate, and Energy Use per Capita in 2007. As it is a linear regression model, I am able to use a quantitative variable. Unlike the previous lesson, I can use GDP per Capita 2007 as is, without having to convert it into a categorical variable. This time, the training data set is 70% and the test data set is 30% of the original data, which means there are 100 observations in my training data set vs. 43 in my test data set. pred_train.shape = (100, 6) …

Random Forests – Machine Learning

Machine Learning Data Analysis This is the second lesson of the fourth course of my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera. If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? For this assignment, the goal is to create a random forest that identifies the varying importance of my explanatory variables: Urban Population, Urban Population Growth, GDP Growth, Population Growth, Employment Rate, and Energy Use per Capita in 2007. For my response variable, I created a categorical variable from GDP per Capita 2007. I separated the data into two levels, where GDP per Capita 2007 is lower than 10000 is 0 or low and where GDP per Capita 2007 is higher than 10000 is 1 or high. Just as in the last assignment, when my test sample is set at 40%, the result is 58 test samples and 85 training samples out of 143 total, with …

Decision Trees – Machine Learning

Machine Learning Data Analysis This is the start of the fourth course of my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera. If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? Now, as I have started working, I do not have as much time. For this course, I decided to focus solely on Python, instead of both Python and SAS as in the past. I am not abandoning SAS but I will probably take the time to learn SAS after this course ends. For this assignment, the goal is to create a decision tree that correct classifies samples according to a binary, categorical response variable. For my response variable, I created a categorical variable from GDP per Capita 2007. I separated the data into two levels, where GDP per Capita 2007 is lower than 10000 is 0 or low and where GDP per Capita 2007 is …