All posts filed under: Research

Short Post: Biology and Life

I have been looking for a steady job now for almost a year now and one of the questions I get asked the most is why? Why am I pursuing or want to pursue a career in such and such? People really want to know where you are coming from and what your vision of the future is. Honestly, even though I know my answers, sometimes I have to look back to the beginning and ask myself why? Why am I now fascinated by the use of space in cities and urban environments? Why am I pursuing a career in understanding the urban? Like most stories, there is a beginning. As a young child, I was always chasing after butterflies, digging up earthworms, and collecting fish. Their movements and their features fascinated me. Metamorphosis, the transformation from an ugly caterpillar to a beautiful butterfly, captivated my imagination and in many ways it is a metaphor for life. I never though much of it until high school, where I met a wonderful biology teacher by the …

WLM Financial Marketing and Branding

During September 2015 to January 2016, I worked as a Marketing Coordinator/Analyst for the real-estate broker WLM Financial. Based in Inglewood, CA, the company focused on providing first-time home buyers with financial advice and loans needed to purchase their home. Using my knowledge of GIS and demographics, I identified the locations of their target markets. I proposed ten cities in the Los Angeles Metropolitan Area that they can look to expand marketing operations into. On the broker side of business operations, I looked at home sales data, mortgage data, and property prices to locate other states that WLM Financial can look to apply for broker licenses. After getting to know their operations, targets and goals better, I created a marketing and a branding plan for the company. In terms of brand building, I used their current website and Facebook page as points of interest and set goals to be reached by July 2016 and July 2017. I created a social media schedule for them to post select content and to generate more reach and views to …

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 …