All posts tagged: Urban Development

Public Policy 101: Understanding Policy

It is important for the public to understand how to understand policy, especially when it is in the process of being deliberated and adopted. As a policymaker, I want to share some of the finer points of policy making, especially into today political climate. *My views do not represent the City of Los Angeles or the Department of City Planning. Every year, a large number of policies are deliberated at all levels of government. Some are passed, some are postponed, some are dead upon arrival. In a democratic government, almost all of these policies are heard in some form or another by the public. However, there are a lot of nuances to understanding them and because the public are not generally versed in understanding policy, there are ways to get policies passed by influencing public sentiment or despite public sentiment. To make it easier for you to understand policies, especially those you care about, the following are three important things to look for to avoid supporting a policy on misguided assumptions. They are listed in …

Los Angeles – No to Measure S

Dear Friends in the City of Los Angeles, For those of you who are residents and are able to vote, there is a ballot measure to take an important stand against in next week’s local elections: Measure S. L.A. Times, Governor Brown, Mayor Garcetti, and many others have came out against this measure, (here, here, here, here, here, here, here, and here) which will basically prohibit development in the City for the next two years and make it extremely difficult in the years after (to be explained below). For those of you that don’t know, the City of Los Angeles like the rest of California, is in the midst of a housing crisis. With a vacancy rate hovering around 2%, the supply of housing is extremely tight and housing costs are skyrocketing. What most people don’t realize is that at around $56,000, the median wage in Los Angeles is actually not that high, yet average home sale prices have now soared above half a million. That is lunacy. Renters are also suffering, with many paying more than 30% of …

A Short About Policy Making

Have you ever wondered about the city you live in: its history, its planning, its development? The Guardian has an incredible 50-part series on the history of urbanization from around the world. The more you read about cities, the more they become a metaphor for life – patience, plans, foundations, and changes. Any sort of urban development can takes years and decades. The saying goes, “Rome wasn’t built in a day”. Furthermore, even the best laid plans can be easily swept aside by unforeseen circumstances or self-created consequences. Yet, without plans and goals, a city will cease to exist. Therein lies the paradox of urban planning (and of life) – each action results in an infinite possibility of reactions. You want to capture the current circumstances and anticipate future change, but it is always an impossibility. You create that which you hope to contain, and yet what you hope to contain is based on projections, assumptions, and visions that can easily fall apart in an instant… The building of cities always serves as an expression of the political …

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 …