A unique and culture-rich city in California is Los Angeles. It is a diverse city filled with many different identities that make up the population. The people of Los Angeles come from many different walks of life. It is a place that has a reputation for the most beautiful million-dollar homes. But Los Angeles also has many neighborhoods with low-income multi-family homes. Many factors play into this. What needs to be understood is who lives in Los Angeles to understand the discrepancies that exist among the people.
What I want to know is who lives in Los Angeles. Who makes up the communities of Los Angeles? Are people segregated in specific areas based on race? What interests me is what each community’s education level is. The data used will show what communities make up each sub-county including race and education. To understand the value of the land Angelinos are living on, I will also include housing costs in each area.
One of the software I used to collect the data was Social Explorer. From here, I found data from the United States 2020 Census. Social Explorer helps to gather the data and map it which can then be exported to an excel sheet that contains all of the information. The data is used on a projection of the United States. The data is masked to only include sub-counties of Los Angeles. Masking the data allows for only the key data to be shown. This way the most important information to visualize can be focused on.
The report that was created using Social Explorer is exported to Excel. Excel assists in filtering out the data to only include the essential information that was pulled. To create the visualization, the excel sheet is then imported into Tableau. Tableau is a visualization tool that aids in laying out data to fit what a user wants to show. This helps select specific data from the input and visualize it in different forms. After editing the details of the visualization, the data is ready to be shared.
To make the visualization, I start by gathering the data collected. Social Explorer holds the US Census data from 2020 that is filtered out. The data collected is only relevant to the topic of discussion and what we are trying to learn. I collected data about race, education, income, and property value. For race, I use data on population and percentage of the population that makes up each Los Angeles Sub-County. For education, I use data of people with Bachelor’s degrees or higher in each sub-county. For Income, I use Median Household Income. I also collected median house value for owner-occupied units. The data recognizes area name as county subdivision which is renamed to be sub-county.
Once the Data is collected in Social Explorer, the data can be opened up in an Excel Sheet. The Excel sheet is cleaned out and organized to only include the essential and necessary data to create the wanted visualization. I remove information in the three different sheets of data that I use. The essential data includes sub-county names and the numbers needed (population by race, population over 25 education level, and median property value in each sub-county).
The data from the Excel sheet is then imported into Tableau to create the visualizations with the necessary data that is found. With this software, I made a bar chart, a circle graph, and a tree map. Each visualization is taking and using different dimensions of the data imported from the excel sheet. The commonality carried throughout the data is each sub-county name. Different variables are used to display the story.
There are 20 county subdivisions in Los Angeles County: Agoura Hills-Malibu, South Bay Cities, Palos Verdes, Newhall, Santa Monica, Pasadena, Torrance, East San Gabriel Valley, Whittier, San Fernando Valley, Downey-Norwalk, Upper San Gabriel Valley, Long Beach-Lakewood, South Antelope Valley, Compton, Southwest San Gabriel Valley, Los Angeles City, Inglewood, North Antelope Valley, South Gate-East Los Angeles.
The first visualization separates each sub-county into a different bar. Each bar’s size shows the population size. Each bar color represents the number of people within that population’s race they identify with. It is categorized by highest population to lowest.
The second visualization represents everything in the first, except that each race in the population is considered as a percentage of the entire population. This makes it clearer to understand the general racial makeup of each sub-county.
The third visualization is a tree map. Each sub-county is separated into a different square. The size of the square is the total population of that sub-county. The shade of color represent the percentage of the population that has a Bachelor’s degree or higher. The darker the color the higher the percentage of people within the population with degrees.
The fourth visualization is a circle graph. The graph compares Median Owner-Occupied House Value versus Median Household Income (In 2020 Inflation Adjusted Dollars). Each circle represent a sub-county. The size of the circle represents the total population in that sub-county. Each sub-county is plotted on the graph. The sub-counties with higher median household income have larger median house value.
Looking at the data, it is fascinating to see patterns arise in different ways the data is presented. Looking at race, education, and incomes, there exists a pattern of wealthier people living in more expensive homes, which also are mostly made up of white people in these sub-county populations.
This shows a common theme throughout Los Angeles and shows it in a very factual and objective way. The racial makeup of each sub-county differs. The smaller sub-county communities are predominantly more white. These communities have more number of Bachelor’s degrees per percentage of population. These same sub-counties also have the higher median house value and larger median household income. There is a clear disctinction pattern that exists in the Los Angeles region.
After visualizing the data, it shows that there does exist a pattern between education, household value, and race. The relationship can be assumed to be related to each other, but more information needs to be found to justify why these patterns arise. I am still curious to know what other factors impact the communities of Los Angeles. Why does there exist a gap and how much further does it stretch?