Background
Born and raised in Los Angeles, I know that many neighborhoods in L.A. differ in their way of life. I ask myself the question, what disparities exist amongst the people that live in the different sub-counties of Los Angeles? To analyze the key differences between Angelinos, I analyze the median household income of each sub-county in L.A. Having some of the wealthiest people like celebrities living in the city and some of the worst homelessness issues in the United States, it is interesting data to compare what each sub-county income levels look like per number of households.
What I want to know is how income inequality is reflected in the location people live in Los Angeles.Are some areas of Los Angeles wealthier than others? Does the location affect what people can afford? The data used takes into account households of all family types, including owner and renter occupied households. Some questions can be answered by visualizing this data.
Lab Materials
The materials used are Social Explorer to find the data from the United States 2020 Census. Social Explorer assists in collecting, gathering, and mapping this data on a projection of the United States. This projections can be divided amongst States to Counties down to census tracts. After masking the data to only show Los Angeles sub-counties, a file is exported with all the data to Excel. Excel is used to filter out unnecessary data that was pulled from the report in Social Explorer. To create the data visualizzation, the excel sheet with only the data I want to visualize is sent to DataWrapper. DataWrapper takes the data input and creates various vizualizations to choose from. After editing details of the visualization, the data is ready to be shared.
Methodology
To create the visualization, we begin by collecting the data. Using social Explorer, I was able to mask the data from the census to only reflect median household income (In 2022 Adjusted Inflation Dollars). Median household income is used instead of other data to better reflect what each household earns instead of counting individuals. For this data collection, multiple people may live in one household. To get more detail on income locations, I categorized the data by county subdivision or what I refer to as sub-counties.
After creating a report in Social Explorer, the data is exported to Excel. With Excel, I organize the data to clean out unnecessary data points. The data that isn’t needed like geography codes and population are removed. Only the essential data is kept. For this visualization, I keep the sub-county names, median household income, and number of household total. This data is essential to show the number of households is used to calculate the average income in each sub-county.
The Excel sheet is imported to Datawrapper for the final steps of the visualization. The steps include uploading the clean data sheet. The next step is checking to see if the data was uploaded correctly. The columns should be labeled and the data should be checked to see if DataWrapper interpreted it correctly. After the check is done, we can visualize the data by choosing the graph type, labeling, and adjusting the color. I chose to do a stacked bar chart because it will show the income found from the total number of households. There can be a clear distinction as to what the income is and how many households are accounted for.
Results
The map of shows Los Angeles County. The subdivision of each county are outlined. There are 20 county sub-divisions: 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 darker shaded regions show higher median household income while the lighter regions show lower income. The interactive map can be found here.
When plotting the data to visualize it, the dark green represent the median household income and the light green represents the number of households within that sub-county. The data was best to visualize with a stacked bar chart because we can now see how many households were taken into account to find the average income. Some sub-counties can be shows to have a higher average income have fewer households being used to find the average. Some other areas have many more households with a very low median household income average.The data is organized with highest median household income to the lowest in each sub-county. The link to the visualization can be found here.
Reflection
Reflecting on the data, it is interesting to see my thoughts on disparities existing in Los Angeles in a visual form. When looking at what sub-counties have higher median household incomes, it makes sense that the ideal place to live for a person with the higher income is near the beach or in the mountains. These areas tend to have higher property taxes because of their ideal living locations.
After visualizing the data on a bar chart, it is clear that there do exist some type of income disparity in Los Angeles. What I want to find out is what other contributing factors aids to this disparity. My curiosity is to understand who lives in each county regarding: who are they, what is their background, what is their education level.