COVID-19, RACE, and INCOME
A map of San Diego County that shows Race, Poverty, and Covid-19.
There has been a connection between lower-income areas based on zip codes and certain racial groups in the past years; based on this, how will Covid affect this pattern? Understanding the connection between Covid, race, and income is crucial and relevant today. If there is a correlation between income, race, and the number of Covid cases, we should assist the groups that are struggling because of those circumstances. In this story map, I will be comparing different map data layers of San Diego County's average household income, Covid cases, and race demographics to see if they are connected. In the past year, a worldwide pandemic has broken out; I want to see how my County has been affected and see if specific demographics are being hit the hardest. Are Covid, race, and income connected in San Diego County?
How to use?
Click on each zip-code boundary to see the average income for that place. To zoom in and out, click the button on the bottom right corner with a plus and minus sign. To see the Legend, click on the button on the left bottom corner that looks like bullet points in a white circle. Click on the button that looks like a house to reset the map to what it was originally. To move the map click down on the map and move the mouse/screen.
What Does this show us?
The visual representation of the data in the map helps us better analyze the Average Household Income because we can see with the different colors of purple of the higher-income and lower-income households in San Diego County. The colors of purple on this map show San Diego County Average Household Income. The darkest purple is the income that is $131,535+ and the lightest tan is the average income of $42,526 and below. I found this data on ArcGIS Online Lara019 made it. I then imported it on my map, and I change the symbol to change the color to be clearly seen on the map.
What does this show us?
This map layer shows the predominant race for each zip code. Red represents the predominant 2017 Non-Hispanic White Population. Blue represents the predominant 2017 Hispanic Population. Light Green represents the predominant 2017 Non-Hispanic Asian Population. The sizes of the circles show the sum of categories. The largest circle is > 38,332 people in total, and the smallest circle is < 2 people in total.
How was it made?
Rizbee made this data layer. I found this layer on ArcGIS Online. I took this data layer and added it on my map I then changed the symbols to change the select a drawing style to make it show the size and the predominant category.
How to use?
Click on each circle to see the predominant race for that category and see a Pie Chart of the different races. To zoom in and out, click the button on the bottom right corner with a plus and a minus sign. To see the Legend click on the button on the left bottom corner that looks like bullet points in a white circle. Click on the button that looks like a house to reset the map to what it was originally. To move the map click down on the map and move the mouse/screen.
How was it made?
I made this data from other layers created by jamesjimenez and Lara019 on ArcGIS. When I had both of those layers, I used the Analysis tool. I then Managed my Data and used the Overlay Layers tool. I input the layer I wanted to use then choose the method. When I was done, I then changed the style to properly show the Household Income and the Case count for Covid.
Connection?
Based on this map you can see a correlation between the larger circles and the dark color circles. This shows zip codes in San Diego with lower Average Household Income also have high Covid-19 cases per area.
Maps of Covid cases per the zip code and predominant race for that category.
How to use this map?
Move the line in the middle to see each side that shows different maps. To zoom in and out, click the button on the bottom right corner with a plus and a minus sign. Click on the button that looks like a house to reset the map to what it was originally. To move the map click down on the map and move the mouse/screen.
What does this show us?
This slide map shows two map data layers side by side. Move the line in the middle to see more of one side of the map in order to get a closer look. As stated above the map on the left shows the amount of Covid cases per the zip code. The map on the left shows the predominant race for that category. The different green color dots on the right represent the amount of Covid cases. With the lighter green showing 1000 plus Covid cases and the darker green showing 500 cases below. And the map on the left shows the predominant race for that zip code. Red represents the 2017 Non-Hispanic White Population. Blue represents the 2017 Hispanic Population. Light Green represents the 2017 Non-Hispanic Asian Population.
Is their a connection ?
As you can see by moving the slide. The zip codes that have a predominant Hispanic Population which is represents by blue, and the zip codes that have a predominant Non-Hispanic Asian population represents by light green line up perfectly with the other map where the light green dots are. This means the places that have a higher Hispanic population and Asian population, have the most Covid cases for their jurisdiction.
Answering my geo-inquiry question
My map and research helped answer my question,"Are Covid, race, and income connected in San Diego County?" Yes, you can see this between the different data layers. From this map, I was able to figure out a clear connection by comparing Covid cases and low-income zip codes and comparing Covid cases and demographics in San Diego County.
What Could We Do?
Based on the conclusion of the data, it proves that Covid, race, and income are connected. We should provide lower-income areas struggling with the highest Covid cases more resources such as masks, hand sanitizer, and perhaps enforcing stricter laws on social distancing and mask-wearing.
A Long Road Ahead?
People of color and lower-income families were most affected by Covid-19. As a young person, it would be disappointing if this pattern were to occur in the future. We need to address the inequality in healthcare and consider the circumstances. According to NPR, KPBS article, Studies Confirm Racial, Ethnic Disparities In COVID-19 Hospitalizations And Visits, by Rachel Treisman, it is stated that "Researchers noted that these racial and ethnic groups are impacted by long-standing and systemic inequities that affect their health, such as limited access to quality health care and disproportionate representation in "essential" jobs with less flexibility to take leave or work remotely." This article points to Hispanic and American Indian Populations. A solution for the immediate future is to fund public health and make it, so everyone has access to good healthcare. In addition, we must create better education, allowing for greater advocacy for better healthcare policies.