IIMMLA Research Project 2019
Interactive Visualizations
Maps
On this map each county is color coordinated according to the five different counties in the Los Angeles Metropolitan area including, Los Angeles County, Orange County, Riverside County, San Bernardino County, and Ventura County. The labels placed in different counties represent highest total personal income for 2003, of participants in said counties. This map along with the other maps show us the highest income in Los Angeles area, and looking back at the “Ethnic Group in Different Cities” map, we see that that county is greater populated with Asian individuals. In our research, we have found that Asian American achievement stems from cultural essentialism (Hsin), the role of culture and how it enables socio-economic mobility in Asian Americans.
This point distribution map shows education level across different cities in the Los Angeles Metropolitan area; color coordinates according to the different education levels including College Graduate, Did not complete high school, Graduate School, High School, Some College, and Vocational or Trade School.
The map shows certain color clusters of data, which allows us to see which education level is more predominant in the cities of the five counties. Comparing this map to the “Ethnic Groups in Different Cities” map , we also see what ethnic group in these cities had a higher education levels; which leads us to ask why the maps shows the other ethnic groups Latin American and Black non-Hispanic with less of the higher education points. In our research, we were able to see that amongst the lower performing ethnic groups, there seems to be a lot of criminalization of immigrants which affects their children and their schooling (Gallo).
On this map, each point is mapped according to the latitude and longitude of the location of cities in the Los Angeles Metropolitan area and color coordinated according to the ethnicities (Asian, Black non-Hispanic, Latin American, White non-Hispanic). When you hover over a specific point, you also see the number of records; so out of participants who answered, how many live in that specific city. It is important to note that this map cannot represent the entire dataset. The data was very large, consisting of 4,656 data entries; 3,570 of the 4,656 (77.7%) data entries did not contain any specific information cities. Viewing the point clusters on the map and the correlating colors, we can also see what which ethnic groups are predominant in which counties.