Background:

Prior to Covid 19, the population who received tertiary education tended to move to mega cities or richer areas for work, healthcare, and education. With such a migration trend, it seems the trend will shift the education percentage level of population in cities to an extreme, which assumes one day in the future, majority of residents who live in a mega city hold at least a bachelor’s degree. But is it really so? There are still so many essential workers work in the city who are not college graduates. 2019-2020 is the last year where the economy grew without being impacted by the global pandemic. The question here is to dig out whether there is significant or slow increase, or decrease in bachelor degree or higher holders’ percentage in New York City during a normal year. Data from 2021 and 2022 is not counted as the pandemic changes migration and work mode, meanwhile 2021 and 2022 datas are insufficient at the moment.

Materials:

Datasets:

In order to conduct the analysis, the datasets used are from Social Explorer.

Software:

Tools used are MS Excel software to organize data spreadsheets and Datawrapper to visualize percentage changes.

The process is to export the ACS 2020(5-Year Estimates) and ACS 2019(5-Year Estimates) from Social Explorer as Excel forms, and then import Excel spreadsheet data to Datawrapper for visualization.

Method:

By selecting ACS data focusing on the population over 25 who hold a bachelor’s degree or higher in New York City for 2019 and 2020. At the beginning stage, an approach towards looking at the data by census tracts was made, but several parts of census tract data are not available on the map for both Year 2019 and 2020. The next approach was made to select the five counties’ data instead and change the color palette to blue which indicates education is beneficial to the public. County data from the five boroughs of New York City was downloaded as Excel files, after reorganizing the datasets, the data was made to focus on numbers of population from each education level.

A stacked column chart is chosen to visualize the population percentage allocated to each education level, corresponding to the five counties and the total. A cool color blue is applied again here to display and visualize education as a benefit to society.

In a more detailed perspective, the stacked column bar chart was made with percentage number values labeled in each vertical bar to compare the change of percentage of each education level’s population. Applying such a label is to make an overview for number sensitive audience. This is clearer in determining the change instead of a vivid change in bars’ length. After that, visualizations are improved by adding category labels to distinguish the three levels of education. Furthermore, a reverse rank change was made to put the “highest” education level (Bachelor’s Degree or Better Category) at the top instead of the bottom. Hence the most desirable data among the population would be convenient to capture.

Results and Interpretation

After creating the two charts and observing the data change in a clearer view. It is noticed that the percentage population who holds a bachelor’s degree was slowly increasing with the population influx to New York City from 2019 to 2020. Moreover, by observing the data trend from the other two education level groups. The population percentage of those who received education less than high school is slightly decreasing in the major four counties and in total, which could symbolize a positive sign.

The other interpretation that could be made is that Bronx County could have the highest criminal offense rate and lowerest average income among the five, and New York County probably has the highest average income level with its significant education level difference compared to the other four counties. Moreover, Bronx county has the slowest increasing rate of 0.24% from bachelor’s degree holders compared to the other four. It suggests education level correlates with income level and crime level, and wealthier counties are more likely to attract the population who received higher education, and vice versa.

Reflections:

Observing and visualizing the change in Education percentage level in New York City is insightful but follows the general assumption listed at the beginning. Limitations exist as 2017 and 2018 data were originally selected at the start to run a four-year change view, while it failed as 2017 and 2018 data is insufficient in multiple major counties. Moreover, other limitations could include foreigners who work here, they may not be willing to be counted in census data while they count a significant portion of the population.

Furthermore, for future work on this visualization task, I still prefer to use tract data for visualizations rather than county data, as that gives a clearer view of which specific region has the most significant increase. This is another limitation that county data does not work. From what I observed: midtown manhattan, downtown manhattan, and Long Island City are three regions that have a rapid growth of people holding bachelor’s degrees or higher. That could reflect the relatively higher increase in rent for those regions recently. It could be insightful to import data from 2022 once it is released to observe the new trend when social activities return to normal.