In this project, we will look at the 2020 estimate of the employment rates, unemployment rates, and labor force for New York City and California sub-county areas of Los Angeles. The dataset was pulled from their Local Area Unemployment Statistics ( https://www.labor.ny.gov/stats/LSLAUS.shtm). The Local Area Unemployment Statistics for (LAUS) and The Local Area Unemployment Statistics for (NYC)( https://www.labor.ny.gov/stats/LSLAUS.shtm). This project will be focusing on the records captured for the monthly labor force, the rate for both Unemployment and employed. I will draw visual comparison mostly by mapping with the hope to answer the following questions and more:
Which city has the highest Un-employment rate?
Which city has the largest labor force etc?
This project will review the employment rate between two major cities New York and California. I will highlight some comparisons between both cities, but I will be focusing on New York City for the most part. I will answer the above questions and more by way of Carto DB map and tableau charts.
- Carto DB – is a cloud computing platform that provides GIS, web mapping, and spatial data science tools for visualization. Companies use Carto DB to analyze and visualize their dataset. Carto DB does not require GIS or developer expertise to use.
- Tableau – Is a visualization tool used to simplify the raw dataset into a visual and more understandable format by presenting the raw data on dashboards and or worksheets using charts and different diagrams.
- Microsoft Excel- is a spreadsheet that has numerous features like calculation, tables, etc. But for this purpose, I used it to purify my dataset.
To complete this project, I used Carto DB, Tableau, and excel. After retrieving the datasets from the department of labor official website as per the links above in the introduction. Carto was used as the primary visualization tool to interpret the datasets. However, before uploading the statistical information into Carto DB, I used excel to clean the datasets up. I also used that same cleaned data to run charts in Tableau to assist with the explanation of my dataset. Fig 1 and 1a show the dataset retrieval and clean up. At the same time, Fig1b and Fig1c show the incorporation of Tableau to aid with the explanation of the data for the datasets used.
Interpretation of the data
Let us analyze the dataset. Fig2 shows us the initial layout from the raw dataset, not clear. However, we understand what the image represents employment data for 2020 between January and May for two major cities in the United States of America ( California and New York City).
Fig 2a will show us the employment detail per Month for California and New York City. This represents this sum of Labor Force, the sum of Employed, the sum of Unemployed and the sum of Unemployment Rate for each Area Name broken down by Month. The color shows details about Area Name. I also paired this with the total sum of unemployment for both cities.
In Fig2a, we can see that California has the highest rate of unemployment from January to May. We also see California moving upwards steadily in the high teens while New York City started January with a rate of unemployment of 3.5% then went to the teens in April; this is a direct effect from COVID 19 that hit New York mid-March. As it relates to California’s rate, it could be because of the population size, and this reason, most people moved to California ( pursuing an acting career ), in which the job market is tiny. Since April, it is at an all-time high (16.30%) and still trending upwards. The latest report says California can reach a whopping 25% because of COVID 19.
In Fig 3 below, we will zoom in on New York City; We will analyze unemployment by Borough. As shown below, Brooklyn and Queens have the highest unemployment; 18.2% and 19.9%, respectively. However, a recent poll indicates that the Bronx has surpassed Brooklyn and Queens, Bronx is at 21.6%. See Fig3a. and an Article
Let us look at seven years ago and compare it to today’s unemployment in New York City. We see in Fig4; that the cause for the high unemployment rate was due to homelessness, mainly students. However, the high rate in 2020 is COVID 19 related. New York City went on shutdown for three months and counting, and as a result, many small businesses have to close, which added to the unemployment job loss for thousands of new York residents. Only a few companies could and can afford to have their employees work from home.
This project was very inciteful because of its current data. I drilled down on the employment dataset for New York City and California. Although I understand the dataset, it was hard to represent is in Carto. There could be many factors why; it could be my lack of full knowledge of the platform in a short time or the dataset chosen to present. I am happy I did it because it pushed me to learn Carto and merge it with other platforms to explain data visually. Hence, the datasets are easier to understand just by looking at images.
New York City
Rate increase in California