Discovering the best locations to live in as a Data Analyst, Product Designer, UX Designer, and UX Researcher


Lab Reports, Maps, Visualization

Background

After starting my M.S. in Data Analytics and Visualization, my Advanced Certificate in User Experience, and joining the Ignition Lab at Pratt Institute, I felt more confident in my ability to narrow down potential career paths that I believe are best suited for me to UX Design, UX Research, Product Design, and Data Analytics. This drove me to take the next step and explore which states in the U.S. would be most optimal to live in for each of these careers. More specifically, I want to determine which states have the highest pay and most job opportunities available in each field I am interested in exploring. I am also curious to see which states would allow me to save at least 10% of my salary across each field.

This project aims to help me discover where I can live the most comfortably in the U.S. and have the most opportunities depending on the field I go into, as well as the states that would allow me to save enough money for my future in each field.

Note: for simplification purposes, the term “states” in this report includes the District of Columbia

Methods

I utilized Zippia to collect data on the Data Analytics, Product Design, UX Design, and UX Research fields due to their comprehensive data collection methods and up to date information on these fields. I extracted data on a few key variables to create four separate datasets for each field in Microsoft Excel. These variables include the total number of jobs as well as the annual salary in each field by state (how much the lowest 10% earn, meaning the starting salary, as well as the average salary and how much the highest 10% earn). 

Prior to calculating my potential savings, I had to first figure out how much I would likely spend each year on housing and other expenses. The U.S. Bureau of Labor Statistics’ 2021 Consumer Expenditure Survey states that the average single person in the U.S. spends around $48,108 annually. Since there is no standard cost of living calculation for each state, I had to get creative in order to come up with an estimate. The Missouri Economic Research and Information Center provides a cost of living index for each state. This index is based on a U.S. baseline of 100, meaning if one state has a cost of living index of 200, it is twice as expensive to live there compared to the baseline. Provided the cost of living changes between each state, it is reasonable to assume that the average annual expenditure also varies state-by-state. Thus, in order to find an estimate of how much I would spend annually in each state, I divided the cost of living index by 100 (the percent of the baseline) then multiplied this value by the annual expenditure of the average single person in the U.S. as a whole. I then subtracted this value from all three salary values (lowest 10%, average, and highest 10%) for all of the states in order to come up with estimates of how much of my salary I could put towards my savings. 

Since I needed location data in order to visualize the variables of interest on a map in ArcGIS, I manually imputed the longitude and latitude for each state within the four datasets on Microsoft Excel. After uploading all four datasets as a .cvs file into ArcGIS as a hosted feature layer, my data points were showing as dots on the map rather than shading the boundaries for each state. Due to this, I had to take a few extra steps to properly visualize my data. More specifically, I joined two feature layers together (the imported dataset and the “USA States Generalized Boundaries” layer provided by Esri) using a spatial relationship join so that my data values would be shaded within each state border rather than imputed as dotted coordinates of each state. I repeated this process for all four datasets and added the four joined layers onto a single map on ArcGIS. 

Within this map, I visualized the relationship between two specific attributes for each field: the total number of jobs and the starting salary. To elaborate, I utilized color to visualize which states have the highest number of jobs and highest starting salary in each field. I also made use of the pop-up section, which is shown by clicking on a state within a layer on the map, to include more detailed information for each state, such as the lowest 10% salary, average salary, highest 10% salary, and the estimated savings for each salary range. 

Although my optimal goal is to save 20% of my salary, this is likely an unrealistic expectation on a starting salary. Thus, I decided to try and save at least 10% of my starting salary. In order to determine an estimate of how much this would be for each field, I took 10% the average starting salary in the U.S. overall for each field. This provided me with a minimum value of how much money I would need to have leftover from my salary in each field after factoring in the average annual expenditure for a single person in the U.S. The reason I calculated it based on the average starting salary in the U.S. as a whole is because I was unable to add a state by state filter in ArcGIS. 

I added these values in the filter function on ArcGIS to each corresponding field layer in the map in order to narrow down the states that would allow me to save 10% of my starting salary for each field I am interested in. For example, to narrow down the states for the Data Analyst job, I created a filter where the starting salary for a Data Analyst minus the average annual expenditure for a single person equals at least 10% of the average starting salary of a Data Analyst in the U.S. 

I then created a copy of this map and repeated these steps in the second map, but this time I visualized the relationship between the total number of jobs and the average salary rather than the starting salary. I was also curious to see if, while earning an average salary, I could save at least 20% of my salary. To do so, I repeated the steps above but this time I altered my calculations to 20% of the average salary in each field rather than 10% of the starting salary in each field. 

Results

The figures below include two sets of data visualizations for each of the fields I am exploring. Click on the links below to fully interact with the maps in this report:

Career map 1 (starting salary): https://arcg.is/0zq89u2

Career map 2 (average salary): https://arcg.is/Pjqej

Data Analyst

Figure 1: Joined feature layer for the Data Analytics field visualizing the relationship between the total number of jobs in each state and the salary in each state (starting and average salary). The remaining highlighted states indicate that those are the states where I have the ability to save 10% of the starting salary in the top map and 20% of the average salary in the bottom map. Swipe down to view the starting salary map and up to view the average salary map.

If I go into the Data Analytics field, I am incredibly limited in where I can live if I want to save at least 10% of my starting salary. Virginia would be the most optimal location for me starting off as a Data Analyst as it is the only state remaining with a high starting salary and high job availability compared to the other states. Fortunately, if I were to stay in Virginia, I would be able to save at least 20% of my salary once I start earning an average salary or higher. If I decide to move once I have gained enough experience to earn an average salary as a Data Analyst, the best locations to live would be California, Texas, Illinois, Michigan, Georgia, North Carolina, Maryland, and New Jersey.

Product Design

Figure 2: Joined feature layer for the Product Design field visualizing the relationship between the total number of jobs in each state and the salary in each state (starting and average salary). The remaining highlighted states indicate that those are the states where I have the ability to save 10% of the starting salary in the top map and 20% of the average salary in the bottom map. Swipe down to view the starting salary map and up to view the average salary map.

As a product designer, I would have significantly more options of where I could live and save 10% of my starting salary compared to a Data Analyst. However, there would only be three optimal locations for me to live that have a high starting salary and a high number of jobs available: Washington, California, and Arizona. Although there are several more states I could choose from that allow me to save 20% once I start earning an average salary, these three states remain the best options for me if I choose to go into the Product Design field.

UX Design

Figure 3: Joined feature layer for the UX Design field visualizing the relationship between the total number of jobs in each state and the salary in each state (starting and average salary). The remaining highlighted states indicate that those are the states where I have the ability to save 10% of the starting salary in the top map and 20% of the average salary in the bottom map. Swipe down to view the starting salary map and up to view the average salary map.

As a UX Designer, the best locations for me to live on a starting salary would be Washington, California, Virginia, and New Jersey as these states have the highest number of jobs available, the highest starting salaries, and would allow me to save 10% of my salary. Once I start earning an average salary or higher, I would be able to save 20% of my salary in the same four states as well as Oregon, Maryland, New York, and Massachusetts, while also having a higher salary and number of job opportunities available relative to the other states.

UX Research

Figure 4: Joined feature layer for the UX Research field visualizing the relationship between the total number of jobs in each state and the salary in each state (starting and average salary). The remaining highlighted states indicate that those are the states where I have the ability to save 10% of the starting salary in the top map and 20% of the average salary in the bottom map. Swipe down to view the starting salary map and up to view the average salary map.

Washington, California, Texas, New Jersey, and New York would be the best places to live starting off as a UX Researcher as these states have the highest number of jobs available, the highest starting salaries, and would allow me to save 10% of my salary. These are also the best states to live in once I gain more experience and start earning an average salary as I would be able to save 20% of my salary in these locations.

Key Takeaways

The UX Research career seems to be the best field in terms of salary as there are a lot more opportunities of where I could live and still save 10% of my starting salary and 20% of my average salary once I gain more experience. In fact, the only state where I would not be able to save 20% of my average salary as a UX Researcher would be Hawaii. In contrast, the Data Analyst position seems to be the worst field in terms of salary as there are significantly less places I could move that would allow me to save at least 10% of my starting salary and 20% of my average salary. Unfortunately, there is no single state that overlaps between all four careers in terms of having a relatively high starting and average salary, high job availability, while also allowing me to save 10% of a starting salary and 20% of an average salary. Thus, I will have to prioritize which factor is most important when deciding where to live after graduate school.

Limitations and Future Considerations

A significant limitation in the analysis process was that the most recently released Consumer Expenditure Survey was from 2021, the cost of living index released by the Missouri Economic Research and Information Center was from the third quarter of 2022, and the field data released by Zippia is from this year (2023). It is safe to assume that due to inflation, the cost of living index for each state will likely increase this year as well as the average expenditure in the U.S. Thus, there is a chance that there are less states I could live in if my primary goal is to save 10% of my starting salary and 20% of my average salary once I gain more experience in the field. Once the available data is updated, I would like to repeat this analysis to visualize which states remain. 

Another limitation is that these salary values are averages of the states as a whole, and there may be a considerable variation in how much I would earn and spend depending on the city. My salary could also vary depending on the company and industry. Despite this, these visualizations provide a useful estimate for my decision making process.

ArcGIS also made it quite difficult to compare the fields with each other, and so developing more visualizations on a different platform could allow for a more seamless comparison, especially if my goal is to find the best states to live in for all four fields combined. 

Resources

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