Failure to show county population within visualizations

Almost all of the visualzations lacked one very useful feature of showing the county population. Using the entire county space can be a little misleading, as the poulation density of some counties is much less than the others.

Lack of using predictive analysis

None of the visualizations made use of Predicitve Analysis to show future trends in COVID cases. Spatial predicitve analysis can help identify trends and better allocate resources accordingly.

Using point size to show the county population

In order to resolve the issue of users confusing the size of the county with the population size, I used the point size variable to highlight the county population.

my role

I worked individually for this particular project. I started off by collecting all the required datasets and cleaning them in Open Refine. For some of the more complex datasets I had to use a combination of Open Refine and Carto in order to get the desired results. After completing the final visualizations I conducted a brief user study with 2 participants to better understand their needs while using these visualizations. I wanted the visuals to be simple so that they can be used by an array of users.

define+design+discover+dicuss

Data Cleaning

Initially the data for the total number of COVID cases was organized in separate columns, and the latitude and longitude columns were in a different table all together. Since the dataset with county latitudes and longitudes was in a zip format I had to do a little bit of extra work in order to merge the tables and get the desired final dataset.

1. Import the county Latitude and Longitude dataset into Carto and download it as a CSV.
2. Import the datasets in OpenRefine and import the latitude and longitude rows into the original dataset using the following function.
   For adding Longitude row: cell.cross(“c_03mr20_1″,”fips”).cells[“lon”].value[0]
   For adding Latitude row: cell.cross(“c_03mr20_1″,”fips”).cells[“lat”].value[0]
3. Perform Transpose operation to interchange the rows and columns, and export the Final Dataset.

Analysis

There are a lot of visualization out there to depict the COVID data, but their lack of highlighting the county population might result in inaccurate processing by the user. In order to tackle this problem I used the size variable to highlight the county population.

The first visualization I created aimed at highlighting the total number of COVID cases over time in every US county. I varied the point size of each county according to its population. Although I went back and forth while choosing the classification method for the point size, I finally settled for Jenks as it breaks the data into classes based on natural groupings inherent in the data. The final visualization has 5 data buckets and the size varies from 2 to 15. I also added a date widget which gives the user the flexibility to filter out and see only specific dates, in addition to this it also helps us analyze the propagation of the virus throughout the states.

The second visualization was aimed at conducting the predictive analysis on the US county COVID data, to determine which counties might act as the next hotspots. For running the Predictive Analysis I used the Predict Trends and Volatility (PTV) analysis tool built into Carto. PTV uses Spatial Markov Chains to calculate the trends and volatility. Since the system changes randomly, it is difficult to predict with certainty the next state of the Markov chain. However, the statistical probability of a systems future can be predicted.

The changes of states are called transitions, and the probabilities associated with various state-changes is called transition probabilities. The deviation of sum of these probabilities trending up (relative to the unit index of that probability) is given by trend_up, trending down by trend_down and overall trend (where sign signifies the direction negative for down and positive for up) by trend. Volatility is the degree of variation of the event series data over time, measured by the standard deviation of probabilities within the trends.

Through my third visualization is wanted to predict the number of cases based on the mask use data. So in order to do that I used the mask use dataset from New York Times, and based on this data I ran PTV analysis on my original dataset to see how the trend changes based on weather the county tends to use masks always or never. In order to facilitate user interaction with this visualization, I added the always and never widgets. These widgets let the user view weather a particular county is gonna trend up or down based on how they use the masks.

UX Study

In order to be able to cater to a larger audience, I wanted my visualization to be easy to interact with and comprehensible. I planned my user study with this idea in mind. I conducted my user study with a total of 2 participants, 1 out of the 2 participants had limited technological experience, but knew a lot about the COVID-19 pandemic. For this particular user study I figured that the think aloud method would work the best, as it would not only help me get a idea about how the user feels about the maps but also about the aesthetics like color and widget placements.

I started the process by defining the goals of the study and what I wanted to understand about my user behavior. The goals were as follows:

1. Can the user interact with the visualizations without feeling lost?
2. Is the purpose of the visualization and the widgets clear?
3. Is the user able to use this visualization to determine the counties with higher risk of infections?
4. Are the aesthetics of the visualization intriguing to the user?

After defining the goals of the study, I worked out a series of questions that would help me better understand the user needs and behavior. Initially I wanted to go ahead with the unguided review method, where the user has more freedom to interact with the visuals, but then I felt it might get to overwhelming for the users with limited technological experience and they might judge the visuals on the basis of ease of use. I defined the following tasks and questions for my user study.

1. Can you identify what this visualization is all about? What are your initial thoughts after seeing these visualizations?
2. Can you tell me the total number of COVID cases in New York City during the first week of September?
3. What do you understand by the trend_up and trend_down values?
4. What do you think about future COVID cases in New York City?
5. Can you determine the counties where people “always” tend to use a mask?

Every question was followed up by two further questions.

1. On a scale of 1-5 how well do you think you did on this task.
2. Do you have any other questions or suggestions.

Throughout my user study I tried to be a passive viewer and not an active participant, because I believe that is the best way to understand a users point of view. One of the participants asked me what does “volatility” mean in the context of the map, to which I responded “what do you think it means”. This gave me a better understanding of what a potential user might think of the terminology.