For the final assignment of dashboard week, we were tasked with creating a visualization using the same dataset that the applicants for DSNY 4 had used for their application. The dataset is on the topic of gift traveling filings, which can be defined as payment for services that don’t have a set fee. The data details the congress member’s sponsored trips from their state to a destination country/city. The dataset includes the non-governmental organizations that sponsored that congress member, the date of departure, the date of the return date, the name of the filers, the year of the trips, the filing type, and the district that the congress member represents.
Looking at the data, there were a lot of things that I needed to add and clean. The data was mostly just qualitative data but there’s a lot of analysis that could be done with the date data and location data. Due to time constraints, I was only able to modify the dataset to list the political affiliation of every congress member in the dataset. Rather than web-scrapping the political affiliation from Wikipedia, I reused a Makeover Monday dataset on the 117th Congress as a look-up table that already had the political affiliation of every congress member. Since the gift traveling dataset had members from the most recent congress, I had to manually add in the congress members that were missing from the dataset.
While in Tableau, I removed 5 members from the member's column because they were a part of Congress staff but not actually elected members of Congress. Some of the rows in the dataset were amendments of data that are already in the dataset. This dataset needed to be normalized but I was unable to normalize it due to the time crunch.
I focused on prioritizing questions I could answer. Such as:
- Is there a political party that receives more sponsorships than another party? Which organizations unevenly support a political party?
- Year comparison between the number of sponsored congress members, number of international trips, number of domestic trips
- Are there more international trips or domestic trips that are sponsored?
- Which countries or cities are the most popular destinations that congress members are flown to? Breakdown of destinations by sponsorships
- Further analysis of the sponsors: congress members sponsored by an organization, number of congress members sponsored throughout the years by a particular sponsor, total days of travel, # of international and domestic trips by congress members
Here’s an Excalidraw of what I envisioned the dashboard to look like:
My dashboard for the last day of dashboard week:
I was able to revise the dashboards above to implement more of the ideas I wanted to include after dashboard week. In my revised dashboard, I was able to include a year parameter that allows users to view information about gift travel filings from 2020-2023. Users can compare the total number of trips, total number of sponsors, and total number of congress members for that selected year. There’s a further breakdown that shows the percentage of the trips that were just domestic trips or international trips as well the percentage of the beneficiaries that were democrats or republicans. I created a drill-down table that allows users to select a sponsor and see a breakdown of the locations that these congress members are flown out to. Selections made to the drill table are reflected in the flow map next to the table. A navigation button on my dashboard will lead to another dashboard with a deeper analysis of congress members and the organizations that sponsors them. I never got to create a filter that allows users to view the dashboard with the normalized or unnormalized data but I would love to come back to this project to incorporate that function. I was able to get the geospatial data for all the destination cities, countries, and states by downloading the latitude and longitude from Tableau. I used these lists of latitudes and longitudes as a lookup table. In Alteryx, I replaced the nulls in the missing destination cities’ latitudes and longitudes with the latitudes and longitudes of their respective countries.
