PP434 Portfolio

Challenge 1: Hosting and embedding charts

Here's one chart.

Challenge 2: Building and creating my own visualizations

A chart of UK unemployment over time made with the Economic Observatory "Create" tool.

A chart of UK debt over time made with the Economic Observatory "Create" tool.

Challenge 3: Debate - Use a visualization in policy commentary

It seems like the second Trump administration is moving faster than the first Trump administration to implement its policy changes. Are ICE arrests ramping up faster under the second Trump administration compared to the first?

Here are two charts that I made with data from the Deportation Data Project obtained through Freedom of Information Act requests from the U.S. Immigration Customs and Enforcement (ICE) Agency. The first shows occurrences of ICE arrests by month from the beginning of Fiscal Year 2016 to July 2017 and the second shows occurrences of ICE arrests Fiscal Year 2024 through July 2025. The red lines indicate the dates of the first and second inaugurations of President Trump, respectively.

Challenge 4: Replication - Re-create, then improve someone else's chart

This chart is located in an article from the Migration Policy Institute titled, "U.S. Immigrant Detention Grows to Record Heights under Trump Administration". You're able to see a general trend in the chart, but it could be more clear.

I used the same data set from TRAC Reports to recreate the chart with grid lines to more clearly identify the x and y values on the line. I also included a y-axis label and a hover function to show more precisely the exact number of detentions on all given dates in the dataset.

Challenge 5: Accessing data - Scrapers and APIs

API

Base URL: https://api.worldbank.org/v2/
Indicator for fertility rate: indicator/SP.DYN.TFRT.IN
JSON format: ?format=json
Date range: &date=1960:2025
Getting data in one call: &per_page=160

Final URL: https://api.worldbank.org/v2/country/kor;prk/indicator/SP.DYN.TFRT.IN?format=json&date=1960:2025&per_page=160

Scraper

I scraped roll call data from the House of Representatives on a bill called the Laken Riley Act and created a bar chart that illustrates votes by party.
View on Google Colab

Challenge 6: Loops - Build a dashboard

Six countries showing the proportion of seats held by women in national parliaments over time, displayed in a grid format using a JavaScript loop.
View on Colab

Challenge 7: Maps - Base maps and chloropleths

Coordinate

Here is my coordinate map of authorised landfills in Wales. Locations were in UTM, so I converted each point to Longitude/Latitude.

Chloropleth

Here is a chloropleth map that uses data from the UK Office of National Statistics that shows the percentage of cigarette smokers by local authority.

Challenge 8: Big Data

I used AutoCPI time series data to map prices of products over time. In the first chart, I mapped the mean and median price of can of beans. In the second chart, I compare the prices of a kilo of canned tuna with a kilo of fresh fish over time.
View on Colab

Here is my bean chart.

Here is my fish chart.

Challenge 9: Interactive Charts

Here are some interactive charts that are also in my project!

Challenge 10: Advanced Analysis and Machine Learning

Advanced Chart

Machine Learning

Cluster 0: Higher share of people from Ecuador and Dominican Republic, and who do not have no criminal record

Cluster 1: Higher share of people from Mexico, and who have criminal charges, either pending or convicted

Cluster 2: Higher share of people from Mexico, and who do not have no criminal record

Cluster 3: Higher share of people from El Salvador and Guatemala, and who do not have no criminal record

I used K-means clustering to find out which regions had ICE arrests with similar characteristics (age, gender, nationality, and criminal record). I found that there was one cluster that included the central and Southern parts of the United States that was defined by high shares of people arrested who have criminal records. The rest of the clusters had lower rates of people arrested with criminal records, but varied with higher shares of different nationalities.
View on Google Colab