is a multidisciplinary designer, with focus on: data visualization and coding. Parsons’ 2018 MSc in Dataviz
As the data visualization designer at USA for UNHCR (the fundraising/awareness arm of UNHCR in the United States), I was part of a team that created the concept for this piece: a data piece that shows total funds raised in specific cities in the US, and how much this money is able to support refugees with specific objects and funds. Website
Demo animation of the project
3d assets by Marcus Penna
The team is:
Gabriel Gianordoli (http://gianordoli.com)
- Jieqian Zhang (https://jieqianzhang.github.io)
Marcus Penna (https://www.behance.net/marcuspenna)
My roles were:
Creation of concept (feasibility of data assets, finding the right message, coming up with the visual metaphors): done together with rest of the team
Coding: mainly took care of all styling of elements. Worked on the main app (figuring out the logic behind the random array parser) together with team; debugged and worked to figure out some small kinks between the modules and Webpack configuration
Art direction: worked closely with the illustrator responsible for the 3d assets we're loading with the three.js library (still figuring out some texture issues).
Work in progress
Computational Learning VS Computational Reality
Work still in progress; v2 should be released by the end of Summer.
The relationship between computer-science education and computational trends in Github
Teaching children how to code is a common goal in education in the US. Initiatives by the public and private sectors – such as code.org and CSforAll – are trying to address this with extensive funding and recommendations for courses and syllabi.
But the long-term objectives of such initiatives are not clear. Will they create better citzens and more informed students? Or are they focused on a more productive workforce? These different agendas need to be aligned with concrete paths for the children and also juxtaposed with their professional and lived experience.
Based on CSTA K 12 Computer Science Standards, and Github repositories as proxies for applied computer science, this visualization shows gaps and opportunities on both sides.
The robustness of a scientific finding must be judged not just by the merits of the original experiments, but also by the ability of these findings to be independently reproduced. Concerns that published findings, however, are commonly failing to reproduce have shaken trust in science, and led to calls for reforms in how scientific findings are evaluated and transmitted. Website
Work made with Steven Hubbard and Marcy Hudson.
What does the economic complexity index look like near GDP per capita and GINI?
This is a project to focus on how to develop African economies (with all its weakness and strenghs). The Economic Complexity Index is chosen as a tool to understand sub-Saharan countries. But the ECI is complex: it tries to assess how the economy of a country is developed and its potential to breach into other areas of economic activity.
The slopegraph has 3 steps; 1st, the GDP per capita; 2nd, the ECI; 3rd, GINI. The vertical position of the corners of each line was normalized to the axis height. When the user clicks on one of the slopes, a histogram for that specific country and its indexes for the last 24 years loads on the left side.
The visualization was rendered with D3. GitHub live link