Results of the SDG DataViz Camp 2018

here you can find the results of the challenges of the sdg dataviz camp 2018.

The SDG DataViz Camp 2018 was a success! From the 28th of May until the 1st of June multidisciplinary teams of highly motivated researchers, students, professionals and civil servants worked together on various Challenges. Guided by mentors and inspired by the workshops and presentations from experts, the teams aimed for making the Sustainable Development Goals tangible. This page is dedicated to publishing the results of each Challenge. Discover the Challenges and data visualizations regarding the Sustainable Development Goals related theme 'Social Inequalities'.

The following Challenges have been worked on:

Challenge 1:  Visualizing transport inequalities: accessibility to jobs in Bogota. - Visualizing urban transport inequalities in Bogota to better inform policy-makers.

Challenge 2:  Sustainable Development Goal Uncertainty Dashboard. - Visualization of uncertainty and incompleteness of data for: Proportion of seats held by women in national parliaments and local governments, using a dashboard approach.

Challenge 3:  Exploring Isotype maps and cartograms for showing SDG data. - Showing change in proportion of women in national parliaments via an isotype map and discovering trends in world nourishment with choropleth maps.

Challenge 4:  Developing water footprint caps for the world’s basins. - Interactive dashboard to inform policy makers on blue water availability, scarcity and footprint.

Challenge 5:  Creating memorable visualizations for broader well-being in Europe. - Using gamification and charts to show change in indicators of the Dutch well-being landscape.

Challenge 6:  Inequalities in the geographical distribution of human resources with high educational profile in Costa Rica. - Main trends in human capital distribution with technological and science background in Costa Rica.

Challenge 7:  Visualization tool for Curacao. - Exploring ways to visualize well-being data of Curacao, resulting in a poster regarding how people feel about discrimination.

Challenge 8: Analysis of the Social Quality-program projects. - Analyzing inequalities in the acceptance process and exploring the financial flows of the Social Quality program of the Province of Overijssel.

Challenge 1

Visualizing transport inequalities: accessibility to jobs in Bogota

by Faculty of Geo-information Science and Earth Observation (University of Twente)

Challenge owner: Mark Brussel

Challenge participants: Elles Blanken, Zahra Hamid, Marija Smiljanic


Urban transport inequalities are among the most persistent and difficult to solve. Especially in the global South, poor citizens may spend half of their income on transportation to access jobs, school and health facilities, yet their accessibility remains low. The concept of accessibility combines transport system, cost and activities in one spatial measure and is very suited to evaluate the effect of new infrastructure. Does it serve the poor in equal measure as the rich? In this challenge we create attractive and convincing spatial visualizations of inequalities of a new metro in Bogota, Colombia to better inform policymakers.


This challenge focused on transport in the urban area of Bogota, Colombia. Access to transport is an important factor in accessing other important areas of life, such as work, education, health care and leisure. The challenge was to visualize inequalities regarding access to transport.
To break down the access to transport into different factors and show the impact it may have on access to other areas we chose to focus on one specific area (work) and counting two major factors (time and cost). To bring out inequalities we took a twofold approach:

  • First, we decided to translate (abstract) inequalities to the personal level by showcasing individual journeys of representatives from very different socio-economical classes. The video follows two people who work in the same area, but with very different living conditions and access to transport. By presenting a timeline and the transport cost in comparison to monthly income the inequalities become visible.
  • Secondly, we decided to present a scatter plot to show the magnitude of these inequalities within the 8 million inhabitants of Bogota, combining three elements: travel time from home to work, travel cost as % of income, and socio-economical strata.

Video 1.1: Visualizing transport inequalities by showcasing individual journeys to work of representatives from very different socio-economical classes.

Presentation 1.2: Final presentation presenting the results of the challenge; the journeys and scatter plots (slide 16/19).


The personal journeys and the scatter plot will give viewers insight in how different limiting factors stack up and influence other basic needs. Comparing the two personal journeys will show the differences in access to transport and subsequently how this diminishes people’s opportunities and creates barriers in other areas.
For example, people who need to spend 3 hours or more on home-work travel will have less time to spend on other tasks like, running household, caring for other family members (children, elderly), pursue studies, participate in community work/activities etc.
Also, having to spend a larger percentage of your income on transport will leave less money to spend on other necessities, like food, housing, clothing, education for children, medication etc.

An additional inequality that the personal journeys show, is the safety issues and other risks that the distance and routes pose to people. People from the lower strata will have less possibility to avoid high risk accident areas. Also having to take public transport and multiple transits means higher crime risks (theft, sexual harassment or assault etc.)
The scatter plot specifically will show how the previous points apply to those from the lowest social-economical strata, and these represent the majority of the population of Bogota.


We experienced two main challenges:

  • The data sets were quite vast, but still it was difficult to extract some of the information we needed. For example: finding average income for each socio-economical stratum or calculating travel costs and travel time.
  • We were too inexperienced to work with specific data visualization tools and software to be able to present finalized visualizations in time.

“Juggling with the data and trying to visualize them in a compelling way, we felt like we were volleyball players who have to play on a basketball court. So, in a way we were experiencing one of the key factors that shape the inequalities that we tried to visualize.”

- Team 1


Challenge 2

Sustainable Development Goal Uncertainty Dashboard

By Geo-information Science and Earth Observation (University of Twente)

Challenge owner: Ieva Dobraja, Jessica Gosling-Goldsmith

Challenge participants: Melis Baloglu, Ieva Dobraja, Adelene Lai, Ragindra Man Rajbhandari, Wouter van Rossem


Design and create a visual dashboard to explore the uncertainty of Sustainable Development Goal indicator data. This graphical and interactive environment will offer a more complete view of global social inequality data by visualizing components that might be overlooked or generalized, or are simply unknown. It will include interactive maps to draw attention to smaller nations and highlight the availability or recency of data. It might also include a bar chart to make comparisons or a timeline to display temporal distribution in addition to other representations that illustrate the uncertainty or incompleteness of indicator data. The project will consider data at the regional and country scale.


We tried to visualize the uncertainty and incompleteness of the data used for the SDG indicator “5.5.1 Proportion of seats held by women in (a) national parliaments and (b) local governments” by using a dashboard approach. This dashboard includes the following visualizations: an animation going over each year and highlighting which countries have missing data, line graphs showing the different proportions of all the countries (Figure 2.1), visualizations to see data of Small Island Developing States (Figure 2.2).

Figure 2.1: Proportion of seat held by women in parliament worldwide visualized in a dashboard.

Figure 2.2: Proportion of seat held by women in parliament in Small Island Developing States.

The dashboard is interactive and it is possible to explore the data in the following element. (Use the full screen mode at the bottom right.) Or use this link to find the interactive dashboard on Tableau Public

We also created a physical representation of this indicator for the data of the year 2017. We visually represent the proportion of women in the national parliament of one country on one straw with a pin indicating this percentage. If there is no pin then the percentage is zero, or we do not have any data. We also use the color of the straw to distinguish between the different continents. All this straws are then put into styrofoam base to order them from highest to lowest and with different rows for the continents. Some extra elements are also added on the base to mark the physical axis, and some pins are different colors depending on their percentage range to have a better visual distinction.

Figure 2.3:  Physical representation of the proportion of women in the national parliament of one country on one straw with a pin indicating this percentage.

"When we were creating the physicallisation it was really interesting for getting a real hands-on experience with the data: you start seeing patterns just by physically putting them on the piece. Things you might miss unless you would also go through every single entry. And it also allows for a lot of creativity since you are not restricted by electronic tools or your programming experience, so you can focus on the problem."

- Wouter

“Let’s play with data!” “Think outside the screen”

I believe that people can understand and internalize any abstract / hidden / invisible things by playing and building with interesting tools and materials. When we were building the tangible model with straws, we experience learning by making.

- Melis


The timeline of missing data on the world map gives an indication that the data is not always complete or available. But it also gives no information of why this might be, as it could for example be because of a particular political situation in that year.
The visualizations concerning the Small Island Developing States show that it is difficult to see them in some graphs and we need to adapt them for these cases.
The physicallisation doesn’t have the problem of small countries, as each is equally represented, i.e. one straw. It can also give some insights on how data is distributed for different continents.


The data are more-or-less already clean coming from UNstats, so that was not an issue.
A general challenge is being critical about the data and whether they are actually “measuring the change you want to see”.
We also relied on alternative data sources to get a better idea of gender equality (SDG5). The challenge here was to incorporate the information from the alternative data sources, e.g. parliamentary composition.

"As a data scientist/chemist, I was trained to present data as they appear or were measured. At this camp, I learned how the design and story-telling aspects of a dataviz can be critical for communication, especially to a wide audience."

- Adelene

Challenge 3

Exploring Isotype maps and cartograms for showing SDG data

By Geo-information Science and Earth Observation (University of Twente)

Challenge owner: Natasha Pirani

Challenge participants: Marieke Abbink, Simbarashe Chereni, Sang Pham


Explore isotype maps and cartograms as alternatives to traditional choropleth maps for visualizing SDG data on a global map by country. Isotype maps portray classified data using one or more symbols; to represent data globally these symbols should be carefully designed, sensitive to international and other diversities and contexts, and embody the data. How can we also use isotype maps to display bivariate data on a global map given the challenges of size and scale and symbology? Cartograms come in many styles. How do the different types compare for visualizing uni- and bi-variate SDG data? Can these maps work effectively in static form to provide an overview and opportunities to explore the data as it relates to population?  

Visualization of proportion of women in national parliament

The results are repeating icon tile maps for South America (Figure 3.1) and Europe (Figure 3.2) which show SDG indicator 5.5.1a, the proportion of women in national parliament. The maps, which are gifs, transition between 2000 and 2015 to show the proportion of women in national parliament in those years. The base tile map of squares was created by Jon Schwabish at Policy Viz, and was downloaded from that website. This form of map is useful because it can represent each country at an equal size, larger countries do not dominate and smaller countries are also easy to see. The square shape also allowed us to depict the data as repeated icons to clearly but simply show the data in a way that might be more precise than using color shading.

The icon was designed to embody the data, by depicting a female in parliament by showing a woman speaking at a lectern. Each icon represents 4% of the total number of seats in parliament, which are held by women.

Figure 3.1: The proportion of women in national parliament in South America for the years 2000 and 2015.

Figure 3.2: The proportion of women in national parliament in Europe for the years 2000 and 2015.  


You can see the increase of women in parliament by comparing 2000 and 2015. Only Suriname had a decrease of women in parliament. You can also see Bolivia has the highest proportion of women in Parliament in South America.

Visualization of trends in world undernourishment

Sustainable Development Goal number 2 is to "end hunger, achieve food security and improved nutrition and promote sustainable agriculture". For policy-makers to be able to assess their efforts indicators have been developed, e.g. 'proportion of undernourishment'. To be able to show in simple ways, a country or region's performance in one indicator over time, we demonstrate how a choropleth map can be used. The choropleth maps for levels of malnourishment in the world for the years 2001 and 2015 are shown in figures 3.1 and 3.2.
The colors in the map indicate the proportion of malnourished people per country. Therefore, green represents none / very low malnourishment and red represents very high malnourishment.  


Asia shows the most changes in level of malnourishment per country (14) of which 13 indicate progress (into higher notches of nourishment) and 1 regression.
Africa shows the second most changes (10) of which 8 indicate progress (many in the lower notches of nourishment) and 2 regressions.
Central and South America shows 3rd most changes, where all indicate progress into higher notches of nourishment.
The Middle East and Eastern Europe indicate the least changes, because most states are already in the higher notches.
Africa has the most stagnated countries regarding malnourishment (6), followed by Asia. These stayed within the same notch of nourishment.

Figure 3.1: Proportion of malnourishment in 2001 globally.

Figure 3.2: Proportion of malnourishment in 2015 globally.

Presentation 3.3: Final presentation: Visualizing trends in world undernourishment

Challenge 4

Developing water footprint caps for the world’s basins

By Water Engineering and Management (University of Twente)

Challenge owner: Rick Hogeboom

Challenge participants: Ehsan Barati, Erik Kemp, Michelle Peters, Ashwin Sadananda Bhat


An adequate freshwater supply is essential to life, nature and the economy. Unfortunately, humanity’s growing water footprint already exceeds what is sustainable available at many locations during the time of use, and scarcity is only expected to worsen. SDG 6 is fully devoted to water and the prestigious World Economic Forum even declared water crises a top-3 systemic risk globally in terms of impact. But how much freshwater do we actually have? What is the maximum water footprint that can be sustainable allocated within each river basin? Are the endowments fairly shared? Dive into our data set and find out!


The visualizations try to show the variations in blue water availability, scarcity, and footprint over each month using aggregated monthly data from 1970 to 2004. The map visualizations are based on data on each basin across the world and their corresponding blue water availabilities, scarcity etc. For both the figures (Figure 4.2 and figure 4.3), the color of the circles shows the water scarcity of each basin. For Water Availability (Figure 4.2), the size of each circle corresponds to the availability of blue water in each basin, while the Water Footprint (Figure 4.3) uses the size of each circle to show the footprint of each basin.

Presentation 4.1: Final presentation: Developing water footprint caps for world's basins

Figure 4.2: Global Water Availability aggregate per month over 1970-2004. Animated loop over 12 months in full-screen view.

Figure 4.3: Global Water Footprint aggregate per month over 1970-2004. Animated loop over 12 months in full-screen view.


The viewers can see the variations in water availability and scarcity (green vs red), as well as the water footprint (size) across aggregated months from 1970-2004. This shows which basins in the world have more availability/scarcity across months. This helps in identifying areas/basins around the world where water scarcity is an issue, the months when it becomes an issue and the months when it usually isn’t. This kind of data lets us know when to withdraw water from certain basins and when to cap our withdrawal limit, enabling better informed policy decisions and leading to a sustainable water policy.  


The main challenges were data wrangling and getting the data in the correct format for visualization tools. While the workshops were quite useful, problems that arise when handling real-world data can’t really be prepared for. Also, lack of in-depth domain knowledge was a bottleneck. Knowing what might be important to look for will often help in deciding what to try to visualize.

Data wrangling is a pain in the ass, but the visualizations make it worth it.Team 4

When’s the next dataviz camp?Team 4

Challenge 5

Creating memorable visualizations for broader well-being in Europe

By Centraal Bureau voor de Statistiek Nederland (Statistics Netherlands)

Challenge owner: Edwin Horlings

Challenge participants: Prerna Bhardwaj, Manuel Garcia,  Xenia Una Mainelli


There is growing demand for better ways to measure well-being. Statistics Netherlands has developed a new statistical instrument – the Monitor Broader Well-Being –to provide basic information on all aspects of well-being in the Netherlands. Broader well-being is defined as the way in which the present inhabitants of a country shape their quality of life (“Here and Now”) and how this impacts on the well-being of future generations (“Later”) and on the well-being of people in other countries (“Elsewhere”). Among the c. 160 indicators in the Monitor are more than 50 indicators for the Sustainable Development Goals. We hope that the researchers who take up our challenge can develop understandable and memorable visualizations that solve one or both of our challenges: (1) charting the development over time of broader well-being “Here and Now” and its trade-offs with broader well-being “Later” and “Elsewhere” for a single nation; and (2) comparing levels of broader well-being “Here and Now”, “Later” and “Elsewhere” between nations.


We decided to focus on the impact of what we do today, on future generations. The goal was to create a “memorable” visualization, so we went for something playful, that would connect parents and children. The result is visualization in the style of a classic children’s game: “spot the difference”. An illustration portrays two similar landscapes, and there are nine hidden differences between the two. Each difference represents a change in a compared indicator (from 2017 to 2027). The indicators are: biodiversity, nitrogen and phosphorus surplus, protected areas in nature, renewable energy resources, CO2 emissions, and fossil fuel reserves. The viewer (roughly children between 7 and 12 years old) should be able to spot the differences with the help of eight “fill in the blank” questions.

Figure 5.1: 'Spot the difference'- game with explanations of the difference for each indicator.

We hope that this game will spark the interest of children in sustainability issues in an understandable way for them, but also that it will trigger parents to think more about their own actions and how this will influence the lives of their children. There is also a more mature version for adults to view side-by-side with the game, in the form of a bar chart. Fortunately for us, the changes in lifestyle and the environment in the Netherlands are fairly positive between now and in ten years, and so this visualization will not likely call people to action. Potentially it could be more illuminating for other countries, or if it were used to compare the differences between countries.

Figure 5.2: Dutch well being landscape, relative change in the indicators for 2027 compared to 2017.


The viewer or the user can gain insights into the physical resources and state of the natural environment that will be available by the year 2027. The information presents the changes in the quality and quantity of these physical resources. Ideally, this "Spot the Difference" game would be digital. When the user clicks on a difference that they find, a pop-up will display, explaining what this difference represents in the real world.

Presentation 5.3: Final presentation:  Creating memorable visualizations for broader well-being in Europe


During the week we encountered the following six challenges:

  1. Translation of data to English.
  2. Many indicators (~160), many of them are subjective, e.g., trust in institutions. Such indicators are difficult to represent in a visual way.
  3. Differences in types of data (qualitative vs quantitative), different scales of measurements.
  4. The data was a bit unclear. Data for the component of “later” only contained data for the years gone by, so it was hard to understand the reasoning behind the different indicators used for the different components of the broader well-being.
  5. Forecast of values to 2017. The results of the projection of the indicators contained errors, these are not portrayed in the final visualization.
  6. The visualization itself is not mathematically accurate – we had to dramatize some changes e.g. water resources and CO2 emissions so that a difference can be seen.

Technology is the answer, but what was the question?Cedric Prince 1979, quoted by Edwin Hans

Not only publish, publicize your work!Robert Kosara, quoted by Maarten Lambrechts

Challenge 6

Inequalities in the geographical distribution of human resources with high educational profile in Costa Rica

By State of the Nation Program (PEN) Costa Rica

Challenge owners: Esteban Durán Monge & Steffan Gomez-Campos

Challenge participants: Marlijn Aarts, Vincent Kierkels, David Klein, Bart Meijer, Jorim Theuns


Want to help trigger development in Costa Rica? We have the data and you may have a great approach on how to do it. Costa Rica (51.100 km2) a country in land similar to Netherlands but with less than one third of its population, is facing important gaps on education and productivity between the central part of the country and the rural and coastal regions. How to reduce those gaps in order to promote sustainable and equity development? We have the data and need an interdisciplinary team to think out of the box. Join us with the ¡Pura Vida!


Our main visualization is a video that shows the main trends in human capital distribution with science and technological background in Costa Rica. Our findings are connected in a story line showing geographical insights that we could reach after a cluster analysis (k-means).
There are two complementary visualizations. One is a dashboard to explore the data after watching the video, and the second is a short fiction story based on the two main characters in the video (José y María).

Video 6.1: Main trends in human capital distribution with science and technological background in Costa Rica

The dashboard is interactive and it is possible to explore the data in the following element. (Use the full screen mode at the bottom right.) Or use this link to find the interactive dashboard on Tableau Public


  • The main trends for the whole country
  • Distribution of human capital by field
  • Gender gap in some fields, especially in engineering and technology
  • Distribution of human capital by the concentration of people with education in science and technology in different cities outside the Central Valley (variable of interest)


There is a presentation with some slides and videos that explain the whole process we followed and the challenges in every phase. In brief the phases were:

  • Starting discussion on how to combine the skills in the group
  • Main discussion on the methodological steps
  • Data analysis and preparation of draft visualizations to explore and understand the data for the video, the dashboard and the short story
  • Script of the video
  • Working on the materials for the filming
  • Filming and editing
  • Final outcomes

Presentation 6.2: Showing the process of designing the visualization.


We will use the results of our Challenge and the results of the other Challenges on the website for two main things:

  1. We will present all the work we have done in the DataViz Camp and the work of the other challenges to our colleagues here in Costa Rica.
  2. We will continue working on our Challenge subject, as starting point we take what we already achieved during the DataViz. 

Challenge 7

Visualization tool for Curacao

By the Statistics office of Curacao

Challenge owner: Leander Kuijvenhoven & Barteld Braaksma

Challenge participants: Hilje de Boer, Jose Requena, Gert Versteeg


The statistics office of Curacao carried out a first Social Cohesion Survey (SCS) in 2015. The study provides important information on aspects of well-being in Curacao. For example, does the society promote inclusion and offer its members the opportunity of upward mobility? The challenge is to choose three topics from the SCS study and design a composite visualization tool in which the data can be used as a proxy for calculating SDGs. A data set containing information on 2626 households is available.


Starting with the assignment a process was initiated to look for a nice way to visualize the data. Considering various types of graphs, the spider plot brought the most information together in an attractive way. Using our background in graphical design an appealing poster is created to attract people passing by and inviting them to explore the data. The steps taken in the process are shown in the presentation 7.1. The spiderplot poster shows the subcategories within the Curacao population and if they have felt discriminated based on color of skin, language, gender, disability, etc. The poster is shown in figure 7.2.

Presentation 7.1: Showing the process from assignment to spider plot poster.

Figure 7.2: Final poster showing how the people of Curacao feel regarding discrimination.


Visual patterns were found in the subcategories of the Curacao population.


Unfortunately, on Wednesday we had only two team members left. Therefore, we had to re-shift our focus.

Very interesting presentations.Team 7

No matter what, you are not alone when it comes to the data world. - “It’s kind of difficult to put all my impressions into word. When I was a kid I always had that feeling of trying to help others. While I was growing up, becoming an adult, I always wanted to describe everything precisely, to find solutions for ambiguous questions and to satiate my curiosity. That is why I came here, in order to find ways to make a better world, and I found some ways to do it”.

- José Raquena

Challenge 8

Analysis of the Social Quality-program projects

By the Province of Overijssel

Challenge owners: Margreet Hogenkamp & Rosalie Bosman

Challenge participants: Sila Akman, Yohang Gu, Shoba Poudel


The Province of Overijssel invests via the program ‘Social quality’ in social enterprises, projects of municipalities and community initiatives. Overijssel provides practical information, knowledge, attention, advice and a big network. With a budget of 3.4 million euro per year this program has a large impact. In this challenge we will focus on the following questions: What do communities and/or initiators need to design and implement their projects? Who are the initiators and what kind of people are they: what is their drive, background and ambition? What is their dream and what do they need to fulfill it? How do their projects contribute to the society of Overijssel?  


We aimed to understand the case while telling the story. According to data set, Social quality Program of Overijssel Province has got 620 applications from 2012 to 2017. First we checked our data set and realized that we could focus on the financial flow within the program according to municipalities, subsidy types and pillars of the program. In the beginning, we mapped the applications spatially considering its volume by using Tableau and Photoshop software. It showed us the distribution of the applications per municipality. And then, we overlapped the spatial request volume with the ratio of acceptance and rejection.

Figure 8.1: Rejected and accepted requests per municipality.

We created a website with an interactive map of Overijssel for exploring the financial flows of the Social Quality Program. The map is available at the following website and shown below. (The website is buggy and sometimes needs a page refresh to work properly.)

Figure 8.2: Still of the interactive map of Overijssel for exploring financial flows of the Social Quality Program

Figure 8.2: Still of the interactive map of Overijssel for exploring financial flows of the Social Quality Program

On the website are a couple of visualizations available to give insight in the financial flows. Four Sankey diagrams are made to show relations between fund and project theme, theme and cities, fund and cities, fund and people who will benefit from this. We also created two bubble charts to show constitution of organizations applying funds and their projects. A ring chart showing components of theme for every city.

Figures 8.3-8.6: Sankey diagrams for the relations between: Pillars and funds, pillars and cities, cities and funds, and funds and people.

Figures 8.7 and 8.8: Bubble diagrams showing the constitution of organizations applying funds and their project


Maps of the applications and their results per municipality in Overijssel give viewers a chance to analyze if there is any inequality in the evaluation process. Since we didn’t have adequate information to reason the results, it is hard to conclude absolutely. However we can stress the high acceptance ratio of Zwolle Municipality which is the capital of Overijssel.

Furthermore, viewers can examine the pattern of the financial flow within the Social Quality Program. Users can get knowledge of how and where the money spend on social quality goes to. And also can get an overview of type of institution and projects.


The biggest challenge for our team was to understand Social Quality Program of Overijssel Province and its data set because all the information and the data set were in Dutch Language and neither of us knows the language. That’s why we had difficulties while articulating our story. Moreover, we were 3 people and since our disciplines and cultural backgrounds are completely different from each other, our approach to the challenge was also quite different.  Therefore, we hardly found a common ground to discuss on the issue comprehensively. In spite of these differences and short time, we could deal with the challenge quite well and we have learned a lot not only from each other but also from other teams and the lectures within the camp.

Quality is only possible if we mind the (in)equality!Team 8


The Challenge owner, Province of Overijssel, will meet with the team at the provincial governmental building in Zwolle to discuss the results of the Challenge and to fine tune the results for publication. The results will also be shared with other departments within the Province.