Projects submitted to the Data Journalism Awards competition

Right here you will find a list of all the projects submitted to the Data Journalism Awards competition.  

BB-resan
Country: Sweden
Organisation: Ottar
Data visualisation of the year
InteractiveInvestigationVisualisationPublic institutions
Applicant
Leonard
Wallentin
Team Members
Jens Finnäs, founder and datajournalist at Journalism++ StockholmSascha Granberg, datajournalist at Journalism++ StockholmAnders Birgersson (graphical design)Åse Bengtsson Helin (graphical design)Josefin Herolf (illustration)
Project Description
We used current and historical population grids to calculate the average distance to the nearest labour ward for citizens in each Swedish municipality, now and in the past. An ambitious GIS research, resulting in a web app: http://bb-resan.ottar.se/ (and a series of articles) for Ottar, a Swedish paper covering sexual and reproductive health and rights and other aspects of sexual politics.The web app takes the user for a journey to the nearest maternity clinic, given a certain municipality. This gives a sense of not only the differences in distances, but also how they have changed over the past decades.By showing how much the distances have changed, and how many more people have further than 100 kilometers to the nearest clinic, compared to two decades ago, we could clearly show that clinics have been closing at a higher rate than urbanization has moved people to the cities.The simple interface works well on any device size.
What makes this project innovative?
The research goes further than most similar projects, calculating actual average distances based on very detailed, large population data sets.The visualisation focusing on conveying the main findings and main angle (the increasing number of people who have more than 100 kilometers to travel to give birth), rather than showing everything at once. We do this by creating one “journey” for each municipality. That way, we could offer something that\'s easier to share on Facebook than a general exploratory interface.This is also a case where we would argue that the data, despite being geographic, has a stronger impact without using a map.
What was the impact of your project? How did you measure it?
We have seen multiple local news pieces on the results from this investigation, as well as the numbers we calculated used in political debates.The research was copied to Finland by Helsingin Sanomat.
Source and methodology
Population grids are available as ESRI shapefiles from SCB, the Swedish statistics agency. Maternity clinics had to be gathered by hand, but we could use the Google Maps API to geocodes their addresses.The analysis was done in QGIS in the following steps: - Convert grid to points (centroids) - For each population point and clinic, calculate the distance (the data is small enough to be done in a few hours on a consumer laptop, so we could do it locally), and save the smallest distance - Map population points to municipalities - Calculate a weighted average of the distances for each municipality, based on the number of inhabitants at each point - Repeat for earlier dates in historyWe were also able to find the people in Sweden with the longest distance to travel, the places where distances had increased the most, and other figures, some of which are used in the web app, and some that are only used in the articles.
Technologies Used
Python+Google Maps API for geocodingQGIS and Python+Pandas for analysisNode JS for building web appPugJS for text templating