INTRODUCTION & INSPIRATION
The public transport infrastructure in Europe is convenient for travel from one country to another. As an efficient and economical way for passengers to organize the route it also provides congestion reduction and health-harming emissions in urban areas. One of the cleanest and energy-efficient public transportation vehicles is trains which also allows passengers to avoid traffic and quickly reach your destination. The idea of creating the train stations chain and their proximity across European countries was inspired by an aspiration to provide the readers with information about routes availability and strongly encourages the use of public transport as an environmentally friendly way of transportation. As an inspiration, I used winter’s cold austerity map from mapbox.com website and some other design examples from pinterest.com such as Blue-green world map and Uber map visualization.
PROCESS & MATERIALS
Train Stations in Europe dataset from Kaggle.com was used as a data source to create a map. This dataset contained the names, coordinates, and basic properties of more than 36K train stations in Europe. The data also contained a few train stations in the European parts of Russia and Turkey, as well as a small number of stations in the African country of Morocco. I downloaded the CSV dataset, cleaned it from rows missing latitude and longitude information (about 5% of all stations had no coordinates) which is crucial for building a map. Then the dataset was uploaded to Carto, the open-source spatial analytics software. Two layers were used to build the map: train stations’ coordinates and European countries. To design the map green color tones were used to show the level of train stations’ intensity by value. Yellow (points) was chosen as a contrast color to demonstrate the specific location of each station. Borders between countries are white with lowered transparency to have a clearer understanding of where the countries are. I also added the legend that shows what colors mean and two widgets that help to filter search results by train station name or country.
RESULTS
As a result, we got the map where green color saturation shows train stations’ intensity. We can see that France, Germany, Belgium, Italy, Andorra, Spain, and the United Kingdom have the highest number of stations. Countries located closer to the east have lower numbers of stations. Latvia, Moldova, Albania have the lowest number of stations. This might be related to the number of landmarks and popularity for travelers. The highest train stations’ intensity is concentrated in central and western Europe.
Yellow dots represent stations and based on this pattern we can see railways and its intersections in the capitals and large cities. For example, we can clearly distinguish France’s capital, Paris, and the United Kingdom’s capital, London, based on station patterns.
I think that the combination of color saturation by country and railway routes work in conjunction and creates the whole picture for this type of transportation which might be particularly useful for planning European trips.
REFLECTION & NEXT STEPS
I found Carto to be more flexible than most of the previous tools we used for map building in terms of functionality and design options. I liked built-in geographical layers that help to create maps with a particular focus on spatial analysis. To design the map I used stroke modification and blending options to find balanced opacity. Various cold and warm color palettes combinations were also useful to demonstrate the intensity of train stations within different countries. Having some issues with my wi-fi connection I also noticed that map preview works pretty well on mobile devices — using the zoom option I was able to clearly see the map, legend, and use widgets. The main limitation of Carto was widget usage because it wasn’t possible to add the filters such as city or region; also dataset editing functionality within Carto is a little limited and it’s easier to work on data first and then visualize cleaned data. For further work, I’d like to explore other European datasets using Carto to see how different environmental conditions (air/water quality, emissions, forests, etc) correlates with mortality rates.
SOURCES
Lè Shine. A restrained color palette reminiscent of winter’s cold, glaring austerity, https://www.mapbox.com/gallery/#l%C3%A8-shine
Mapbox, https://www.mapbox.com/gallery/
Train Stations in Europe, https://www.kaggle.com/headsortails/train-stations-in-europe
Kaggle, https://www.kaggle.com/
Carto, https://carto.com/
Pinterest, https://www.pinterest.com/
Blue-green world map, https://www.pinterest.com/pin/159033430580985000/
Uber map visualization, https://www.pinterest.com/pin/333547916153952347/