The UK traffic data 2012-2014

Maps, Visualization


The map is now an interface that can support productive information access and knowledge construction activities. In modern map-based environments, the map can literally use the World Wide Web as its “database.” Carto as a world’s leading Location Intelligence platform provides a possibility to visualize data on GIS workflows. Therefore, I pick up the abroad database UK traffic data from Karggle because I’m curious about what are the most influential elements for UK transportation.


This poster is from The World Data Visualization Prize for 2019 was awarded to Dimiter Toshkov, Associate Professor at the Institute of Public Administration. I like the way Dimiter uses colors. Yellow, red and blue is primary colors in art and design, particularly painting. Those colors are very strong identities and intuitive.Red as a strong color represents small states(less than 5 million); yellow represents midsized; blue as big states.



The UK government amassed traffic data from 2000 and 2016, recording over 1.6 million accidents in the process and making this one of the most comprehensive traffic data sets out there. It’s a huge picture of a country undergoing change. My focus city is London. (Note that all the contained accident data comes from police reports, so this data does not include minor incidents.)

Variables: Accident_IndexLocation_Easting_OSGR / Unique ID.Longitude / Local British coordinates x-value.Latitude / Local British coordinates y-value / Police_ForceAccident_Severity / Number_of_Vehicles / DateDay_of_WeekTime / Road_NumberRoad_Type / Speed_limit / Light_Conditions / Weather_Conditions / Road_Surface_Conditions / Special_Conditions_at_Site / Urban_or_Rural_Area


CARTO (formerly CartoDB) is a Software as a Service (SaaS) cloud computing platform that provides GISweb mapping, and spatial data science tools. The company is positioned as a Location Intelligence platform due to tools with an aptitude for data analysis and visualization that do not require previous GIS or development experience. The cartography is being undisciplined; that is, freed from the confines of the academic and opened up to the people.


After multiple tests by visualizing variables, I select four of them that may influence the traffic accidents: Number of Vehicles, Weather_condition, Road Type, Light_condition, Road Surface. I also pick up ‘filter’ as my algorithm method to manipulate data because my database is complicated. It includes many different variables and hard to display or analyze at the same time. To be more detailed, I set point size by the number of vehicles in an accident so that it will be directed to know which area has more severe traffic. I use the color from the UK flag, red, white and blue. Just for indentation. Also, those three colors have a strong contrast which helps me analyze for the next step. Between the extremes of traditional map presentation and visual data exploration, map-based visualization also supports goal-driven analysis and information synthesis.

Today’s cartographic environments are characterized by two keywords: interaction and dynamics.” To design the map more interactive, I add the pop-up function. The background is dark-grey emphasis the dot’s information effectively.

Results and Interpretation

If I set ‘day_of_week’ as my dots’ size, according to my Cartograph, there are more big dots than small dots so that more traffic appears on weekends than on weekdays. I use the filter to hide the accidents that less than 3 vehicles. The more vehicle involved the traffic the severe the situation will be. Plus, there are too much data on the map I have to make some emphasize. For the road_type, Single carriageway has the most accidents followed by Dual carriageway; under the weather condition bar, data shows ‘fine without high winds’ has the most accidents while ‘raining without high winds’ is the next; ‘Daylight: street lights present’ dots appear the most and ‘Darkness: street lights present’ is the second.

After explorations, I come up with some summaries about 2012-2014 London traffic data. There are more traffic accidents happened during the weekends; Single carriageway happens more accidents. Most of the time, the weather doesn’t trigger the traffic. However, the data ‘raining with high winds’ tells me the wet road does some damage to transportation. Most of the traffic appears under a normal light condition so that it isn’t the main cause of accidents.

The data itself is just number but making a brunch of data into visualization and analyze it with social reality can generate more content. Pickles rethinks mapping as the production of space, geography, place, and territory as well as the political identities people have who inhabit and make up these spaces (Pickles 1991, 1995). Maps are active; they actively construct knowledge, they exercise power and they can be a powerful means of promoting social change.


When I first imported my data into Carto. I was flustered because it was too many variables and dots on the map. It was hard to do to analyze. Fortunately, I found many algorithm methods that can clear my data. I choose the ‘filter by column value’ after reading the function intro. Visualizing data has a similar principle to graphic design: each visual element, decoration, and color should have a reason behind them. For instance, if I set the size of the dots by ‘day of the week’, the bigger dots are shown as weekends and small dots are weekdays. Details generate content. Therefore, in Data Visualization, details are more important. It means a lot behind the tiny difference.

Nowadays, mapping isn’t only used for checking location. As more information layer added upon the map, it became a very comprehensive social science. In the reading: ‘the relationship between the body, art, mapping and digital media (Silver and Balmori, 2003) and the impulse to escape embodied gendered and racialized subjects through scientific mapping projects (Piper, 2002). Artistic communities seem to be increasingly interested in psychogeographical engagements with urban life.’ From my perspective, the usage of mapping will be expanded to a broad field in contemporary society. More knowledge will be related to mapping for practical or academic improvements.


Alan M. MacEachren and Menno-Jan Kraak1, Research Challenges in Geovisualization

Chris Perkins, Cartography – cultures of mapping: power in practice

Jeremy W. Crampton & John Krygier, An Introduction to Critical Cartography