Monthly Archives: May 2016

What is the importance of Big Data in public transportation?

The current trend of some companies to use Big Data analytic tools in order to create predictive models of business intelligence is a reality that is impacting the managerial processes in different business environments. However, the fact that the public sector is using more often Big Data analytics to foresee citizens’ needs, change the traditional government management approach that has been in place for decades.

A specific case that elucidates how municipal governments are employing Big Data analytics in their management processes happens in the department of transportation of the city of London, England; TfL, the local government body responsible for the city’s transportation system. This municipal agency has implemented a Big Data analytics strategy that helped to find predictive behaviors of transit users, to identify the needs of the city transportation infrastructure and to improve the agency’s decision making processes.

Similarly, through partnership agreements with different businesses of the city of London, TfL encouraged public transportation users to download a mobile phone application about routes and timetables in their smart phones. The information generated in such app, was intersected with databases that contain information related to routes frequencies in peak hours, routes with greater passenger mobility and annual number of trips per capita using TfL services.

Which aspects of the public transportation in the city of London improved from using big data?

  • Transportation user experience: After the analysis of large data sets, Tfl could answer questions such as: What is the main purpose of London citizens to use transit systems? What is the preferred mode of transportation among transit users? What is the main source of information used by citizens to obtain information about routes and schedules and What are the main concerns of transit users about the city’s transportation system?
  • Mobility and sustainability: Using big data, Tfl could forecast important variables such as: transit systems future demand, citizens’ ride time using public transit, transportation network planning and its relationship with the environment (stops, routes and frequency), scope of intermodal transportations systems offered by TfL.
  • Infrastructure and street furniture: Analyzing large data sets of information, TfL could find out, the impact of new infrastructure in London’s mobility, the use of public space by transit system users and the social and environmental impact of public transportation systems.
  • Communications: Based on the amount of data generated through the interactions with TfL users, the agency could design strategies to improve the communication among TfL and transit users and to discover what technological platforms were more popular among citizens to communicate with TfL.

The use of Big Data analytic tools by the TfL, helped to bring effective solutions to the needs of public transit users and to identify the most prominent infrastructure and mobility problems of the municipality.

Transportation and Big Data


The following article portrays the use of Big Data analytical tools to resolve the needs of public transportation systems in other cities in the world:

Why using visual metaphors improve big data visualization?

The popular adage that says: “a picture is worth a thousand words”, could be considered the statement that best explains the fact that a complex idea can be communicated with greater effectiveness through a graphical representation. Such adage can also be extrapolated to the concept of  big data visualization, as it is a communication instrument that helps to illustrate specific volumes of numeric text data in form of visual metaphors using different kinds of charts.

Usually, when communicating large volumes of data represented in numeric text, it becomes difficult for the human brain to symbolize its meaning using the verbal processing function that we employ to interpret daily life phenomena. Therefore, the graphical tools developed to display numerical data sets, allows the human brain to understand information more effectively through visual metaphors.

Likewise, it is pertinent to mention that analogies and metaphors are brain’s cognitive processes that play an essential role in abstract reasoning abilities and human communication. Human brain constantly uses previously acquired knowledge about things we are familiar with, to be aware of unknown phenomena. Thus, graphic metaphors are created in our minds to allow us to understand abstract concepts, such as numeric text data in visual terms.

Additionally, and in order to understand the meaning of a visual metaphor in the analysis of a numerical data set, it is important to remark that the symbols and the graphic resources employed to depict such data must describe numeric values in the most accurate manner. Otherwise, the information authenticity that is intended to be communicated by such data set might be misinterpreted by the final user.

The aforementioned ideas help us to deduce that there is a symbiotic relationship between the types of charts and the sets of numerical data to be analyzed, which influences the way numerical data sets must be presented in the charts and the way users’ perceive data information.

Important questions to ask before designing visual metaphors.

  • Does the chart aim to compare different values? Are the highest and lowest values of the data set clearly shown in the chart?
  • Does the chart aim to display the composition of something? Does the chart intents to show independent values as part of a whole set of data?
  • Does the chart intent to show the distribution of different values? Does the chart help to show statistics outliers, normal trends and the whole range of values expressed in the data set?
  • Does the chart intent to express the relationship between different groups of data sets? Does the chart show an interrelationship between different variables?

Data visualization greatest strength is that it empowers our brain capacity to process visual information more efficiently than numerical information. Therefore, if a visual metaphor is constructed following a systematic methodology that takes into account data accuracy and graphic coherence; it will communicate charts’ information comprehensively, generating at the same time confidence in the analyzed data by users.

Data visualization