Tailoring Analytic Algorithms and Visualization to Address User Requirements - how do you get from a user with data to one acting on the basis of said data?

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Commercially Confidential
Generating Insight from Data
Tailoring Analytic Algorithms and Visualization to
Address User Requirements

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The Challenge
How do you get from a user with data to one acting on the basis of said data?
 We will elaborate by way of example(s)
Data
+
User
Action !
Needs
Analysis
Visualisation

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TfL Data Overview
The Transport for London (TfL) Tube travel data set provides a large, open-
access data set we can play around with to demonstrate the process
 Over ½ million journeys logged giving location and time of the start and end of each.
– Needs cleaning (unstarted, unfinished, not applicable etc…)
 While nominally about journeys, the data can be re-analysed to give information
about:
– Stations
– Lines
 Other meta data allows for potentially interesting analyses, such as user type (elderly
pass user, season ticket user etc…)

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User: Needs
Consider a potential user. What questions do they want answers to? What
information is of use to them?
 We consider a user who wants to know about stations – not just in terms of usage
but in terms of which stations are similar to other stations. This may be for several
reasons:
– Interested in advertising based on likely users;
– Interested in appropriate staffing and rostering of stations;
– Interested in issue tracking, learning and apply lessons to similar stations;
 Alternative users might be interested in the traffic flow on lines and how they are
affected by station closure:
– Emergency / contingency planning;
– Sophisticated travel advice apps;

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Analysis: Station Profiles
We refocus the data set to give a profile of the usage of a station – recording
both arrival and departure rates across the working day
 Comparing total usage of stations is easily done by the users (already) so we scale
each of these profiles to have a maximum value of 1.

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Analysis: Dissimilarity Metric
User is interested in type of station (e.g. commuter source) but not interested in
the precise timing of the commuter rushes
 Stations close to the centre (e.g.
Harrow) have later morning
departure peaks than stations
further out (e.g. Chorleywood)
 The reverse is true for the
evening arrivals rush
 Dissimilarity between stations is
determined by minimum
Euclidian distance between
arrival and departure profiles
allowing for small timeshifts
 Timeshifts must be applied in
opposite directions for arrivals
and departures

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Analysis: Automatic Clustering
Agglomerative hierarchical clustering technique was used with group average
linkage to merge clusters
 Complete dendrogram is easy to calculate – deciding where to split is
 Splitting into 6 clusters provided useful insight (more clusters are also insightful)

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Analysis: 6 Clusters
Some insights can be gained just from looking at the clusters – e.g. the clusters
were labelled by observing their membership
 Commuter Source: 168 stations, characterised by a morning departures peak
and an evening arrivals peak, mainly located in the suburbs (e.g. Barnet)
 Commuter Destination: 44 stations, characterised by a morning arrivals peak
and an evening departures peak, mainly central London (e.g. Canary Wharf)
 Transit: 44 stations, with peaks as a commuter destination but also keeping high
usage throughout the day, includes most rail/tube interchanges, (e.g. Kings Cross)
 Social: 3 stations, with peaks as a commuter destination, but with extra arrivals
early evening and many departures very late in the evening, (e.g. Covent Garden)
 Heathrow Terminal 4: Cluster of one whose behaviour is highly variable -
dependent upon flights rather than typical work patterns.
 Heathrow Terminals 1,2,&3: Cluster of one whose behaviour is highly variable -
dependent upon flights rather than typical work patterns.
 …Text is a poor way of displaying these

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Geographic Visualisation
Further insight can be achieved by using an interactive, web based,
visualisation tool to show the location, cluster and current usage of each station

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Geographic Visualisation
Rush-hour becomes startlingly clear as the size of each station is proportional
to how busy it is

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Geographic Visualisation
Pan and zoom (inherited from Google Maps) allow a user to focus their interest
in an intuitive manner – clicking on a station brings up details on the right

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Conclusions
To generate new insight you need to determine users needs, apply appropriate
analytics and display with suitable visualisations.
 Data analysis without an understanding of the goal may just be empty maths;
 Data visualisation on it’s own may be very pretty, but not useful;
 New insights can be generated from analytics + visualisation
 Target these to address user needs and you have something useful

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