Specific Frequented Locations
(Instances of Category x Location)
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Specific Frequented Locations portray map points of only transport related transactions (i.e. Taxi, Bus, Train) over the period of the research study. ​On the right, a screengrab of the visualisation is displayed, showing the large concentration of transport transactions in the central and city area. By having such concentrations, it can be inferred that the user frequently travel in those locations.
Above Note the dots that seem to continue blinking at one location. Where do you think the user lives, work and play?
Twenty years ago, if a supermarket had asked to put a microphone in our houses, or a landlord had asked to put in a camera, or a train company had asked our whereabouts in the station, we would have said no. Now we buy Amazon Alexas, rent Airbnbs and use London Underground’s free WiFi
"With information collected that can identify our every move, is privacy dead?" CNA Luxury (2019) Henry Mance
Just like regular commuters, the user constantly transacts on moving around, making the single category of transportation 78% of all transactions in the data pool. When placed together both data sets combine to represent the correlation and similarities in the use of location data in both data sets.
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With Big Data available in the public domain and capable to be mined into harmful means, the usage of such data posts a plethora of risks to people, including identity theft, scams and digital theft. Nonetheless, issues on the collection of data represents only the tip of the iceberg, apart from many other challenges in the data-ecosystem and its stakeholders. Learn more about the many issues by reading the thesis here.