What does
that mean?
I came
across the term yesterday for the first time, and wanted to know more. IBM were
showcasing their product offerings that use it, and I stopped for a chat.
IBM had been
tasked with identifying citizen movement trends in Istanbul to assist in that
city’s future planning of roads and public transport. The city officials needed
to know how, where and when the masses move. In smaller areas this can be done
via aerial tracking and sensors (for vehicular traffic) and manually or via
turnstile tracking for public transport. However, having to monitor millions of
people travelling in every direction IBM took novel approach using mobile
carrier location data. Partnering with Vodafone, IBM got access to the mobile
carrier’s Home Location Register (HLR) data and carried out Big Data analysis
to establish the people movement trends they needed.
In addition
to the obvious benefits for planning public transport and roads, people
movement data can be handy for retail strategies (branch locations and open
hours), traffic planning, insurance, public safety (where and when to provide
security), road works planning, etc. It could also provide interesting insights
in to crowd behaviour following irregular incidents/event s (weather events,
public disturbances, major sports events, etc.)
This is ‘treasure
from trash’ solution.... or rather more akin to producing energy out of corn husks or
renting out your back yard for your neighbour's cows to graze J. Normally, the masses
of HLR data would be give limited value, and only just for the mobile carrier.
The mobile carriers would be interested with the numbers within an area at various
times, but would be less concerned with
the movements of individual users. Opening up this data to government
and others creates a potential new revenue stream for a Telco, and broad
benefits for the public (by way of more targeted services).
So what are
the limitations?
The most
obvious is that not everyone has a mobile phone, hence students and lower
income people may be under-represented in the numbers. Secondly, unless you get
all the mobile carriers you only get a sample. Another is that you need to
perform some hefty Big Data analysis, which is a specialized area and could be
costly. Hence, various assumptions will need to be made and possibly some
tweaking of the data.
Overall,
when speed (translating to cost savings and quicker solutions) is more
important than accuracy, this is a very good method.
