A Location-Tracking Mobile App
Advisor: Jeff Ondich
Times: Winter 3a
Location data is powerful. If you know an individual's location every 15 minutes over a period of
several months, you can identify with high probability where the person lives and works and
goes to school, where they go for fun, what kind of medical care they receive, etc. If you have
a database of many people's locations, it's easy to search for friend groups, work groups,
participants in political protests, weekly meetings,
and other kinds of relationships (illicit or not). Even if the data is ostensibly "anonymized",
location data is so particular to an individual that it's very easy to translate a detailed
stream of locations into a single individual's identity.
and intelligence agencies
love location data, and there is an
marketplace making large datasets of location data available for sale.
Sometimes, sharing your phone's location with an app leads to great features for you.
Mapping apps like Google Maps, Apple Maps, and Waze are very popular for good reason—they
help you get from your current location to your destination, they help you route around traffic
delays, etc. And fitness apps like Strava and Runkeeper can give you helpful
guidance about your exercise. But because the data market
is mostly unregulated, the creators of these apps can also sell your location data or put
it to uses unrelated to the purposes of the apps that collected the data in the first place.
The problem with location data is that it can lead to extraordinarily intrusive, detailed
inferences about the behavior of individuals and groups of people. Despite the fact that this
has been widely studied and publicized, there's still a
surprising amount of apathy
around the subject of data collection and exploitation.
To help us understand more concretely how location data can be translated into
inferences about a person, this project will focus on creating a location-tracking mobile app.
This app will collect its user's location over time and provide the user with a variety
of data visualization and analysis features intended to demonstrate the power of this kind of data.
What you will do
- Decide whether to create an app for iOS or Android or both.
- Study privacy policies and write one suitable for your app.
- (Weeks 1-4) Use the built-in iOS/Android location services to create an app that
collects location data at fixed intervals (say, 5 or 15 minutes), stores it
in a local database on the phone, includes very simple data visualization
(e.g., a daily map and a tabular list of GPS coordinates), and allows export of the data as,
say, an emailed CSV file. At this stage, the app should also include an
On/Off setting to allow the user to easily enable and disable data collection.
- Incorporate existing location databases (e.g., from Google Maps or OpenStreetMap)
to tie the user's GPS record to named locations like specific businesses, restaurants,
street addresses, etc.
- Conceive, design, and implement a suite of analytical tools for the data. For a simple example,
your app could try to identify the location of the user's home. You could show a list
of the user's top-ten locations (by name and coordinates), predict the user's likely location
at a future date and time, etc. etc. There's a lot of room for creativity in this
- Design and execute a few simple tests for your analytics (e.g., how effective
is the app at detecting people's homes?). Because we won't have the time to
collect medium-to-large datasets for this project, we won't be shooting for
formal statistical studies during this comps period. But we want to do some simple
some plausibility tests to make sure our analysis features aren't obviously dumb,
which could help set up the potential for more formal studies in the future.
- (Optional) Enable two users to share their data with each other and provide
two-person analytical features like "how much time do they spend together?" and
"how far apart do they live?"
(Also, if you like combining entertainment with existential dread,
you should probably watch the
Data Brokers episode
of John Oliver's Last Week Tonight.)