Understanding Mobility Data

Jackie Barbieri • July 3, 2020


Location data is any data processed by an electronic communications service indicating the geographical position of the device of a user of the service. This data usually includes the latitude, longitude, and altitude of the terminal equipment; the direction of travel of the user; or the time the location information was recorded. It is also referred to as geospatial data.


Simply put, mobility data is location data collected by mobile devices. It is information generated by activities, events, or transactions using digitally-enabled mobility devices or services. Mobility data is often recorded as a series of points with latitude and longitude collected at regular intervals by devices such as smartphones, bikes, scooters, vehicles, or navigation apps (e.g. Google Maps). It also has a temporal element to it, so the time is recorded at each point. Some devices will capture the speed of travel, who is making the trip, altitude, and store that data with each point as well.


GPS, or global positioning system, is a system originally developed by the U.S. military. Using satellites, receivers, and ground stations, a device’s exact location on the Earth’s topology can be precisely pinpointed. Geospatial data is the backend data collected by GPS receivers or devices. Some of the components (fields) captured include: time, longitude, latitude, altitude, time, horizontal accuracy, vertical accuracy, speed, and direction. A GPS receiver or device repeatedly polls the system for its location, so it can be spatially tracked as it moves over time.


There are a variety of coordinate systems out there. Some coordinate system uses numbers to identify a unique position in three-dimensional space. Degrees, minutes, and seconds or DMS is commonly used system. In DMS, you store the D, M, and S for both latitude and longitude. This system can be converted into a single decimal number for latitude and a single decimal number for longitude. This is the system that we utilize and it is called decimal degrees (DD). The precision of the location is related to the number of decimal places stored.


Precision is a measure of reproducibility (how easy is it to get the same results?). Accuracy is a measure of truth (how close is the measurement to its actual value?). Since all geospatial data is measured or captured with technology, we must consider that these measurements may sometimes not be precise, or accurate, or both.

With respect to geospatial data, the precision comes with the number of decimal places in the decimal degrees representation for latitude and longitude. A latitude of 0.001 is going to be less precise than 0.000001. This means that the higher the number of decimals recorded, the more reproducible that measurement is to that specific location. A value in decimal degrees to 6 decimal places is precise to 111.32 mm at the equator. Individual humans can be unambiguously recognized at this scale.

The two common data types of “float32” and “float64”, give about 7 and 16 decimal digits of precision, respectively. The former gives meter precision and the latter gives nanometer precision. Some providers, like Google and Apple, use the float64 data type to store latitude and longitude coordinates, giving us far more precision than necessary.


Horizontal accuracy gives us a radius around a two-dimensional location point. It implies that the true, unknown location is somewhere within the circle formed around the point. It gives us our margin of error (100 meters, for example).


Many potential clients ask if mobility data is truly anonymous. Whitespace Solutions and our mobility data provider, X-Mode, go to great lengths to ensure privacy. Unfortunately, the answer to this question is not easy. With enough time and resources, a bad actor could potentially reverse engineer the data to figure out movements of particular people of interest.

However, certain techniques are used to achieve an acceptable level of privacy such as introducing noise and a data anonymization technique called generalization. Generalization aggregates devices together forming “crowds” to show movement of groups of people rather than individuals. The upside is that this generalization technique has practical applications – we are often more concerned with how crowds move than certain individuals.


Mobility data is the foundation of our analytics. We develop analytics to help professionals in a variety of fields from the public sector, healthcare, research, and academia. By using anonymized mobility data, we can analyze the behaviors and answer questions about device movements in a region over time. More importantly, we take this a step further and make predictions to help you with making informed decisions about your organization with respect to COVID-19.

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