There’s a saying that many things don’t happen unless people make them happen. But where and when people do things can give important context to what they actually do. That’s why it’s important to have mobility data when building a geospatial data ecosystem.
But what is mobility data, exactly? Where does it come from? Why is it advantageous to have? And what kinds of applications can you use it for? We’ll answer all of these questions and more as we cover the following:
We’ll start with a mobility data definition, and an explanation of how mobility data fits into larger geospatial and big data systems.
Mobility data, in a geospatial context, is an aggregated, anonymized measurement of people’s movements surrounding points of interest (POIs) or neighborhoods (i.e. census block groups or dissemination areas). It can include where people come from, how long they stay, and where they go afterwards.
Social mobility data is sometimes also referred to as “footfall data” or “foot traffic data”. It can be collected manually, through GPS signals, through connections to WiFi networks, through mobile beacons, and more. Apps often ask users if they want to opt into mobility data sharing before their data can be collected and anonymized as geospatial data.
An important note is that, for privacy reasons, mobility data collection does not mean tracking specific people or their activities. Rather, it only involves anonymously noting how many devices enter the proximity of a point of interest or other area, when they enter, and how long they stay.
Mobility data is an integral part of the geospatial big data ecosystem, often combined with data types like property/boundary (i.e. the physical and jurisdictional dimensions of a place) and POI (i.e. what a place actually is). Together, these data types make it a little easier to see not only where people go, but why they might go there and what they might be doing there.
Other types of big data and mobility data can form powerful combinations as well, for use in both the commercial and civic sectors. We’ll discuss some specific use cases a bit later.
Mobility data management and exploration comes with several advantages. Here are a few of the most significant benefits:
Mobility datasets are most powerful when combined with other datasets (geospatial or otherwise). Here are some examples of what global mobility data can do when it’s used as part of a larger data ecosystem.
Trade area analysis involves figuring out what types of businesses have opportunities in a geographic area, and who their competitors (or businesses that may complement them) might be. This is where adding data on human mobility to POI information on what businesses already have a foothold in an area can come in handy.
For example, if people have to travel a long distance to shop at a certain type of store or access a certain type of service, it could be a signal that there’s room in the local market for a similar service that’s more accessible. Or the flow of human traffic inside or out of an area could point to consumers visiting (or avoiding) certain types of businesses after others. There may be complementary businesses in the area, or competitors that people are passing by because they already have what they need (or perhaps patronizing if they couldn’t find exactly what they wanted at stores they already visited).
Check out our mobility vs. buffer trade areas dashboard to see how much more accurately trade areas can be defined by using mobility data, as opposed to distance-based buffer zones.
As a business owner, you usually want to move your store(s) to areas that have high foot traffic. That way, you get exposure to more potential customers for your business (though not necessarily if there are too many established competitors already in the area, which is why you should do trade area analysis first).
Conversely, if you notice an area where one of your stores is located isn’t getting as much foot traffic as it used to, perhaps it’s time to close down and/or move out of that area. You can make that decision in concert with POI data on that particular store to see if it’s costing more than it’s bringing in.
For a more in-depth look at how to do this, watch our webinar on how to use mobility data for real estate site selection and deselection.
Even if a business can’t set up their store in an area with high foot traffic, they may still be able to draw in a larger customer base by setting up advertisements there. That way, people will know where to go if they can’t find what they need at stores in the high-traffic area.
Of course, the company will also need to look at property data to see what advertising space is available in the area. They may also need to analyze street-level data to see how accessible their store is from where they place their ads, or even explore opportunities for mobile advertising (on buses, taxis, etc.).
For a deeper dive into how to use mobility data this way, check out our guide to better location-based marketing.
Cloud mobility data never reveals any personally-identifiable information, as per privacy regulations. But there are ways to get a general sense of who consumers are based on where they go and what they do. This is where footfall data shines when combined with datasets like POI, demographics, and anonymized purchase records.
To illustrate, a company could look at demographics data for neighborhoods around their stores to get a sense of the average age, gender ratio, income, etc. of the people who live there. They can then compare that with other behavioral data (including anonymized credit or debit card transactions or WiFi network connections) for the area around their stores to discover shopping and other activity patterns that approximate their clientele’s lifestyles.
Then, based on data about the company’s own stores, they can modify their operations to appeal to the demographics and shopping patterns of people in nearby neighborhoods. For example, they could arrange a store so that departments with products nearby consumers are likely to buy are easily accessible. Or they could design their advertising so that products popular with surrounding demographics and lifestyles are front and center.
For another example of this kind of consumer research in action, watch our webinar with Spatial.ai on analyzing mobility patterns in the context of geosocial profiles.
Traditional official financial data is often produced too infrequently to be useful these days, so investment firms are increasingly using alternative data for faster insights. One basic way they can do this is by joining mobility data with accurate property data to perform visit attribution analysis. This involves measuring how many people in a geographic area around a store actually entered the grounds of the store, and how long they stayed, versus how many simply walked past the store.
Understanding mobility based on GPS data alongside POI data, property data, credit/debit transaction data, and other metrics provides a clearer picture of an individual store’s sales performance. And when performed on several stores, visit attribution can give clues regarding a
company’s financial health long before quarterly reports or other official indicators are released.
Our webinar with Goldman Sachs discusses other ways of using alternative data to improve the speed and accuracy of investment decisions.
Insurance companies can use mobility data modeling, management, and understanding alongside POI, property, and even environmental data to create more accurate liability policies for both commercial and residential spaces. Average foot traffic plays a role in how likely a person is to be accidentally injured on a particular property, along with the property’s location (relative to weather patterns and other hazards) and layout.
Also remember that footfall traffic patterns can increase or decrease depending on time and other factors. For example, some places are busy during the day, while others are patronized mostly at night. Other places may see foot traffic spikes on certain days of the week, or during certain seasons. These increases, combined with environmental hazards (darkness, rain, ice/snow, fog, etc.), can make risk profiles for some places different from others.
To visualize how it all works, check out this interactive set of maps we made together with AtomicMaps and Esri that explains how to use mobility data to assess general liability.
Mobility data can be used for the public good as well. Government agencies can combine mobility patterns, big data, and transport analytics to compare where people go throughout a day versus how easily they can get to each place with existing transportation routes and methods. This may tell them that they should plan to locate more essential services (such as hospitals) near places where people typically gather. Or it could be a signal to build out critical infrastructure to make it easier for people in outlying areas to access important facilities.
Check out our blog for a further discussion of how measuring human mobility can inform urban development and transportation planning.
Knowing where people go, and when, can complement information on what they do. And this can lead to insights concerning why people go certain places, as well as do certain things at particular places and times. This can give you a sizable and timely advantage in determining what you should do, whether you’re trying to set up a business, attract more customers, make safer or more lucrative investments, accurately assess risk, or serve your community better.