Population Insights

Leveraging mobile location data to understand visitor footfall and customer behaviour


We analyse anonymised mobile location data and integrate it with other data sources to provide dynamic population maps, floating population statistics, visitor footfall data, and other customisable presence and activity indicators. Nommon Population Insights answers the questions of how many people are located at a particular location or a defined geographical space throughout the day, what their profile is, and why they are there.

Key features

  • Detailed activity characterisation: type of activity (home, work, education, pass-through traffic, etc.), length of stay, frequency of visits, places visited before and after, etc.
  • Segmentation by visitors’ sociodemographic profile: age, gender, income, place of residence, etc.
  • Fully customisable study area, zoning system, study period and temporal resolution
  • Seamless integration with surveys, visitor counts, and other relevant data.
  • Quick access to historical and present information.
  • Robust analytics technology, validated across numerous projects for a variety of industries.

One of the main challenges for Nommon Population Insights was to meet the needs of a variety of clients with very different business models, ecosystems, value chains, cultures and processes. Where many of our competitors have failed in their attempt to develop a one-size-fits-all solution, our technical team has done an amazing job in developing a highly flexible product. Building on our internationally recognised, well-proven mobility analytics technology, Nommon Population Insights is versatile enough to deliver the information that best suits the requirements, business practices and willingness-to-pay of industries as diverse as smart cities and urban planning, environmental health, geomarketing or tourism, among others.

Antonio Pinel
Nommon Chief Business Development Officer


Smart cities and urban planning

Understand how people choose the places in which they live, work, shop and socialise, and the way they use public spaces.

Retail and geomarketing

Find out where your customers come from, where to find new customers, and how to optimise your product distribution and advertising strategies.

Real estate

Identify the areas that offer the most potential for residential, retail, office, leisure and tourism development.


Identify the most visited places in a city/region and understand where visitors come from, the places they choose to spend the night, and the places they go before and after.

Health and environment

Measure population exposure to air pollution, noise, heat and other relevant environmental factors with an unprecedented level of detail.

Key benefits

  • High-quality presence and activity information thanks to the use of large-scale, well-distributed samples of the whole population.
  • Highly flexible product, which can be easily tailored to the needs of each specific application.
  • Information obtained at a fraction of the cost and time required by traditional methods.
  • The availability of historical and continuously updated information enables the study of specific periods (holiday periods, special events, etc.), the before-after analysis of a certain policy or measure (e.g., a marketing campaign), and the monitoring and early detection of new trends.

What we deliver (and how)

  • Data can be accessed on demand through our API-based self-service platform or delivered through bespoke projects tailored to individual client needs.
  • Data can be delivered in different formats (CSV, JSON…) according to client needs.
  • Data is made available under different schemes, from restricted-use to open data licences.
  • Complementary visualisation tools and specialised consulting services to help our clients make the most of our data are available upon request (e.g., support for using the data in the development of location intelligence tools).
Dynamic population maps for the Madrid metropolitan area during the COVID-19 pandemic lockdown. Courtesy of Gustavo Romanillos, tGIS Research Group, Complutense University of Madrid.