Advancements in data science are rapidly creating a new field: spatial demography. In partnership with GeoVille and the European Space Agency, we have developed a new technique for predicting and forecasting the age structure of a population living in a given city block. By integrating earth observation data with sophisticated demographic techniques (including Bayesian Model Averaging), we have pioneered a new product called AgeSpot.
AgeSpot computes the number of persons in a designated age bracket likely to live in a particular area. Results can be obtained up to the 50 by 50 meters building block level. AgeSpot’s methodology involves establishing a class of linear regression models. These models are then integrated into a Bayesian Model Averaging approach where we estimate the explanatory power of each linear model. The resulting estimates are then used to create a weighted average of the results of all models. To execute forecasts, we also include an Urban Growth Model which shows which areas are expected to be urban or rural in future years. The main sources of input data are satellite imagery and census information.
Given the broad applicability of Agespot, the spectrum of potential use cases is wide. The group of potential users is expansive and diverse, ranging from commercial enterprises to public sector organizations and non-profit entities.
Following use-cases may be implemented:
Agespot’s products allows customers to project future demand realistically on a city-block level. This enables Telecom companies to deploy their 5G Network infrastructure in the most effective way.
Retail Banking and Insurance companies’ have the possibility of using Agespot’s combined age distributions and education-level datasets to determine the optimal location of branches. Higher educated and older age groups are more lucrative customers for banks and insurance companies while younger age groups can be targeted for mobile banking offerings.
Healthcare Workers and Facilities:
Agespot allows for the deployment of healthcare services based on age-specific needs. This concerns both the private sector, such as pharmacies, and the public sector when planning the future health infrastructure in cities.
Using AgeSpot, we were able to identify the density of seniors in London at a 50x50 square meter level and make projections of where they're expected to move over the next decade. To fight COVID-19 and future epidemics more effectively, cities have to better understand their vulnerabilities and leveraging big data can help.