Population estimation beyond counts—Inferring demographic characteristics

Abstract

Mapping population distribution at a fine spatial scale is essential for urban studies and planning. Numerous studies, mainly supported by geospatial and statistical methods, have focused primarily on predicting population counts. However, estimating their socio-economic characteristics beyond population counts, such as average age, income, and gender ratio, remains unattended. We enhance traditional population estimation by predicting not only the number of residents in an area, but also their demographic characteristics: average age and the proportion of seniors. By implementing and comparing different machine learning techniques (Random Forest, Support Vector Machines, and Linear Regression) in administrative areas in Singapore, we investigate the use of point of interest (POI) and real estate data for this purpose. The developed regression model predicts the average age of residents in a neighbourhood with a mean error of about 1.5 years (the range of average resident age across Singaporean districts spans approx. 14 years). The results reveal that age patterns of residents can be predicted using real estate information rather than with amenities, which is in contrast to estimating population counts. Another contribution of our work in population estimation is the use of previously unexploited POI and real estate datasets for it, such as property transactions, year of construction, and flat types (number of rooms). Advancing the domain of population estimation, this study reveals the prospects of a small set of detailed and strong predictors that might have the potential of estimating other demographic characteristics such as income.

Mapping refugee populations at high-resolution by unlocking humanitarian administrative data

“…Dasymetric mapping that is a non-uniform spatial cartography has a long history as a technique for re ning the spatial representation of population by excluding unpopulated areas from the map. It has been however so far restricted to total population count, sometimes broken down by age and sex (7,8,31), or in data-rich context, to map transient population (32) or race (33). To our knowledge however, no population dasymetric mapping research has focused on the refugee's subgroup in particular and none has been using register data.…”

Mining real estate ads and property transactions for building and amenity data acquisition

“…Information on nearby amenities (POIs) and the surroundings are also important in this context, as they are often associated with buildings, e.g. as indicators of housing value, demographics, and accessibility (Feng & Humphreys 2012;Kang et al 2021;Yang et al 2021;Mirkatouli et al 2018;Szarka & Biljecki 2022;Su et al 2021).…”