Mapping local-scale working population and daytime population densities using points-of-interest and nighttime light satellite imageries

Abstract

In this study, we proposed a multi-source approach for mapping local-scale population density of England. Specifically, we mapped both the working and daytime population densities by integrating the multi-source data such as residential population density, point-of-interest density, point-of-interest category mix, and nighttime light intensity. It is demonstrated that combining remote sensing and social sensing data provides a plausible way to map annual working or daytime population densities. In this paper, we trained models with England-wide data and subsequently tested these models with Wales-wide data. In addition, we further tested the models with England-wide data at a higher level of spatial granularity. Particularly, the random forest and convolutional neural network models were adopted to map population density. The estimated results and validation suggest that the three built models have high prediction accuracies at the local authority district level. It is shown that the convolutional neural network models have the greatest prediction accuracies at the local authority district level though they are most time-consuming. The models trained with the data at the local authority district level are less appropriately applicable to test data at a higher level of spatial granularity. The proposed multi-source approach performs well in mapping local-scale population density. It indicates that combining remote sensing and social sensing data is advantageous to mapping socioeconomic variables.

Keywords

• Nighttime light imagery
• point-of-interest
• working population
• daytime population
• convolutional neural network