Where do university graduates live? – A computer vision approach using satellite images

Abstract

AbstractIn this article, we examine to what extent the settlement of university graduates can be derived from satellite images. We apply a convolutional neural network (CNN) to grid images of a city and predict five density classes of university graduates at a micro level (250 m × 250 m grid size). The CNN reaches an accuracy rate of 40.5% (random approach: 20%). Furthermore, the accuracy increases to 78.3% when considering a one-class deviation compared to the true class. We also examine the predictability of inhabited and uninhabited grid cells, where we achieve an accuracy of 95.3% using the same CNN. From this, we conclude that there is information that correlates with graduate density that can be derived by analysing only satellite images. The findings show the high potential of computer vision for urban and regional economics. Particularly in data-poor regions, the approach utilised facilitates comparative analytics and provides a possible solution for the modifiable aerial unit (MAU) problem. The MAU problem is a statistical bias that can influence the results of a spatial data analysis of point-estimate data that is aggregated in districts of different shapes and sizes, distorting the results.