Time Series Analysis of Urban Liveability
Alex Levering Diego Marcos Devis Tuia
In this paper we explore deep learning models to monitor longitudinal
liveability changes in Dutch cities at the neighbourhood level. Our liveability
reference data is defined by a country-wise yearly survey based on a set of
indicators combined into a liveability score, the Leefbaarometer. We pair this
reference data with yearly-available high-resolution aerial images, which
creates yearly timesteps at which liveability can be monitored. We deploy a
convolutional neural network trained on an aerial image from 2016 and the
Leefbaarometer score to predict liveability at new timesteps 2012 and 2020. The
results in a city used for training (Amsterdam) and one never seen during
training (Eindhoven) show some trends which are difficult to interpret,
especially in light of the differences in image acquisitions at the different
time steps. This demonstrates the complexity of liveability monitoring across
time periods and the necessity for more sophisticated methods compensating for
changes unrelated to liveability dynamics.