Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities.

• Forecasting urban growth via machine learning models can improve city planning. • We turn pan-European spatial data into tabular data for our predictive model. • We apply our Artificial Neural Network model in five major Greek cities. • We present our training and validation process using goodness-of-fit metrics. • We make an urban growth prediction for each city for a 12-year cycle to the year 2030. Urban development if not planned and managed adequately can be unsustainable. Urban growth models have been a powerful toolkit to help tackling this challenge. In this paper, we use a machine learning approach, to apply an urban growth model to five of the largest cities in Greece. Specifically, we first develop a methodology to collect, organise, handle and transform historical open spatial data, concerning various impact factors, into machine learning data. Such factors involve social, economic, biophysical, neighbouring-related and political driving forces, which must be transformed into tabular data. We also provide an artificial neural network (ANN) model and the methodology to train and evaluate it using goodness-of-fit metrics, which in turn provide the best weights of impact factors. Finally, we execute a prediction for 2030, presenting the results and output maps for each of the five case study cities. As our study is based on pan-European datasets, our model can be used for any area within Europe, using the open-source utility developed to support it. In this sense, our work provides local policy-makers and urban planners with an instrument that could help them analyse various future development scenarios and take the right decisions going forward. [ABSTRACT FROM AUTHOR]