  In this paper, we investigate the significance of choosing an appropriate
tessellation strategy for a spatio-temporal taxi demand-supply modeling
framework. Our study compares (i) the variable-sized polygon based Voronoi
tessellation, and (ii) the fixed-sized grid based Geohash tessellation, using
taxi demand-supply GPS data for the cities of Bengaluru, India and New York,
USA. Long Short-Term Memory (LSTM) networks are used for modeling and
incorporating information from spatial neighbors into the model. We find that
the LSTM model based on input features extracted from a variable-sized polygon
tessellation yields superior performance over the LSTM model based on
fixed-sized grid tessellation. Our study highlights the need to explore
multiple spatial partitioning techniques for improving the prediction
performance in neural network models.
