When modeling geo-spatial data, it is critical to capture spatialcorrelations for achieving high accuracy. Spatial Auto-Regression (SAR) is acommon tool used to model such data, where the spatial contiguity matrix (W)encodes the spatial correlations. However, the efficacy of SAR is limited bytwo factors. First, it depends on the choice of contiguity matrix, which istypically not learnt from data, but instead, is assumed to be known apriori.Second, it assumes that the observations can be explained by linear models. Inthis paper, we propose a Convolutional Neural Network (CNN) framework to modelgeo-spatial data (specifi- cally housing prices), to learn the spatialcorrelations automatically. We show that neighborhood information embedded insatellite imagery can be leveraged to achieve the desired spatial smoothing. Anadditional upside of our framework is the relaxation of linear assumption onthe data. Specific challenges we tackle while implementing our frameworkinclude, (i) how much of the neighborhood is relevant while estimating housingprices? (ii) what is the right approach to capture multiple resolutions ofsatellite imagery? and (iii) what other data-sources can help improve theestimation of spatial correlations? We demonstrate a marked improvement of 57%on top of the SAR baseline through the use of features from deep neuralnetworks for the cities of London, Birmingham and Liverpool.
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