Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities

TL;DR

This paper introduces a hierarchical Gaussian process model that refines coarse spatial data using auxiliary datasets with varying granularities, enabling effective fine-grained spatial data inference.

Contribution

It presents a novel probabilistic model that integrates multiple auxiliary spatial datasets with different granularities for refining coarse spatial data.

Findings

Model effectively utilizes auxiliary data with various granularities.

Accurate inference of fine-grained spatial data from coarse data.

Demonstrated success on real-world spatial datasets.

Abstract

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The fine-grained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of fine-grained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.