SPATIAL DATA UPSAMPLING METHOD, SPATIAL DATA UPSAMPLING APPARATUS AND PROGRAM
A spatial data downscaling method executed by a computer including a memory and a processor, includes acquiring point data where a point in a geographical space and a value at the point are associated with each other and region data in which a region in the geographical space and a value in the region are associated with each other as training data; estimating, with the training data acquired in the acquiring, parameters of a multivariate Gaussian process model represented by a linear mixture of a plurality of latent Gaussian processes; and calculating resolution enhance data in which a region having a finer granularity than the region and a value in the region having the finer granularity are associated with each other from the region data designated by a user with the multivariate Gaussian process model in which the parameters estimated in the estimating have been set.
1 . A spatial data downscaling method executed by a computer including a memory and a processor, the method comprising:
acquiring point data where a point in a geographical space and a value at the point are associated with each other and region data in which a region in the geographical space and a value in the region are associated with each other as training data;
estimating, with the training data acquired in the acquiring, parameters of a multivariate Gaussian process model represented by a linear mixture of a plurality of latent Gaussian processes; and
calculating resolution enhance data in which a region having a finer granularity than the region and a value in the region having the finer granularity are associated with each other from the region data designated by a user with the multivariate Gaussian process model in which the parameters estimated in the estimating have been set.
2 . The spatial data downscaling method according to claim 1 , wherein the estimating includes estimating a spatial scale parameter, a mixing coefficient, a residual variance parameter, and a noise variance parameter as the parameters of the multivariate Gaussian process model.
3 . The spatial data downscaling method according to claim 1 , wherein the estimating comprises representing a value of the region data using a realization value of a Gaussian distribution having an integrated value of a Gaussian process in the region as an average based on the multivariate Gaussian process model, representing a value of the point data using a realization value of a Gaussian distribution having a value of a Gaussian process at the point as an average, and estimating the parameters by maximum likelihood estimation.
4 . The spatial data downscaling method according to claim 1 , wherein the geographical space includes a plurality of geographical spaces representing a plurality of different cities.
5 . A spatial data downscaling apparatus comprising:
a memory; and
a processor configured to execute
acquiring point data where a point in a geographical space and a value at the point are associated with each other and region data in which a region in the geographical space and a value in the region are associated with each other as training data;
estimating, with the training data acquired in the acquiring, parameters of a multivariate Gaussian process model represented by a linear mixture of a plurality of latent Gaussian processes; and
calculating resolution enhance data in which a region having a finer granularity than the region and a value in the region having the finer granularity are associated with each other from the region data designated by a user with the multivariate Gaussian process model in which the parameters estimated in the estimating have been set.
6 . A non-transitory computer-readable recording medium having computer-readable instructions stored thereon, which when executed, cause a computer including a memory and a processor to execute a method comprising:
acquiring point data where a point in a geographical space and a value at the point are associated with each other and region data in which a region in the geographical space and a value in the region are associated with each other as training data;
estimating, with the training data acquired in the acquiring, parameters of a multivariate Gaussian process model represented by a linear mixture of a plurality of latent Gaussian processes; and
calculating resolution enhance data in which a region having a finer granularity than the region and a value in the region having the finer granularity are associated with each other from the region data designated by a user with the multivariate Gaussian process model in which the parameters estimated in the estimating have been set.