Joint predictive modeling for geospatial data at various locations

Abstract

Predictive modeling uses statistics to predict unknown outcomes. In general, there are two categories of predictive modeling, parametric and non-parametric. There are many applications of predictive modeling, for example, it can be used to predict the risk score of a credit card transaction, it can also be used in health care to identify the probability of having certain disease. When it comes to geospatial data, there are some unique characteristics of the problem. Predictive modeling of geospatial data naturally involves multiple response variables at various locations. The response variables are not independent with each other and thus building separate models for each individual response variable is not appropriate. In addition, many geospatial data has strong spatial auto-correlation such that data from nearby locations are more similar with each other. A joint modeling takes into account of both the correlation among response variables and relationship among different locations, and can make predictions for locations with no training data. In this paper, we review works on joint predictive modeling for multiple response variables at various locations.