Predictive regional trees to supplement geo-physical random fields

Nowadays ubiquitous sensor stations are deployed to measure geophysical fields for several ecological and environmental processes. Although these fields are measured at the specific location of stations, geo-statistical problems demand for inference processes to supplement, smooth and standardize recorded data. We study how predictive regional trees can supplement data sampled periodically in an ubiquitous sensing scenario. Data records that are similar one to each other are clustered according to a rectangular decomposition of the region of analysis; a predictive model is associated to the region covered by each cluster. The cluster model depicts the spatial variation of data over a map, the predictive model supplements any unknown record that is recognized belong to a cluster region. We illustrate an incremental algorithm to yield time-evolving predictive regional trees that account for the fact that the statistical properties of the recorded data may change over time. This algorithm is evaluated with spatio-temporal data collections.

Nowadays ubiquitous sensor stations are deployed to measure geophysical fields for several ecological and environmental processes. Although these fields are measured at the specific location of stations, geo-statistical problems demand for inference processes to supplement, smooth and standardize recorded data. We study how predictive regional trees can supplement data sampled periodically in an ubiquitous sensing scenario. Data records that are similar one to each other are clustered according to a rectangular decomposition of the region of analysis; a predictive model is associated to the region covered by each cluster. The cluster model depicts the spatial variation of data over a map, the predictive model supplements any unknown record that is recognized belong to a cluster region. We illustrate an incremental algorithm to yield time-evolving predictive regional trees that account for the fact that the statistical properties of the recorded data may change over time. This algorithm is evaluated with spatio-temporal data collections. © Springer-Verlag Berlin Heidelberg 2013.