Paper Info
Title
Scalable local regression for spatial analytics
Abstract
Local regression models are one of the backbones of spatial analytics. Computational scalability of such models can be resolved with a distributed implementation which requires truly local modelling as communication between components is limited or absent at some stages. The use of such models in a streaming context provide further restrictions. A calibration procedure has to be truly incremental, with constant memory and processing time for any sample in a stream. This paper explores a spatially distributed incremental local regression model satisfying these requirements and providing similar functionality in terms of interpretability and modelling accuracy as the widely-used geographically weighted regression. Our experiments were run on a conventional mid-range 8-core server. In the largest scale experiment we processed a stream of 157 million spatially referenced samples simulating power consumption readings taken every 2 hours in a period of 5 months from about 87000 households in a European country. The software implementation we developed for the evaluation and performance analysis is made available as an open source project.
Year
DOI
Venue
2011
10.1145/2093973.2094023
GIS
Keywords
Field
DocType
software implementation,local modelling,widely-used geographically weighted regression,incremental local regression model,local regression model,8-core server,spatial analytics,million spatially referenced sample,european country,modelling accuracy,scalable local regression,calibration procedure,visualization,applications,regression model,satisfiability
Spatial analysis,Data mining,Interpretability,Geographically Weighted Regression,Visualization,Computer science,Local regression,Artificial intelligence,Machine learning,Calibration,Scalability,Power consumption
Conference
Citations 
PageRank 
References 
3
0.48
12
Authors
2
Name
Order
Citations
PageRank
Alexei Pozdnoukhov121618.87
Christian Kaiser230.48