Spatial prediction and spatial dependence monitoring on georeferenced data streams

This paper deals with the analysis of data streams recorded by georeferenced sensors. We focus on the problem of measuring the spatial dependence among the observations recorded over time and with the prediction of the data distribution, where no sensor record is available. The proposed strategy con...

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Bibliographic Details
Published in:Statistical methods & applications 2020-03, Vol.29 (1), p.101-128
Main Authors: Balzanella, Antonio, Irpino, Antonio
Format: Article
Language:English
Subjects:
Chemistry and Earth Sciences
Computer Science
Consumer goods
Data transmission
Dependence
Economics
Finance
Geographic information systems
Health Sciences
Histograms
Humanities
Insurance
Kriging interpolation
Law
Management
Mathematics and Statistics
Medicine
Original Paper
Physics
Statistical Theory and Methods
Statistics
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
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Summary:This paper deals with the analysis of data streams recorded by georeferenced sensors. We focus on the problem of measuring the spatial dependence among the observations recorded over time and with the prediction of the data distribution, where no sensor record is available. The proposed strategy consists of two main steps: an online step summarizes the incoming data records by histograms; an offline step performs the measurement of the spatial dependence and the spatial prediction. The main novelties are the introduction of the variogram and the kriging for histogram data. Through these new tools we can monitor the spatial dependence and to perform the prediction starting from histogram data, rather than from sensor records. The effectiveness of the proposal is evaluated on real and simulated data.
ISSN:1618-2510
1613-981X
DOI:10.1007/s10260-019-00462-0