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2018 | OriginalPaper | Chapter

Spatial Co-location Pattern Mining

Author : Venkata M. V. Gunturi

Published in: Big Data Analytics

Publisher: Springer International Publishing

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Abstract

Given a spatial dataset containing instances of a set of spatial Boolean feature-types, the problem of spatial co-location pattern mining aims to determine a subset of feature-types which are frequently co-located in space. Spatial Co-location patterns have a wide range of applications in the domains such as ecology, public health and public safety. For instance, in an ecological dataset containing event instances corresponding to different bird species and vegetation types, spatial co-location patterns may revel that a particular species of birds prefer a particular kind of trees for their nests. Similarly, in a crime dataset, spatial co-location may revel a pattern that drunk-driving cases are co-located with bar locations. This article presents a gentle introduction to spatial co-location pattern mining. It introduces a well studied interest measure called participation index for co-location mining and, then discusses an algorithm to determine patterns having high participation index in a spatial dataset.

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previous chapter Malware Detection Using Machine Learning and Deep Learning

Whenever the context is clear, we drop the keyword “Spatial” from“Spatial Co-location” to maintain clarity of text.

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Title: Spatial Co-location Pattern Mining
Author: Venkata M. V. Gunturi
Publisher: Springer International Publishing
Book: Big Data Analytics
Print ISBN: 978-3-030-04779-5

Electronic ISBN: 978-3-030-04780-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-04780-1_29

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