Detecting significant multidimensional spatial clusters

Daniel B. Neill, Andrew W. Moore, Francisco Pereira, Tom Mitchell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Assume a uniform, multidimensional grid of bivariate data, where each cell of the grid has a count ci and a baseline bi. Our goal is to find spatial regions (d-dimensional rectangles) where the ci are significantly higher than expected given bi. We focus on two applications: detection of clusters of disease cases from epidemiological data (emergency department visits, over-the-counter drug sales), and discovery of regions of increased brain activity corresponding to given cognitive tasks (from fMRI data). Each of these problems can be solved using a spatial scan statistic (Kulldorff, 1997), where we compute the maximum of a likelihood ratio statistic over all spatial regions, and find the significance of this region by randomization. However, computing the scan statistic for all spatial regions is generally computationally infeasible, so we introduce a novel fast spatial scan algorithm, generalizing the 2D scan algorithm of (Neill and Moore, 2004) to arbitrary dimensions. Our new multidimensional multiresolution algorithm allows us to find spatial clusters up to 1400x faster than the naive spatial scan, without any loss of accuracy.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
PublisherNeural information processing systems foundation
ISBN (Print)0262195348, 9780262195348
StatePublished - 2005
Event18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada
Duration: Dec 13 2004Dec 16 2004

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other18th Annual Conference on Neural Information Processing Systems, NIPS 2004
CountryCanada
CityVancouver, BC
Period12/13/0412/16/04

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications
  • Information Systems

Fingerprint Dive into the research topics of 'Detecting significant multidimensional spatial clusters'. Together they form a unique fingerprint.

  • Cite this

    Neill, D. B., Moore, A. W., Pereira, F., & Mitchell, T. (2005). Detecting significant multidimensional spatial clusters. In Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004 (Advances in Neural Information Processing Systems). Neural information processing systems foundation.