### Abstract

Assume a uniform, multidimensional grid of bivariate data, where each cell of the grid has a count c_{i} and a baseline b_{i}. Our goal is to find spatial regions (d-dimensional rectangles) where the c_{i} 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 language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004 |

Publisher | Neural information processing systems foundation |

ISBN (Print) | 0262195348, 9780262195348 |

State | Published - Jan 1 2005 |

Event | 18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada Duration: Dec 13 2004 → Dec 16 2004 |

### Other

Other | 18th Annual Conference on Neural Information Processing Systems, NIPS 2004 |
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Country | Canada |

City | Vancouver, BC |

Period | 12/13/04 → 12/16/04 |

### Fingerprint

### ASJC Scopus subject areas

- Signal Processing
- Computer Networks and Communications
- Information Systems

### Cite this

*Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004*Neural information processing systems foundation.

**Detecting significant multidimensional spatial clusters.** / Neill, Daniel; Moore, Andrew W.; Pereira, Francisco; Mitchell, Tom.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004.*Neural information processing systems foundation, 18th Annual Conference on Neural Information Processing Systems, NIPS 2004, Vancouver, BC, Canada, 12/13/04.

}

TY - GEN

T1 - Detecting significant multidimensional spatial clusters

AU - Neill, Daniel

AU - Moore, Andrew W.

AU - Pereira, Francisco

AU - Mitchell, Tom

PY - 2005/1/1

Y1 - 2005/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84898986661&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84898986661&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84898986661

SN - 0262195348

SN - 9780262195348

BT - Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004

PB - Neural information processing systems foundation

ER -