Upper level set scan statistic for detecting arbitrarily shaped hotspots

Patil, G. P.; Taillie, C.

doi:10.1023/b:eest.0000027208.48919.7e

Cited by 247 publications

(151 citation statements)

References 17 publications

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“…One statistical approach that overcomes some of these limitations is the spatial scan statistic (Kulldorff and Nagarwalla 1995;Anderson and Titterington 1997;Kulldorff 1997Kulldorff , 2001Patil and Taillie 2004), described later.…”

Section: What Has Been Done Before?mentioning

confidence: 99%

A Genetic Approach to Detecting Clusters in Point Data Sets

Conley

Gahegan

Macgill

2005

Geographical Analysis

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Spatial analysis techniques are widely used throughout geography. However, as the size of geographic data sets increases exponentially, limitations to the traditional methods of spatial analysis become apparent. To overcome some of these limitations, many algorithms for exploratory spatial analysis have been developed. This article presents both a new cluster detection method based on a genetic algorithm, and Programs for Cluster Detection, a toolkit application containing the new method as well as implementations of three established methods: Openshaw's Geographical Analysis Machine (GAM), case point-centered searching (proposed by Besag and Newell), and randomized GAM (proposed by Fotheringham and Zhan). We compare the effectiveness of cluster detection and the runtime performance of these four methods and Kulldorf's spatial scan statistic on a synthetic point data set simulating incidence of a rare disease among a spatially variable background population. The proposed method has faster average running times than the other methods and significantly reduces overreporting of the underlying clusters, thus reducing the user's postprocessing burden. Therefore, the proposed method improves upon previous methods for automated cluster detection. The results of our method are also compared with those of Map Explorer (MAPEX), a previous attempt to develop a genetic algorithm for cluster detection. The results of these comparisons indicate that our method overcomes many of the problems faced by MAPEX, thus, we believe, establishing that genetic algorithms can indeed offer a viable approach to cluster detection.

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Section: What Has Been Done Before?mentioning

confidence: 99%

A Genetic Approach to Detecting Clusters in Point Data Sets

Conley

Gahegan

Macgill

2005

Geographical Analysis

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“…Choosing a different set of search regions would most likely affect the detection power of our methods, however, we expect that the relative performance of different methods will remain approximately the same. The question of choosing an optimal set of search regions (in order to maintain high detection power over a wide range of outbreak shapes and sizes) is orthogonal to our question of choosing the correct statistical method, and has been investigated in detail by Duczmal and Assuncao (2004), Patil and Taillie (2004), Tango and Takahashi (2005), and many others.…”

Section: Discussionmentioning

confidence: 99%

“…While Kulldorff's original method (Kulldorff 1997) assumed circular search regions, other methods have searched over rectangles (Neill et al 2005a), ellipses (Kulldorff et al 2006), and various sets of irregularly shaped regions (Duczmal and Assuncao 2004;Patil and Taillie 2004;Tango and Takahashi 2005). In past work, we demonstrated that the "fast spatial scan" method can be used to speed up scan statistic calculations by 2-3 orders of magnitude when searching over rectangular regions .…”

Section: Related Workmentioning

confidence: 99%

A multivariate Bayesian scan statistic for early event detection and characterization

Neill

Cooper

2009

Mach Learn

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We present the multivariate Bayesian scan statistic (MBSS), a general framework for event detection and characterization in multivariate spatial time series data. MBSS integrates prior information and observations from multiple data streams in a principled Bayesian framework, computing the posterior probability of each type of event in each space-time region. MBSS learns a multivariate Gamma-Poisson model from historical data, and models the effects of each event type on each stream using expert knowledge or labeled training examples. We evaluate MBSS on various disease surveillance tasks, detecting and characterizing outbreaks injected into three streams of Pennsylvania medication sales data. We demonstrate that MBSS can be used both as a "general" event detector, with high detection power across a variety of event types, and a "specific" detector that incorporates prior knowledge of an event's effects to achieve much higher detection power. MBSS has many other advantages over previous event detection approaches, including faster computation and easy interpretation and visualization of results, and allows faster and more accurate event detection by integrating information from the multiple streams. Most importantly, MBSS can model and differentiate between multiple event types, thus distinguishing between events requiring urgent responses and other, less relevant patterns in the data.

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“…Space-time scan statistics (STSS) is a state-of-the-art cluster detection method (Patil and Taillie, 2004). This statistical method is modified for the purpose of NRC detection, and consists of four steps (Anbaroğlu et al, 2015).…”

Section: Stss Based Nrc Detectionmentioning

confidence: 99%

How Travel Demand Affects Detection of Non-Recurrent Traffic Congestion on Urban Road Networks

Anbaroğlu

Heydecker

Cheng

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London's urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

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Upper level set scan statistic for detecting arbitrarily shaped hotspots

Cited by 247 publications

References 17 publications

A Genetic Approach to Detecting Clusters in Point Data Sets

A Genetic Approach to Detecting Clusters in Point Data Sets

A multivariate Bayesian scan statistic for early event detection and characterization

How Travel Demand Affects Detection of Non-Recurrent Traffic Congestion on Urban Road Networks

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