Space‐time cluster identification in point processes

DOI10.1002/cjs.5550350105zbMath1124.62065OpenAlexW2171018733MaRDI QIDQ5421208

Renato M. Assunção, Martin Kulldorff, Andréa I. Tavares, Thais R. Correa

Publication date: 22 October 2007

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.5550350105

zbMATH Keywords

Poisson processpoint patterncluster detectionKnox testspatial statistics.

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to social sciences (62P25) Non-Markovian processes: hypothesis testing (62M07) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (4)

\textit{MAD-STEC}: a method for multiple automatic detection of space-time emerging clusters ⋮ An up-to-date review of scan statistics ⋮ Surveillance to detect emerging space-time clusters ⋮ Power of the Sequential Monte Carlo Test

Uses Software

• CrimeStat

Cites Work

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• A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters
• Geographic and network surveillance via scan statistics for critical area detection
• Modified Randomization Tests for Nonparametric Hypotheses
• A Hierarchical Model for Space–Time Surveillance Data on Meningococcal Disease Incidence
• A spatial scan statistic
• Hierarchical Spatio-Temporal Mapping of Disease Rates
• The Knox Method and Other Tests for Space‐Time Interaction
• Prospective Time Periodic Geographical Disease Surveillance Using a Scan Statistic
• Monitoring Point Patterns for the Development of Space–Time Clusters
• The spectral analysis of two-dimensional point processes

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