ArticleSpatial statistics2023
A Hypothesis Test for Detecting Distance-Specific Clustering and Dispersion in Areal Data.
Article in Spatial statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Spatial and Temporal Analyses of Melioidosis in Kaohsiung, Taiwan.The American journal of tropical medicine and hygiene · 2025Article
- Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project.Ecology and evolution · 2025Article
- From infectious diseases to chronic diseases: the paradigm shift of spatial epidemiology in disease prevention and control.Frontiers in public health · 2025Review
- A Hypothesis Test for Detecting Spatial Patterns in Categorical Areal Data.Spatial statistics · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Spatial clustering detection has a variety of applications in diverse fields, including identifying infectious disease outbreaks, pinpointing crime hotspots, and identifying clusters of neurons in brain imaging applications. Ripley's K-function is a popular method for detecting clustering (or dispersion) in point process data at specific distances. Ripley's K-function measures the expected number of points within a given distance of any observed point. Clustering can be assessed by comparing the observed value of Ripley's K-function to the expected value under complete spatial randomness. While performing spatial clustering analysis on point process data is common, applications to areal data commonly arise and need to be accurately assessed. Inspired by Ripley's K-function, we develop the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.