Issues in the Current Practices of Spatial Cluster Detection and Exploring Alternative Methods

Dados Bibliográficos

ID15515257
AutoresD W S Wong (0000-0002-0525-0071, George Mason University, autor correspondente)
Ano2021
Volume18
Fascículo18
Páginas9848-9848
Data de publicação2021-09-18
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoInternational Journal of Environmental Research and Public Health (JOURNAL)
Identificadores do periódicoISSN: 1661-7827 • E-ISSN: 1660-4601
EditoraMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph18189848
PMID34574771
OpenAlexW3198927044
IdiomaEN
Citações recebidas3
Referências citadas30

Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value (cold spot) spatial clusters for various purposes. However, these popular tools are based on the concept of spatial autocorrelation or association (SA), but do not explicitly consider if values are high or low enough to deserve attention. Resultant clusters may not include areas with extreme values that practitioners often want to identify when using these tools. Additionally, these tools are based on statistics that assume observed values or estimates are highly accurate with error levels that can be ignored or are spatially uniform. In this article, problems associated with these popular SA-based cluster detection tools were illustrated. Alternative hot spot-cold spot detection methods considering estimate error were explored. The class separability classification method was demonstrated to produce useful results. A heuristic hot spot-cold spot identification method was also proposed. Based on user-determined threshold values, areas with estimates exceeding the thresholds were treated as seeds. These seeds and neighboring areas with estimates that were not statistically different from those in the seeds at a given confidence level constituted the hot spots and cold spots. Results from the heuristic method were intuitively meaningful and practically valuable

Cluster (spacecraft · Cold spot · Data mining · Geography · Heuristic · Hot spot (computer programming · Spatial analysis · Statistic · Statistics · Computer Science · Data-Driven Disease Surveillance · Economic and Environmental Valuation · Mathematics · Spatial and Panel Data Analysis · Artificial Intelligence