ArticleScientific reports2023
Spatial autocorrelation equation based on Moran's index.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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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
27 citing papers in PubMed.
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- Hybridizing deep learning algorithms and geostatistical approaches for improved crop yield disaggregation.PloS one · 2026Article
- Analysis of cross-provincial healthcare utilization among hospitalized patients in a tertiary Chinese medicine hospital.Frontiers in public health · 2026Article
- Spatiotemporal patterns and incidence trends of other infectious diarrhea in Qinghai Province, 2009-2023: a surveillance-based analysis.Frontiers in public health · 2026Article
- Unraveling the spatiotemporal clustering of malaria incidence and modeling a decade of epidemiological data in Jimma Zone, Oromia, Ethiopia.Scientific reports · 2025Article
- BioLACE: unifying spatial geometry and marker priors for cohesive cell-type clustering in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- Epidemiology, comorbidities, treatments and outcomes of autoimmune liver diseases: A French nationwide study.JHEP reports : innovation in hepatology · 2025Article
- Clonal dynamics shaped by diverse drug-tolerant persister states in melanoma resistance.bioRxiv : the preprint server for biology · 2025Article
- Spatiotemporal dynamics and driving factors of human resources for health in traditional Chinese medicine in China.Scientific reports · 2025Article
- Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project.Ecology and evolution · 2025Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Moran's index is an important spatial statistical measure used to determine the presence or absence of spatial autocorrelation, thereby determining the selection orientation of spatial statistical methods. However, Moran's index is chiefly a statistical measurement rather than a mathematical model. This paper is devoted to establishing spatial autocorrelation models by means of linear regression analysis. Using standardized vector as independent variable, and spatial weighted vector as dependent variable, we can obtain a set of normalized linear autocorrelation equations based on quadratic form and vector inner product. The inherent structure of the models' parameters are revealed by mathematical derivation. The slope of the equation gives Moran's index, while the intercept indicates the average value of standardized spatial weight variable. The square of the intercept is negatively correlated with the square of Moran's index, but omitting the intercept does not affect the estimation of the slope value. The datasets of a real urban system are taken as an example to verify the reasoning results. A conclusion can be reached that the inner product equation of spatial autocorrelation based on Moran's index is effective. The models extend the function of spatial analysis, and help to understand the boundary values of Moran's index.
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Registered trials
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