Related Experiment Video
Updated: Mar 6, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
Published on: September 5, 2025
Detecting two-dimensional spatial structure in biological data
P A Jumars1, D Thistle2, M L Jones3
1Department of Oceanography, WB-10, University of Washington, 98195, Seattle, WA, USA.
Abstract:
Cliff and Ord (1973) made versatile methods available for the direct utilization of location data in the analysis of dispersion patterns, but their monograph has as yet seen little use in the ecological literature. Application of their weighted forms of Geary's c and Moran's I indices of spatial autocorrelation to some marine benthos data demonstrates a diversity of population structure not anticipated on the basis of more common measures of pattern. These indices provide objective means to evaluate numerous recent spatial models and hypotheses in geographical ecology and genetics. The procedures are particularly attractive because (1) they efficiently utilize data which are often wasted (i.e., sample coordinates), (2) their application puts few constraints on sampling designs which would otherwise be employed, and (3) they reveal and quantify pattern differences which are not obvious to the untrained eye.
More Related Videos
09:56Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
Published on: April 30, 2019
08:59Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
Published on: October 28, 2018
Related Concept Videos
Two-Dimensional (2D) NMR: Overview
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse....
Two-Dimensional Microscopy in Microbiology
Electron Microscope Tomography and Single-particle Reconstruction
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...