Measuring the Spatial Homogeneity in Corneal Endotheliums by Means of a Randomization Test

1999 ◽  
pp. 418-423 ◽  
Author(s):  
M. E. Díaz ◽  
G. Ayala
Keyword(s):  
Randomization Test ◽  
Spatial Homogeneity

scholarly journals Author Correction: Enhancing and quantifying spatial homogeneity in monolayer WS2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yameng Cao ◽  
Sebastian Wood ◽  
Filipe Richheimer ◽  
J. Blakesley ◽  
Robert J. Young ◽  
...  
Keyword(s):  
Spatial Homogeneity

scholarly journals Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity

2013 ◽  
Vol 74 (3) ◽  
pp. 534-544 ◽  
Author(s):  
Sébastien Tisné ◽  
Yann Serrand ◽  
Liên Bach ◽  
Elodie Gilbault ◽  
Rachid Ben Ameur ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatial Homogeneity

Trend to spatial homogeneity for solutions to semilinear damped wave equations

1987 ◽  
Vol 105 (1) ◽  
pp. 117-126 ◽  
Author(s):  
J. Solà-Morales ◽  
M. València
Keyword(s):  
Boundary Conditions ◽  
Wave Equations ◽  
Lipschitz Constant ◽  
Neumann Boundary ◽  
Neumann Boundary Conditions ◽  
Spatial Homogeneity ◽  
Homogeneous Solutions ◽  
Spatially Homogeneous ◽  
Image Position

SynopsisThe semilinear damped wave equationssubject to homogeneous Neumann boundary conditions, admit spatially homogeneous solutions (i.e. u(x, t) = u(t)). In order that every solution tends to a spatially homogeneous one, we look for conditions on the coefficients a and d, and on the Lipschitz constant of f with respect to u.

Synthetic and Comparative Hydrology of Earth, Mars, and Titan

2021 ◽  
Author(s):  
Vojtěch Cuřín ◽  
Johanna Blöcher ◽  
Petr Brož ◽  
Yannis Markonis ◽  
Jan Masner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Precipitation ◽  
Morphological Characteristics ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Carbon Sphere ◽  
Spatial Homogeneity ◽  
Planetary Bodies ◽  
Network Properties ◽  
Visual Properties

<p>Earth, Mars, and Titan are the only known planetary bodies in our solar system where flowing liquids have shaped surface topography and formed extensive river networks. Fed by atmospheric precipitation and carved by fluvial erosion, these channels are observable in remote sensing data. They carry information about the interactions between the atmosphere, the hydro(carbon)sphere, and the lithosphere and allow for investigation of the conditions that had prevailed during their formation. Comparison of drainage basins, which developed in these profoundly different environments, could yield insights into the past and ongoing hydrological processes in addition to climatic, chemical, and topographic conditions of the planetary bodies. Increased computing capacities allow for building and utilization of a vast database of hydrological, climatological, and geological data as well as algorithmic evaluation of remote sensing products. Here, we propose a classification of basins from Earth, Mars, and Titan using several machine learning techniques based on their morphological characteristics, network properties, spatial homogeneity, cross-scale self-similarity, and visual properties. Constraints on climatic and geologic properties of the terrestrial basin classes will be identified, and the results of their morphology-climatic relationship extrapolated to Mars and Titan. To find out more, visit our project’s website https://www.schemata-project.com/.</p>

Statistical Evidence of Discrimination: The Randomization Test

1996 ◽  
pp. 159-162
Author(s):  
Richard L. Scheaffer ◽  
Ann Watkins ◽  
Mrudulla Gnanadesikan ◽  
Jeffrey A. Witmer
Keyword(s):  
Randomization Test ◽  
Statistical Evidence

scholarly journals Fast Identification of Urban Sprawl Based on K-Means Clustering with Population Density and Local Spatial Entropy

2018 ◽  
Vol 10 (8) ◽  
pp. 2683 ◽  
Author(s):  
Lingbo Liu ◽  
Zhenghong Peng ◽  
Hao Wu ◽  
Hongzan Jiao ◽  
Yang Yu ◽  
...  
Keyword(s):  
Population Density ◽  
Urban Sprawl ◽  
Population Data ◽  
Entropy Method ◽  
Mobile Phone Data ◽  
Source Population ◽  
Entropy Model ◽  
Spatial Homogeneity ◽  
Open Population ◽  
Spatial Entropy

As urban sprawl is proven to jeopardize the sustainability system of cities, the identification of urban sprawl is essential for urban studies. Compared with previous related studies which tend to utilize more and more complicated variables to recognize urban sprawl while still retaining an element of uncertainty, this paper instead proposes a simplified model to identify urban sprawl patterns. This is a working theory which is based on a diagram interpretation of the classic urban spatial structure patterns of the Chicago School. The method used in our study is K-means clustering with gridded population density and local spatial entropy. The results and comparison with open population data and mobile phone data verify the assumption and furthermore indicate that the accuracy of source population data will limit the precision of output identification. This article concludes that urban sprawl is mainly dominated by population and surrounding unevenness. Moreover, the Floating Catchment Area (FCA) local spatial entropy method presented in this research brings about an integration of Shannon entropy, Tobler’s first law of geography and the Moore neighborhood, improving the spatial homogeneity and locality of Batty’s Spatial Entropy model which can only be used in a general scope.

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