The interpoint distance distribution as a descriptor of point patterns, with an application to spatial disease clustering

2005 ◽  
Vol 24 (5) ◽  
pp. 753-773 ◽  
Author(s):  
Marco Bonetti ◽  
Marcello Pagano
Keyword(s):  
Distance Distribution ◽  
Interpoint Distance ◽  
Disease Clustering ◽  
Point Patterns

A novel procedure for pattern analysis of features present on freeze-fractured plasma membranes

1985 ◽  
Vol 74 (1) ◽  
pp. 105-117
Author(s):  
S.T. Appleyard ◽  
J.A. Witkowski ◽  
B.D. Ripley ◽  
D.M. Shotton ◽  
V. Dubowitz
Keyword(s):  
Muscle Fibre ◽  
Statistical Methods ◽  
Plasma Membranes ◽  
Pattern Analysis ◽  
Simple Algorithm ◽  
Two Dimensional ◽  
Interpoint Distance ◽  
Distance Analysis ◽  
Point Patterns ◽  
Dimensional Distribution

We have used statistical methods for the analysis of two-dimensional point patterns to derive quantitative descriptions of the distributions of caveolae on freeze-fractured muscle fibre membranes. One method was based on a quadrat analysis while the second was a new procedure that we have called the interpoint distance analysis. We show that the latter analysis can unambiguously distinguish random, clustered and dispersed patterns and that a single parameter can be derived that can be used to compare different distributions. It is readily applicable to patterns containing several hundred points. Practical details of the method are given and a simple algorithm that can be implemented on a microcomputer is provided. The interpoint distance analysis should prove generally useful in situations where the two-dimensional distribution of objects has to be quantified.

Distance Distribution between Complex Network Nodes in Hyperbolic Space

2016 ◽  
Vol 25 (3) ◽  
pp. 223-236 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Miguel A. Andrade-Navarro ◽  
Keyword(s):  
Complex Network ◽  
Hyperbolic Space ◽  
Distance Distribution ◽  
Network Nodes

scholarly journals Does fluvial channel-belt clustering predict net sand to gross rock volume? Architectural metrics and point-pattern analysis of a digital outcrop model

2019 ◽  
Vol 89 (11) ◽  
pp. 1109-1126
Author(s):  
Alexander R. Koch ◽  
Cari L. Johnson ◽  
Lisa Stright
Keyword(s):  
Large Scale ◽  
Small Scale ◽  
Point Pattern ◽  
Point Patterns ◽  
Spatial Point ◽  
Channel Belt ◽  
John Henry Member ◽  
Scale Point ◽  
Belt Width ◽  
Digital Outcrop

ABSTRACT Spatial point-pattern analyses (PPAs) are used to quantify clustering, randomness, and uniformity of the distribution of channel belts in fluvial strata. Point patterns may reflect end-member fluvial architecture, e.g., uniform compensational stacking and avulsion-generated clustering, which may change laterally, especially at greater scales. To investigate spatial and temporal changes in fluvial systems, we performed PPA and architectural analyses on extensive outcrops of the Cretaceous John Henry Member of the Straight Cliffs Formation in southern Utah, USA. Digital outcrop models (DOMs) produced using unmanned aircraft system-based stereophotogrammetry form the basis of detailed interpretations of a 250-m-thick fluvial succession over a total outcrop length of 4.5 km. The outcrops are oriented roughly perpendicular to fluvial transport direction. This transverse cross-sectional exposure of the fluvial system allows a study of the system's variation along depositional strike. We developed a workflow that examines spatial point patterns using the quadrat method, and architectural metrics such as net sand to gross rock volume (NTG), amalgamation index, and channel-belt width and thickness within moving windows. Quadrat cell sizes that are ∼ 50% of the average channel-belt width-to-thickness ratio (16:1 aspect ratio) provide an optimized scale to investigate laterally elongate distributions of fluvial-channel-belt centroids. Large-scale quadrat point patterns were recognized using an array of four quadrat cells, each with 237× greater area than the median channel belt. Large-scale point patterns and NTG correlate negatively, which is a result of using centroid-based PPA on a dataset with disparately sized channel belts. Small-scale quadrat point patterns were recognized using an array of 16 quadrat cells, each with 21× greater area than the median channel belt. Small-scale point patterns and NTG correlate positively, and match previously observed stratigraphic trends in the fluvial John Henry Member, suggesting that these are regional trends. There are deviations from these trends in architectural statistics over small distances (hundreds of meters) which are interpreted to reflect autogenic avulsion processes. Small-scale autogenic processes result in architecture that is difficult to correlate between 1D datasets, for example when characterizing a reservoir using well logs. We show that 1D NTG provides the most accurate prediction for surrounding 2D architecture.

A note on the detection of linear point patterns

1991 ◽  
Vol 55 (1-2) ◽  
pp. 151-152
Author(s):  
Zhang Dayong
Keyword(s):  
Point Patterns
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