Engaging in Point Pattern Analysis

2015 ◽  
pp. 170-201
Keyword(s):  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern

scholarly journals Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Mariem Ben-Said
Keyword(s):  
Spatial Patterns ◽  
Pattern Analysis ◽  
Biotic Interactions ◽  
Plant Ecology ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Summary Statistics ◽  
Spatial Point Pattern ◽  
Spatial Point ◽  
Spatial Point Pattern Analysis

Abstract Background Ecological processes such as seedling establishment, biotic interactions, and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis (SPPA). Being widely used in plant ecology, SPPA is increasingly carried out to describe biotic interactions and interpret pattern-process relationships. However, some aspects are still subjected to a non-negligible debate such as required sample size (in terms of the number of points and plot area), the link between the low number of points and frequently observed random (or independent) patterns, and relating patterns to processes. In this paper, an overview of SPPA is given based on rich and updated literature providing guidance for ecologists (especially beginners) on summary statistics, uni-/bi-/multivariate analysis, unmarked/marked analysis, types of marks, etc. Some ambiguities in SPPA are also discussed. Results SPPA has a long history in plant ecology and is based on a large set of summary statistics aiming to describe species spatial patterns. Several mechanisms known to be responsible for species spatial patterns are actually investigated in different biomes and for different species. Natural processes, plant environmental conditions, and human intervention are interrelated and are key drivers of plant spatial distribution. In spite of being not recommended, small sample sizes are more common in SPPA. In some areas, periodic forest inventories and permanent plots are scarce although they are key tools for spatial data availability and plant dynamic monitoring. Conclusion The spatial position of plants is an interesting source of information that helps to make hypotheses about processes responsible for plant spatial structures. Despite the continuous progress of SPPA, some ambiguities require further clarifications.

Spatiotemporal Point Pattern Analysis Using Ripley's K Function

2017 ◽  
pp. 155-176
Author(s):  
Alexander Hohl ◽  
Minrui Zheng ◽  
Wenwu Tang ◽  
Eric Delmelle ◽  
Irene Casas
Keyword(s):  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Ripley’S K Function ◽  
Ripley’S K ◽  
Ripley's K ◽  
K Function

scholarly journals Analyzing the Spatial Distribution of Drumlins: A Two-Phase Mosaic Approach

1984 ◽  
Vol 30 (106) ◽  
pp. 302-307
Author(s):  
B. N. Boots ◽  
R. K. Burns
Keyword(s):  
Spatial Distribution ◽  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Statistical Bias ◽  
Two Phase ◽  
Alternative Approach ◽  
Mosaic Approach ◽  
The Individual

AbstractResearchers have analyzed various properties of drumlins within individual drumlin fields in order to provide evidence to help in identifying the processes involved in drumlin formation. One property which has been examined is the spatial distribution of drumlins within a field. Traditionally, in such endeavours the individual drumlins have been represented as points and their distribution examined using techniques of point-pattern analysis. We suggest that not only is such a representation inappropriate at this scale, it also introduces statistical bias which makes the results of such analyses questionable. Consequently, we propose an alternative approach which involves representing individual drumlins as areal phenomena and considering their pattern as a two-phase mosaic. The advantages of such an approach are discussed and it is illustrated by applying it to two different drumlin fields.

Un metodo per l'analisi empirica dei fenomeni di concentrazione spaziale delle attivitŕ economiche basato su dati micro-geografici

2012 ◽  
pp. 38-58
Author(s):  
Diego Giuliani ◽  
Giuseppe Espa
Keyword(s):  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Allo Studio

La concentrazione spaziale delle attivitŕ economiche, a causa delle implicazioni che puň avere per la crescita economica locale e le disparitŕ territoriali, č un fenomeno di grande interesse per l'economia e le scienze regionali. Č stato tuttavia riconosciuto che un ostacolo rilevante allo studio di tale fenomeno č rappresentato dalla mancanza di metodi appropriati per la sua misurazione (Combes, Overman, 2004; Combes et al., 2008). Un recente approccio alla misurazione, basato sulle metodologie della point pattern analysis, utilizza dati micro-geografici e considera le imprese come punti privi di dimensione distribuiti nello spazio economico. Rispetto alle misure di concentrazione tradizionali tale approccio non soffre del problema dell'arbitrarietŕ dei confini regionali. Tuttavia, in circostanze pratiche, i punti (imprese) osservati nello spazio economico non possono essere considerati privi di dimensione e sono, al contrario, caratterizzati da dimensioni diverse in termini di numero di occupati, fatturato, capitale ecc. L'approccio basato sulla point pattern analysis trascura l'aspetto della diversa dimensione delle imprese e quindi ignora il fatto che un grado elevato di concentrazione spaziale possa essere dovuto, per esempio, sia a un numero elevato di imprese di piccole dimensioni localizzate in un'area geografica ristretta e sia a poche imprese di grandi dimensioni localizzate vicine. In questo articolo, facendo riferimento alla teoria dei processi di punto marcati (Penttinen, 2006), il problema viene affrontato adattando la funzione K di Ripley in modo tale che tenga conto della dimensione dei punti. Per illustrare il metodo proposto, viene presentata un'applicazione empirica all'analisi della distribuzione spaziale delle imprese manifatturiere ad alta e medio-alta intensitŕ tecnologica nei comuni di Milano e Torino

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