scholarly journals Modelling Aggregation on the Large Scale and Regularity on the Small Scale in Spatial Point Pattern Datasets

2015 ◽  
Vol 43 (2) ◽  
pp. 587-609 ◽  
Author(s):  
Frédéric Lavancier ◽  
Jesper Møller
Keyword(s):  
Large Scale ◽  
Small Scale ◽  
Point Pattern ◽  
Spatial Point Pattern ◽  
Spatial Point

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.

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.

scholarly journals Analysis of conditional colocalization relationships and hierarchies from three-color microscopy images

2021 ◽  
Author(s):  
Jesus Vega-Lugo ◽  
Bruno da Rocha-Azevedo ◽  
Aparajita Dasgupta ◽  
Nicolas Touret ◽  
Khuloud Jaqaman
Keyword(s):  
Molecular Interactions ◽  
Cell Biology ◽  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Spatial Point Pattern ◽  
Spatial Point ◽  
Spatial Point Pattern Analysis ◽  
Cellular Context ◽  
Microscopy Images

Colocalization is a cornerstone approach in cell biology for the analysis of multicolor microscopy images. It provides information on the localization of molecules within various subcellular compartments and allows the interrogation of molecular interactions in their spatiotemporal cellular context. However, the overwhelming majority of colocalization analyses are designed for two-color microscopy images, which limits their applicability and the type of information that they may reveal, leading to underutilization of multicolor microscopy images. Here we describe an approach for analyzing the colocalization relationships between three molecular entities, termed 'conditional colocalization analysis,' based on spatial point pattern analysis of detected objects in microscopy images. Going beyond the question of whether colocalization is present or not, it addresses the question of whether the colocalization between two molecular entities is influenced, positively or negatively, by their respective colocalization with a third entity. We showcase two applications of conditional colocalization analysis, one addressing the question of the compartmentalization of molecular interactions, and one investigating the hierarchy of molecular interactions in a multimolecular complex. The software for conditional colocalization analysis is freely accessible online at https://github.com/kjaqaman/conditionalColoc.

scholarly journals Investigate human responses to Late-Holocene changes of fluvial landforms through Spatial Point Pattern Analysis (Po Plain, N Italy)

2020 ◽  
Author(s):  
Filippo Brandolini ◽  
Francesco Carrer
Keyword(s):  
Pattern Analysis ◽  
Flood Hazard ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Spatial Point Pattern ◽  
Po Valley ◽  
Spatial Point ◽  
Spatial Point Pattern Analysis ◽  
Settlement Strategies ◽  
Alluvial Geomorphology

<p>In fluvial environments, alluvial geomorphological features had a huge influence on settlement strategies during the Holocene. However, a few projects investigate this topic through quantitative and question-driven analyses of the human-landscape correlation. The Po Valley (N Italy) – located between the Mediterranean regions and continental Europe – is as a key area for the investigation of environmental and cultural influences on settlement strategies since prehistoric times. For instance, the transition from Roman to Medieval times represented a crucial moment for the reorganisation of human settlement strategies in the Po Valley; the process was mainly driven by climate changes and socio-political factors. Spatial Point Pattern Analysis (SPPA) was here employed to provide a solid statistical assessment of these dynamics in the two historical phases. A point pattern (PP) corresponds to the location of spatial events generated by a stochastic process within a bounded region. The density of the PP is proportional to the intensity of the underlying process. The intensity, in turn, can be constant within the region or spatially variable, thus influencing the uniformity of distribution of spatial events. SPPA provides powerful techniques for the statistical analysis of PP data that consist of a complete set of locations of archaeological sites/findings within an observation window. The use of spatial covariates enables the investigation of environmental and non-environmental factors influencing the spatial homogeneity of the point process. Archaeologists have increasingly analyzed such datasets to quantify the characteristics of observed spatial patterns with the aims of deriving hypotheses on the underlying processes or testing hypotheses derived from archaeological theory. The aim of this paper is to assess whether a shift in water management strategies between the Roman and Medieval periods influenced the spatial distribution of settlements, and to evaluate the relative importance of agricultural suitability over flood risks in each historical phase. In particular, the variability settlement patterns between Roman and Medieval phases has been assessed against two related proxies for alluvial geomorphology and agricultural suitability: flood hazard and soil texture. The SPPA performed shows that Roman and Medieval settlement patterns mirror two different human responses to the geomorphological dynamics of the area. Roman land- and water-management were able to minimize the flood hazard, to drain the floodplain and organize a complex land use on different soil types. In the Medieval period, the alluvial geomorphology of the area, characterised by wide swampy meadows and frequent flood events, affected the spatial organisation of settlement, which privileged topographically prominent positions. Social and cultural dynamics played a crucial role in responding to alluvial geomorphological environmental challenges in different times.</p>

scholarly journals Spatial point pattern analysis of urban retail stores: the case of twelve large- and medium-sized Greek cities

2020 ◽  
Vol 11 (4) ◽  
pp. 36-63
Author(s):  
Michail-Christos TSOUTSOS ◽  
◽  
Yorgos Photis
Keyword(s):  
Spatial Patterns ◽  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Retail Stores ◽  
High Population Density ◽  
Spatial Point Pattern ◽  
Professional Activities ◽  
Spatial Point ◽  
Spatial Point Pattern Analysis

The retailers’ profitability and the consumers’ satisfaction depend on finding the optimal location for a retail store. When considering the stores’ spatial distribution, business potential can be understood and a squandering planning of resources can be avoided. In this paper we identify the spatial patterns of retail stores located in the traditional commercial centers of twelve large -and medium-sized Greek cities, aiming to explain why such patterns exist. The type of retail activities was determined using the image of the ground-floor stores provided by the Google Street View (GSV) service and thus 7322 stores were recorded in a geodatabase as point features. The results reveal that the retail stores’ distribution has a clustered and random spatial pattern at least in one city, where the high population density and the increase in rental prices of premises for professional activities constitute the factors that form these spatial patterns respectively.

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