spatial point pattern analysis
Recently Published Documents

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.

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.

Geographic Information Systems (GIS) enable the integration, mapping, and analysis of data across numerous domains. It has been estimated that 80 per cent of all data collected by governments and businesses contain geographic references, and the news media are no exception. We will explain how we conceptualize news items as spatial data points and illustrate how GIS can be used to manage and analyze them using a sample of geographic references from local news items published in the Toronto Star newspaper. The analysis makes use of cartographic mapping for visual analysis of local news distribution and geospatial tools for quantitative–statistical analysis of emerging patterns. The objective of this paper is to illustrate how computer-based mapping tools can be used to analyze the geographic distribution of news in order to identify concentrations and gaps in local news coverage within a given area and thus better understand issues and trends in local news reporting. Keywords : geographic distribution, GIS, local news, spatial point pattern analysis

Increasing evidence suggests that infectiousness of hosts carrying parasites of the Leishmania donovani complex, the causative agents of visceral leishmaniasis, is linked to parasite repositories in the host skin. This is particularly true for asymptomatic to moderately symptomatic hosts with no or minimally detectable parasitemia. However, a detailed description of the dispersal and dispersion of parasites and parasitized host phagocytes in the skin is still lacking. Here, we combined image analysis with spatial point pattern models borrowed from ecology, providing a new route to predicting modes of skin parasite dispersal and characterizing their dispersion. Our results suggest that, after initial parasite seeding in the skin, parasites form self-propagating networks of parasite patch clusters in the skin that may contribute to parasite outward transmission. This combination of imaging and ecological pattern analysis to identify mechanisms driving the skin parasite landscape offers new perspectives on parasitism by Leishmania donovani and may also be applicable to elucidating the behavior of other intracellular tissue-resident pathogens.

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.