Spatial Point Pattern Analysis Research Articles

Cluster analysis of spatial point patterns: posterior distribution of parents inferred from offspring

This manuscript demonstrates an empirical Bayesian estimation of cluster centers (parents) in spatial point pattern. We use the Thomas models on a plane, namely, Neyman–Scott process of a two-dimensional Gaussian-type or its extension to the multi-type species. This manuscript firstly confirms restoration method; namely, the numbers of parents and their locations are estimated from simulated datasets of Thomas process or its multi-type process; and the Metropolis’ simulation of parent locations are well performed from their posterior distributions. Then, we further apply the models to three real datasets of plant ecology, volcano swarms, and inland shallow micro-earthquakes. However, in the latter two cases, a preliminary analysis by the pairwise correlation suggests that the classical second-order moment relationship between the ordinary Thomas process and the Palm intensity cannot be applicable. Therefore, alternative method is implemented by applying a Bayesian selection of the optimal number of parents; which is searched by maximizing the marginal likelihood (integrated posterior) by numerically performing the high-dimensional integration with respective to the location coordinates of the parents. Then, the optimal posterior distribution of the selected model provides the image of likely locations of parents.

A name‐led approach to profile urban places based on geotagged Twitter data

AbstractPlace is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowdsourced social media data present a valuable opportunity to develop bottom‐up inferences of places that are integral to social activities and settings. Conventional location‐led approaches use a predefined spatial unit to associate data and space with places, which cannot capture the richness of urban places (i.e., spatial extents and their dynamic functions). This article develops a name‐led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden Borough, London and the results are evaluated through comparisons to the Foursquare point of interest data. This name‐led approach enables the shift from space‐based analysis to place‐based analysis of urban space.

Addressing the data guardian and geospatial scientist collaborator dilemma: how to share health records for spatial analysis while maintaining patient confidentiality

BackgroundThe utility of being able to spatially analyze health care data in near-real time is a growing need. However, this potential is often limited by the level of in-house geospatial expertise. One solution is to form collaborative partnerships between the health and geoscience sectors. A challenge in achieving this is how to share data outside of a host institution’s protection protocols without violating patient confidentiality, and while still maintaining locational geographic integrity. Geomasking techniques have been previously championed as a solution, though these still largely remain an unavailable option to institutions with limited geospatial expertise. This paper elaborates on the design, implementation, and testing of a new geomasking tool Privy, which is designed to be a simple yet efficient mechanism for health practitioners to share health data with geospatial scientists while maintaining an acceptable level of confidentiality. The basic premise of Privy is to move the important coordinates to a different geography, perform the analysis, and then return the resulting hotspot outputs to the original landscape.ResultsWe show that by transporting coordinates through a combination of random translations and rotations, Privy is able to preserve location connectivity among spatial point data. Our experiments with typical analytical scenarios including spatial point pattern analysis and density analysis shows that, along with protecting spatial privacy, Privy maintains the spatial integrity of data which reduces information loss created due to data augmentation.ConclusionThe results from this study suggests that along with developing new mathematical techniques to augment geospatial health data for preserving confidentiality, simple yet efficient software solutions can be developed to enable collaborative research among custodians of medical and health data records and GIS experts. We have achieved this by developing Privy, a tool which is already being used in real-world situations to address the spatial confidentiality dilemma.

Scale-dependent intraspecific competition of Taurus cedar (Cedrus libani A. Rich.) saplings in the Southern Turkey.

Better understanding of the competitive interaction at the early development stages of the stand is crucial to help schedule silvicultural treatments for young stands and for the better management of the future stands. We used scale‐dependent analysis to improve our understanding of sapling dynamics in the pure Taurus cedar (Cedrus libani A. Rich.) stands in Southern Turkey. Using data from nine plots established at the western Taurus Mountains, diameter, height, and crown radii of saplings were compared, and spatial point pattern analyses were performed. We found significant differences for the mean diameter and height, and crown radii of saplings among the plots. Univariate pair correlation function showed that sapling pattern was regular only at small scales (r < 0.4 m) but was predominantly random. Bivariate pair correlation function revealed no evidence of spatial interaction between tall saplings and short saplings. Univariate mark correlation function revealed that strong intraspecific competition was detected at small scales (up to 0.55 m). This distance is reasonable for the juvenile age tending of Taurus cedar saplings and should be under consideration during silvicultural treatments to use the site productivity more efficiently.

Disturbance after Disturbance: Combined Effects of Two Successive Hurricanes on Forest Community Structure

Our attempts to gain knowledge from studying the effects of a single windstorm might be complicated by one or more other events that pass through the same system before it recovers from prior damage. In this article, we had the opportunity to examine permanent plots affected by two consecutive catastrophic storms of comparable intensity within a short time frame (less than a decade). We compared tree damage patterns resulting from Hurricane Rita in 2005 and Hurricane Ike in 2008, which struck the same five forest plots (40 m × 50 m each; 1 ha total) established in the Big Thicket National Preserve of southeast Texas. The results showed that the post-Rita forest structure was characterized by intense damage to short, shade-tolerant stems and increased canopy openness. Hence, trees damaged by Ike were, in general, taller, more shade intolerant, and more spatially distant from their undamaged counterparts than trees damaged by Rita. These contrasting damage patterns indicate that Ike affected the plots differently to a normal windstorm occurring in isolation (after a long absence of prior windstorms). We anticipate that the cumulative, compounded effects of these two storms will potentially have long-lasting footprints on the structure and function of the study forest. The need to account for compounding disturbance interactions in forest research and management will grow, because many atmospheric scientists predict increases in both the intensity and frequency of hurricanes in conjunction with future climate change scenarios. Key Words: Big Thicket, disturbance interaction, forest structure, hurricane, spatial point pattern analysis.

Measuring differential access to facilities between population groups using spatial Lorenz curves and related indices

AbstractAccess to certain types of facilities can promote health and well‐being. When population and facilities are not uniformly distributed across the landscape, inequities in accessibility may occur. Current research into GIS‐based accessibility measures has focused primarily on spatial inequities between different geographic locations but not directly on differences in accessibility between subgroups of the population. The research presented here develops a new method for measuring differential accessibility to facilities between various segments of the population. The method extends concepts and techniques in spatial point pattern analysis that account for the spatial structure of demand and its relationship to supply. In this approach, the traditional Lorenz curve and its associated indices, the Gini coefficient and the dissimilarity index, which are used to measure inequality, are recast in spatial terms for measuring differences in accessibility between population subgroups. An analysis of spatial accessibility to grocery stores in Akron, OH illustrates the value of the spatial Lorenz curve and its associated indices compared to other methods.

Characterization of Territorial Spatial Agglomeration Based on POI Data: A Case Study of Ningbo City, China

Territorial space is a valuable resource, and it is the home on which human beings depend for their survival and development. However, due to disorder, excessive and decentralized development, resource constraints are tightening, ecological environment deterioration, regional development imbalance and other prominent problems. Agglomeration development has become the strategic choice of territorial space development in China. Therefore, understanding the agglomeration characteristics of territorial space is helpful for local governments to carry out territorial spatial planning. Based on POI (point of interest) data, the method of spatial point pattern analysis is used to characterize the territorial spatial agglomeration in Ningbo city. We found that the elliptic center of Ningbo’s territorial space is distributed in Haisu district. The whole trend is along the “northwest to southeast” direction, and there is a significant directionality. The entire territorial space presents spatial agglomeration, and the agglomeration characteristics of the commercial space are more significant. Moreover, there is a spatial scale effect on the agglomeration of territorial space, which changes with the geographical distance attenuation and presents an inverted “U” structure. With Sanjiangkou as the core, the agglomeration hotspots of all levels distribute in a gradient manner, and the spatial differentiation of “center-periphery” is obvious. The characteristics of its spatial distribution are “river-oriented” and “sea-oriented”. In conclusion, the locking effect of a single center is still significantly higher than the evacuation “magnetic force” of multiple centers in Ningbo city. In the future, we should effectively disperse the densely clustered areas in the central urban area, and focus on cultivating peripheral sub-centers/groups to enhance the positive interaction between multiple centers.

Spatial patterns of discovery points and invasion hotspots of non‐native forest pests

AbstractAimEstablishments of non‐native forest pests (insects and pathogens) continue to increase worldwide with growing numbers of introductions and changes in invasion pathways. Quantifying spatio‐temporal patterns in establishment locations and subsequent invasion dynamics can provide insight into the underlying mechanisms driving invasions and assist biosecurity agencies with prioritizing areas for proactive surveillance and management.LocationUnited States of America.Time period1794–2018.Major taxa studiedInsecta, plant pathogens.MethodsUsing locations of first discovery and county‐level occurrence data for 101 non‐native pests across the contiguous USA, we (a) quantified spatial patterns in discovery points and county‐level species richness with spatial point process models and spatial hotspot analyses, respectively, and (b) identified potential proxies for propagule pressure (e.g., human population density) associated with these observed patterns.ResultsDiscovery points were highly aggregated in space and located in areas with high densities of ports and roads. Although concentrated in the north‐eastern USA, discovery points also occurred farther west and became less aggregated as time progressed. Invasion hotspots were more common in the north‐east. Geographic patterns of discovery points and hotspots varied substantially among pest origins (i.e., global region of pests’ native ranges) and pest feeding guilds. Significant variation in invasion richness was attributed to the patterns of first discovery locations. Data and shapefiles comprising analyses are provided.Main conclusionsUse of spatial point pattern analyses provided a quantitative characterization of the central role of human activities in establishment of non‐native pests. Moreover, the decreased aggregation of discovery points through time suggests that invasion pathways to certain areas in the USA have either been created or intensified by human activities. Overall, our results suggest that spatio‐temporal variability in the intensity of invasion pathways has resulted in marked geographic patterns of establishment and contributed to current macroscale patterns of pest invasion in the USA.

The shape is more important than we ever thought: Plant to plant interactions in a high mountain community

Abstract Plant to plant interactions are probably the most important driver of species coexistence at fine spatial scales, but their detection represents a challenge in Ecology. Spatial point pattern analysis (SPPA) is likely the approach most used to identify them, however, it suffers from some limitations related to the over‐simplification of individuals to points. Here, we propose a new approach called Overlapping Area Analysis (OAA) to test whether the consideration of the shape and orientation of the individuals reveal signs of interactions between species that would remain undetected with SPPA. We used this approach to analyse a fully mapped cryophilic grassland in the Sierra de Guadarrama National Park (Spain), where the crown of each individual plant (i.e. the species canopy) was approximated by a polygon. We then computed and compared the total overlapping area between the canopy of a focal species and that of any other species in the community with the expectations of a null model of random rotation of each plant around its centroid. We complemented the results of our new approach by comparing with that of SPPA of plants’ centroids. Results of OAA showed that up to 41% of species pairs had less canopy overlap than expected, suggesting that many interspecific canopy associations in this plant community were significantly negative at the finest spatial scale. Contrarily, SPPA estimated that 12% of species pairs were positively associated at spatial scales up to 20 cm, confirming the facilitative effect displayed by the main engineer in the community (Festuca curvifolia Lag.) and by some other dominant species. Our new approach quantifying canopy associations provides new insights into the processes guiding community assembly. While the results of SPPA suggested the prevalence of traditional ‘stress gradient hypothesis’ (i.e. prevalence of positive interactions under stressful abiotic conditions), OAA revealed that many interspecific canopy associations were significantly negative. Overall, most facilitated species optimized this positive effect by placing their centroids as close to the benefactor species as their foraging behaviour allowed while avoiding crown overlap. The method proposed is available in a dedicated r‐package that will facilitate its application by other ecologists.

Temporal Point Pattern Analysis of Human Activities Using GIS Methods: A Case Study of Library Visiting Activities in Chengdu City, China

This article aims to develop a new type of temporal pattern analysis—temporal point pattern analysis (TPPA)—by treating the distribution of activities as a point pattern on a two-dimensional plane using the start time and end time of activities as axes. Geographic information systems (GIS) methods, which are originally used in spatial point pattern analysis in GIS, are introduced to support TPPA. This article presents a case study to understand the temporal patterns of the library visiting activities of university students using a four-week smart card data set in Chengdu City, China. Several methods from GIS are applied, including the measurement of mean centers, kernel density, nearest neighbor distances, and optimized hot spot analysis. Results show that the GIS methods can reveal a lot of information on the temporal pattern of activities, thereby proving the reasonability of the proposed TPPA of activities. Key Words: GIS, human activities, library visiting, smart card data, visualization.

Detecting plant spatial pattern change after disperser loss: A simulation and a case study

AbstractDisruption of seed dispersal processes may affect plant population spatial structure. We used a spatial simulation model and an empirical case study to assess the conditions under which the loss of seed dispersers has a detectable effect on a species' spatial pattern. Our simulation experiments suggested that detecting spatial change following disperser loss will be difficult, except when rates of fruit removal are initially high and then completely disappear. To contextualize the simulation modeling, we used spatial point pattern analyses to characterize the spatial pattern of two large‐seeded species (Leucopogon nutans, a fire‐killed seeder shrub and Macrozamia riedlei, a long‐lived, resprouting cycad) in the jarrah (Eucalyptus marginata) forests of southwestern Australia. The plant species' primary disperser, the emu (Dromaius novaehollandiae), was absent from one of the sites we considered, but present at the other two. There was no detectable difference for either plant species in the strength of aggregation between sites with and without emu. However, even if disperser loss may not greatly affect local spatial structure for most plant species, it is likely to be important for long distance dispersal and genetic structuring of populations, so accurate characterization of the dispersal kernel is critical, especially in terms of plant emigration.

Locality-adapted kernel densities of term co-occurrences for location prediction of tweets

While geographical metadata referring to the originating locations of tweets provides valuable information to perform effective spatial analysis in social networks, scarcity of such geotagged tweets imposes limitations on their usability. In this work, we propose a content-based location prediction method for tweets by analyzing the geographical distribution of tweet texts using Kernel Density Estimation (KDE). The primary novelty of our work is to determine different settings of kernel functions for every term in tweets based on the location indicativeness of these terms. Our proposed method, which we call locality-adapted KDE, uses information-theoretic metrics and does not require any parameter tuning for these settings. As a further enhancement on the term-level distribution model, we describe an analysis of spatial point patterns in tweet texts in order to identify bigrams that exhibit significant deviation from the underlying unigram patterns. We present an expansion of feature space using the selected bigrams and show that it eventually yields further improvement in prediction accuracy of our locality-adapted KDE. We demonstrate that our expansion results in a limited increase in the size of feature space and it does not hinder online localization of tweets. The methods we propose rely purely on statistical approaches without requiring any language-specific setting. Experiments conducted on three tweet sets from different countries show that our proposed solution outperforms existing state-of-the-art techniques, yielding significantly more accurate predictions.

Utility of Spatial Point-Pattern Analysis Using Residential and Workplace Geospatial Information to Localize Potential Outbreak Sources.

Identifying the source of an outbreak facilitates its control. Spatial methods are not optimally used in outbreak investigation, due to a mix of the complexities involved (e.g., methods requiring additional parameter selection), imperfect performance, and lack of confidence in existing options. We simulated 30 mock outbreaks and compared 5 simple methods that do not require parameter selection but could select between mock cases’ residential and workplace addresses to localize the source. Each category of site had a unique spatial distribution; residential and workplace address were visually and statistically clustered around the residential neighborhood and city center sites respectively, suggesting that the value of workplace addresses is tied to the location where an outbreak might originate. A modification to centrographic statistics that we propose—the center of minimum geometric distance with address selection—was able to localize the mock outbreak source to within a 500 m radius in almost all instances when using workplace in combination with residential addresses. In the sensitivity analysis, when given sufficient workplace data, the method performed well in various scenarios with only 10 cases. It was also successful when applied to past outbreaks, except for a multisite outbreak from a common food supplier.

Models of the fine-scale spatial distributions of trees in managed and unmanaged forest patches with Abies alba Mill. and Fagus sylvatica L.

Fine-scale spatial point-pattern analysis can give insights into the underlying pattern-creating biological processes in an environmental context. However, there are few studies comparing the spatial distribution of trees in managed stands and unmanaged forests that take into account their developmental stage. The main aim of this study was to assess and model the spatial patterns of Abies alba Mill. and Fagus sylvatica L. trees in managed and unmanaged forest patches in the early developmental stage.This study investigated tree communities dominated by A. alba and F. sylvatica in the Świętokrzyskie Mountains in Central Europe. From 2016 to 2018, 60 sample plots representing the growing-up developmental stage were randomly selected: 30 from managed stands and 30 from unmanaged forests. Models of the fine-scale spatial distributions of the trees were distinguished using hierarchical cluster analysis (HCA), analysis of group similarities (ANOSIM), and multiple response permutation procedures (MRPP). Indices based on the K-function and the O-ring statistic, ILr and IOr, respectively, were developed and used as clustering variables. The proposed indices assume values from −1 to 1 for random patterns; values > 1 indicate aggregation, and values < −1 indicate regularity.Models of the fine-scale spatial distributions of the trees differed only slightly between the managed and unmanaged patches. These models suggest that in the investigated sample plots, the fine-scale spatial patterns (with distances among the trees not exceeding 10 m) were usually random. Only in a few patches were the tree distributions found to be aggregated, from 5 to 10 m and from 7 to 10 m in the managed stands and the unmanaged forests, respectively. The relatively similar models of the fine-scale spatial distributions of the trees in the managed and unmanaged forest patches in the growing-up developmental stage show that early successional forest dynamics follow a parallel trajectory of recovery. The long-term application of the shelterwood system as well as self-regeneration and self-regulation natural processes led to the creation of the similar spatial structures of the trees. This observation may be helpful in the design of planting patterns and/or harvesting schedules.

Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors.

Quantitative characterization of the tumor microenvironment, including its immuno-architecture, is important for developing quantitative diagnostic and predictive biomarkers, matching patients to the most appropriate treatments for precision medicine, and for providing quantitative data for building systems biology computational models able to predict tumor dynamics in the context of immune checkpoint blockade therapies. The intra- and inter-tumoral spatial heterogeneities are potentially key to the understanding of the dose-response relationships, but they also bring challenges to properly parameterizing and validating such models. In this study, we developed a workflow to detect CD8+ T cells from whole slide imaging data, and quantify the spatial heterogeneity using multiple metrics by applying spatial point pattern analysis and morphometric analysis. The results indicate a higher intra-tumoral heterogeneity compared with the heterogeneity across patients. By comparing the baseline metrics with PD-1 blockade treatment outcome, our results indicate that the number of high-density T cell clusters of both circular and elongated shapes are higher in patients who responded to the treatment. This methodology can be applied to quantitatively characterize the tumor microenvironment, including immuno-architecture, and its heterogeneity for different cancer types.

小兴安岭天然针阔混交林主要树种空间格局及其关联性

PDF HTML阅读 XML下载 导出引用 引用提醒 小兴安岭天然针阔混交林主要树种空间格局及其关联性 DOI: 10.5846/stxb201808101708 作者: 作者单位: 东北林业大学林学院,东北林业大学,东北林业大学林学院,东北林业大学,东北林业大学,东北林业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（31670627，31270666） Spatial point patterns and associations of populations in the coniferous and deciduous broadleaved mixed forest in Xiaoxing'an Mountain Author: Affiliation: College of Forestry，Northeast Forestry University,Northeast Forestry University,,,, Fund Project: The National Natural Science Fundation of China(31670627,31270666) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在小兴安岭天然针阔混交林1.1 hm2的固定样地内，本文调查和统计了其物种组成和林分径级结构。运用空间点格局分析方法中的O-ring函数，对种群的空间分布格局及其关联性进行分析。结果表明：样地内共有乔木树种15种，重要值排在前四位的依次为红皮云杉（Picea koraiensis）、枫桦（Betula costata）、春榆（Ulmus davidiana）和色木槭（Acer mono），红皮云杉和枫桦属于样地内的优势种，重要值分别为15.81%和11.10%。林下更新状态良好，枫桦的径级结构有呈正态分布的趋势，其余三种均呈现倒"J"形。O-ring函数结果证实，四个主要树种的种群空间分布格局相似，均在0-5 m小尺度上呈聚集分布，随着尺度的增大，逐渐变为随机分布；红皮云杉与其他三个主要种群之间在较大尺度上均呈现显著正相关或无相关，在小尺度上仅与枫桦呈极显著正相关；色木槭在较小尺度上与其他三个种群呈弱负相关性，随着尺度的增大，逐渐变为无相关性。 Abstract:Species composition and populations' diameter class structure were investigated in a 1.1 hm2 permanent plot in the coniferous and deciduous broadleaved mixed forest in Xiaoxing'an Mountain. Using O-ring functions in the spatial point pattern analysis. the populations' spatial distribution patterns and spatial association are analyzed. The results showed that there were 15 arbor species in the plot. Picea koraiensis, Betula costata, Ulmus davidiana, and Acer mono ranked as the top 4 important species in our plot. Picea koraiensis and B. costata were the major dominant species with importance values of 15.81% and 11.10%, respectively. This forest was also founf to be in good regeneration condition. The size class structure of B. costata tended to be normally distributed, while that of the other three populations showed an inverted "J" shape. The results from the O-ring function showed that the spatial distribution pattern of the four main populations was similar, with distributions was aggregated at the 0-5 m scale. At increasing scales, distributions shifted from aggregated to random. Picea koraiensis had a significant positive or no correlation with the other three main populations at coarse scales, but only had a significant positive correlation with B. costata at fine scales. Acer mono had a non-significant negative correlation with the other three populations at fine scales. As the scale increased, no correlation was found. 参考文献 相似文献 引证文献

Spatial point pattern analysis of the local current distribution on the surface of multi-tip field emitters

A further development of the method for obtaining local characteristics of multi-tip field emitter using spatial point pattern analysis was presented. The field emission characteristics of nanocomposite emitter single-walled carbon nanotubes / polystyrene were calculated. Two sets of emission sites with different current load distribution were discovered. The application of the of spatial point pattern analysis allow to determine the spatial distribution of emission sites over the cathode surface. The results of the analysis confirm the cluster character of the emitting sites distribution on the cathode surface.

Mark-mark scatterplots improve pattern analysis in spatial plant ecology

Point process statistics provides valuable tools for many ecological studies, where ‘points’ are commonly determined to represent the locations of plants or animals and ‘marks’ are additional items such as species or size. In the statistical analysis of marked point patterns, various correlation functions are used such as the mark variogram or the mark correlation function. Often the interpretation of these functions is not easy and the non-spatial ecologist is in need of support. In order to make the analysis of spatial point patterns more accessible to ecologists, we introduced and tested a new graphical method, the mark-mark scatterplot. This plot visualises the marks of point pairs of inter-point distances r smaller than some small distance rmax. We tested the application of the mark-mark scatterplot by reconsidering three quite different tree patterns: a pattern of longleaf pine trees from the southern US which was strongly influenced by fires, a tropical tree pattern of the species Shorea congestiflora from Sri Lanka and a Scots pine pattern from Siberia (Russia). The new method yielded previously undetected cause-effect information on mark behaviour at short inter-point distances and thus improved the analysis with mark correlation functions as well as complemented the information they provided. We discovered important new correlations in clusters of trees at close proximity. The application of the mark-mark scatterplot will facilitate the interpretation of point process summary statistics and will make point process analysis more accessible to ecologists not specialized in point process statistics.

Co-occurrence patterns of ice-rafted dropstones and brachiopods in the Middle Permian Wandrawandian Siltstone of the southern Sydney Basin (southeastern Australia) and palaeoecological implications

The ecological significance of ice-rafted dropstones in present-day glacial marine benthic environments has received considerable attention from ecologists, but similar studies based on the geological records of dropstones and associated fossils are rare. In this study, we report statistically significant co-occurrences of ice-rafted dropstones with brachiopod shells in multiple stratigraphic horizons at multiple sites from the Middle Permian Wandrawandian Siltstone of the southern Sydney Basin in southeastern Australia. We analysed the distribution patterns of both dropstones and brachiopod-dominated fossil assemblages by using a quadrat-based sampling method and spatial point pattern analysis. It was revealed that the co-occurrences of ice-rafted dropstones and brachiopod shells are not random; rather, they demonstrate statistically significant and stratigraphically recurring associations that are here interpreted to represent dropstone-associated, brachiopod-dominated palaeoecological communities. In these recurring palaeocommunities, the presence of ice-rafted dropstones is considered to have added habitat complexity and heterogeneity to the benthic environment, especially suited to the settlement of brachiopods. In addition, the sinking of dropstones, from floating ice masses through the water column to the silty seafloor, is interpreted to have enriched the nutrient and oxygen supply to the benthic environment, further aiding the flourishing and maintenance of a diverse and stable marine benthic fauna. Supplementary material: Original data collection (Supplementary material 1) and spatial point pattern analysis results of rest quadrats (Supplementary material 2) are available at https://doi.org/10.6084/m9.figshare.c.4101743

Anomaly behaviour detection based on the meta-Morisita index for large scale spatio-temporal data set

In this paper, we propose a framework for processing and analysing large-scale spatio-temporal data that uses a battery of machine learning methods based on a meta-data representation of point patterns. Existing spatio-temporal analysis methods do not include a specific mechanism for analysing meta-data (point pattern information). In this work, we extend a spatial point pattern analysis method (the Morisita index) with meta-data analysis, which includes anomaly behaviour detection and unsupervised learning to support spatio-temporal data analysis and demonstrate its practical use. The resulting framework is robust and has the capability to detect anomalies among large-scale spatio-temporal data using meta-data based on point pattern analysis. It returns visualized reports to end users.