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Previous research shows that large, densely populated urban areas have higher rates of child victimization that have persisted over time. However, few investigations have inquired about the processes that produce and sustain hot and cold spots of child victimization. As a result, the mechanisms that produce the observed spatial clustering of child victimization, and hence "why" harms against children tend to cluster in space, remains unknown. Does the likelihood of being a victim of violence in one location depend on a similar event happening in a nearby location within a specified timeframe? Rather, are child victims of violence more likely to reside in suboptimal neighborhood conditions? This paper aims to present an analytical and theoretical framework for distinguishing between these locational (point) processes to determine whether the empirical spatial patterns undergirding child victimization are more reflective of the "spread" via contagion (i.e., dependency) or whether they are produced by neighborhood structural inequality resulting from spatial heterogeneity. To detect spatial dependence, we applied the inhomogeneous K -function to Los Angeles Medical Examiner data on child homicide victim locations while controlling for regional differences in victimization events (i.e., heterogeneity). Our analysis found strong evidence of spatial clustering in child victimization at small spatial scales but inhibition at larger scales. We further found limited support for the spatiotemporal clustering of child victimization indicative of a contagion effect. Overall, our results support the role of neighborhood structural vulnerability in the underlying mechanisms producing patterns of child victimization across Los Angeles County. We conclude by discussing the policy implications for understanding this spatial patterning in geographical context and for developing effective and targeted preventive interventions. [ABSTRACT FROM AUTHOR]

Caragana tibetica is a common species in the shrub-encroached desert grasslands of Inner Mongolia, China. Studying its distribution and factors can improve our grasp of shrub-encroached grassland dynamics and aid in regional biodiversity conservation. This study examined eight C. tibetica communities using point pattern analysis to assess the spatial distribution pattern (SDP) and the influencing factors of C. tibetica scrubs. We also propose a new index, i.e., the degree of deviation index (DoDI), to quantify the SDP of scrubs. The results revealed the following: (1) The shrubland of C. tibetica in the study area showed aggregated distribution on the scale of 0–30 m. On the scale of 30–50 m, the degree of aggregation gradually weakened and random distribution appeared. (2) There was not a significant correlation between SDP and environmental factors; however, DoDI showed that habitat heterogeneity had a certain impact on C. tibetica in the study area. Our research indicates that spatial heterogeneity contributes to the SDP of shrub plants in the shrub-encroached grasslands of the Inner Mongolia Plateau, and the use of DoDI enhances the ability to quantify and isolate the role of spatial heterogeneity. This study helps to deepen the understanding of the mechanisms of shrub encroachment formation in grasslands. [ABSTRACT FROM AUTHOR]

This paper develops a new method for analyzing the relationship between a set of points and another single point, the latter of which we call a reference point. This relationship has been discussed in various academic fields, such as geography, criminology, and epidemiology. Analytical methods, however, have not yet been fully developed, which has motivated this paper. Our method reveals how the number of points varies by the distance from a reference point and by direction. It visualizes the spatial pattern of points in relation to a reference point, describes the point pattern using mathematical models, and statistically evaluates the difference between two sets of points. We applied the proposed method to analyze the spatial pattern of the climbers of Mt. Azuma, Japan. The result gave us useful and interesting findings, indicating the method's soundness. [ABSTRACT FROM AUTHOR]

Similarity of point patterns is critical to geographic information retrieval. Many methods depend on measuring the similarity between point patterns within the spatial database. However, previous researches mainly focus on point density which is only one aspect of point patterns. A point distribution can be complex by its numerous alignments of point groups, which usually imply different geographical meanings in reality. In this paper, we propose a new method that uses image analysis techniques to comprehensively consider the characteristics of a point pattern. Specifically, given a set of point datasets falling in the same region, our method first generates the point intensity surfaces to transform the original vector space to raster space; then, the method constructs a matrix to describe all the pattern-related information. Finally, the point pattern similarity is calculated by decomposing this matrix into the lower-order representation and the factorized basis features. Due to the use of matrix decomposition, the proposed method has the merits that it can eliminate noises from the original data and assess the similarity of two patterns with emphasis on their major features. As a case study, our method is effective in discovering regularity from the taxi pick-up/drop-off point datasets. [ABSTRACT FROM AUTHOR]

Quercus myrsinifolia is one of the dominant species in the evergreen broad-leaf forest on the southern slope of Shennongjia. The study of spatial distribution pattern and spatial correlation of Q. myrsinifolia population will help to understand population development and potential ecological processes; as well as the structure and biodiversity maintenance mechanism of evergreen broad-leaf forests at the northern edge of the subtropics. Based on forest dynamic monitoring data from one 1 hm2 permanent plot on the southern slope of Shennongjia' we employed pair correlation functions g( r) and marked correlation functions to analyze the diameter structure of the Q. myrsinifolia population; spatial distribution patterns at different diameter classes; and intraspecific and interspecific spatial associations. The results showed that diameter structure of Q. myrsinifolia population exhibited an inverted ' J ' - shaped distribution; suggesting a healthy regeneration status and belonging to a growing population type. The spatial distribution showed a decreasing trend in aggregation with increasing diameter. Positive correlations among individuals strengthened with closer diameter classes; while weakening with larger diameter differences. Interspecific spatial associations showed an increasing correlation of Q. myrsinifolia with understory dominant species with increasing spatial scales; but no correlation was observed with canopy-dominant species. Our results suggested that the spatial pattern of Q. myrsinifolia populations on the southern slope of Shennongjia was mainly influenced by habitat filtering ' seed dispersal limitation; and intraspecific and interspecific competition. Furthermore; the adaptive strategies of Q. myrsinifolia varied when they coexisted with different species. [ABSTRACT FROM AUTHOR]

Drone technology opens the door to major changes and opportunities in our society. But this technology, like many others, needs to be administered and regulated to prevent potential harm to the public. Therefore, national and local governments around the world established regulations for operating drones, which bans drone use from specific locations or limits their operation to qualified drone pilots only. This study reviews the types of restrictions on drone use that are specified in federal drone regulations for the US, the UK, and France, and in state regulations for the US. The study also maps restricted areas and assesses compliance with these regulations by analyzing the spatial contribution patterns to three crowd-sourced drone portals, namely SkyPixel, Flickr, and DroneSpot, relative to restricted areas. The analysis is performed both at the national level and at the state/regional level within each of the three countries, where statistical tests are conducted to compare compliance rates between the three drone portals. This study provides new insight into drone users' awareness of and compliance with drone regulations. This can help governments to tailor information campaigns for increased awareness of drone regulations among drone users and to determine where increased control and enforcement of drone regulations is necessary. [ABSTRACT FROM AUTHOR]

[Objective] The relationship between water adaptation and the distribution pattern of desert shrubs at the regional scale was studied and the process and mechanism of plant population response to environment in arid and semi-arid areas was analyzed in order to provide a basis for further understanding the co-evolution relationship between plants and environments. [Methods] The spatial distribution pattern and water use strategy of Reaumuria songarica populations were analyzed by using point pattern analysis and hydrogen and oxygen stable isotope tracing techniques. [Results] ① Under the complete spatial randomness model, the R. songarica population was uniformly distributed at 32 m scale. The intra-population ecological relationship changed from competition to promotion. After habitat heterogeneity was eliminated, the population showed random distribution at > 30 m scale, and the intra-population ecological relationship showed a weakening trend, indicating that habitat heterogeneity played an important role in the formation of the spatial distribution pattern of the R. songarica population. This result indicated that habitat heterogeneity played an important role in the formation of the spatial distribution pattern of R. songarica populations at large scales. ② As the multi-year average precipitation decreased, the water use depth of the R. songarica population deepened from 0—20 cm to 60—100 cm. The density of the R. songarica population showed a highly significant negative correlation with multi-year precipitation (p

Endangered plants are essential components of most forest ecosystems and reflect the ecological integrity of forests. The study of the population structure and spatial pattern of Alsophila spinulosa is of great significance for its conservation. In the subtropical Chishui Tree fern valley in China, we studied the best structure of A. spinulosa to reflect the environmental change, the range of the most intense spatial aggregation change, and the spatial relationship with the community structure, and explored the survival characteristics of A. spinulosa. Our results showed that the variation in tree height structure was the most obvious response to the change in community type. The spatial aggregation degree of A. spinulosa had the most obvious change in the range of 1–5 m. There was an obvious spatial correlation between the dominant plants, the vertical structure of the community, and the distribution of A. spinulosa. These findings provided a reference for exploring the population structure, distribution pattern, and the influence of community types on A. spinulosa populations. [ABSTRACT FROM AUTHOR]

Dot maps are a type of representation of absolute and aggregated population quantities presented in a disaggregated form, with a planimetric/geometric and graphic point layout. While several such maps are developed, the proposed Dot Cartogram (also known as le cartogramme par points in french) is a new map belonging to the graphic cartograms and resulting from the articulation between three Dot Map: the Proportional Dot Map, the Dot Density Map (Montizon, 1830) and the Dorling’ circular cartogram of population (Dorling, 1996). The Dot Cartogram renews the cartographic image of the Dotmap in two directions: firstly, in terms of the perceived patterns, which is likely to vary according to several parameters; secondly, the steps to which it is subject. Our introduction of the Dot Cartogram is carried out on different static or interactive, executable and reproducible media. Its definition is complete by an application achieved on two data sets examined in parallel: population data and the gross domestic product of the countries of the world, both observed from 1950 to 2018.

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Endangered plants are essential components of most forest ecosystems and reflect the ecological integrity of forests. The study of the population structure and spatial pattern of Alsophila spinulosa is of great significance for its conservation. In the subtropical Chishui Tree fern valley in China, we studied the best structure of A. spinulosa to reflect the environmental change, the range of the most intense spatial aggregation change, and the spatial relationship with the community structure, and explored the survival characteristics of A. spinulosa. Our results showed that the variation in tree height structure was the most obvious response to the change in community type. The spatial aggregation degree of A. spinulosa had the most obvious change in the range of 1–5 m. There was an obvious spatial correlation between the dominant plants, the vertical structure of the community, and the distribution of A. spinulosa. These findings provided a reference for exploring the population structure, distribution pattern, and the influence of community types on A. spinulosa populations.

In this paper, we propose an extension to the barrier model, i.e., the Multi-Barriers Model, which could characterize an area of interest with different types of obstacles. In the proposed model, the area of interest is divided into two or more areas, which include a general area of interest with sampling points and the rest of the area with different types of obstacles. Firstly, the correlation between the points in space is characterized by the obstruction degree of the obstacle. Secondly, multiple Gaussian random fields are constructed. Then, continuous Gaussian fields are expressed by using stochastic partial differential equations (SPDEs). Finally, the integrated nested Laplace approximation (INLA) method is employed to calculate the posterior mean of parameters and the posterior parameters to establish a spatial regression model. In this paper, the Multi-Barriers Model is also verified by using the geostatistical model and log-Gaussian Cox model. Furthermore, the stationary Gaussian model, the barrier model and the Multi-Barriers Model are investigated in the geostatistical data, respectively. Real data sets of burglaries in a certain area are used to compare the performance of the stationary Gaussian model, barrier model and Multi-Barriers Model. The comparison results suggest that the three models achieve similar performance in the posterior mean and posterior distribution of the parameters, as well as the deviance information criteria (DIC) value. However, the Multi-Barriers Model can better interpret the spatial model established based on the spatial data of the research areas with multiple types of obstacles, and it is closer to reality. [ABSTRACT FROM AUTHOR]

Studying the spatial pattern of plant species can improve understanding of ecological processes and mechanisms that maintain forest stand stability. To better understand the population dynamics of two dipterocarp species Dipterocarpus costatus and Dipterocarpus alatus, in the semi-deciduous forest in Southern Vietnam, we used the spatial point-pattern analysis methods to evaluate the spatial patterns and the interaction of these two species. The results showed that the population structure of D. costatus and D. alatus was similar; their diameter distributions were reverse J-shaped, and the numbers of trees were high at the juvenile and premature stages. The spatial patterns of two dipterocarp species tend to shift from aggregation to randomness and regularity as the tree diameter increases. We found negative interactions between mature and premature trees of two dipterocarp species at small scales. Mature and premature trees of two dipterocarp species showed no inhibition with their juveniles. Seed dispersal and natural self-thinning may be two main processes that control the spatial distribution and association patterns of two dipterocarp species. With the obtained results, our study contributes to simulate the population dynamics of tropical forest trees by using time-replacing space. [ABSTRACT FROM AUTHOR]

The Quercus wutaishanica forest influences the ecological environment and climate characteristics and plays an important ecological role in the Loess Plateau region. However, we still know relatively little about the genetic diversity and spatial distribution of Q. wutaishanica. Here, we assessed the genetic diversity of Q. wutaishanica using simple sequence repeats and used the point pattern method to analyze the spatial distribution patterns as well as intraspecific relationships. Our results indicate that the diameter structure of the Q. wutaishanica population was inverted J-type, showing a growing population. In addition, the population maintained high genetic diversity on a small scale. Due to dispersal constraints, the spatial distribution pattern of Q. wutaishanica seedlings (DBH < 1 cm) tended to aggregate at small scales and the degree of aggregation decreased with increasing spatial scale. However, trees (DBH > 5 cm) and saplings (1 cm ≤ DBH < 5 cm) showed more random distribution at the scale, indicating that Q. wutaishanica individuals shift from aggregation to random distribution at the spatial scale. In addition, although individuals of different diameter classes showed facilitative (trees vs. saplings, 5–6.5 m) and competitive effects (trees vs. seedlings, 13.5–16 m) on some scales, they showed no correlation on other scales, especially for saplings and seedlings, where they were not correlated on any scale. The results contribute to revealing the status and dynamics of Q. wutaishanica in the Loess Plateau, thereby providing a theoretical basis for further study on the maintenance mechanism of the population. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this study was to understand the encroachment order, spatial patterns, interspecific associations, and species diversity of a Cunninghamia lanceolata plantation and to provide context for how to improve the spatial structure of C. lanceolata plantations. We investigated a Guanshan (C. lanceolata) Plantation in the Jiangxi Province. The C. lanceolata plantation was divided into three developmental stages (early, middle and late), with a space-for-time substitution method, according to their diameter at breast height (DBH) measurements. Across these plots, we analyzed encroachment patterns of the species and their interspecific associations with C. lanceolata according to the coordinates of trees in the plot, as well as the variation of species diversity after the encroachment of broad-leaf tree species. Our results show that the encroachment of broad-leaf trees into the C. lanceolata plantation followed a clear successional sequence of tree community assembly: intolerant tree species encroachment first, such as Alniphyllum fortunei and Liquidambar formosana, encroached; neutral tree species then encroachment, such as Daphniphyllum oldhamii and Schima superba, and shade-tolerant tree species encroachment last, such as Castanopsis eyrei and Castanopsis tibetana. Further, the spatial pattern of species establishment differed based upon when they encroached on the plantation. Intolerant and neutral tree species of early and middle developmental stages were distributed randomly. However, intolerant and some shade-tolerant tree species of the late developmental stage were aggregated on a scale of about 0–3 m. In addition, the interspecific associations between broad-leaf species encroachment into the C. lanceolata plantation were mainly negative, among which the most competitive ones were A. fortunei, D. oldhamii and Machilus thunbergii. However, with the development of the community, some species with negative correlations changed to having no correlation with C. lanceolata. We found that species diversity in the plantation gradually increased by nearly-five times from the early to the late developmental stage. Based on our collective results, we conclude that the encroachment and assembly of tree species show obvious order and pattern, which has a strong competitive effect with C. lanceolata in the naturalized plantation. Additionally, the community biodiversity has been significantly improved. Therefore, this successional process is important in the transformation of the C lanceolata plantation and different tree species should be replanted according to C. lanceolata plantation developmental stages with priority given to competitive species, such as A. fortunei, D. oldhamii, and M. thunbergii, so as to promote the C. lanceolata plantation change to C. lanceolata and broadleaf mixed forest.

Many spatial analysis methods suffer from the scaling issue identified as part of the Modifiable Areal Unit Problem (MAUP). This article introduces the Pyramid Model (PM), a hierarchical data framework integrating space and spatial scale in a 3D environment to support multi-scale analysis. The utility of the PM is tested in examining quadrat density and kernel density, which are commonly used measures of point patterns. The two metrics computed from a simulated point set with varying scaling parameters (i.e. quadrats and bandwidths) are represented in the PM. The PM permits examination of the variation of the density metrics computed at all different scales. 3D visualization techniques (e.g. volume display, isosurfaces, and slicing) allow users to observe nested relations between spatial patterns at different scales and understand the scaling issue and MAUP in spatial analysis. A tool with interactive controls is developed to support visual exploration of the internal patterns in the PM. In addition to the point pattern measures, the PM has potential in analyzing other spatial indices, such as spatial autocorrelation indicators, coefficients of regression analysis and accuracy measures of spatial models. The implementation of the PM further advances the development of a multi-scale framework for spatio-temporal analysis. [ABSTRACT FROM AUTHOR]

Abstract Background Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design. Results We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection. Conclusions sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.

The spatial analysis of linear features (lines and curves) is a challenging and rarely attempted problem in ecology. Existing methods are typically expressed in abstract mathematical formalism, making it difficult to assess their relevance and transferability into an ecological setting. We introduce a set of concrete and accessible methods to analyze the spatial patterning of line‐segment data. The methods include Monte Carlo techniques based on a new generalization of Ripley's K‐function and a class of line‐segment processes that can be used to specify parametric models: parameters are estimated using maximum likelihood and models compared using information‐theoretic principles. We apply the new methods to fallen tree (dead log) data collected from two 1‐ha Australian tall eucalypt forest plots. Our results show that the spatial pattern of the fallen logs is best explained by plot‐level spatial heterogeneity in combination with a slope‐dependent nonuniform distribution of fallen‐log orientations. These methods are of a general nature and are applicable to any line‐segment data. In the context of forest ecology, the integration of fallen logs as linear structural features in a landscape with the point locations of living trees, and a quantification of their interactions, can yield new insights into the functional and structural role of tree fall in forest communities and their enduring post‐mortem ecological legacy as spatially distributed decomposing logs. [ABSTRACT FROM AUTHOR]

Objectives: Progress in national schistosomiasis control in China has successfully reduced disease transmission in many districts. However, a low transmission rate hinders conventional snail surveys in identifying areas at risk. In this study, Schistosoma japonicum-infected sentinel mice surveillance was conducted to identify high-risk areas of schistosomiasis transmission in Hubei province, China. Methods: The risk of schistosomiasis transmission was assessed using sentinel mice monitoring in Hubei province from 2010 to 2018. Field detections were undertaken in June and September, and the sentinel mice were kept for approximately 35 days in a laboratory. They were then dissected to determine whether schistosome infection was present. Ripley's K-function and kernel density estimation were applied to analyze the spatial distribution and positive point pattern of schistosomiasis transmission. Results: In total, 190 sentinel mice surveillance sites were selected to detect areas of schistosomiasis infection from 2010 to 2018, with 29 (15.26%) sites showing infected mice. Of 4723 dissected mice, 256 adult worms were detected in 112 infected mice. The infection rate was 2.37%, with an average of 2.28 worms detected per infected mouse. Significantly more infected mice were detected in the June samples than in the September samples (χ2 = 12.11, p

Abstract A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community‐level patterns, and ecological processes. In this study, we developed a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub‐blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0–0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a geographical information system, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Since the introduction of drones to the mass market for private users, drone pilots have started to share geo-tagged aerial photos and videos on a variety of drone platforms. This study compares the spatio-temporal contribution patterns of georeferenced drone-based images and videos between three crowd-sourcing platforms, namely Dronestagram, Travelwithdrone, and Flickr. The comparison addresses aspects of spatial accuracy, geographic coverage, contribution inequality, power-law approximations of different contribution characteristics, and negative binomial multilevel regression models that identify man-made and natural features, socio-economic factors, and land use categories that are associated with image and video contribution numbers. This study provides new insight into the abundance of drone-based image and video contributions around the globe, helps to determine which drone platform is best suited to find drone media for a specific location, and discusses a few potential applications that could benefit from crowd-sourced drone imagery and videos. [ABSTRACT FROM AUTHOR]

The description of spatial patterns in species distributions is central to research throughout ecology. In this manuscript, we demonstrate that five of the most widely used species‐level spatial patterns are not only related, but can in fact be quantitatively derived from each other under minimal assumptions: the occupancy area curve, Taylor's Law, the neighborhood density function, a two‐plot variant of Taylor's Law and two‐plot single‐species turnover. We present an overarching mathematical framework and derivations for several theoretical example cases, along with a simulation study and empirical analysis that applies the framework to data from the Barro Colorado Island tropical forest plot. We discuss how knowledge of this mathematical relationship can support the testing of ecological theory, suggest efficient field sampling schemes, highlight the relative importance of plot area and abundance in driving turnover patterns and lay the groundwork for future unified theories of community‐level spatial metrics and multi‐patch spatial patterns. [ABSTRACT FROM AUTHOR]

In this paper, species of the same congeneric species(Litsea pungens and L. elongata )in large sample plot of 15 hm2 in Mulinzi National Nature Reserve of Hubei Province were taken as the study object. The spatial distribution pattern, intraspecies and interspecific spatial correlations of the two species were analyzed by pairwise correlation function and different zero models(complete spatial randomness, heterogeneous Poisson and antecedent condition), and the construction mechanism of the community was discussed from the point of view of the spatial distribution pattern of the same congeneric species. The results were as follows:(1)In the study of population spatial distribution pattern, the spatial distribution pattern of the two species was generally aggregation distribution. The aggregation scales on the basis of the CSR zero model, the L. pungens and the different diameter stages(sapling, small tree, medium tree, adult tree)were 0-114 m, 0-103 m, 0-56 m, 0-34 m, and 0-16 m, respectively, and the aggregation scales of the L. elongata and the different diameter stages were 0-150 m, 0-150 m and 0-59 m, 0-56 m and 0-14 m; On the basis of the HP-zero model, the spatial distribution pattern of the whole L. pungens was 0-23 m, where in all of the sapling, small and medium trees were 0-22 m, and the aggregation distribution of adult trees was only on the scale of 0-3 m. The aggregation scale of the whole spatial distribution pattern of L. elongata was 0-36 m and 140-150 m, and the aggregation scale of each diameter grade was 0-35 m, 0-35 m, 0-12 m and 0-6 m.(2)In the study of intraspecific correlation, the intraspecies correlation of different diameter individuals showed positive correlation on a small scale, and became weakly unrelated or negative with the increase of scale.(3)In the study of interspecific correlation, the spatial relationship between the two species was about the same. On the whole, the two species showed positive correlation with another species within 30 m scale and negative correlation within 40-68 m scale, and there was no correlation between different diameter grades, and there was positive or negative correlation between the two species. It is considered that the spatial distribution pattern of the population is basically aggregation distribution, which is mainly affected by habitat heterogeneity and diffusion. The spatial distribution patterns of L. pungens and L. elongata in large sample plot are similar, and different habitat preferences may be the cause of the coexistence of the two species. [ABSTRACT FROM AUTHOR]

The aim of this article is to construct spatial models for the activation of sweat glands for healthy subjects and subjects suffering from peripheral neuropathy by using videos of sweating recorded from the subjects. The sweat patterns are regarded as realizations of spatial point processes and two point process models for the sweat gland activation and two methods for inference are proposed. Several image analysis steps are needed to extract the point patterns from the videos and some incorrectly identified sweat gland locations may be present in the data. To take into account the errors, we either include an error term in the point process model or use an estimation procedure that is robust with respect to the errors. [ABSTRACT FROM AUTHOR]

Background: Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design. Results: We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection. Conclusions: sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique. [ABSTRACT FROM AUTHOR]

Point cluster generalization is a major concern in cartography. This problem is complicated by how to preserve the distribution patterns (e.g., density) of points during the process of deriving small-scale maps from a large-scale map. To address this problem, existing methods adopt geometric models such as planar Voronoi diagram, which considers the geographic space as an ideal Euclidean one. The associated operations determine the relationships of points based on the Euclidean straight-line distance. However, many map features are in different geographic environments. For example, as one of the most significant features on a map, urban facilities rely on the transport function of street networks to compete for service areas, and thus their distributions, in reality, are constrained to the spatial layout of street networks. To preserve such characteristic after generalization, this paper establishes a network-constrained Voronoi model for delimitating service areas of facility points-of-interest (POIs). With the help of Voronoi model, the proposed method further treats the generalization process of facility POIs as a compete and merge iterative process of service areas: a facility that has a relatively small service area faces more intense competition from its neighbors and by deleting it from the resulting small-scale map in the first priority its service area will be partitioned and merged to the neighbors. The experimental results demonstrate that our method considers the service patterns of facility POIs is particularly useful in applications related to navigation.

Abstract 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.

• More infected mice were detected in June samples than in September samples. • The sentinel mice infection pattern showed aggregation when the distance was less than or equal to 34.52 km. • Hotspots were detected especially in areas along the Yangtze River, such as the Fu-Lun, Dongjing-Tongshun, and Juzhang River basins. Progress in national schistosomiasis control in China has successfully reduced disease transmission in many districts. However, a low transmission rate hinders conventional snail surveys in identifying areas at risk. In this study, Schistosoma japonicum- infected sentinel mice surveillance was conducted to identify high-risk areas of schistosomiasis transmission in Hubei province, China. The risk of schistosomiasis transmission was assessed using sentinel mice monitoring in Hubei province from 2010 to 2018. Field detections were undertaken in June and September, and the sentinel mice were kept for approximately 35 days in a laboratory. They were then dissected to determine whether schistosome infection was present. Ripley's K -function and kernel density estimation were applied to analyze the spatial distribution and positive point pattern of schistosomiasis transmission. In total, 190 sentinel mice surveillance sites were selected to detect areas of schistosomiasis infection from 2010 to 2018, with 29 (15.26%) sites showing infected mice. Of 4723 dissected mice, 256 adult worms were detected in 112 infected mice. The infection rate was 2.37%, with an average of 2.28 worms detected per infected mouse. Significantly more infected mice were detected in the June samples than in the September samples (χ 2 = 12.11, p < 0.01). Ripley's L (d) index analysis showed that, when the distance was ≤34.52 km, the sentinel mice infection pattern showed aggregation, with the strongest aggregation occurring at 7.86 km. Three hotspots were detected using kernel density estimation: at the junction of Jingzhou District, Gong'an County, and Shashi District in Jingzhou City; in Wuhan City at the border of the Huangpi and Dongxihu Districts, and in the Hannan and Caidian Districts. The results showed that sentinel mice surveillance is useful in identifying high-risk areas, and could provide valuable information for schistosomiasis prevention and control, especially concerning areas along the Yangtze River, such as the Fu-Lun, Dongjing-Tongshun, and Juzhang River basins. [ABSTRACT FROM AUTHOR]

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community‐level patterns, and ecological processes. In this study, we developed a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub‐blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0–0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a geographical information system, to collect experimental data on the spatial point patterns for the populations in this grassland community. [ABSTRACT FROM AUTHOR]

The Baneh- Saqqez zone is part of the Sanandaj‒Sirjan Zone, northwest of Iran. Generally, this zone is recognized as a gold bearing zone, and the mineralization in it is controlled by faults. In this research, structural lineaments were extracted using Landsat 8 (OLI) image, in which the main lineaments trend NE‒SW, EW and NW‒SE. Spatial distributions of the mineralization are examined by using the point pattern, Fry and fractal analyses. The pattern of structural controls on the gold mineralization in southwest Saqqez, is almost regular, and the trend of gold mineralization and Fry points show NE‒SW and EW trends. Comparison of rose diagrams for the Fry analysis final translation points and faults in southwest Saqqez show that the orientations of mineralization are similar to NE‒SW and EW trending faults and that the mineralization intersects the NW‒SW trending faults. The results of fractal analysis suggest that gold occurrences in the area have higher fractal dimension. The deep regional faults cause a way for ore-bearing fluids to migrate and control the emplacement of the intrusive bodies and the position of ore mineralization in the Saqqez‒Baneh sub-zone. On that basis, a survey of spatial data and its relationship to structural features can be useful for future exploration and discovery of new occurrences of gold mineralization. [ABSTRACT FROM AUTHOR]

Presence–absence sampling is an important method for monitoring state and change of both individual plant species and communities. With this method, only the presence or absence of the target species is recorded on plots and thus the method is straightforward to apply and less prone to surveyor judgement compared to other vegetation monitoring methods. However, in the basic setting, all plots must be equally large or otherwise it is unclear how data should be analysed. In this study, we propose and evaluate five different methods for estimating plant density based on presence–absence registrations from surveys with variable plot sizes.Using artificial plant population data as well as empirical data from the Swedish National Forest Inventory, we evaluated the performance of the proposed methods. The main analysis was conducted through sampling simulation in artificial populations, whereby bias and variance of density estimators for the different methods were quantified and compared.Both for state and change estimation of plant density, we found that the best method to handle variable plot size was to perform generalized least squares regression, using plot size as an independent variable. Methods where plots smaller than a certain threshold were excluded or their registrations recalculated were, however, almost as good. Using all registrations as if they were obtained from plots with the nominal plot size resulted in substantial bias.Our findings are important for plant population studies in a wide range of environmental monitoring programmes. In these programmes, plots are typically randomly laid out and may be located across boundaries between different land‐use or land‐cover classes, resulting in subplots of variable size. Such splitting of plots is common when large plots are used, for example, with the 100 m2 plots used in the Swedish National Forest Inventory. Our methods overcome problems to estimate plant density from presence–absence data observed in plots that vary in size. [ABSTRACT FROM AUTHOR]

Large animals provide crucial seed dispersal services, yet face continued threats and are susceptible to changes in landscape composition and configuration. Thus, there is a growing imperative to improve understanding of animal‐generated seed dispersal using models that incorporate spatial complexity in a realistic, yet tractable, way.We developed a spatially explicit agent‐based seed dispersal model, with disperser movements informed by biotelemetry data, to evaluate how landscape composition and configuration affect seed dispersal patterns. We illustrated this approach for the world's second largest ratite, the emu (Dromaius novaehollandiae), a highly mobile generalist frugivore considered an important long‐distance disperser for many plant species across Australia.When animal movement is unrestricted, model parameters related to seed gut passage largely determine seed dispersal kernels. However, as habitat loss and fragmentation increase, the extent of long‐distance dispersal events is reduced and seed shadows became progressively more aggregated. This effect is due to the emu not being able to move between disconnected parts of the landscape, with small changes in habitat structure causing decreased long‐distance dispersal.We simulated seed dispersal patterns generated by three commonly used generic models of animal movement – unbiased and biased correlated random walks and Lévy walks – to evaluate how different representations of movement affect estimations of animal movements and emergent seed dispersal patterns. Simulated movements informed by the emu biotelemetry data resulted in longer median seed dispersal distances than do the three generic models.Synthesis. Changes in landscape composition and configuration can dramatically alter patterns of zoochorous seed dispersal as they influence animal movement. However, when models are used to simulate the patterns of seed dispersal, decisions about how animal movement is represented also affect estimates of seed dispersal. [ABSTRACT FROM AUTHOR]

Spatial point-pattern analysis can give insights to the underlying processes of patch succession and restoration. It is unclear whether inter-shrub competition determines patch succession. In this paper, we assessed the spatial patterns along patch succession using spatial statistics such as univariate and bivariate O-ring statistics, in the water-wind erosion crisscross region in semi-arid ecosystems of the Loess Plateau. Point pattern analysis results showed that there were no significant difference in three positions of the slope. The small and middle shrub patches were aggregatedly distributed in small spatial scale, meanwhile the large shrub patches were regularly distributed and dead shrub patches were randomly distributed. The small shrub patches were respectively aggregated to the middle and large patches at fine scales. Competition-induced regular distribution or negative relationship becomes obvious when analyzing the shift towards less aggregated perceptible effect of competition, a time component should always be included in spatial pattern-based inference of competition. Our results revealed that regular, clumped and random shrub patch patterns could occur, pending on size of shrub patches, and the shrub patches are distributed in different ways and they can present variant spatial point pattern features along patch size succession.

Cat-borne parasites and their associated diseases have substantial impacts on human, livestock, and wildlife health worldwide. Despite this, large and detailed datasets that allow researchers to study broad-scale trends in the ecology of cat-borne diseases are either difficult to obtain or non-existent. One condition that is easily detected at slaughter is macroscopic sarcocystosis, a cat-borne parasitosis of sheep (Ovis aries). We conducted a cross-sectional study to describe the geographic distribution of sarcocystosis in sheep throughout South Australia and investigate ecosystem characteristics associated with the presence of disease. Data were obtained from two slaughterhouses which processed 3,865,608 sheep from 4,204 farms across 385,468 km2 of South Australia's land mass for the period 2007–2017. A Poisson point process model was developed to quantify environmental characteristics associated with higher densities of sarcocystosis-positive farms. Sarcocystosis was highly clustered on a large island off of the Australian coast and the density of sarcocystosis-positive farms increased in areas of low soil pH (intensity ratio: 0.86, 95% CI: 0.78, 0.95) and high clay content. We hypothesize that region was confounded by, and predominately acted as a proxy for, cat density. Our results have broader implications regarding the health, welfare, economic, and conservation impacts of other cat-borne parasitosis, such as toxoplasmosis.

Quercus myrsinifolia is one of the dominant species in the evergreen broad-leaf forest on the southern slope of Shennongjia. The study of spatial distribution pattern and spatial correlation of Q. myrsinifolia population will help to understand population development and potential ecological processes, as well as the structure and biodiversity maintenance mechanism of evergreen broad-leaf forests at the northern edge of the subtropics. Based on forest dynamic monitoring data from one 1 hm 2 permanent plot on the southern slope of Shennongjia, we employed pair correlation functions g ( r ) and marked correlation functions to analyze the diameter structure of the Q. myrsinifolia population, spatial distribution patterns at different diameter classes, and intraspecific and interspecific spatial associations. The results showed that diameter structure of Q. myrsinifolia population exhibited an inverted 'J'-shaped distribution, suggesting a healthy regeneration status and belonging to a growing population type. The spatial distribution showed a decreasing trend in aggregation with increasing diameter. Positive correlations among individuals strengthened with closer diameter classes, while weakening with larger diameter differences. Interspecific spatial associations showed an increasing correlation of Q. myrsinifolia with understory dominant species with increasing spatial scales, but no correlation was observed with canopy-dominant species. Our results suggested that the spatial pattern of Q. myrsinifolia populations on the southern slope of Shennongjia was mainly influenced by habitat filtering, seed dispersal limitation, and intraspecific and interspecific competition. Furthermore, the adaptive strategies of Q. myrsinifolia varied when they coexisted with different species.

Spatial heterogeneity of cells in liver biopsies can be used as biomarker for disease severity of patients. This heterogeneity can be quantified by non-parametric statistics of point pattern data, which make use of an aggregation of the point locations. The method and scale of aggregation are usually chosen ad hoc, despite values of the aforementioned statistics being heavily dependent on them. Moreover, in the context of measuring heterogeneity, increasing spatial resolution will not endlessly provide more accuracy. The question then becomes how changes in resolution influence heterogeneity indicators, and subsequently how they influence their predictive abilities. In this paper, cell level data of liver biopsy tissue taken from chronic Hepatitis B patients is used to analyze this issue. Firstly, Morisita-Horn indices, Shannon indices and Getis-Ord statistics were evaluated as heterogeneity indicators of different types of cells, using multiple resolutions. Secondly, the effect of resolution on the predictive performance of the indices in an ordinal regression model was investigated, as well as their importance in the model. A simulation study was subsequently performed to validate the aforementioned methods. In general, for specific heterogeneity indicators, a downward trend in predictive performance could be observed. While for local measures of heterogeneity a smaller grid-size is outperforming, global measures have a better performance with medium-sized grids. In addition, the use of both local and global measures of heterogeneity is recommended to improve the predictive performance., Competing Interests: Declaration of competing interest None Declared, (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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