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A relatively new sub-area within this is the statistical analysis of point processes on linear networks, that is, processes of events occurring randomly in space but with locations constrained to lie on a linear network. For example, traffic accidents occur at random locations constrained to lie on a network of streets. In this case, the network is idealized as a network of line segments in the plane or three-dimensional space. The development of statistical techniques for the analysis of point processes on linear networks is still in its infancy. Many standard statistical techniques for analyzing point processes cannot be directly applied to data arising from linear networks and require suitable modification. Test of Complete Spatial Randomness (CSR) for point processes on the plane based on quadrat counts or nearest neighbors cannot be applied to point processes on linear networks. This paper defines a Voronoi tessellation of the linear network which uses the shortest path distance along the network instead of Euclidean distance, and then develops a chi-square test of CSR for linear networks based on the event counts in the tiles of this tessellation. This test is applied to data on traffic accidents in Leon County, Florida, USA., Nokta süreçlerinin incelenmesi ve uzamsal nokta modellerini içeren verilerin analizi, uzun bir geçmişe ve geniş bir literatüre sahip konumsal istatistikte önemli konulardır. Bunun içinde nispeten yeni bir alt alan, doğrusal ağlar üzerindeki nokta süreçlerinin istatistiksel analizidir, yani uzayda rastgele meydana gelen ancak doğrusal bir ağ üzerinde uzanmak için kısıtlı konumlara sahip olayların süreçleri. Örneğin, trafik kazaları, bir sokak ağı üzerinde yer almakla sınırlandırılmış rastgele konumlarda meydana gelir. Diğer örnekler, nehir ağlarında veya nöron ağlarında meydana gelen olayları içerir. Tüm bu durumlarda ağlar, düzlemde veya üç boyutlu uzayda bir doğru parçaları ağı olarak idealleştirilir. Doğrusal ağlarda nokta süreçlerinin analizi için istatistiksel tekniklerin geliştirilmesi henüz emekleme aşamasındadır. Nokta süreçlerini analiz etmek için birçok standart istatistiksel teknik, doğrusal ağlardan kaynaklanan verilere doğrudan uygulanamaz ve uygun modifikasyon gerektirir. Kuadrat sayılarına veya en yakın komşulara dayalı düzlemdeki nokta süreçleri için Tam Konumsal Rastgelelik Testi (CSR), doğrusal ağlardaki nokta süreçlerine uygulanamaz. Bu makale, Öklid mesafesi yerine ağ ile birlikte en kısa yol mesafesini kullanan doğrusal ağın bir Voronoi mozaiklemesini tanımlar ve ardından bu mozaiklemenin döşemelerindeki olay sayılarına dayalı olarak doğrusal ağlar için bir ki-kare CSR testi geliştirir. Bu test, ABD, Florida, Leon County'deki trafik kazalarına ilişkin verilere uygulandı.

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., We show that several widely used single species spatial metrics (the occupancy area curve, Taylor's Law, the neighborhood density function and plot‐based turnover) are all derivable from each other under minimal assumptions. We provide example equations showing this relationship and demonstrate its application to simulated and empirical data.

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

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.

Quaking aspen is found in western forests of the United States and is currently at risk of loss due to conifer competition at within-stand scales. Wildfires in these forests are impactful owing to conifer infilling during prolonged fire suppression post-Euro-American settlement. Here, restoration cuttings seek to impact wildfire behavior and aspen growing conditions. In this study, we explored how actual and hypothetical cuttings with a range of conifer removal intensity altered surface fuel and overstory structure at stand and fine scales. We then simulated wildfires, examining fire behavior and effects on post-fire forest structures around aspen trees. We found that conifer removal constrained by lower upper diameter limits (&lt, 56 cm) had marginal effects on surface fuel and overstory structure, likely failing to enhance resource conditions sufficiently to sustain aspen. Increasing the diameter limit also led to a higher likelihood of fire spread and a higher rate of spread, owing to greater within-canopy wind speed, though crown fire activity decreased. Our simulations suggest heavier treatments could facilitate reintroduction of fire while also dampening the effects of wildfires on forest structure. Cutting specifications that relax diameter limits and remove a substantial portion of conifer overstory could better promote aspen restoration and mitigate fire hazard.

Not all environmental processes are observed in a way that allows a straight forward easy modelling. Nevertheless, insights can also be gained by exploring weakly dependent covariates paying attention to details of the distribution. Using the concept of copulas, it is possible to explore the dependence of a multivariate distribution without the distortion of the marginal distribution functions acting on typical correlation measures. Furthermore, copulas turn the attention to the dependence across the entire range of the multivariate distribution and do not only summarise it in a single correlation measure. In our application, we study counts of rat sightings in the city of Madrid. The brown rat lives with mankind and adversely affects public health by transmission of diseases, bites and allergies. Better understanding behavioural and spatial correlation aspects of this species can contribute to its effective management and control. We explore weakly to moderately correlated covariates based on distances to broken sewers, feeding grounds and markets as well as population density. The use of copulas is motivated by the different dependence structures of the four covariates and the asymmetries therein. In order to deal with the discrete zero-inflated counts, we present a new approach that assigns conditional random ranks to discrete data. This way, we mimic an underlying continuous variable easing the vine copula estimation, but do not destroy the dependence as in a uniform randomisation. We show that a 5-dimensional vine copula model is able to capture the dependence in our application.

The Gask Ridge system is a series of forts, fortlets, and timber watchtowers situated along a Roman road in northern Scotland. The high intervisibility of the watchtowers in the Gask Ridge system has resulted in the proposal of two main functions: one that the watchtowers were a signalling system and two that the watchtowers provided visibility of the Gask Ridge road for surveillance and monitoring. Despite this, only the former function has been assessed. This paper explores the function of the watchtowers along the Gask Ridge road using computational methods, including Monte Carlo hypothesis testing. The analytical approach, which is documented and reproducible with accompanying code, rejects that the watchtowers were randomly located along the Gask Ridge road, instead favouring the alternative hypothesis that the watchtowers were located to maximise the visibility of the road. Furthermore, it is possible to claim that the need to monitor the road shows a causal relationship with the location of the watchtowers, rather than associative. The findings support the interpretation that the main function of the watchtowers was for the surveillance and monitoring of the Gask Ridge road, providing an early warning system of an attack from the Highlands (Woolliscroft, 1993).

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.

Spatial statistics are widely used in studies of ecological processes in plant communities, especially to provide evidence of neutral or non‐neutral mechanisms that might support species coexistence. The contribution of such statistics has been substantial, but their ability to identify any links between underlying processes and emergent patterns is not certain. We investigate the ability of a number of spatial statistics to distinguish theorized mechanisms of species coexistence (spatial and temporal niche differentiation, neutrality, the Janzen–Connell effect and heteromyopia) in a simulated plant community. We find that individual statistics differ substantially in their sensitivity to these mechanisms, with those based on nearest neighbour species identities being the most sensitive. These differences are largely robust to changes in the strength of the modelled mechanisms when simulated independently and in combination. The spatial signal of niche differentiation is always distinct in simulations that combine mechanisms. Synthesis. We describe full spatial signals of modelled coexistence mechanisms that are observed consistently across statistics and simulated strengths and combinations of mechanisms, and identify a set of spatial statistics that holds particular promise for empirical studies designed to investigate mechanisms of these kinds.

Aggregation patterns are often visually detected in sets of location data. These clusters may be the result of interesting dynamics or the effect of pure randomness. We build an asymptotically Gaussian test for the hypothesis of randomness corresponding to a Poisson point process. We first compute the exact first and second moment of the Ripley K-statistic under the homogeneous Poisson point process model. Then we prove the asymptotic normality of a vector of such statistics for different scales and compute its covariance matrix. From these results, we derive a test statistic that is chi-square distributed. By a Monte-Carlo study, we check that the test is numerically tractable even for large data sets and also correct when only a hundred of points are observed., Comment: 26 pages, 3 figures, 2 tables

While much research in ecology has focused on spatially explicit modelling as well as on measures of biodiversity, the concept of spatial (or local) biodiversity has been discussed very little. This paper generalises existing measures of spatial biodiversity and introduces a family of spatial biodiversity measures by flexibly defining the notion of the individuals’ neighbourhood within the framework of graphs associated to a spatial point pattern. We consider two non-independent aspects of spatial biodiversity, scattering, i.e. the spatial arrangement of the individuals in the study area and exposure, the local diversity in an individual’s neighbourhood. A simulation study reveals that measures based on the most commonly used neigh-bourhood defined by the geometric graph do not distinguish well between scattering and exposure. This problem is much less pronounced when other graphs are used. In an analysis of the spatial diversity in a rainforest, the results based on the geometric graph have been shown to spuriously indicate a decrease in spatial biodiversity when no such trend was detected by the other types of neighbourhoods. We also show that the choice neighbourhood markedly impacts on the classification of species according to how strongly and in what way different species spatially structure species diversity. Clearly, in an analysis of spatial or local diversity an appropriate choice of local neighbourhood is crucial in particular in terms of the biological interpretation of the results. Due to its general definition, the approach discussed here offers the necessary flexibility that allows suitable and varying neighbourhood structures to be chosen. Postprint

International audience; The analysis of territorial changes over time raises many methodological questions. Despite a very frequent use of spatial analysis methods, few works underline their methodological shortcomings. This paper considers a comparison and discussion of exploratory statistical methods (notably Ripley's K function and its derivatives). It is worth noting three parameters in archaeological spatial analysis: scale, quality and quantity of input data. Archaeological data are intrinsically heterogeneous. Therefore, our approach proposes a multiscalar process integrating heterogeneous data. We describe a range of methods for the description and analysis of heterogeneous spatial point patterns in archaeology. These methods are described through the spatial analysis of neolithic sites in the South-East of the Paris Basin (France). We conclude by discussing the problems of the logical link between spatial pattern recognition and the archaeological process.

This paper aims to compare the spatial distributions of road accidents involving a cyclist that are officially reported to the police with those not reported (user-sourced dataset). We show that reported and unreported cycling accidents have similar spatial patterns and exhibit similar locational tendencies with respect to specific infrastructures and facilities. We illustrate the importance of defining a study area in the interpretation of the (cross) K-function results and show that cycling accidents are more likely to be unreported in areas where a small speed difference between slow and fast modes is imposed (traffic-calming areas). Because safety measures influence both accident rate and reporting rate, evaluation of their effect on cycling accidents requires careful spatial analysis. Cet article a pour objectif de comparer la distribution spatiale des accidents impliquant un cycliste qui ont été constatés par la police et ceux non enregistrés (données générées par utilisateurs). Nous montrons que les distributions spatiales des deux types d’accidents se ressemblent et révèlent des tendances spatiales similaires par rapport à certaines infrastructures. Nous insistons ici sur l’importance de la définition de la zone d’étude mais aussi sur le fait que la police a tendance à moins être appelée pour des accidents dans des zones où la vitesse des différents modes de transport est contrôlée (petits écarts de vitesse entre modes lents et rapides). Puisque les mesures de sécurité routière influencent à la fois le taux d’accident et le taux de constatation par la police, l’évaluation de leurs effets sur des accidents de cyclistes doit être analysée spatialement avec prudence. 本文旨在比较正式报告警方的涉及自行车骑行者的交通事故与未报告的交通事故（用户来源数据集）的空间分布。 研究表明，经报告和未报告的自行车事故具有相似的空间模式，并且在特定的基础设施和其他设施方面表现出类似的位置分布倾向。 我们说明了在解释（交叉）K函数结果时定义研究区域的重要性，并表明在慢速和快速模式之间速度差异较小的区域（平静交通区域，traffic-calming areas），涉自行车的交通事故不被报告警方的可能性更大。安全措施既会对事故发生率也会对报告率产生影响，因此评估它们对自行车事故的影响需要精细的空间分析。