Internal cohesion and geometric shape of spatial clusters

The geographic delineation of irregularly shaped spatial clusters is an ill defined problem. Whenever the spatial scan statistic is used, some kind of penalty correction needs to be used to avoid clusters’ excessive irregularity and consequent reduction of power of detection. Geometric compactness and non-connectivity regularity functions have been recently proposed as corrections. We present a novel internal cohesion regularity function based on the graph topology to penalize the presence of weak links in candidate clusters. Weak links are defined as relatively unpopulated regions within a cluster, such that their removal disconnects it. By applying this weak link cohesion function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. A multi-objective genetic algorithm (MGA) has been proposed recently to compute the Pareto-sets of clusters solutions, employing Kulldorff’s spatial scan statistic and the geometric correction as objective functions. We propose novel MGAs to maximize the spatial scan, the cohesion function and the geometric function, or combinations of these functions. Numerical tests show that our proposed MGAs has high power to detect elongated clusters, and present good sensitivity and positive predictive value. The statistical significance of the clusters in the Pareto-set are estimated through Monte Carlo simulations. Our method distinguishes clearly those geographically inadequate clusters which are worse from both geometric and internal cohesion viewpoints. Besides, a certain degree of irregularity of shape is allowed provided that it does not impact internal cohesion. Our method has better power of detection for clusters satisfying those requirements. We propose a more robust definition of spatial cluster using these concepts.     

Upper level set scan statistic for detecting arbitrarily shaped hotspots

A declared need is around for geoinformatic surveillance statistical science and software infrastructure for spatial and spatiotemporal hotspot detection. Hotspot means something unusual, anomaly, aberration, outbreak, elevated cluster, critical resource area, etc. The declared need may be for monitoring, etiology, management, or early warning. The responsible factors may be natural, accidental, or intentional. This proof-of-concept paper suggests methods and tools for hotspot detection across geographic regions and across networks. The investigation proposes development of statistical methods and tools that have immediate potential for use in critical societal areas, such as public health and disease surveillance, ecosystem health, water resources and water services, transportation networks, persistent poverty typologies and trajectories, environmental justice, biosurveillance and biosecurity, among others. We introduce, for multidisciplinary use, an innovation of the health-area-popular circle-based spatial and spatiotemporal scan statistic. Our innovation employs the notion of an upper level set, and is accordingly called the upper level set scan statistic, pointing to a sophisticated analytical and computational system as the next generation of the present day popular SaTScan. Success of surveillance rests on potential elevated cluster detection capability. But the clusters can be of any shape, and cannot be captured only by circles. This is likely to give more of false alarms and more of false sense of security. What we need is capability to detect arbitrarily shaped clusters. The proposed upper level set scan statistic innovation is expected to fill this need     

Spatial and space-time scan statistics to detect low rate clusters of sex ratio

Whether general environmental exposures to endocrine disrupting chemicals (including pesticides and dioxin) might induce decreased sex ratios (male/female ratio at birth) is discussed. To address this issue, the authors looked for a space-time clustering test which could detect local areas of significantly low risk, assuming a Bernoulli distribution. As a matter of fact, if the endocrine disruptor hypothesis holds true, and if the sex ratio is a sentinel health event indicative of new reproductive hazards ascribed to environmental factors, then in a given region, either a cluster of low male/female ratio among newborn babies would be expected in the vicinity of polluting municipal solid waste incinerators (MSWIs) (supporting the dioxin hypothesis), or local clusters would be expected in some rural areas where large amounts of pesticides are sprayed. Among cluster detection tests, the spatial scan statistic has been widely used in various applications to scan for areas with high rates, and rarely (if ever) with low rates. Therefore, the goal of this paper was to check the properties of the scan statistics under a given scenario (Bernoulli distribution, search for clusters with low rates) and to assess its added value in addressing the sex ratio issue. This study took place in the Franche-Comté region (France), mainly rural, comprising three main MSWIs, among which only one had high dioxin emissions level in the past. The study population consisted of 192,490 boys and 182,588 girls born during the 1975–1999 period. On the whole, the authors conclude that: (i) spatial and space-time scan statistics provide attractive features to address the sex ratio issue; (ii) sex ratio is not markedly affected across space and does not provide a reliable screening measure for detecting reproductive hazards ascribed to environmental factors.     

Local multiplicity adjustments for spatial cluster detection

The spatial scan statistic is a widely applied tool for cluster detection. The spatial scan statistic evaluates the significance of a series of potential circular clusters using Monte Carlo simulation to account for the multiplicity of comparisons. In most settings, the extent of the multiplicity problem varies across the study region. For example, urban areas typically have many overlapping clusters, while rural areas have few. The spatial scan statistic does not account for these local variations in the multiplicity problem. We propose two new spatially-varying multiplicity adjustments for spatial cluster detection, one based on a nested Bonferroni adjustment and one based on local averaging. Geographic variations in power for the spatial scan statistic and the two new statistics are explored through simulation studies, and the methods are applied to both the well-known New York leukemia data and data from a case–control study of breast cancer in Wisconsin.     

An improved approach for identifying suitable habitat of Sambar Deer (Cervus unicolor Kerr) using ecological niche analysis and environmental categorization: Case study at Phu-Khieo Wildlife Sanctuary,Thailand

To assess habitat suitability (HS) has become an increasingly important component of species/ecosystem management. HS assessment is usually based on presence/absence data related to environmental variables. An exception is Ecological Niche Factor Analysis (ENFA), which uses only presence data and which does not require absence data. Most HS modelling is based on input of all environmental parameters (EnvPs) without environmental categorization, and does not take into account species interaction and human intervention for an assessment of HS. In this study, the EnvPs are arranged into four features: geographical features, consumable features, human-factor features, and species–human interaction features. These features affect species with respect to movement, behavior and activity. The research presented here has used an already existing dataset of wildlife species and human activities/visitations, which was compiled during 2004–2006 in Phu-Khieo Wildlife Sanctuary (PKWS). Data from 2004 to 2005 were used to produce HS maps, while the data of 2006 were used for evaluating these maps. Sambar Deer (SD) was chosen to predict its own HS. Six HS maps of SD were analyzed using ENFA in the following manner: (1) inputting all EnvPs together, (2) inputting each feature, separately and (3) integrating the four resulting HS maps by model averaging. It was found that model averaging was capable of predicting the HS of SD more reliably than the model with all EnvPs put in together. Multiple linear regressions were computed between the HS map with all EnvPs and the HS maps with each feature. The results show that the HS map with only geographical features has the highest coefficient value (0.516) while the coefficient values of other HS maps with the above features are 0.296, 0.53 and −0.006, respectively. This indicates that the geographical features have an influence on the other features and that the predicting power is lower when all EnvPs are computed in the ENFA model. Therefore, in order to generate HS, each feature should at first be put into the model separately. Following that, the average of all features will be combined.     

Multi-lag cluster designs for estimating the semivariogram for sediments affected by effluent discharges offshore in San Diego

Maps are useful tools for understanding, managing, and protecting the marine environment, yet few useful and statistically defensible maps of environmental quality and aquatic resources have been developed in near-coastal regions. Current environmental management efforts, such as ocean monitoring by sewage dischargers, routinely sample areas of potential impact using sparse sampling grids. Heterogeneous oceanic conditions often make extrapolation from these grids to non-sampled locations questionable. Although rarely applied in coastal monitoring, kriging offers a more rigorous statistical approach to mapping and allows confidence intervals to be calculated for predictions. Its usefulness relies on accurate models of the spatial variability through estimating the semivariogram. Many optimal designs for estimating the semivariogram have been proposed, but these designs are often difficult to implement in practice. In this paper, we present simple design strategies for augmenting existing monitoring designs with the goal of estimating the semivariogram. In particular, we investigate a multi-lag cluster design strategy, where clusters of sites, spaced at various lag distances, are placed around fixed stations on an existing sampling grid. We find that these multi-lag cluster designs provide improved accuracy in estimating the parameters of the semivariogram. Based on simulation study findings, we apply a multi-lag cluster enhancement to the monitoring grid for the City of San Diego’s Point Loma Wastewater Treatment Plant as part of a special study to map chemical contaminants in sediments around its sewage outfall.     

A spatial scan statistic for zero-inflated Poisson process

The scan statistic is widely used in spatial cluster detection applications of inhomogeneous Poisson processes. However, real data may present substantial departure from the underlying Poisson process. One of the possible departures has to do with zero excess. Some studies point out that when applied to data with excess zeros, the spatial scan statistic may produce biased inferences. In this work, we develop a closed-form scan statistic for cluster detection of spatial zero-inflated count data. We apply our methodology to simulated and real data. Our simulations revealed that the Scan-Poisson statistic steadily deteriorates as the number of zeros increases, producing biased inferences. On the other hand, our proposed Scan-ZIP and Scan-ZIP+EM statistics are, most of the time, either superior or comparable to the Scan-Poisson statistic.     

Comparing and Integrating Community-Based and Science-Based Approaches to Prioritizing Marine Areas for Protection

Abstract:  We compared and integrated marine protected areas proposed through community and scientific assessments in 2 regions of British Columbia, Canada. The community priorities were identified during individual and group interviews with knowledgeable resource users. The scientific priorities were developed with abiotic and biotic data in Marxan, a decision-support tool. The resulting maps of community-based and science-based priorities were very similar for the inshore areas, which lent credibility to both approaches. The resource users thought the science-based maps were fairly good at highlighting areas important for conservation, but preferred the scenarios that integrated the 2 maps to either constituent map. Incorporating spatial variation in human impacts on the marine areas and commercial fishing, which are both costs of protection, into our Marxan analyses led to scenarios that were different from either constituent map. Our results show the value of integrating community-based and science-based approaches in conservation planning to achieve community acceptance and conservation utility. They also reveal that people's assessments on the basis of their traditional ecological knowledge may serve as a reasonable proxy for scientific approaches in selecting areas of ecological value.     

Power evaluation of focused cluster tests

Many statistical tests have been developed to assess the significance of clusters of disease located around known sources of environmental contaminants, also known as focused disease clusters. The majority of focused-cluster tests were designed to detect a particular spatial pattern of clustering, one in which the disease cluster centers around the pollution source and declines in a radial fashion with distance. However, other spatial patterns of environmentally related disease clusters are likely given that the spatial dispersion patterns of environmental contaminants, and thus human exposure, depend on a number of factors (i.e., meteorology and topography). For this study, data were simulated with five different spatial patterns of disease clusters, reflecting potential pollutant dispersion scenarios: (1) a radial effect decreasing with increasing distance, (2) a radial effect with a defined peak and decreasing with distance, (3) a simple angular effect, (4) an angular effect decreasing with increasing distance and (5) an angular effect with a defined peak and decreasing with distance. The power to detect each type of spatially distributed disease cluster was evaluated using Stone’s Maximum Likelihood Ratio Test, Tango’s Focused Test, Bithell’s Linear Risk Score Test, and variations of the Lawson–Waller Score Test. Study findings underscore the importance of considering environmental contaminant dispersion patterns, particularly directional effects, with respect to focused-cluster test selection in cluster investigations. The effect of extra variation in risk also is considered, although its effect is not substantial in terms of the power of tests.     

Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India

Fire is the most spectacular natural disturbance that affects the forest ecosystem composition and diversity. Fire has a devastating effect on the landscape and its impact is felt at every level of the ecosystem and it is possible to map forest fire risk zone and thereby minimize the frequency of fire. There is a need for supranational approaches that analyze wide scenarios of factors involved and global fire effects. Fires can be monitored and analyzed over large areas in a timely and cost effective manner by using satellite imagery. Also Geographical Information System (GIS) can be used effectively to demarcate the fire risk zone map. Bhadra wildlife Sanctuary located in Kamataka, India was selected for this study. Vegetation, slope, distance from roads, settlements parameters were derived for a study area using topographic maps and field information. The Remote Sensing (RS) and Geographical Information System (GIS)-based forest fire risk model of the study area appeared to be highly compatible with the actual fire-affected sites. The temporal satellite data from 1989 to2006 have been analyzed to map the burnt areas. These classes were weighted according to their influence on forest fire. Four categories of fire risk regions such as Low, Moderate, High and Very high fire intensity zones were identified. It is predicted that around 10.31% of the area falls undermoderate risk zone.     

West Indian Ocean phytoplankton: a numerical investigation of phytohydrographic regions and their characteristic phytoplankton associations

Phytoplankton samples were collected from a large area of the West Indian Ocean and 237 different species identified. The object of this study was to define structure in the species distributions and to relate this to environmental factors. Numerical methods of cluster analysis are described, with adaptation and application to this ecological study of phytoplankton. The phytoplankton stations were grouped according to their phytoplankton populations; these groupings were related firstly to the monsoon seasons, and secondly to the hydrology, in order to derive phytohydrographic regions. The species were then grouped according to hydrographical distribution in order to derive the floral elements (species associations) which typify the different regions. The samples divided seasonally into 2 overlapping clusters, those sampled prior to the South West Monsoon, and those sampled subsequently. In both seasonal groups 4 main phytohydrographic regions were isolated from (1) equatorial subsurface water; (2) the equatorial undercurrent boundary; (3) the South Equatorial Current; (4) the surface tropical water. A total of 11 different floral elements were derived to account for the phytohydrographic groupings. The distribution of the largest element (50 species) was centred in equatorial subsurface water. This element dominated all samples, and was considered to comprise the endemic Indian Ocean flora. The effects of the other minor floral elements were superimposed on the dominance or otherwise of this element in the samples. These different minor floral elements were characteristic of (a) different seasonal divisions; (b) regional differences in equatorial subsurface water; (c) traversing currents; (d) nutrientrich regions of instability.     

风险地图—环境风险管理的有效新工具

中国不断发生的重大环境突发事故使得环境风险管理提上了政府的议事日程,建立有效和实用的环境风险管理体系势在必行。近年来,风险地图已成为环境风险管理中迅速崛起的新领域。风险地图不但可以探讨污染物效应的空间特征,而且可作为风险可视化的重要工具。通过调研和分析大量文献,在描述了环境风险地图的功能、重要的风险地图类型基础上,以一个城市为例展示了风险图制作流程,最后,对环境风险地图在中国的应用和发展提出了建议。     

A fair comparison between the spatial scan and the Besag–Newell Disease clustering tests

Routine surveillance of a large geographic region for clusters of adverse health events, particularly cancers, often involves small area health data, possibly controlling for exposure information. Many different methods have been proposed to test for the presence of geographical clusters. Two of the most popular methods are the spatial scan method proposed by Kulldorff and that using a fixed number of cases within scanning circles proposed by Besag and Newell. Although the second test is very popular, it has some difficulties. While the scan test controls for the multiple testing problem, the Besag and Newell test does not. Additionally, the latter method requires the setting of several tuning parameters whose values affect the test performance and are subjectively chosen by the user. This creates a difficulty to make a fair comparison between the two methods and it explains why there have been few formal studies evaluating their relative performances. In this paper, we modify the Besag and Newell test allowing for the control of the error type I probability and compare its power with respect to that of the spatial scan test. We used data sets from a publicly available simulated benchmark. We found that the two methods have similar results, except for clusters located in sparsely populated regions, where the spatial scan method presented a better performance.     

Interactions between environment, species traits, and human uses describe patterns of plant invasions

Although invasive alien species (IAS) are a major threat to biodiversity, human health, and economy, our understanding of the factors controlling their distribution and abundance is limited. Here, we determine how environmental factors, land use, life-history traits of the invaders, residence time, origin, and human usage interact to shape the spatial pattern of invasive alien plant species in South Africa. Relationships between the environmental factors and the extrinsic and intrinsic attributes of species were investigated using RLQ analysis, a multivariate method for relating a species-attribute table to an environmental table by way of a species presence/absence table. We then clustered species according to their position on the RLQ axes, and tested these groups for phylogenetic independence. The first three axes of the RLQ explained 99% of the variation and were strongly related to the species attributes. The clustering showed that, after accounting for environmental factors, the spatial pattern of IAS in South Africa was driven by human uses, life forms, and reproductive traits. The seven clusters of species strongly reflected geographical distribution, but also intrinsic species attributes and patterns of human use. Two of the clusters, centered on the genera Acacia and Opuntia, were phylogenetically non-independent. The remaining clusters comprised species of diverse taxonomic affinities, but sharing traits facilitating invasion in particular habitats. This information is useful for assessing the extent to which the potential spread of recent introductions can be predicted by considering the interaction of their biological attributes, region of origin, and human use.     

土壤可蚀性（K）值图编制方法的初步研究

利用土壤普查图件和剖面理化分析的成果资料编制了土壤可蚀性（Ｋ）值图，介绍了求取剖面点Ｋ值的查图表法、图斑分并与图斑Ｋ值的确定原则和编制程序等，应用上述方法，完成了我国第一张地区级Ｋ值图，经分析，与公式算法比，查图表法的Ｋ值精度为８５．４％，与径流小区实测值比，用Ｋ值图上相应Ｋ值监测的土壤年流失量的精度为８６．０％。     

东北红豆杉植物地理学研究

总结了东北红豆杉的地理分布概况和分布区特点，绘制了分布图，以在我国境内分布区域的主导气象因子构成指示指标组建立了东北红豆杉自然地理分布模型，并通过实例证明了该模型的有效性。     

Multiscale advanced raster map analysis system: Definition, design and development

This paper brings together a multidisciplinary initiative to develop advanced statistical and computational techniques for analyzing, assessing, and extracting information from raster maps. This information will provide a rigorous foundation to address a wide range of applications including disease mapping, emerging infectious diseases, landscape ecological assessment, land cover trends and change detection, watershed assessment, and map accuracy assessment. It will develop an advanced map analysis system that integrates these techniques with an advanced visualization toolbox, and use the system to conduct large case studies using rich sets of raster data, primarily from remotely sensed imagery. As a result, it will be possible to study and evaluate raster maps of societal, ecological, and environmental variables to facilitate quantitative characterization and comparative analysis of geospatial trends, patterns, and phenomena. In addition to environmental and ecological studies, these techniques and tools can be used for policy decisions at national, state, and local levels, crisis management, and protection of infrastructure. Geospatial data form the foundation of an information-based society. Remote sensing has been a vastly under-utilized resource involving a multi-million dollar investment at the national levels. Even when utilized, the credibility has been at stake, largely because of lack of tools that can assess, visualize, and communicate accuracy and reliability in timely manner and at desired confidence levels. Consider an imminent 21st century scenario: What message does a multi-categorical map have about the large landscape it represents? And at what scale, and at what level of detail? Does the spatial pattern of the map reveal any societal, ecological, environmental condition of the landscape? And therefore can it be an indicator of change? How do you automate the assessment of the spatial structure and behavior of change to discover critical areas, hot spots, and their corridors? Is the map accurate? How accurate is it? How do you assess the accuracy of the map? How do we evaluate a temporal change map for change detection? What are the implications of the kind and amount of change and accuracy on what matters, whether climate change, carbon emission, water resources, urban sprawl, biodiversity, indicator species, human health, or early warning? And with what confidence? The proposed research initiative is expected to find answers to these questions and a few more that involve multi-categorical raster maps based on remote sensing and other geospatial data. It includes the development of techniques for map modeling and analysis using Markov Random Fields, geospatial statistics, accuracy assessment and change detection, upper echelons of surfaces, advanced computational techniques for geospatial data mining, and advanced visualization techniques.     

Mitochondrial DNA Variation in Black Rhinoceros (Diceros bicornis): Conservation Management Implications

Cell cultures have been established from 33 individual black rhinoceroses. These were from wild populations from various localities in southern Africa and include representatives from three geographical regions (southwestern, south-central, and eastern) corresponding to currently accepted conservation units, and include individuals previously attributed to one of the four subspecies, Diceros b. minor, D. b. bicornis, D. b. michaeli , and D. b. chobiensis (du Toit et al. 1987). Comparative mitochondrial DNA restriction maps were constructed using 16 restriction enzymes. These showed in each case two site differences between representative individuals from any two of the above geographical regions. Maps were monomorphic within geographical regions and, therefore, have the potential to provide diagnostic markers. The map from a single individual attributed to the D. b. chobiensis subspecies was identical to other individuals (attributed to D. b. minor ) in the south-central geographical region. The low amount of genetic diversity implied by these few differences renders it unlikely that problems with outbreeding depression will arise if, given the continuing decline in numbers of black rhinoceroses, it becomes necessary to supplement wild or captive populations with individuals from a different conservation unit in order to avoid inbreeding depression.     

European Atlas of the Seas: “a picture is worth a thousand words”

The European Atlas of the Seas offers a snapshot of environmental and socio-economic features that characterize the coastal and marine environment. The latest release (Version 4) addresses the public in general, but also non-specialist experts involved with environmental issues, human activities or policies related to Europe’s coasts and seas. The information content of the Atlas comprises a series of geographical layers, subdivided in “background maps”, “thematic maps” (i.e. maritime Europe, natural setting, sea bottom, sea level rise, security, transport, tourism, energy, wind, fisheries and fish consumption) and “do-it-yourself maps” (dealing with marine knowledge, nature and environment, socio-economics, fisheries, aquaculture, transport, energy, sea bed mining, coastal tourism, Maritime Spatial Planning, integrated maritime surveillance, and international ocean governance). All maps follow consistent cartographic rules and can be extracted for external use. The Atlas database is updated regularly, but historical data remain accessible after the updates, so that time series may be constructed. Tools for map exploration and combination can be used to combine together more layers, providing professional users with analysis and interpretation capabilities, to couple data into graphical indicators. The Atlas aims to supports also policy making, on marine environment, maritime issues and economic sectors, both within and outside the European Institutions (e.g. on Common Fisheries Policy or Maritime Spatial Planning). Further, it expands the same support to near-coastal issues and matters related to land-sea interactions. The web application for accessing Atlas contents offers links to other Marine Information Systems, and is available to a broad audience from computers, tablets and mobile devices.