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     

Infant mortality and social environment in Georgia: an application of hotspot detection and prioritization

Recent years have witnessed the growth of new information technologies and their applications to various disciplines. The goal of this paper is to demonstrate how the two innovative methods, upper level set scan (ULS) hotspot detection and the multicriteria prioritization scheme, facilitate population health and break new ground in public health surveillance. It is believed that the social environment (i.e. social conditions and social capital) is one of the determinants of human health. Using infant health data and 10 additional indicators of social environment in the 159 counties of Georgia, ULS identified 52 counties that are in double jeopardy (high infant mortality and a high rate of low infant birth weight). The multicriteria ranking scheme suggested that there was no conspicuous spatial cluster of ranking orders, which improved the traditional decision making by visual geographic cluster. Both hotspot detection and ranking methods provided an empirical basis for re-allocating limited resources and several policy implications could be drawn from these analytic results.     

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.     

Biodiversity protection funding preference: a case study of hotspot geoinformatics and digital governance for the Map of Italian Nature in the presence of multiple indicators of ecological value,ecological sensitivity and anthropic pressure for the Oltrepò Pavese and Ligurian-Emilian Apennine study area in Italy

The environmental decision-maker is aware of the increasing difficulties in finding sufficient financial resources for nature conservation. So he must focus his attention on ecological situations that more than the others merit considering and defending because of elevated value but also because of risk for their intrinsic characteristics and for human pressure acting on them. Usually an ecological scientist focuses his attention on the natural patches of the landscape, analyzing their peculiar ecological traits forgetting that, even if we want to protect some environmental critical situations, this can be done only moving to the administrative partition of the territory since the central and local environmental stakeholders have primary interest in providing funds to those involved in those critical situations. The present work shows a methodological approach, consisting of a set of statistical and geoinformational tools, considering both ecological and socio-demographical indicators. The goal is not simply to give some general guidelines for environmental policies to the involved stakeholders but focuses more on finding out which administrative local partitions in a study area are more worthy to receive urgently the priority funds for biodiversity protection to face critical environmental situations often due to a combination of intrinsic ecological parameters and external human pressure ones. Obtaining results that cover 5% of the Communes involved in the area seems to be a realistic result that a decision-maker can support and fund. Methodologically and geospatial data analytically, the investigation offers interesting challenges for surveillance geoinformatics of hotspot detection and prioritization, because of the presence of multiple hotspots and multiple sets of multiple indicators.     

基于RS和GIS的生态系统健康评价

生态系统健康评价可以认识区域生态系统健康状况、识别生态环境问题,为制定科学的生态保护对策提供依据,对提高可持续发展和环境管理具有重要的指导作用。文章由压力-状态-响应（P-S-R）概念模型建立了生态系统健康评价指标体系和评价模型,运用RS和GIS技术及统计学分析法,形成程序化、系统化的生态系统健康评价技术方法体系,并以高平市为案例,运用定量的方法对其生态系统健康进行综合评价及分级研究,通过对压力、状态、响应评价结果及健康综合评价结果的分析,结合高平市生态系统健康的自然条件状况与人类活动影响,探讨了影响区域健康的因素,为高平市资源的合理利用与保护提供科学依据。结果表明该技术对生态系统健康评价是切实可行的。     

PULSE,progressive upper level set scan statistic for geospatial hotspot detection

This paper presents a scan statistic, progressive upper level set (PULSE) scan statistic, for geospatial hotspot detection and its software implementation. Like ULS, the PULSE scan statistic is based on the arbitrarily shaped scan window and can be adapted for a network setting. PULSE is a refinement of the upper level set (ULS) scan statistic. Like some other likelihood based scanning devices, the ULS scan statistic identifies maximum likelihood estimate (MLE) zones that tend to be ‘stringy’ and sprawling. Its search path increases possibility of inclusion of extraneous cells in its MLE zones and, to a smaller extent, of exclusion of cells that belong to a true hotspot from its MLE zone. The PULSE scan statistic achieves improvement over the ULS scan statistic in two ways. First, it begins its search for a most likely zone with a large population of candidate zones obtained by modifying the ULS tree structure and continues its search using a genetic algorithm. Secondly, to reduce chances of generating an MLE that is excessively stringy and that includes extraneous cells in the MLE zone, PULSE uses cardinality and compactness of zones along with their likelihoods as the fitness function in the genetic algorithm and uses several pertinent criteria including evenness of intra-zone cellular response ratios to determine the MLE zone. To reduce computation, Gumbel distribution of extreme values is used to determine the p-value of the MLE zone. Better results come at the cost of increased processing time. An evaluative performance study is presented.     

Indicators of ecosystem function identify alternate states in the sagebrush steppe

Models of ecosystem change that incorporate nonlinear dynamics and thresholds, such as state-and-transition models (STMs), are increasingly popular tools for land management decision-making. However, few models are based on systematic collection and documentation of ecological data, and of these, most rely solely on structural indicators (species composition) to identify states and transitions. As STMs are adopted as an assessment framework throughout the United States, finding effective and efficient ways to create data-driven models that integrate ecosystem function and structure is vital. This study aims to (1) evaluate the utility of functional indicators (indicators of rangeland health, IRH) as proxies for more difficult ecosystem function measurements and (2) create a data-driven STM for the sagebrush steppe of Colorado, USA, that incorporates both ecosystem structure and function. We sampled soils, plant communities, and IRH at 41 plots with similar clayey soils but different site histories to identify potential states and infer the effects of management practices and disturbances on transitions. We found that many IRH were correlated with quantitative measures of functional indicators, suggesting that the IRH can be used to approximate ecosystem function. In addition to a reference state that functions as expected for this soil type, we identified four biotically and functionally distinct potential states, consistent with the theoretical concept of alternate states. Three potential states were related to management practices (chemical and mechanical shrub treatments and seeding history) while one was related only to ecosystem processes (erosion). IRH and potential states were also related to environmental variation (slope, soil texture), suggesting that there are environmental factors within areas with similar soils that affect ecosystem dynamics and should be noted within STMs. Our approach generated an objective, data-driven model of ecosystem dynamics for rangeland management. Our findings suggest that the IRH approximate ecosystem processes and can distinguish between alternate states and communities and identify transitions when building data-driven STMs. Functional indicators are a simple, efficient way to create data-driven models that are consistent with alternate state theory. Managers can use them to improve current model-building methods and thus apply state-and-transition models more broadly for land management decision-making.     

Residential radon concentration in the Abruzzo region (Italy): a different perspective for identifying radon prone areas

Indoor radon is an important risk factor for human health. Indeed radon inhalation is considered the second cause of lung cancer after smoking. During the last decades, in many countries huge efforts have been made in order to measuring, mapping and predicting radon levels in dwellings. Various researches have been devoted to identify those areas within the country where high radon concentrations are more likely to be found. Data collected through indoor radon surveys have been analysed adopting various statistical approaches, among which hierarchical Bayesian models and geostatistical tools are worth noting. The essential goal of this paper regards the identification of high radon concentration areas (the so-called radon prone areas) in the Abruzzo Region (Italy). In order to accurately pinpoint zones deserving attention for mitigation purpose, we adopt spatial cluster detection techniques, traditionally employed in epidemiology. As a first step, we assume that indoor radon measurements do not arise from a continuous spatial process; thus the geographic locations of dwellings where the radon measurements have been taken can be viewed as a realization of a spatial point process. Following this perspective, we adopt and compare recent cluster detection techniques: the simulated annealing scan statistic, the case event approach based on distance regression on the selection order and the elliptic spatial scan statistic. The analysis includes data collected during surveys carried out by the Regional Agency for the Environment Protection of Abruzzo (ARTA) in 1,861 random sampled dwellings across 277 municipalities of the Abruzzo region. The radon prone areas detected by the selected approaches are provided along with the summary statistics of the methods. Finally, the methodologies considered in this paper are tested on simulated data in order to evaluate their power and the precision of cluster location detection.     

Preliminary prioritization based on partial order theory and R software for compositional complexes in landscape ecology,with applications to restoration,remediation, and enhancement

The present purpose is to provide convenient computational capability and visualizations for preliminary partial or progressive prioritization based largely on concepts of partial order theory and implemented in R software as illustrated in a context of conservation and sustainable stewardship across landscapes with ecosystem services as a complex multidimensional domain that must be placed in public and private perspective in pursuit of multi-resource management. Practical perspective is promoted by graphic visualization with local partial order modeling (LPOM) methods for screening of settings and scenarios involving interactions of ecosystem elements as evidenced by environmental indicators. ORDIT ordering and precedence plots arise from ascribed advantage as an outcome of a rating regime. Representative ranks constitute criteria drawn from the rank distribution for the case in question. Distal data are determined with regard to remediation and retention. Median mismatches reflect interplay of indicators appearing as isolated instances in plotting patterns. A suggested strategy to circumvent computational constraints is partitioning the pool of cases into collectives by clustering, pursuing classes of partitions, and then prioritizing in particular partitions. When prime prospects have been obtained, detailed determinations can be done with partial ordering procedures involving Hasse diagrams and similarly complex constructs that are difficult to apply with many cases and/or indicator criteria.     

国际生态学研究发展态势文献计量分析

目前,全球的生态环境问题已经引起了各国政府和人民的高度关注,而生态学研究可以为人类与自然生态系统的共存提供理论依据和行动指南。利用Thomson Data Analyzer、NetDraw和Aureka等分析工具,结合Pathfinder算法,对SCIE和SSCI数据库中国际生态学研究相关文献进行统计分析,发现：北美、欧洲、澳大利亚等国及其研究机构在生态学领域的论文影响力较大,论文质量较高;美国是全球生态学研究合作网络的中心,其次是英国和德国;在机构层面上,加州大学戴维斯分校和马普学会是两个比较明显的合作中心;我国在生态学研究领域发文量上具有一定的优势,但论文总体质量仍有待继续提高,目前我国在生态学领域的主要合作国家有美国、英国、加拿大、德国、日本、澳大利亚和法国;2008—2010年国际生态学研究热点主要集中在生物多样性、气候变化、基因变异、物种间相互作用和性选择等研究领域。     

Uranium mining in Portugal: a review of the environmental legacies of the largest mines and environmental and human health impacts

The history of uranium mining in Portugal during almost one century has followed international demand peaks of both radium and uranium, which in turn were driven by medical, military, and civil applications. Nowadays, following price drop in the 1980s, mining activities decreased and ceased in 2001. The current challenge is to deal with environmental legacies left by old uranium mines, mainly located in Viseu and Guarda districts. In 2001, based on several radiological surveys carried out, the Portuguese government assumed the remediation costs of abandoned mine areas for environmental safety and public health protection. Detailed environmental and public health risk assessments were performed under the scope of studies both requested by the government and by funded research projects. It was found that the existing risks, due to radiological and chemical exposures to metals and radionuclide’s, were particularly high at the old milling facilities and mines where in situ and heap leaching of low-grade ore occurred. The different studies, involving both humans and non-human species from different trophic levels, demonstrated the existence of effects at different levels of biological organization (molecular, cellular, tissues, individuals, and populations) and on ecosystem services. To mitigate the risks, the environmental rehabilitation works at the Urgeiriça mine complex are almost complete, while at Cunha Baixa mine, they are presently in progress. These works and environmental improvements achieved and expected are described herein.     

Land-cover change within and around protected areas in a biodiversity hotspot

The landscape surrounding protected areas influences their ability to maintain ecosystem functions and achieve conservation goals. As anthropogenic intensification continues, it is important to monitor land-use and land-cover change in and around protected areas. We measure land-cover change surrounding protected areas in the Maputaland-Pondoland-Albany Biodiversity hotspot from the 1980s to present. Using Landsat imagery, we classified land cover within and around each protected area. Agricultural land uses were increasing and often directly border protected area boundaries. Human settlements increased around every protected area, potentially increasing human activity along the edges of protected areas and threatening their ecological integrity. Urban expansion around protected areas varied but increased as much as 10%. Woody vegetation cover varied both within and around protected areas with possible evidence of deforestation and shrub encroachment throughout the hotspot. We recommend monitoring land cover across southeastern Africa to better understand regional trends in land-use impacts to protected areas.     

湿地生态风险评价中生物完整性指数研究进展

湿地是陆地与水域之间的过渡地带,是地球上生产力最高的生态系统。湿地生态风险评价的实际应用将使人们更好地理解物理、化学和生物风险源如何影响湿地,并为湿地管理提供科学支撑,这就要求确定湿地生态健康评价指标的完整性。生物完整性指数以环境生态毒理学数据为依据,是进行生态系统健康风险评价的最有力工具。大中型无脊椎动物作为易选择的分类群,可用于湿地评估的生物完整性指数的建立,土壤动物特别是线虫类群作为湿地土壤和水环境健康评价的指示生物具有广阔前景。通过线虫分子毒理学等研究方法,可优化出生物完整性指数体系,建立扰动背景下的湿地生态风险评价模型,为湿地污染的监测、防控和修复,提供理论依据和实践方法。     

A new scheme for initializing process-based ecosystem models by scaling soil carbon pools

Process-based ecosystem models are useful tools, not only for understanding the forest carbon cycle, but also for predicting future change. In order to apply a model to simulate a specific time period, model initialization is required. In this study, we propose a new scheme of initialization for forest ecosystem models, which we term a “slow-relaxation scheme”, that entails scaling of the soil carbon and nitrogen pools slowly during the spin-up period. The proposed slow-relation scheme was tested with the CENTURY version 4 ecosystem model. Three different combinations of scaled soil pools were also tested, and compared to the results from a fast-relaxation regime. The fast-relaxation of soil pools produced unstable, transient model behaviour whereas slow-relaxation overcame this instability. This approach holds promise for initializing ecosystem models, and for starting simulations with more realistic initial conditions.     

Urban ecosystem health assessment based on emergy and set pair analysis—A comparative study of typical Chinese cities

Regarding various energy and materials flowing in the urban ecosystem and the merit of emergy as an embodied energetic equivalent for integrated ecological economic evaluation, an evaluation framework of emergy-based urban ecosystem health indicators (UEHI_em) was established in view of five aspects including vigor, structure, resilience, ecosystem service function maintenance and environmental impact to depict the urban ecosystem health states. Further, set pair analysis (SPA) was employed to assess the urban ecosystem health level based on the UEHI_em, by which the approximate degree of real index set to the optimal one was defined and evaluated to describe the relative health state of the concerned urban ecosystems. Choosing twenty typical Chinese cities in 2005 as cases, we evaluated and compared their urban ecosystem health levels based on UEHI_em and SPA. The results showed that health levels of Xiamen, Qingdao, Shenzhen and Shanghai are pretty well, while those of Wuhan, Harbin, Yinchuan, Beijing and Urumchi are relatively weak. Moreover, the relative health levels were analyzed by SPA to discern the influences of the mentioned five aspects on the UEHI_em. It is concluded that emergy synthesis combined with SPA can serve as an effective relative-measure to compare different ecosystem health levels of urban ecosystems.     

重要生态功能区典型生态服务及其评估指标体系的构建

科学评估重要生态功能区的生态服务是管理者制定相关政策的基础,对促进重要生态功能区维护区域生态安全、支撑经济社会可持续发展具有重要意义。依据生态服务的空间转移特性,探索提出了重要生态功能区典型生态服务的概念与内涵,并在此基础上综合运用频度分析法、专家咨询法和层次分析法,构建了重要生态功能区水源涵养、土壤保持、防风固沙、生物多样性保护和洪水调蓄等典型生态服务的评估指标体系。结果表明：在指标体系中,影响水源涵养服务的主要是土壤质地和降水量;影响土壤保持服务的主要是植被覆盖度和丰富度指数;影响防风固沙服务的主要是植被覆盖度和大风时速;影响生物多样性服务的主要是植被景观多样性指数;影响洪水调蓄服务的主要是湿地容积和降水量。土壤、植被、地形和气候等生态因子是重要生态功能区典型生态服务产生与发挥的重要基础。     

Spatial information in ecosystem service assessment: data applicability in the cascade model context

Spatial information and geographical information systems (GISs) are widely used in ecosystem service research, but both the information and the methods need to be properly understood in order to make coherent analyses. We discuss the practical challenges of incorporating spatial data to ecosystem service assessment in an agricultural landscape and apply the ecosystem service cascade model to put different data into context. We review the prerequisites and practices for successful ‘ecosystem service GIS’ and provide a structured view of the information and data needed in the assessment of ecosystem services at a regional scale. Due to the heterogeneity of the spatial data, the regional characteristics should be considered in environmental decision-making through ethnographic research on local expertise to make optimal choices in using spatial information.     

Predicting breeding habitat for amphibians: a spatiotemporal analysis across Yellowstone National Park

The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000-2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probability, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from (1) field survey site data only, (2) field data combined with data from geospatial models, and (3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 to 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of "core" breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative areas that could be protected for each species, including 6828-10 764 ha for tiger salamanders, 971-3017 ha for western toads, 4732-16 696 ha for boreal chorus frogs, and 4940-19 690 ha for Columbia spotted frogs.     

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.     

基于属性理论的长株潭城市群生态系统健康评价

城市生态系统是一种高度人工化的自然-社会-经济复合生态系统,其健康状况直接影响到城市的可持续发展。针对城市生态系统健康评价标准的不确定性问题,在构建评价指标体系的基础上,提出了基于属性理论的城市生态系统健康评价模型及评价方法。以长株潭城市群为研究区域,运用该模型和方法对其生态系统健康进行了综合评价。评价结果表明：长株潭城市群的生态系统现状属于一般健康类,其中自然生态子系统对区域整体健康状况有较大影响,评价结果与实际情况大体吻合。通过对各子系统层的健康度分析,识别了健康限制因素,并提出了相应的调控措施。采用属性理论方法开展城市生态系统健康评价,能较好地识别系统层综合健康状况和子系统层的健康状况,具有一定的实用价值,其评价结果为促进城市生态建设,有效实施生态系统健康管理提供了科学依据。