Robert N. Stewart

An environmental decision support system for spatial assessment and selective remediation

Spatial Analysis and Decision Assistance (SADA) is a Windows freeware program that incorporates spatial assessment tools for effective environmental remediation. The software integrates modules for GIS, visualization, geospatial analysis, statistical analysis, human health and ecological risk assessment, cost/benefit analysis, sampling design, and decision support. SADA began as a simple tool for integrating risk assessment with spatial modeling tools. It has since evolved into a freeware product primarily targeted for spatial site investigation and soil remediation design, though its applications have extended into many diverse environmental disciplines that emphasize the spatial distribution of data. Because of the variety of algorithms incorporated, the user interface is engineered in a consistent and scalable manner to expose additional functionality without a burdensome increase in complexity. The scalable environment permits it to be used for both application and research goals, especially investigating spatial aspects important for estimating environmental exposures and designing efficient remedial designs. The result is a mature infrastructure with considerable environmental decision support capabilities. We provide an overview of SADAs central functions and discuss how the problem of integrating diverse models in a tractable manner was addressed.

Formal Scenario Development for Environmental Impact Assessment Studies

Abstract Scenario analysis is a process of evaluating possible future events through the consideration of alternative plausible, though not equally likely, states (scenarios). The analysis is designed to enable improved decision making and assessment through a more rigorous evaluation of possible outcomes and their implications. For environmental impact and integrated assessment studies, the process of scenario development typically involves making explicit and/or implicit assumptions about potential future conditions, such as climate change, land cover and land use changes, population growth, economic development and technological changes. Realistic assessment of scenario impacts often requires complex integrated modelling frameworks that represent environmental and socioeconomic systems to the best of our knowledge, including assumptions about plausible future conditions. In addition, scenarios have to be developed in a context relevant to the stakeholders involved, and include estimation and communication of uncertainties, to establish transparency, credibility and relevance of scenario results among the stakeholders. This paper reviews the state of the art of scenario development and analysis, proposes a formal approach to scenario development in environmental studies and discusses existing issues. Major recommendations for future research in this area include proper consideration of uncertainty involved in scenario studies, construction of scenarios of a more variable nature, and sharing of information and resources among the scenario development research community.

SADA: Ecological Risk Based Decision Support System for Selective Remediation

Spatial Analysis and Decision Assistance (SADA) is freeware that implements terrestrial + criteria were applied to determine a spatially explicit remedial design that reduced shrew exposures to protective levels.

Uncertainty quantification techniques for population density estimates derived from sparse open source data

The Population Density Tables (PDT) project at Oak Ridge National Laboratory (www.ornl.gov) is developing population density estimates for specific human activities under normal patterns of life based largely on information available in open source. Currently, activity-based density estimates are based on simple summary data statistics such as range and mean. Researchers are interested in improving activity estimation and uncertainty quantification by adopting a Bayesian framework that considers both data and sociocultural knowledge. Under a Bayesian approach, knowledge about population density may be encoded through the process of expert elicitation. Due to the scale of the PDT effort which considers over 250 countries, spans 50 human activity categories, and includes numerous contributors, an elicitation tool is required that can be operationalized within an enterprise data collection and reporting system. Such a method would ideally require that the contributor have minimal statistical knowledge, require minimal input by a statistician or facilitator, consider human difficulties in expressing qualitative knowledge in a quantitative setting, and provide methods by which the contributor can appraise whether their understanding and associated uncertainty was well captured. This paper introduces an algorithm that transforms answers to simple, non-statistical questions into a bivariate Gaussian distribution as the prior for the Beta distribution. Based on geometric properties of the Beta distribution parameter feasibility space and the bivariate Gaussian distribution, an automated method for encoding is developed that responds to these challenging enterprise requirements. Though created within the context of population density, this approach may be applicable to a wide array of problem domains requiring informative priors for the Beta distribution.

Attribute Portfolio Distance: A Dynamic Time Warping-Based Approach to Comparing and Detecting Common Spatiotemporal Patterns Among Multiattribute Data Portfolios

Frequently questions we ask cannot be answered by simply looking at one indicator. To answer the question asking which countries are similar to one another economically over the past 20 years is not just a matter of looking at trends in gross domestic product (GDP) or unemployment rates; “economically” encompasses much more than just one or two measures. In this chapter, we propose a method called attribute portfolio distance (APD) and a variant trend only APD (TO-APD) to address questions such as these. APD/TO-APD is a spatiotemporal extension of a data-mining algorithm called dynamic time warping used to measure the similarity between two univariate time series. We provide an example of this method by answering the question, Which countries are most similar to Ukraine economically from 1994–2013?