April Morton

US cities can manage national hydrology and biodiversity using local infrastructure policy

Significance We introduce a unique and detailed data-driven approach that links cities’ hard infrastructures to their distal ecological impacts on streams. Although US cities concentrate most of the nation’s population, wealth, and consumption in roughly 5% of the land area, we find that city infrastructures influence habitats for over 60% of North America’s fish, mussel, and crayfish species and have contributed to local and complete extinctions in 260 species. We also demonstrate that city impacts are not proportionate to city size but reflect infrastructure decisions; thus, as US urbanization trends continue, local government and utility companies have opportunities to improve regional aquatic ecosystem conditions outside city boundaries through their hard infrastructure policies. Cities are concentrations of sociopolitical power and prime architects of land transformation, while also serving as consumption hubs of “hard” water and energy infrastructures. These infrastructures extend well outside metropolitan boundaries and impact distal river ecosystems. We used a comprehensive model to quantify the roles of anthropogenic stressors on hydrologic alteration and biodiversity in US streams and isolate the impacts stemming from hard infrastructure developments in cities. Across the contiguous United States, cities’ hard infrastructures have significantly altered at least 7% of streams, which influence habitats for over 60% of North America’s fish, mussel, and crayfish species. Additionally, city infrastructures have contributed to local extinctions in 260 species and currently influence 970 indigenous species, 27% of which are in jeopardy. We find that ecosystem impacts do not scale with city size but are instead proportionate to infrastructure decisions. For example, Atlanta’s impacts by hard infrastructures extend across four major river basins, 12,500 stream km, and contribute to 100 local extinctions of aquatic species. In contrast, Las Vegas, a similar size city, impacts <1,000 stream km, leading to only seven local extinctions. So, cities have local policy choices that can reduce future impacts to regional aquatic ecosystems as they grow. By coordinating policy and communication between hard infrastructure sectors, local city governments and utilities can directly improve environmental quality in a significant fraction of the nation’s streams reaching far beyond their city boundaries.

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.

Can Social Media Play a Role in the Development of Building Occupancy Curves

The demand for building occupancy estimation continues to grow in a wide array of application domains, such as population distribution modeling, green building technologies, public safety, and natural hazards loss analytics. While much has been gained in using survey diaries, sensor technologies, and dasymetric modeling, the volume and velocity of social media data provide a unique opportunity to measure and model occupancy patterns with unprecedented temporal and spatial resolution. If successful, patterns or occupancy curves could describe the fluctuations in population across a 24 h period for a single building or a class of building types. Although social media hold great promise in responding to this need, a number of challenges exist regarding representativeness and fitness for purpose that, left unconsidered, could lead to erroneous conclusions about true building occupancy. As a mode of discussion, this chapter presents an explicit social media model that assists in delineating and articulating the specific challenges and limitations of using social media. It concludes by proposing a research agenda for further work and engagement in this domain.

A Hybrid Dasymetric and Machine Learning Approach to High-Resolution Residential Electricity Consumption Modeling

As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for detailed information regarding residential energy consumption patterns has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy consumption, the majority of techniques are highly dependent on region-specific data sources and often require building- or dwelling-level details that are not publicly available for many regions in the United States. Furthermore, many existing methods do not account for errors in input data sources and may not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more general hybrid approach to high-resolution residential electricity consumption modeling by merging a dasymetric model with a complementary machine learning algorithm. The method’s flexible data requirement and statistical framework ensure that the model both is applicable to a wide range of regions and considers errors in input data sources.

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?