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Morton, April

Publications and source records attributed to Morton, April.

13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Installation Assignment Durations and Patterns of Army Personnel

The purpose of this document is to report on the development of personnel assignment duration statistical metrics and statistical descriptions for groups of Army personnel aggregated by rank, career management field, and other descriptors for 32 Continental United States (CONUS) Army installations. This information can be used for generating evidence-based measures of personnel exposure times associated with environmental health considerations at the installation and career management field levels. Assignment duration statistical descriptions were developed to provide temporal metrics for groups of personnel at Army installations as foundational data to support a variety of assessments. Statistical results of personnel assignment durations at the installation level are provided in several formats. The 50th percentile assignment duration represents the median number of days personnel in groups aggregated by rank and career management field spend at the 32 CONUS Army installations in this study. Likewise, other duration percentiles (90th) and quartiles of assignment durations for groups aggregated by rank and career management field spend at each installation were developed. Assignment path analysis provide statistical descriptions of assignment durations at Army installations throughout increments of time (e.g., 5 years, 10 years). Key groupings of assignment paths were achieved by analyzing the duration of assignments at installations in chronological order from entry throughout specified increments of military service to generate the most common assignment paths across the 32 CONUS Army installations in this study. The developed assignment duration analytics and duration statistical descriptions provide foundational temporal metrics for groups of personnel at 32 CONUS Army installations to support a variety of assessments including those by human resource planners and environmental health specialists.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

High performance Data Driven Agent-based Modeling Framework for Simulation of Commute Mode Choices in Metropolitan Area

Active transportation, human-powered transportation modes such as walking and bicycling, not only reduces the carbon footprint from the transportation sector but also promotes healthy living by offering opportunities for people to build physical activity into their daily routine. To encourage active transportation through urban planning and public campaigns, it is of significant importance to infer factors that substantially influence commuters in their transportation mode choice process. This necessitates a flexible and repeatable tool that can evaluate how a policy is perceived by individual commuters and convert their decisions into macro level understanding. This paper introduces one such effort that is specifically designed for studies of transportation mode choices in metropolitan areas. It provides results from a high-resolution data driven simulation based on high performance computing implementation of the agent-based model framework for home-to-work commute trips. The framework uses a graph-partition based technique that can leverage the interaction structure of agents within a geographic proximity and can boost the simulation execution time. Further, based on a flexible design, it can run ABM with different levels of computing resources-from multi core workstations to an HPC grid. The framework has been tested on the Titan Cray XK7 supercomputer of the Oak Ridge Leadership Computing Facility.

Park, Byung↗
DOE OSTI2018

Need A Boost? A Comparison of Traditional Commuting Models with the XGBoost Model for Predicting Commuting Flows

Commuting models estimate the number of commuting trips from home to work locations in a given area. Since their infancy, they have been increasingly used in a variety of fields to reduce traffic and pollution, drive infrastructure choices, and solve a variety of other problems. Traditional commuting models, such as gravity and radiation models, typically have a strict structural form and limited number of input variables, which may limit their ability to predict commuting flows as well as machine learning models that might better capture the complex dynamics of the commuting process. To determine whether machine learning models might add value to the field of commuter flow prediction, we compare and discuss the performance of two standard traditional models with the XGBoost machine learning algorithm for predicting home to work commuter flows from a well-known United States commuting dataset. We find that the XGBoost model outperforms the traditional models on three commonly used metrics, indicating that machine learning models may add value to the field of commuter flow prediction.

Morton, April↗
DOE OSTI2017

A Simulation Approach for Modeling High-Resolution Daytime Commuter Travel Flows and Distributions of Worker Subpopulations

High-resolution daytime travel flows and population distribution data are critical for addressing important issues ranging from business planning to emergency preparedness and response. Though dasymetric modeling approaches and Census datasets have been developed to capture daytime populations and travel flows, they often lack the spatial resolution, demographic detail and uncertainty measures needed by decision-makers. In this paper, we propose a high-resolution simulation approach for estimating and quantifying the uncertainty of worker subpopulation distributions and their commuter travel flows in the United States (U.S.). An early case study and novel visualization illustrate the model’s potential utility and impact.

Morton, April↗
DOE OSTI2017

An Approximate Entropy Based Approach for Quantifying Stability in Spatio-Temporal Data with Limited Temporal Observations

Identifying erratic or unstable time-series is an area of interest to many fields. Recently, there have been successful developments towards this goal. These newly developed methodologies however come from domains where it is typical to have several thousand or more temporal observations. This creates a challenge when attempting to apply these methodologies to time-series with much fewer temporal observations such as for spatio-temporal socio-cultural understanding, a domain where a typical time series of interest might only consist of 20-30 annual observations. Identifying instability in spatio-temporal trends is critical for understanding global dynamics and finding areas of potential concern or intervention. In the case of spatio-temporal socio-cultural understanding, instability is marked by two characteristics, 1) how widely varying the values are and 2) how predictable that variance is from one observation to the next. Approaches for simultaneously addressing both of these concerns have been limited. In this paper, we introduce an approximate entropy based method for characterizing the behaviour of a time series with limited temporal observations. For Geocomputation, this methodology represents a novel additional tool for researchers to use for exploring and understanding spatio-temporal data, specifically with limited temporal observations. As a case study, we look at national youth male unemployment across the world from 1991-2014.

Piburn, Jesse↗
DOE OSTI2017

A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables

Monte Carlo simulation is a popular numerical experimentation technique used in a range of scientific fields to obtain the statistics of unknown random output variables. Despite its widespread applicability, it can be difficult to infer required input probability distributions when they are related to population counts unknown at desired spatial resolutions. To overcome this challenge, we propose a framework that uses a dasymetric model to infer the probability distributions needed for a specific class of Monte Carlo simulations which depend on population counts.

Morton, April↗
DOE OSTI2017

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 increasingenvironmental awareness, the need for detailed information regarding residentialenergy consumption patterns has become increasingly important. Though currentmodeling efforts mark significant progress in the effort to better understand thespatial distribution of energy consumption, the majority of techniques are highlydependent on region-specific data sources and often require building- ordwelling-level details that are not publicly available for many regions in the UnitedStates. Furthermore, many existing methods do not account for errors in input datasources and may not accurately reflect inherent uncertainties in model outputs. Wepropose an alternative and more general hybrid approach to high-resolution residentialelectricity consumption modeling by merging a dasymetric model with acomplementary machine learning algorithm. The method’s flexible data requirementand statistical framework ensure that the model both is applicable to a widerange of regions and considers errors in input data sources.

cities↗
DOE OSTI2017

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.

Stewart, Robert↗
DOE OSTI2016

The Science Behind Pokemon GO

Jesse Piburn and April Morton of Oak Ridge National Laboratory's Geographic Information Science & Technology Group discuss the science and technology Pokémon GO and how it is used every day to solve real-world problems. (Pokémon GO photos/videos courtesy of Pokémon/Nintendo).

58 GEOSCIENCES↗
DOE OSTI2016

A High-Resolution Spatially Explicit Monte-Carlo Simulation Approach to Commercial and Residential Electricity and Water Demand Modeling

As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for high resolution spatially explicit estimates for energy and water demand has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy and water consumption, many are provided at a course spatial resolution or rely on techniques which depend on detailed region-specific data sources that are not publicly available for many parts of the U.S. Furthermore, many existing methods do not account for errors in input data sources and may therefore not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more flexible Monte-Carlo simulation approach to high-resolution residential and commercial electricity and water consumption modeling that relies primarily on publicly available data sources. The method’s flexible data requirement and statistical framework ensure that the model is both applicable to a wide range of regions and reflective of uncertainties in model results.

Energy Modeling↗
DOE OSTI2013

Uncertainty Quantification Techniques for Population Density Estimates Derived from Sparse Open Source Data

The Population Density Tables (PDT) project at the 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 40 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.

Bayesian↗