scholarly journals Wildfire burn scar encapsulation

2021 ◽  
Author(s):  
Jorge A. Duarte ◽  
Andrés D. González ◽  
Jonathan J. Gourley
Keyword(s):  
Debris Flows ◽  
Synthetic Data ◽  
The United States ◽  
Data Set ◽  
Computational Tools ◽  
Burn Scar ◽  
Burned Areas ◽  
Close Proximity ◽  
Severe Erosion ◽  
Land Surfaces

AbstractWildfires burn annually across the United States (US), which threaten those in close proximity to them. Due to drastic alterations of soil properties and to the land surfaces by these fires, risks of flash floods, debris flows, and severe erosion increases for these areas, which can have catastrophic consequences for biota, people and property. Computational tools, such as the WildfireRain algorithm, have been designed and implemented to assess the potential occurrence of debris flows over burn scars. However, in order to efficiently operate these tools, they require independent, non-overlapping buffers around burned areas to be defined, which is not a trivial task. In this paper we consider the problem of efficiently subsetting the conterminous US (CONUS) domain into optimal subdomains around burn scars, aiming to enable domain-wide WildfireRain product outputs to be used for operations by the National Weather Service (NWS). To achieve this, we define the Object Encapsulation Problem, where burn scars are represented by single-cell objects in a gridded domain, and circular buffers must be constructed around them. We propose a Linear Programming (LP) model that solves this problem efficiently. Optimal results produced using this model are presented for both a simplified synthetic data set, as well as for a subset of burn scars produced by severe wildfires in 2012 over the CONUS.

Consideration of the Validity of Debris-flow Bulking Factors

2017 ◽  
Vol 23 (4) ◽  
pp. 291-298
Author(s):  
Holly Brunkal ◽  
Paul Santi
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Peak Discharge ◽  
Data Set ◽  
Burned Areas ◽  
The Given ◽  
Basin Area ◽  
Bulking Factor ◽  
Significant Underestimation

Abstract Compilation of a database of debris-flow peak discharges (Q) allowed for a comparison with the expected basin discharge, as computed using the rational equation, Q=CIA. The observed values of Q for debris flows in unburned and burned areas were divided by the computed Q values of runoff using the rational method. This ratio is the bulking factor for that debris-flow event. Unburned and burned basins constitute two distinct populations; analysis shows that the bulking factors for burned areas are consistently higher than those for unburned basins. Previously published bulking factors for unburned areas fit the data set in about 50 percent of the observed cases in our compiled data set. Bulking factors for burned areas that were found in the published literature were well below the observed increases in peak discharge in over 50 percent of the cases investigated. If used for design purposes, these bulking factors would result in a significant underestimation of the peak discharge from a burned basin for the given rainfall intensity. Peak discharge bulking rates were found to be inversely related to basin area.

scholarly journals Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: Results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C)

2021 ◽  
Author(s):  
Jason A Thomas ◽  
Randi E Foraker ◽  
Noa Zamstein ◽  
Philip RO Payne ◽  
Adam B Wilcox ◽  
...  
Keyword(s):  
Synthetic Data ◽  
Patient Characteristics ◽  
Original Data ◽  
The United States ◽  
Small Sample ◽  
Sample Sizes ◽  
Data Set ◽  
Data Utility ◽  
Small Sample Sizes ◽  
Zip Code

Objective: To evaluate whether synthetic data derived from a national COVID-19 data set could be used for geospatial and temporal epidemic analyses. Materials and Methods: Using an original data set (n = 1,854,968 SARS-CoV-2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip-code level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. Results: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean = 2.9 ± 2.4; max = 16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5,819), respectively. In small sample sizes, synthetic data utility was notably decreased. Discussion: Analyses on the population-level and of densely-tested zip codes (which contained most of the data) were similar between original and synthetically-derived data sets. Analyses of sparsely-tested populations were less similar and had more data suppression. Conclusion: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression - an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

State Medical Boards' Perceptions of a Minimum Data Set and Current Practices for Collecting Physician Information

2013 ◽  
Vol 99 (4) ◽  
pp. 40-45 ◽  
Author(s):  
Aaron Young ◽  
Philip Davignon ◽  
Margaret B. Hansen ◽  
Mark A. Eggen
Keyword(s):  
Media Coverage ◽  
The United States ◽  
Minimum Data Set ◽  
Direct Patient Care ◽  
Physician Workforce ◽  
Data Set ◽  
Minimum Data ◽  
State Medical Boards ◽  
The Impact ◽  
Current Practices

ABSTRACT Recent media coverage has focused on the supply of physicians in the United States, especially with the impact of a growing physician shortage and the Affordable Care Act. State medical boards and other entities maintain data on physician licensure and discipline, as well as some biographical data describing their physician populations. However, there are gaps of workforce information in these sources. The Federation of State Medical Boards' (FSMB) Census of Licensed Physicians and the AMA Masterfile, for example, offer valuable information, but they provide a limited picture of the physician workforce. Furthermore, they are unable to shed light on some of the nuances in physician availability, such as how much time physicians spend providing direct patient care. In response to these gaps, policymakers and regulators have in recent years discussed the creation of a physician minimum data set (MDS), which would be gathered periodically and would provide key physician workforce information. While proponents of an MDS believe it would provide benefits to a variety of stakeholders, an effort has not been attempted to determine whether state medical boards think it is important to collect physician workforce data and if they currently collect workforce information from licensed physicians. To learn more, the FSMB sent surveys to the executive directors at state medical boards to determine their perceptions of collecting workforce data and current practices regarding their collection of such data. The purpose of this article is to convey results from this effort. Survey findings indicate that the vast majority of boards view physician workforce information as valuable in the determination of health care needs within their state, and that various boards are already collecting some data elements. Analysis of the data confirms the potential benefits of a physician minimum data set (MDS) and why state medical boards are in a unique position to collect MDS information from physicians.

Legislative Diversity and the Rise of Women Lobbyists

2021 ◽  
pp. 106591292110093
Author(s):  
James M. Strickland ◽  
Katelyn E. Stauffer
Keyword(s):  
Gender Diversity ◽  
Personal Characteristics ◽  
The United States ◽  
Revolving Door ◽  
Women And Politics ◽  
Political Equality ◽  
Data Set ◽  
Women Legislators ◽  
Organized Interests ◽  
American States

Despite a growing body of literature examining the consequences of women’s inclusion among lobbyists, our understanding of the factors that lead to women’s initial emergence in the profession is limited. In this study, we propose that gender diversity among legislative targets incentivizes organized interests to hire women lobbyists, and thus helps to explain when and how women emerge as lobbyists. Using a comprehensive data set of registered lobbyist–client pairings from all American states in 1989 and 2011, we find that legislative diversity influences not only the number of lobby contracts held by women but also the number of former women legislators who become revolving-door lobbyists. This second finding further supports the argument that interests capitalize on the personal characteristics of lobbyists, specifically by hiring women to work in more diverse legislatures. Our findings have implications for women and politics, lobbying, and voice and political equality in the United States.

scholarly journals Actors, Partisan Inclination, and Emotions: An Analysis of Government Shutdown News Stories Shared on Twitter

2021 ◽  
Vol 7 (2) ◽  
pp. 205630512110088
Author(s):  
Colin Agur ◽  
Lanhuizi Gan
Keyword(s):  
Emotional Responses ◽  
The United States ◽  
Online Information ◽  
News Frames ◽  
Data Set ◽  
Political Issues ◽  
News Stories ◽  
News Sharing ◽  
Government Shutdown

Scholars have recognized emotion as an increasingly important element in the reception and retransmission of online information. In the United States, because of existing differences in ideology, among both audiences and producers of news stories, political issues are prone to spark considerable emotional responses online. While much research has explored emotional responses during election campaigns, this study focuses on the role of online emotion in social media posts related to day-to-day governance in between election periods. Specifically, this study takes the 2018–2019 government shutdown as its subject of investigation. The data set shows the prominence of journalistic and political figures in leading the discussion of news stories, the nuance of emotions employed in the news frames, and the choice of pro-attitudinal news sharing.

Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

2021 ◽  
pp. 036119812110088
Author(s):  
Raul E. Avelar ◽  
Karen Dixon ◽  
Boniphace Kutela ◽  
Sam Klump ◽  
Beth Wemple ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Synthetic Data ◽  
Calibration Procedure ◽  
Safety Performance ◽  
Absolute Deviation ◽  
Data Set ◽  
Safety Database ◽  
Simple Index ◽  
Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

STEM-Focused Career Courses and College Pipeline for Students with Learning Disabilities

2021 ◽  
pp. 089590482110199
Author(s):  
Jennifer A. Freeman ◽  
Michael A. Gottfried ◽  
Jay Stratte Plasman
Keyword(s):  
High School ◽  
Learning Disabilities ◽  
High School Students ◽  
The United States ◽  
Students With Learning Disabilities ◽  
School Students ◽  
Data Set ◽  
Stem Majors ◽  
Career And Technical ◽  
Nationally Representative

Recent educational policies in the United States have fostered the growth of science, technology, engineering, and mathematics (STEM) career-focused courses to support high school students’ persistence into these fields in college and beyond. As one key example, federal legislation has embedded new types of “applied STEM” (AS) courses into the career and technical education curriculum (CTE), which can help students persist in STEM through high school and college. Yet, little is known about the link between AS-CTE coursetaking and college STEM persistence for students with learning disabilities (LDs). Using a nationally representative data set, we found no evidence that earning more units of AS-CTE in high school influenced college enrollment patterns or major selection in non-AS STEM fields for students with LDs. That said, students with LDs who earned more units of AS-CTE in high school were more likely to seriously consider and ultimately declare AS-related STEM majors in college.

scholarly journals The COVID Connection: Pandemic Anxiety, COVID-19 Comprehension, and Digital Confidence

2021 ◽  
pp. 000276422110031
Author(s):  
Laura Robinson ◽  
Jeremy Schulz ◽  
Øyvind N. Wiborg ◽  
Elisha Johnston
Keyword(s):  
Logistic Models ◽  
The United States ◽  
Sociodemographic Factors ◽  
Future Research ◽  
Lower Probability ◽  
Data Set ◽  
Digital Inequality ◽  
Partner Status ◽  
General Anxiety ◽  
Nationally Representative

This article presents logistic models examining how pandemic anxiety and COVID-19 comprehension vary with digital confidence among adults in the United States during the first wave of the pandemic. As we demonstrate statistically with a nationally representative data set, the digitally confident have lower probability of experiencing physical manifestations of pandemic anxiety and higher probability of adequately comprehending critical information on COVID-19. The effects of digital confidence on both pandemic anxiety and COVID-19 comprehension persist, even after a broad range of potentially confounding factors are taken into account, including sociodemographic factors such as age, gender, race/ethnicity, metropolitan status, and partner status. They also remain discernable after the introduction of general anxiety, as well as income and education. These results offer evidence that the digitally disadvantaged experience greater vulnerability to the secondary effects of the pandemic in the form of increased somatized stress and decreased COVID-19 comprehension. Going forward, future research and policy must make an effort to address digital confidence and digital inequality writ large as crucial factors mediating individuals’ responses to the pandemic and future crises.

scholarly journals A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Elahe Jamalinia ◽  
Faraz S. Tehrani ◽  
Susan C. Steele-Dunne ◽  
Philip J. Vardon
Keyword(s):  
Numerical Simulation ◽  
Water Flux ◽  
Temporal Stability ◽  
Synthetic Data ◽  
Climatic Conditions ◽  
Training Data ◽  
Data Driven ◽  
Data Set ◽  
Surface Cracking ◽  
Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

scholarly journals 910. A Comprehensive, One Year, Hospital-Wide Snapshot of All Serious Infectious Complications in People Who Inject Drugs

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S489-S490
Author(s):  
John T Henderson ◽  
Evelyn Villacorta Cari ◽  
Nicole Leedy ◽  
Alice Thornton ◽  
Donna R Burgess ◽  
...  
Keyword(s):  
Multivariable Analysis ◽  
Vertebral Osteomyelitis ◽  
The United States ◽  
Infectious Complications ◽  
Data Set ◽  
Cumulative Impact ◽  
Mortality And Morbidity ◽  
Icu Admission ◽  
Risk Of Death ◽  
The Impact

Abstract Background There has been a dramatic rise in IV drug use (IVDU) and its associated mortality and morbidity, however, the scope of this effect has not been described. Kentucky is at the epicenter of this epidemic and is an ideal place to better understand the health complications of IVDU in order to improve outcomes. Methods All adult in-patient admissions to University of Kentucky hospitals in 2018 with an Infectious Diseases (ID) consult and an ICD 9/10 code associated with IVDU underwent thorough retrospective chart review. Demographic, descriptive, and outcome data were collected and analyzed by standard statistical analysis. Results 390 patients (467 visits) met study criteria. The top illicit substances used were methamphetamine (37.2%), heroin (38.2%), and cocaine (10.3%). While only 4.1% of tested patients were HIV+, 74.2% were HCV antibody positive. Endocarditis (41.1%), vertebral osteomyelitis (20.8%), bacteremia without endocarditis (14.1%), abscess (12.4%), and septic arthritis (10.4%) were the most common infectious complications. The in-patient death rate was 3.0%, and 32.2% of patients were readmitted within the study period. The average length of stay was 26 days. In multivariable analysis, infectious endocarditis was associated with a statistically significant increase in risk of death, ICU admission, and hospital readmission. Although not statistically significant, trends toward mortality and ICU admission were identified for patients with prior endocarditis and methadone was correlated with decreased risk of readmission and ICU stay. FIGURE 1: Reported Substances Used FIGURE 2: Comorbidities FIGURE 3: Types of Severe Infectious Complications Conclusion We report on a novel, comprehensive perspective on the serious infectious complications of IVDU in an attempt to measure its cumulative impact in an unbiased way. This preliminary analysis of a much larger dataset (2008-2019) reveals some sobering statistics about the impact of IVDU in the United States. While it confirms the well accepted mortality and morbidity associated with infective endocarditis and bacteremia, there is a significant unrecognized impact of other infectious etiologies. Additional analysis of this data set will be aimed at identifying key predictive factors in poor outcomes in hopes of mitigating them. Disclosures All Authors: No reported disclosures

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