• Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products

      Tang, Guoqiang; Behrangi, Ali; Long, Di; Li, Changming; Hong, Yang; Univ Arizona, Dept Hydrol & Atmospher Sci (ELSEVIER SCIENCE BV, 2018-04)
      Rain gauge observations are commonly used to evaluate the quality of satellite precipitation products. However, the inherent difference between point-scale gauge measurements and areal satellite precipitation, i.e. a point of space in time accumulation v.s. a snapshot of time in space aggregation, has an important effect on the accuracy and precision of qualitative and quantitative evaluation results. This study aims to quantify the uncertainty caused by various combinations of spatiotemporal scales (0.1 degrees-0.8 degrees and 1-24 h) of gauge network designs in the densely gauged and relatively flat Ganjiang River basin, South China, in order to evaluate the state-of-the-art satellite precipitation, the Integrated Multi satellite Retrievals for Global Precipitation Measurement (IMERG). For comparison with the dense gauge network serving as "ground truth", 500 sparse gauge networks are generated through random combinations of gauge numbers at each set of spatiotemporal scales. Results show that all sparse gauge networks persistently underestimate the performance of IMERG according to most metrics. However, the probability of detection is overestimated because hit and miss events are more likely fewer than the reference numbers derived from dense gauge networks. A nonlinear error function of spatiotemporal scales and the number of gauges in each grid pixel is developed to estimate the errors of using gauges to evaluate satellite precipitation. Coefficients of determination of the fitting are above 0.9 for most metrics. The error function can also be used to estimate the required minimum number of gauges in each grid pixel to meet a predefined error level. This study suggests that the actual quality of satellite precipitation products could be better than conventionally evaluated or expected, and hopefully enables non-subject-matter expert researchers to have better understanding of the explicit uncertainties when using point-scale gauge observations to evaluate areal products. (C) 2018 Elsevier B.V. All rights reserved.
    • Contextualizing climate science: applying social learning systems theory to knowledge production, climate services, and use-inspired research

      Owen, Gigi; Ferguson, Daniel B.; McMahan, Ben; Univ Arizona, Inst Environm, Climate Assessment Southwest (SPRINGER, 2019-06-04)
      Scientists need to acknowledge the inherent social contexts that drive the scientific process if they want their research to improve complex societal problems such as vulnerability to climate change. Social interactions and relationships are essential elements for conducting use-inspired research, creating usable knowledge, and providing climate services. The Climate Assessment for the Southwest (CLIMAS) program was founded on theories of use-inspired research and co-producing knowledge with non-academic partners. A recent program evaluation illuminated gaps in these underlying program models and led to the inclusion of social learning systems theory and communities of practice. Using grounded examples, we demonstrate the CLIMAS program's ongoing role in fostering, maintaining, and expanding a climate resilience social learning system in the U.S. Southwest. Broader implications from the evaluation focus on the importance of establishing and maintaining relationships, increasing institutional and individual flexibility in response to change, and improving the practice of transdisciplinarity. These findings inform new program evaluation metrics and data collection techniques. This paper contributes to current theory and practice of use-inspired science and climate services by identifying and demonstrating how social interactions inform climate knowledge production. The reconceptualization of the CLIMAS program as part of a growing regional social learning system serves as an example for similar types of programs. We encourage climate services and use-inspired research programs to explore applications of this framework to their own operations.
    • Evaluating Object Manipulation Interaction Techniques in Mixed Reality: Tangible User Interfaces and Gesture

      Bozgeyikli, Evren; Bozgeyikli, Lal Lila; School of Information, University of Arizona (IEEE, 2021-03)
      Tangible user interfaces (TUIs) have been widely studied in computer, virtual reality and augmented reality systems and are known to improve user experience in these mediums. However, there have been few evaluations of TUIs in wearable mixed reality (MR). In this study, we present the results from a comparative study evaluating three object manipulation techniques in wearable MR: (1) Space-multiplexed identical-formed TUI (i.e., a physical cube that acted as a dynamic tangible proxy with identical real and virtual forms); (2) Time-multiplexed TUI (i.e., a tangible controller that was used to manipulate virtual content); (3) Hand gesture (i.e., reaching, pinching and moving the hand to manipulate virtual content). The interaction techniques were compared with a user study with 42 participants. Results revealed that the tangible cube and the controller interaction methods were comparative to each other while both being superior to the hand gesture interaction method in terms of user experience, performance, and presence. We also present suggestions for interaction design for MR based on our findings. © 2021 IEEE.
    • Introducing Explorer of Taxon Concepts with a case study on spider measurement matrix building

      Cui, Hong; Xu, Dongfang; Chong, Steven S.; Ramirez, Martin; Rodenhausen, Thomas; Macklin, James A.; Ludäscher, Bertram; Morris, Robert A.; Soto, Eduardo M.; Koch, Nicolás Mongiardino; et al. (BIOMED CENTRAL LTD, 2016-11-17)
      Background: Taxonomic descriptions are traditionally composed in natural language and published in a format that cannot be directly used by computers. The Exploring Taxon Concepts (ETC) project has been developing a set of web-based software tools that convert morphological descriptions published in telegraphic style to character data that can be reused and repurposed. This paper introduces the first semi-automated pipeline, to our knowledge, that converts morphological descriptions into taxon-character matrices to support systematics and evolutionary biology research. We then demonstrate and evaluate the use of the ETC Input Creation - Text Capture - Matrix Generation pipeline to generate body part measurement matrices from a set of 188 spider morphological descriptions and report the findings. Results: From the given set of spider taxonomic publications, two versions of input (original and normalized) were generated and used by the ETC Text Capture and ETC Matrix Generation tools. The tools produced two corresponding spider body part measurement matrices, and the matrix from the normalized input was found to be much more similar to a gold standard matrix hand-curated by the scientist co-authors. Special conventions utilized in the original descriptions (e.g., the omission of measurement units) were attributed to the lower performance of using the original input. The results show that simple normalization of the description text greatly increased the quality of the machine-generated matrix and reduced edit effort. The machine-generated matrix also helped identify issues in the gold standard matrix. Conclusions: ETC Text Capture and ETC Matrix Generation are low-barrier and effective tools for extracting measurement values from spider taxonomic descriptions and are more effective when the descriptions are self-contained. Special conventions that make the description text less self contained challenge automated extraction of data from biodiversity descriptions and hinder the automated reuse of the published knowledge. The tools will be updated to support new requirements revealed in this case study.
    • Women’s Health Leadership Training to Enhance Community Health Workers as Change Agents

      Ingram, Maia; Chang, Jean; Kunz, Susan; Piper, Rosie; Zapien, Jill Guernsey de; Strawder, Kay; University of Arizona; Mariposa Community Health Center; U.S. Office of Women's Health (SAGE PUBLICATIONS INC, 2016-05)
      Objectives. A community health worker (CHW) is a frontline public health worker who is a trusted member of and/or has an unusually close understanding of the community served. While natural leadership may incline individuals to the CHW profession, they do not always have skills to address broad social issues. We describe evaluation of the Women’s Health Leadership Institute (WHLI), a 3-year training initiative to increase the capacity of CHWs as change agents. Methods. Pre-/postquestionnaires measured the confidence of 254 participants in mastering WHLI leadership competencies. In-depth interviews with CHW participants 6 to 9 months after the training documented application of WHLI competencies in the community. A national CHW survey measured the extent to which WHLI graduates used leadership skills that resulted in concrete changes to benefit community members. Multivariate logistic regressions controlling for covariates compared WHLI graduates’ leadership skills to the national sample. Results. Participants reported statistically significant pre-/post improvements in all competencies. nterviewees credited WHLI with increasing their capacity to listen to others, create partnerships, and initiate efforts to address community needs. Compared to a national CHW sample, WHLI participants were more likely to engage community members in attending public meetings and organizing events. These activities led to community members taking action on an issue and a concrete policy change. Conclusions. Leadership training can increase the ability of experienced CHWs to address underlying issues related to community health across different types of organizational affiliations and job responsibilities.