Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages

We present a case study applying hierarchical Bayesian estimation on high-throughput protein melting-point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in melting-temperature posterior-distribution estimates to enable principled decision-making in common high-throughput measurement tasks, and contrast the decision-making workflow against simple maximum-likelihood curve-fitting. We conclude with a discussion of the relative merits of each workflow.

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Hierarchical Bayesian models for soil CO2flux using soil texture: a case study in central Hokkaido, Japan

Soil Science & Plant Nutrition ◽

10.1080/00380768.2014.978728 ◽

2014 ◽

Vol 61 (1) ◽

pp. 116-132 ◽

Cited By ~ 10

Author(s):

Xi Li ◽

Kiwamu Ishikura ◽

Chunying Wang ◽

Jagadeesh Yeluripati ◽

Ryusuke Hatano

Keyword(s):

Soil Texture ◽

Bayesian Models ◽

Hierarchical Bayesian ◽

Hierarchical Bayesian Models

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Conceptualizing community resilience to natural hazards – the emBRACE framework

Natural Hazards and Earth System Science ◽

10.5194/nhess-17-2321-2017 ◽

2017 ◽

Vol 17 (12) ◽

pp. 2321-2333 ◽

Cited By ~ 16

Author(s):

Sylvia Kruse ◽

Thomas Abeling ◽

Hugh Deeming ◽

Maureen Fordham ◽

John Forrester ◽

...

Keyword(s):

Natural Hazards ◽

Community Resilience ◽

Scientific Concept ◽

Risk Governance ◽

Community Stakeholders ◽

Societal Context ◽

Key Drivers ◽

Community Forces ◽

The One

Abstract. The level of community is considered to be vital for building disaster resilience. Yet, community resilience as a scientific concept often remains vaguely defined and lacks the guiding characteristics necessary for analysing and enhancing resilience on the ground. The emBRACE framework of community resilience presented in this paper provides a heuristic analytical tool for understanding, explaining and measuring community resilience to natural hazards. It was developed in an iterative process building on existing scholarly debates, on empirical case study work in five countries and on participatory consultation with community stakeholders where the framework was applied and ground-tested in different contexts and for different hazard types. The framework conceptualizes resilience across three core domains: (i) resources and capacities, (ii) actions and (iii) learning. These three domains are conceptualized as intrinsically conjoined within a whole. Community resilience is influenced by these integral elements as well as by extra-community forces comprising disaster risk governance and thus laws, policies and responsibilities on the one hand and on the other, the general societal context, natural and human-made disturbances and system change over time. The framework is a graphically rendered heuristic, which through application can assist in guiding the assessment of community resilience in a systematic way and identifying key drivers and barriers of resilience that affect any particular hazard-exposed community.

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Hierarchical Bayesian Parameter Estimation for Modeling and Analysis of User Affective Influence

Volume 3B: 39th Design Automation Conference ◽

10.1115/detc2013-13693 ◽

2013 ◽

Cited By ~ 1

Author(s):

Feng Zhou ◽

Jianxin (Roger) Jiao

Keyword(s):

Decision Making ◽

Interior Design ◽

Computational Models ◽

Human Perception ◽

Model Parameters ◽

Hierarchical Bayesian ◽

Affective States ◽

Modeling And Analysis ◽

Human Decision

Traditional user experience (UX) models are mostly qualitative in terms of its measurement and structure. This paper proposes a quantitative UX model based on cumulative prospect theory. It takes a decision making perspective between two alternative design profiles. However, affective elements are well-known to have influence on human decision making, the prevailing computational models for analyzing and simulating human perception on UX are mainly cognition-based models. In order to incorporate both affective and cognitive factors in the decision making process, we manipulate the parameters involved in the cumulative prospect model to show the affective influence. Specifically, three different affective states are induced to shape the model parameters. A hierarchical Bayesian model with a technique called Markov chain Monte Carlo is used to estimate the parameters. A case study of aircraft cabin interior design is illustrated to show the proposed methodology.

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The Central Arizona Conservation Alliance Programs: Use of Social Media and App-Supported Community Science for Landscape-Scale Habitat Restoration, Governance Support, and Community Resilience-Building

Land ◽

10.3390/land11010137 ◽

2022 ◽

Vol 11 (1) ◽

pp. 137

Author(s):

Aireona B. Raschke ◽

Jeny Davis ◽

Annia Quiroz

Keyword(s):

Social Media ◽

Habitat Restoration ◽

Positive Impact ◽

Community Resilience ◽

Open Spaces ◽

Awareness Raising ◽

Use Of Social Media ◽

Community Science ◽

Central Arizona

Land managers are currently faced with a nexus of challenges, both ecological and social, when trying to govern natural open spaces. While social media has led to many challenges for effective land management and governance, the technology has the potential to support key activities related to habitat restoration, awareness-raising for policy changes, and increased community resilience as the impacts of increased use and climate change become more apparent. Through the use of a case study examining the work of the Central Arizona Conservation Alliance’s social media ambassadorship and its app-supported community science projects, we examine the potential and realized positive impact that technology such as social media and smartphone apps can create for land managers and surrounding communities.

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