Differential drivers of rent burden in growing and shrinking cities

Housing affordability is an issue of increasing importance and interest, particularly in the United States. Much of this interest is due to skyrocketing rents in coastal cities with tight housing markets. Shrinking cities, in contrast, are often characterized as rich in low-cost housing, providing an affordable alternative to superstar cities. This paper compares income and rent dynamics in cities with growing versus shrinking populations. While costs may be lower in shrinking cities, falling incomes have likely rendered housing unaffordable for many residents. We employ multiple lines of evidence to test for different dynamics between growing and shrinking cities. Matching is used to explore changes in income and rent between 1980 and 2017 in shrinking and the most similar non-shrinking cities. After controlling for baseline conditions, shrinking cities exhibit faster falling incomes and growing cities exhibit faster rising rents, while rent burden increases at a very similar rate in both groups. We also use a fixed effects regression model to test for differences between growing and shrinking cities in sensitivity of rent burden to changes in income and rent. Rent burden has considerably increased across US cities since 1980, yet growing and shrinking cities exhibit clearly different pathways toward that end. Shrinking cities are more sensitive to identical changes in income and rent, likely because a greater share of their residents live near the edge of affordability

Addressing accountability in highly autonomous virtual assistants

Building from a survey specifically developed to address the rising concerns of highly autonomous virtual assistants; this paper presents a multi-level taxonomy of accountability levels specifically adapted to virtual assistants in the context of Human-Human-Interaction (HHI). Based on research findings, the authors recommend the integration of the variable of accountability as capital in the development of future applications around highly automated systems. This element inserts a sense of balance in terms of integrity between users and developers enhancing trust in the interactive process. Ongoing work is being dedicated to further understand to which extent different contexts affect accountability in virtual assistants

Trust in an autonomously driven simulator and vehicle performing maneuvers at a T-junction with and without other vehicles

Autonomous vehicle (AV) technology is developing rapidly. Level 3 automation assumes the user might need to respond to requests to retake control. Levels 4 (high automation) and 5 (full automation) do not require human monitoring of the driving task or systems [1]: the AV handles driving functions and makes decisions based on continuously updated information. A gradual switch in the role of the human within the vehicle from active controller to passive passenger comes with uncertainty in terms of trust, which will likely be a key barrier to acceptability, adoption and continued use [2]. Few studies have investigated trust in AVs and these have tended to use driving simulators with Level 3 automation [3, 4]. The current study used both a driving simulator and autonomous road vehicle. Both were operating at Level 3 autonomy although did not require intervention from the user; much like Level 4 systems. Forty-six participants completed road circuits (UK-based) with both platforms. Trust was measured immediately after different types of turns at a priority T-junction, increasing in complexity: e.g., driving left or right out of a T-junction; turning right into a T-junction; presence of oncoming/crossing vehicles. Trust was high across platforms: higher in the simulator for some events and higher in the road AV for others. Generally, and often irrespective of platform, trust was higher for turns involving an oncoming/crossing vehicle(s) than without traffic, possibly because the turn felt more controlled as the simulator and road AVs always yielded, resulting in a delayed maneuver. We also found multiple positive relationships between trust in automation and technology, and trust ratings for most T-junction turn events across platforms. The assessment of trust was successful and the novel findings are important to those designing, developing and testing AVs with users in mind. Undertaking a trial of this scale is complex and caution should be exercised about over-generalizing the findings

Exploring the usability of a connected autonomous vehicle human machine interface designed for older adults

Users of Level 4–5 connected autonomous vehicles (CAVs) should not need to intervene with the dynamic driving task or monitor the driving environment, as the system will handle all driving functions. CAV human-machine interface (HMI) dashboards for such CAVs should therefore offer features to support user situation awareness (SA) and provide additional functionality that would not be practical within non-autonomous vehicles. Though, the exact features and functions, as well as their usability, might differ depending on factors such as user needs and context of use. The current paper presents findings from a simulator trial conducted to test the usability of a prototype CAV HMI designed for older adults and/or individuals with sensory and/or physical impairments: populations that will benefit enormously from the mobility afforded by CAVs. The HMI was developed to suit needs and requirements of this demographic based upon an extensive review of HMI and HCI principles focused on accessibility, usability and functionality [1, 2], as well as studies with target users. Thirty-one 50-88-year-olds (M 67.52, three 50–59) participated in the study. They experienced four seven-minute simulated journeys, involving inner and outer urban settings with mixed speed-limits and were encouraged to explore the HMI during journeys and interact with features, including a real-time map display, vehicle status, emergency stop, and arrival time. Measures were taken pre-, during- and post- journeys. Key was the System Usability Scale [3] and measures of SA, task load, and trust in computers and automation. As predicted, SA decreased with journey experience and although cognitive load did not, there were consistent negative correlations. System usability was also related to trust in technology but not trust in automation or attitudes towards computers. Overall, the findings are important for those designing, developing and testing CAV HMIs for older adults and individuals with sensory and/or physical impairments

From apology to compensation: A multi-level taxonomy of trust reparation for highly automated virtual assistants

This paper presents a multi-level taxonomy of reparation levels specifically adapted to virtual assistants in the context of Human-Human-Interaction (HHI) with a specific focus on maintaining trust in the system. This taxonomy ranges from current models of apology to the newly integrated compensation area via a range of case studies specifically developed to address the rising concerns of unsupervised interactions in the context of Virtual Assistants (VA). Based on preliminary research, the author recommends the integration of reparation strategies as a fundamental variable in the ongoing development of VAs, as this element inserts a sense of balance in terms of vulnerability between users and developers to enhance trust in the interactive process. Present and future work is being dedicated to further understand how different contexts may affect integrity in highly automated virtual assistants

The role of expertise in dynamic risk assessment: A reflection of the problem-solving strategies used by experienced fireground commanders

Although the concept of dynamic risk assessment has in recent times become more topical in the training manuals of most high risk domains, only a few empirical studies have reported how experts actually carry out this crucial task. The knowledge gap between research and practice in this area therefore calls for more empirical investigation within the naturalistic environment. In this paper, we present and discuss the problem solving strategies employed by sixteen experienced operational firefighters using a qualitative knowledge elicitation tool — the critical decision method. Findings revealed that dynamic risk assessment is not merely a process of weighing the risks of a proposed course of action against its benefits, but rather an experiential and pattern recognition process. The paper concludes by discussing the implications of designing training curriculum for the less experienced officers using the elicited expert knowledge

The Influence of Social-Cognitive Factors on Personal Hygiene Practices to Protect Against Influenzas: Using Modelling to Compare Avian A/H5N1 and 2009 Pandemic A/H1N1 Influenzas in Hong Kong

# The Author(s) 2010. This article is published with open access at Springerlink.com Background Understanding population responses to influenza helps optimize public health interventions. Relevant theoretical frameworks remain nascent. Purpose To model associations between trust in information, perceived hygiene effectiveness, knowledge about the causes of influenza, perceived susceptibility and worry, and personal hygiene practices (PHPs) associated with influenza. Methods Cross-sectional household telephone surveys on avian influenza A/H5N1 (2006) and pandemic influenza A/ H1N1 (2009) gathered comparable data on trust in formal and informal sources of influenza information, influenzarelated knowledge, perceived hygiene effectiveness, worry, perceived susceptibility, and PHPs. Exploratory factor analysis confirmed domain content while confirmatory factor analysis was used to evaluate the extracted factors. The hypothesized model, compiled from different theoretical frameworks, was optimized with structural equation modelling using the A/H5N1 data. The optimized model was then tested against the A/H1N1 dataset. Results The model was robust across datasets though corresponding path weights differed. Trust in formal information was positively associated with perceived hygien

JWST UNCOVER: Extremely Red and Compact Object at z phot ≃ 7.6 Triply Imaged by A2744

Recent JWST/NIRCam imaging taken for the ultra-deep UNCOVER program reveals a very red dropout object at z phot ≃ 7.6, triply imaged by the galaxy cluster A2744 (z d = 0.308). All three images are very compact, i.e., unresolved, with a delensed size upper limit of r e ≲ 35 pc. The images have apparent magnitudes of m F444W ∼ 25−26 AB, and the magnification-corrected absolute UV magnitude of the source is M UV,1450 = −16.81 ± 0.09. From the sum of observed fluxes and from a spectral energy distribution (SED) analysis, we obtain estimates of the bolometric luminosities of the source of L bol ≳ 1043 erg s−1 and L bol ∼ 1044–1046 erg s−1, respectively. Based on its compact, point-like appearance, its position in color–color space, and the SED analysis, we tentatively conclude that this object is a UV-faint dust-obscured quasar-like object, i.e., an active galactic nucleus at high redshift. We also discuss other alternative origins for the object’s emission features, including a massive star cluster, Population III, supermassive, or dark stars, or a direct-collapse black hole. Although populations of red galaxies at similar photometric redshifts have been detected with JWST, this object is unique in that its high-redshift nature is corroborated geometrically by lensing, that it is unresolved despite being magnified—and thus intrinsically even more compact—and that it occupies notably distinct regions in both size–luminosity and color–color space. Planned UNCOVER JWST/NIRSpec observations, scheduled in Cycle 1, will enable a more detailed analysis of this object

Crowdsourcing Coordination: A Review and Research Agenda for Crowdsourcing Coordination Used for Macro-tasks

Crowdsourcing has become a widely accepted approach to leveraging the skills and expertise of others to accomplish work. Despite the potential of crowdsourcing to tackle complex problems, it has often been used to address simple micro-tasks. To tackle more complex macro-tasks, more attention is needed to better comprehend crowd coordination. Crowd coordination is defined as the synchronization of crowd workers in an attempt to direct and align their efforts in pursuit of a shared goal. The goal of this chapter is to advance our understanding of crowd coordination to tackle complex macro-tasks. To accomplish this, we have three objectives. First, we review popular theories of coordination. Second, we examine the current approaches to crowd coordination in the HCI and CSCW literature. Finally, the chapter identifies shortcomings in the literature and proposes a research agenda directed at advancing our understanding of crowd coordination needed to address complex macro-tasks.National Science Foundation grant CHS-1617820Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150620/1/Kim and Robert 2019 Preprint Chapter 2.pdfDescription of Kim and Robert 2019 Preprint Chapter 2.pdf : Preprint Versio

Developing cognitive task analysis and the importance of sociocognitive competence/insight for professional practice

Accelerating the cognitive expertise of professionals is a critical challenge for many organizations. This paper reports a collaborative, longitudinal, academic practitioner project which aimed to elicit, document, and accelerate the cognitive expertise of engineering professionals working with the manufacture and management of petroleum additives. Twenty-five engineering experts were trained by three academic psychologists to use applied cognitive task analysis (ACTA) interview techniques to document the cognition of their expert peers. Results had high face validity for practitioners who elicited hot/ sensory-based cognition, a number of perceptual skills and mental models, highlighting undocumented context specific expertise. We conclude from a peer review of findings, combined with experienced CTA analysts that ACTA technique