Environmental influences on affect and cognition: A study of natural and commercial semi-public spaces

Research has consistently shown differences in affect and cognition after exposure to different physical environments. The time course of these differences emerging or fading during exploration of environments is less explored, as most studies measure dependent variables only before and after environmental exposure. In this within-subject study, we used repeated surveys to measure differences in thought content and affect throughout a 1-h environmental exploration of a nature conservatory and a large indoor mall. At each survey, participants reported on aspects of their most recent thoughts (e.g., thinking of the present moment vs. the future; thinking positively vs. negatively) and state affect. Using Bayesian multi-level models, we found that while visiting the conservatory, participants were more likely to report thoughts about the past, more positive and exciting thoughts, and higher feelings of positive affect and creativity. In the mall, participants were more likely to report thoughts about the future and higher feelings of impulsivity. Many of these differences in environments were present throughout the 1-h walk, however some differences were only evident at intermediary time points, indicating the importance of collecting data during exploration, as opposed to only before and after environmental exposures. We also measured cognitive performance with a dual n-back task. Results on 2-back trials replicated results from prior work that interacting with nature leads to improvements in working-memory performance. This study furthers our understanding of how thoughts and feelings are influenced by the surrounding physical environment and has implications for the design and use of public spaces

Investigating the role of social media and smart device applications in understanding human-environment relationships in urban green spaces

Urban green spaces are integral components of urban landscapes and the cultural ecosystem services afforded to human populations by these green spaces are of particular relevance to human and societal well-being. Urban green spaces provide opportunities for human interaction, physical activity and recreation, stress alleviation and mental restoration, economic opportunity, cultural activities and interactions with nature. To understand how these benefits are received by human populations it is vital to understand when and how individuals interact with urban green spaces. The rapid development and uptake of technologies such as smart phones, social networks and apps provides new opportunity to investigate the human interactions occurring in urban green spaces. Using the city of Birmingham as a case study, this thesis aims (i) to $demonstrate$ the utility of data obtained from smart device enabled platforms (social networks and apps) in understanding socio-ecological interactions in urban areas and (ii) to $evaluate$ the utility of these data sources for researchers and policy makers. The successful identification of a range of socio-ecological interaction suggest these data sources provide a viable method if investigating such interactions; however, there remain a number of limitations to consider to ensure they are employed appropriately in research contexts

Quantifying the health benefits of nature contact in cities across the US

Urbanization, the rise of sedentary lifestyles, and increasing screen time have led to a significant decline in nature contact, or how much time people spend in greenspace. At the same time, urban populations are experiencing declining physical and mental well-being. While nature contact has been shown to have a variety of health benefits, these benefits have not been well-quantified or verified across different geographic contexts. In addition, there is a lack of clarity around how the benefits of nature contact vary temporally (e.g. seasonally) and between different types of greenspaces. In this dissertation, I investigate the health benefits of urban parks at three spatial scales. In Chapter One, I used the Hedonometer, a sentiment analysis tool, to show that people write happier words on Twitter when visiting parks in San Francisco. The sentiment benefit, or change in average word happiness from baseline, was highest for the larger and greener regional parks. In Chapter Two, I applied similar methods to the 25 largest cities in the US and again found higher sentiment during park visits. The sentiment benefit was highest for the largest parks and during weekends and the summer. In Chapter Three, using national health surveys and a database of municipal park systems, I found that increased park access is associated with lower prevalence of negative health outcomes (including obesity and mental health) across the 500 largest cities in the US. The findings of this research support the importance of protecting and enhancing urban nature and can be used by urban planners and public health officials to better inform nature contact recommendations for growing urban populations

Punishing Homelessness

Homelessness is punishing to those who experience it, not just from the inherent and protracted trauma of living exposed on the street, but also due to widespread and pervasive laws that punish people for being homeless. People experiencing homelessness, particularly chronic homelessness, often lack reasonable alternatives to living in public. Yet cities throughout the country are increasingly enacting and enforcing laws that punish the conduct of necessary, life-sustaining activities in public, even when many people have no other option. These laws are frequently challenged in court and often struck down as unconstitutional. But legally sound, cost-effective, and non-punitive alternatives to ending chronic homelessness exist. This article exposes some of the problems with criminalization laws, not only for people experiencing homelessness, but also for the broader community. It discusses how current approaches often make chronic homelessness worse and explains why non-punitive alternatives, especially Housing First and permanent supportive housing, are the most cost-effective means of addressing chronic homelessness. Ultimately, this article urges cities and their constituents to stop punishing homelessness and instead to start solving it

Assessing mental wellbeing in urban areas using social media data: understanding when and where urbanites stress and de-stress

Are Americans more stressed out by living in dense, urbanized areas or less dense, car-oriented areas? To answer this question, can we use people's expressions of stress in different environments to understand what kinds of spaces help them de-stress? This study uses stress levels of geolocated tweets to help us answer such inquiries and resolve the longstanding disparities between the field of psychology and urban planning about mental health impacts of cities. This is important because more than 75 percent of Americans are moderately stressed. Long-term stress is associated with mental health disorders, including sleeplessness, anxiety, and depression. Additionally, chronic stress is linked to physical ailments, including high blood pressure, cardiovascular diseases, and diabetes. The psychology literature claims that urban areas witness elevated levels of mental health problems, manifested as stress, mood disorders, and anxiety issues. Density, crowding, traffic, crime, and pollution are identified as stressors associated with urban living conditions. Contending this claim, the urban planning literature positions stress in the context of longer commutes, lack of accessibility, and social isolation that comes with suburban living conditions. Urban Planners and urban designers have advocated for density. With rapid urbanization, 60 percent of the world population will live in urban areas by 2030, making it crucial for urban planners to address these disparities to support the mental wellbeing of the urbanites. This research uses multi-headed attention transformer model to classify tweets (token sequences), and assesses the stress levels of custom-defined assessment grids of ten acres within the city area of Atlanta and Boston. The assessed stress level of these assessment grids is called the mental wellbeing score (MWS). Mental wellbeing score is defined in this research as a measure of `mental wellbeing' of any given grid (higher score is better). Using this measure, the research investigates the relationship between mental wellbeing and built environment characteristics in urban areas to uncover the impact of long-term stress triggered by the conditions of the built environment in urban settings. In summary, the results of the exploration shed light on three critical aspects: 1. Mental wellbeing score increases with increasing urbanness. 2. The mental wellbeing score increases with the increase in the diversity of escape facilities, including green parks, open spaces, and other points of interest. 3. The mental wellbeing score is positively impacted by accessible high-density spaces with high symbolic value. The research also investigates the impact of safety perception and socio-economic status on mental wellbeing scores. The results show that addressing socio-economic disparity, crime, and investment in green infrastructure can improve mental wellbeing of urbanites. The methods and findings of the research show that 'urban areas' can positively impact mental health if designed appropriately. Furthermore, this study can empower urban planners and policymakers to develop tools to assess the mental wellbeing of urbanites, adjust infrastructure needs, and improve the urban amenities that support mental wellbeing.Ph.D

E-Quality: An Analysis of Digital Equity Discourse and Co-Production in the Era of COVID-19

The digital divide refers to the social stratification due to an unequal ability to access, adapt, and create knowledge via information and communication technologies (Andreasson, 2015). Digitally disadvantaged individuals have inadequate access to services and resources, exacerbating existing vulnerabilities. The COVID-19 pandemic instigated a new model of digital equity policymaking that leverages co-production between numerous actors. As citizens faced new financial and community constraints and governments reached administrative capacities, both the digital divide and the policymaking process evolved. This inductive study explores how digital equity policymaking shifted to a co-production model (Ostrom, 1996) amid the pandemic. Using a sequential mixed-methods approach, this research considers the interconnections of digital equity, co-production, and crisis policymaking. Digital divide discourse was first examined through a large-scale text analysis of verified tweets. Methods of investigation include natural language processing techniques, regression modeling, and unsupervised machine learning topic modeling. Descriptive and inferential analyses demonstrate a statistically significant increase in policy discourse as well as a diversification of topics, though suggest a disconnect between outputs and on-the-ground needs. Next, semi-structured interviews were conducted with City of Boston policymakers, and the resulting data was open-coded and axially coded to reveal insights into the design and implementation of co-productive solutions. Additionally, interviews detail what conditions contribute to successful outcomes while working with limited time, knowledge, and resources. Analyses reveal that co-productive behavior is critical to coping with the effects of the pandemic and highlight the influential role of community-based organizations. Furthermore, the study provides contextual information on co-production prerequisites that were previously understood, and sheds light on interpersonal conditions that Ostrom does not address. This dissertation contributes to the developing body of scholarly literature on the digital divide in the era of COVID-19. This case study also advances theoretical knowledge, offers methodological innovations, and provides concrete policy recommendations to promote more egalitarian digital use

Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development

Attention Restraint, Working Memory Capacity, and Mind Wandering: Do Emotional Valence or Intentionality Matter?

Attention restraint appears to mediate the relationship between working memory capacity (WMC) and mind wandering (Kane et al., 2016). Prior work has identifed two dimensions of mind wandering—emotional valence and intentionality. However, less is known about how WMC and attention restraint correlate with these dimensions. Te current study examined the relationship between WMC, attention restraint, and mind wandering by emotional valence and intentionality. A confrmatory factor analysis demonstrated that WMC and attention restraint were strongly correlated, but only attention restraint was related to overall mind wandering, consistent with prior fndings. However, when examining the emotional valence of mind wandering, attention restraint and WMC were related to negatively and positively valenced, but not neutral, mind wandering. Attention restraint was also related to intentional but not unintentional mind wandering. Tese results suggest that WMC and attention restraint predict some, but not all, types of mind wandering

Proceedings of the 2nd African Operations Management Conference: Competitive Operations Management for Driving Automation in Africa Forward

Conference proceedingsColleges of Economic and Management Science