Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery

The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age

Assessing care

The objective of this report is to summarize progress towards measurement of selected childcare and feeding practices, and to discuss the feasibility and usefulness of these measurements in research and program contexts. This is the third in a series of reports documenting insights regarding care and measurement of care gained from the Accra Urban Food and Nutrition Study (AUFNS). This last report complements the previous two by providing an extensive review of the published literature on experience with the measurement of selected dimensions of care.FCND ,Child Feeding. ,Child care. ,

Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals

General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/

Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior

This white paper examines four topics addressing evaluation, measurement, and attribution of direct and indirect effects to energy efficiency and behavioral programs: Estimates of program savings (gross); Net savings derivation through free ridership / net to gross analyses; Indirect non-energy benefits / impacts (e.g., comfort, convenience, emissions, jobs); and, Persistence of savings

Soft Skills Centrality in Graduate Studies Offerings

Is it possible to measure how important soft skills like leadership or teamwork are from the point of view of graduate studies offerings? This paper provides a framework that introduces the concept of network centrality as a practical way to measure the individual importance of soft skills in graduate studies. We examine 230 graduate programs offered by 49 higher education institutions in Colombia to estimate the empirical importance of soft skills in the context of graduate studies offerings. The results show that: a) graduate programs in Colombia tend to share a common set of 31 soft skills in their intended learning outcomes; b) the centrality of these skills varies as a function of the graduate program, although this variation was not statistically significant; and c) while most central soft skills tend to be those related to creativity (i.e., creation or generation of ideas or projects), leadership (to lead or teamwork), and analytical orientation (e.g., evaluating situations and solving problems), less central were those related to empathy (i.e., understanding others and acknowledgment of others), ethical thinking, and critical thinking, posing the question if too much emphasis on most visible skills might imply an unbalance in the opportunities to enhancing other soft skills such as ethical thinking

Interconnectedness in Education Systems

Candia, C., Pulgar, J., & Pinheiro, F. L. (2022). Interconnectedness in Education Systems. Manuscript submitted for publication. arXiv: Physics: Physics Education. https://doi.org/10.48550/arXiv.2203.05624Traditional methods used in education sciences often disregard the underlying complexity of the educational system and consequently its emergence phenomena. Underlying complex systems, there is a rich web of interconnected components that determine the relational properties of the system. Here, we argue that an interconnected vision of educational systems -- from classrooms to an organizational level -- is key to improving learning, social integration, well-being, and decision making, all fundamental aspects of the educational experience. Hence, understanding the education system as an interconnected network of people, degree programs, and/or institutions requires methods and concepts from computational social sciences. Thus, we can leverage institutional records and experimental designs to elicit the relational maps of key players in education and derive their implications in their functioning at all scales. Here, in different settings, from elementary classrooms to higher education programs, we show how mapping the network relationships between entities can lead to the inference of novel insights about education systems and the development of solutions with societal implications.preprintsubmitte

Strategic Knowledge Measurement and Management

Knowledge and intellectual capital are now recognized as vital resources for organizational survival and competitive advantage. A vast array of knowledge measures has evolved, spanning many disciplines. This chapter reviews knowledge measures focusing on groups of individuals (such as teams, business and organizations), as they reflect the stock or flow of knowledge, as well as enabling processes that enhance knowledge stocks and flows. The chapter emphasizes the importance of organizational value chains, pivotal talent pools and the link between knowledge and competitive success, in understanding the significance of today’s knowledge measures, and opportunities for future research and practice to enhance them

Innovations in home energy use

As a collection of essays that explore innovations to encourage reduction in homeowner energy use, this volume reflects a confluence of ideas and initiatives rather than a narrow look at what a single, particular line of academic literature suggests might be possible to shape homeowner behavior. Not only do the contributors represent a wide array of institutions and backgrounds, but the very intellectual infrastructure that encouraged and allowed the summit that inspired this book itself represents a conscious effort to facilitate multidisciplinary and interdisciplinary collaboration for the purpose of addressing salient societal concerns. With this volume, we hope to provide a source of ideas for behavior change that will appeal to a range of people charged with curbing residential energy use through communication-based intervention.Publishe

Selective exposure: Exposing a Few Selected Theoretical Aspects

Selective exposure is a phenomenon studied by scholars for decades. Its prominence can be explained by certain potential consequences for democratic societies which include polarization and growing support for extreme views.The media selective exposure approach generated hundreds of publications, however, this growth in new facts and information does not seem to advance much of a paradigmatic consensus on theoretical foundations and practical utility of this line of research.This article aims at assessing whether the key concepts and models of selective exposure represent a cohesive body of knowledge empowering researchers. It also encourages them to seek new knowledge, and test new links. Researchers can also evaluate whether there are some controversial or not sufficiently explicated elements requiring reassessment.This article is a modest effort to assess what is really known and agreed upon in such important pillars of any theory such as definitions and models of selective exposure. This piece also suggests which aspects of selective exposure may need further clarification