Context Trees: Augmenting Geospatial Trajectories with Context

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information

Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data

The meteoric rise in the popularity of public engagement platforms such as social media, customer review websites, and public input solicitation efforts strives for establishing an inclusive environment for the public to share their thoughts, ideas, opinions, and experiences. Many decisions made at a personal, local, or national scale are often fueled by data generated by the public. As such, inclusive collection, analysis, sensemaking, and utilization of pubic-generated data are crucial to support the exercise of successful decision-making processes. However, people often struggle to engage, participate, and share their opinions due to inaccessibility, the rigidity of traditional public engagement methods, and the lack of options to provide opinions while avoiding potential confrontations. Concurrently, data analysts and decision-makers grapple with the challenges of analyzing, sensemaking, and making informed decisions based on public-generated data, which includes high dimensionality, ambiguity present in human language, and a lack of tools and techniques catered to their needs. Novel technological interventions are therefore necessary to enable the public to share their input without barriers and allow decision-makers to capture, forage, peruse, and sublimate public-generated data into concrete and actionable insights. The goal of this dissertation is to demonstrate how human-centered approaches involve the stakeholders in the design, development, and evaluation of tools and techniques that can lead to inclusive, effective, and efficient approaches to public-generated data collection and analysis to support informed decision-making. To that end, in this dissertation, I first addressed the challenges of empowering the public to share their opinions by exploring two major opinion-sharing avenues --- social media and public consultation. To learn more about people\u27s social media experiences and challenges, I built two technology probes and conducted a qualitative exploratory study with 16 participants. This study is followed up by exploring the challenges of inclusive participation during public consultations such as town halls. Based on a formative study with 66 participants and 20 organizers, I designed and developed CommunityClick to enable reticent share their opinions silently and anonymously during town halls. Equipped with the knowledge and experiences from these works, I designed, developed, and evaluated technologies and methods to facilitate and accelerate informed data-driven decision-making based on increased public-generated data. Based on interviews with 14 analysts and decision-makers in the civic domain, I built a visual analytics system CommunityClick that can facilitate public input analysis by surfacing hidden insights, people\u27s reflections, and priorities. Leveraging the lessons learned during this work, I created a visual text analytics system that supports serendipitous discovery and balanced analysis of textual data to help make informed decisions. In this work, I contribute an understanding of how people collect and analyze public-generated data to fuel their decisions when they have increased exposure to alternative avenues for opinion-sharing. Through a series of human-centered studies, I highlight the challenges that inhibit inclusivity in opinion sharing and shortcomings of existing methods that prevent decision-makers to account for comprehensive public input that includes marginalized or unpopular opinions. To address these challenges, I designed, developed, and evaluated a collection of interactive systems including CommunityClick, CommunityPulse, and Serendyze. Through a rigorous set of evaluation strategies which include creativity sessions, controlled lab studies, in-the-wild deployment, and field experiments, I involved stakeholders to assess the effectiveness and utility of the built systems. Through the empirical evidence from these studies, I demonstrate how alternative designs for social media could enhance people\u27s social media experiences and enable them to make new connections with others to share opinions. In addition, I show how CommunityClick can be utilized to enable reticent attendees during public consultation to share their opinions while avoiding unwanted confrontation and allowing organizers to capture and account for silent feedback. I highlight how CommunityPulse allowed analysts and decision-makers to examine public input from multiple angles for an accelerated analysis and more informed decision-making. Furthermore, I demonstrate how supporting serendipitous discovery and balanced analysis using Serendyze can lead to more informed data-driven decision-making. I conclude the dissertation with a discussion on future avenues to expand this research including the facilitation of multi-user collaborative analysis, integration of multi-modal signals in the analysis of public-generated data, and potential adoption strategies for decision-support systems designed for inclusive collection and analysis of public-generated data

Global COVID-19 lockdown highlights humans as both threats and custodians of the environment

Funding: Gordon and Betty Moore Foundation; National Geographic Society (NGS-82515R-20).The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness.PostprintPeer reviewe

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Mobility is the Message: Experiments with Mobile Media Sharing

This thesis explores new mobile media sharing applications by building, deploying, and studying their use. While we share media in many different ways both on the web and on mobile phones, there are few ways of sharing media with people physically near us. Studied were three designed and built systems: Push!Music, Columbus, and Portrait Catalog, as well as a fourth commercially available system – Foursquare. This thesis offers four contributions: First, it explores the design space of co-present media sharing of four test systems. Second, through user studies of these systems it reports on how these come to be used. Third, it explores new ways of conducting trials as the technical mobile landscape has changed. Last, we look at how the technical solutions demonstrate different lines of thinking from how similar solutions might look today. Through a Human-Computer Interaction methodology of design, build, and study, we look at systems through the eyes of embodied interaction and examine how the systems come to be in use. Using Goffman’s understanding of social order, we see how these mobile media sharing systems allow people to actively present themselves through these media. In turn, using McLuhan’s way of understanding media, we reflect on how these new systems enable a new type of medium distinct from the web centric media, and how this relates directly to mobility. While media sharing is something that takes place everywhere in western society, it is still tied to the way media is shared through computers. Although often mobile, they do not consider the mobile settings. The systems in this thesis treat mobility as an opportunity for design. It is still left to see how this mobile media sharing will come to present itself in people’s everyday life, and when it does, how we will come to understand it and how it will transform society as a medium distinct from those before. This thesis gives a glimpse at what this future will look like