Human dynamics in the age of big data: a theory-data-driven approach

The revolution of information and communication technology (ICT) in the past two decades have transformed the world and people’s lives with the ways that knowledge is produced. With the advancements in location-aware technologies, a large volume of data so-called “big data” is now available through various sources to explore the world. This dissertation examines the potential use of such data in understanding human dynamics by focusing on both theory- and data-driven approaches. Specifically, human dynamics represented by communication and activities is linked to geographic concepts of space and place through social media data to set a research platform for effective use of social media as an information system. Three case studies covering these conceptual linkages are presented to (1) identify communication patterns on social media; (2) identify spatial patterns of activities in urban areas and detect events; and (3) explore urban mobility patterns. The first case study examines the use of and communication dynamics on Twitter during Hurricane Sandy utilizing survey and data analytics techniques. Twitter was identified as a valuable source of disaster-related information. Additionally, the results shed lights on the most significant information that can be derived from Twitter during disasters and the need for establishing bi-directional communications during such events to achieve an effective communication. The second case study examines the potential of Twitter in identifying activities and events and exploring movements during Hurricane Sandy utilizing both time-geographic information and qualitative social media text data. The study provides insights for enhancing situational awareness during natural disasters. The third case study examines the potential of Twitter in modeling commuting trip distribution in New York City. By integrating both traditional and social media data and utilizing machine learning techniques, the study identified Twitter as a valuable source for transportation modeling. Despite the limitations of social media such as the accuracy issue, there is tremendous opportunity for geographers to enrich their understanding of human dynamics in the world. However, we will need new research frameworks, which integrate geographic concepts with information systems theories to theorize the process. Furthermore, integrating various data sources is the key to future research and will need new computational approaches. Addressing these computational challenges, therefore, will be a crucial step to extend the frontier of big data knowledge from a geographic perspective. KEYWORDS: Big data, social media, Twitter, human dynamics, VGI, natural disasters, Hurricane Sandy, transportation modeling, machine learning, situational awareness, NYC, GI

Understanding the Socio-infrastructure Systems During Disaster from Social Media Data

Our socio-infrastructure systems are becoming more and more vulnerable due to the increased severity and frequency of extreme events every year. Effective disaster management can minimize the damaging impacts of a disaster to a large extent. The ubiquitous use of social media platforms in GPS enabled smartphones offers a unique opportunity to observe, model, and predict human behavior during a disaster. This dissertation explores the opportunity of using social media data and different modeling techniques towards understanding and managing disaster more dynamically. In this dissertation, we focus on four objectives. First, we develop a method to infer individual evacuation behaviors (e.g., evacuation decision, timing, destination) from social media data. We develop an input output hidden Markov model to infer evacuation decisions from user tweets. Our findings show that using geo-tagged posts and text data, a hidden Markov model can be developed to capture the dynamics of hurricane evacuation decision. Second, we develop evacuation demand prediction model using social media and traffic data. We find that trained from social media and traffic data, a deep learning model can predict well evacuation traffic demand up to 24 hours ahead. Third, we present a multi-label classification approach to identify the co-occurrence of multiple types of infrastructure disruptions considering the sentiment towards a disruption—whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). We validate our approach for data collected during multiple hurricanes. Fourth, finally we develop an agent-based model to understand the influence of multiple information sources on risk perception dynamics and evacuation decisions. In this study, we explore the effects of socio-demographic factors and information sources such as social connectivity, neighborhood observation, and weather information and its credibility in forming risk perception dynamics and evacuation decisions

Why Information Matters: A Foundation for Resilience

Embracing Change: The Critical Role of Information, a research project by the Internews' Center for Innovation & Learning, supported by the Rockefeller Foundation, combines Internews' longstanding effort to highlight the important role ofinformation with Rockefeller's groundbreaking work on resilience. The project focuses on three major aspects:- Building knowledge around the role of information in empowering communities to understand and adapt to different types of change: slow onset, long-term, and rapid onset / disruptive;- Identifying strategies and techniques for strengthening information ecosystems to support behavioral adaptation to disruptive change; and- Disseminating knowledge and principles to individuals, communities, the private sector, policymakers, and other partners so that they can incorporate healthy information ecosystems as a core element of their social resilience strategies