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

Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

The U.S. household (HH) energy consumption is responsible for approximately 20% of annual global GHG emissions. Identifying the key factors influencing HH energy consumption is a major goal of policy makers to achieve energy sustainability. Although various explanatory factors have been examined, empirical evidence is inconclusive. Most studies are either aspatial in nature or neglect the spatial non-stationarity in data. Our study examines spatial variation of the key factors associated with HH energy expenditures at census tract level by utilizing geographically weighted regression (GWR) for the 14 metropolitan statistical areas (MSAs) in North Carolina (NC). A range of explanatory variables including socioeconomic and demographic characteristics of households, local urban form, housing characteristics, and temperature are analyzed. While GWR model for HH transportation expenditures has a better performance compared to the utility model, the results indicate that the GWR model for both utility and transportation has a slightly better prediction power compared to the traditional ordinary least square (OLS) model. HH median income, median age of householders, urban compactness, and distance from the primary city center explain spatial variability of HH transportation expenditures in the study area. HH median income, median age of householders, and percent of one-unit detached housing are identified as the main influencing factors on HH utility expenditures in the GWR model. This analysis also provides the spatial variability of the relationship between HH energy expenditures and the associated factors suggesting the need for location-specific evaluation and suitable guidelines to reduce the energy consumption

Geographical Assessment of Low-Carbon Transportation Modes: A Case Study from a Commuter University

This case study examines the geographic variation in students’ low-carbon transportation (LCT) modes to a commuter university campus. Three major goals are accomplished from this research: (1) identifying commuting zones for the bicycling, walking, and transit mode choice for UNCG students; (2) understanding whether the real vs. perception of space can be predictive to mode choice; and (3) understanding the relative importance of demographic, psychological, and logistic factors on students’ mode choice, using a suite of variables developed in multiple fields. Our analyses support the assertion that various physical, demographic, and psychological dimensions influence LCT mode choice. While the presence of sidewalks is conducive to walking, the distance, either perceived or actual, within 1.61 km from UNCG is the most important factor for walking mode share. The bicycling commute is not associated with either the distance or presence of bicycle lanes, while transit ridership most likely increases if students live >8 km from the UNCG campus with the nearest bus stop within 1 km from home. Given the limited bicycle lanes in Greensboro, students who commute to campus by bicycle are resilient to unfavorable bicycle conditions by sharing the road with cars and adjusting their travel routes. Our findings also concur with previous studies showing that bicycle commuters are disproportionately represented by self-identified whites while bus riders are disproportionately comprised of self-identified non-whites. Our analyses support Greensboro’s current planning and policy emphasis on low-carbon travel behaviors via equitable and safe transit-oriented multi-modal infrastructures, and suggest that UNCG should utilize its influence to advocate and further facilitate these ongoing efforts