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Human Dynamics in the Age of Big Dat...

Pourebrahim, Nastaran .

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Human Dynamics in the Age of Big Data: A Theory-Data-Driven Approach.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Human Dynamics in the Age of Big Data: A Theory-Data-Driven Approach./
作者:	Pourebrahim, Nastaran .
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	136 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-09, Section: A.
Contained By:	Dissertations Abstracts International81-09A.
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27545452
ISBN:	9781392632260

Human Dynamics in the Age of Big Data: A Theory-Data-Driven Approach.
Pourebrahim, Nastaran .

Human Dynamics in the Age of Big Data: A Theory-Data-Driven Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 136 p.

Source: Dissertations Abstracts International, Volume: 81-09, Section: A.

Thesis (Ph.D.)--The University of North Carolina at Greensboro, 2019.

This item must not be sold to any third party vendors.

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.

ISBN: 9781392632260Subjects--Topical Terms:

524010
Geography.
Subjects--Index Terms:

Big data

Human Dynamics in the Age of Big Data: A Theory-Data-Driven Approach.
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520 $a 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.
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