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Using Socially Sensed Big Data to Mo...

Fu, Cheng.

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Using Socially Sensed Big Data to Model Patterns and Geographic Context of Human Activities in Cities = = 使用社会感知大数据对城市中人类行为的模式和地理上下文建模.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using Socially Sensed Big Data to Model Patterns and Geographic Context of Human Activities in Cities =/
其他題名:	使用社会感知大数据对城市中人类行为的模式和地理上下文建模.
作者:	Fu, Cheng.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	160 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-02, Section: B.
Contained By:	Dissertations Abstracts International80-02B.
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10791635
ISBN:	9780438183155

Using Socially Sensed Big Data to Model Patterns and Geographic Context of Human Activities in Cities = = 使用社会感知大数据对城市中人类行为的模式和地理上下文建模.
Fu, Cheng.

Using Socially Sensed Big Data to Model Patterns and Geographic Context of Human Activities in Cities =使用社会感知大数据对城市中人类行为的模式和地理上下文建模. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 160 p.

Source: Dissertations Abstracts International, Volume: 80-02, Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2018.

This item is not available from ProQuest Dissertations & Theses.

Understanding dynamic interactions between human activities and land-use structure in a city is a key lens to explore the city as a complex system. This dissertation contributes to understanding the complexity of urban dynamics by gaining knowledge of the interactions between human activities and city land-use structures by utilizing free-accessible socially sensed data sources, and building upon recent research trend and technologies in geographical information science, urban study, and computer science. This dissertation addresses three main questions related to human dynamics: 1) how human activities in an urban environment are shaped by socioeconomic status and the intra-city land-use structure, and how in turn, the knowledge of socioeconomic status-activity relationships can contribute to understanding the social landscape of a city; 2) how different types of activities are located in space and time in three U.S. cities and how the spatiotemporal activity patterns in these cities characterize the activity profile of different neighborhoods in the cities; and 3) how recent socially sensed information on human activities can be integrated with widely-used remotely sensed geographical data to create a novel approach for discovering patterns of land use in cities that are otherwise lacking in up to date land use information. This dissertation models the associations between socioeconomics and mobility in the Washington, D.C. metropolitan area as a case study and applies the learned associations for inferring geographical patterns of socioeconomic status (SES) solely using the socially sensed data. This dissertation also implements a semi-automated workflow to retrieve activity details from socially sensed Twitter data in Washington, D.C., the City of Baltimore, and New York City. The dissertation integrates remotely-sensed imagery and socially sensed data to model the dynamics associated with changing land-use types in the Washington, D.C.-Baltimore metropolitan area over time.

ISBN: 9780438183155Subjects--Topical Terms:

524010
Geography.

Using Socially Sensed Big Data to Model Patterns and Geographic Context of Human Activities in Cities = = 使用社会感知大数据对城市中人类行为的模式和地理上下文建模.
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520 $a Understanding dynamic interactions between human activities and land-use structure in a city is a key lens to explore the city as a complex system. This dissertation contributes to understanding the complexity of urban dynamics by gaining knowledge of the interactions between human activities and city land-use structures by utilizing free-accessible socially sensed data sources, and building upon recent research trend and technologies in geographical information science, urban study, and computer science. This dissertation addresses three main questions related to human dynamics: 1) how human activities in an urban environment are shaped by socioeconomic status and the intra-city land-use structure, and how in turn, the knowledge of socioeconomic status-activity relationships can contribute to understanding the social landscape of a city; 2) how different types of activities are located in space and time in three U.S. cities and how the spatiotemporal activity patterns in these cities characterize the activity profile of different neighborhoods in the cities; and 3) how recent socially sensed information on human activities can be integrated with widely-used remotely sensed geographical data to create a novel approach for discovering patterns of land use in cities that are otherwise lacking in up to date land use information. This dissertation models the associations between socioeconomics and mobility in the Washington, D.C. metropolitan area as a case study and applies the learned associations for inferring geographical patterns of socioeconomic status (SES) solely using the socially sensed data. This dissertation also implements a semi-automated workflow to retrieve activity details from socially sensed Twitter data in Washington, D.C., the City of Baltimore, and New York City. The dissertation integrates remotely-sensed imagery and socially sensed data to model the dynamics associated with changing land-use types in the Washington, D.C.-Baltimore metropolitan area over time.
520 $a 理解人类与城市用地的动态交互是理解城市复杂系统的重要手段。本论文通过使用免费获取的空间感知数据，以及基于目前在地理信息科学，城市科学以及计算机科学的研究进展，对城市中人类行为及其与城市用地结构交互的进行建模以理解城市复杂系统的动态过程。本论文由三个主要研究构成：1）城市空间中人类行为是如何被社会经济状态和城市内用地结构所塑造，以及两者的关系如何反向贡献于理解城市中社会地形；2）三个城市中不同类型的行为如何在时空中定位以及行为的时空模式如何描述城市社群的特征；3）关于人类活动的社会感知数据和广泛使用的遥感数据如何被一种新方法结合起来以发现城市中用地类型分布从而可以对缺乏用地类型分布图的城市提供相关信息。本论文以华盛顿都市区为例建模了社会经济状态与移动性之间的关系，并将该关系应用到了以社会感知数据单独对社会经济状态的地理特征进行推断的实验中。本论文也实现了一种半自动的工作流以使用推特数据来感知城市中具体行为，并应用到了华盛顿，巴尔的摩和纽约的实验中。本论文还将遥感图像和社会感知数据结合来对华盛顿-巴尔的摩都市区的用地类型变化过程进行建模。.
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