東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Visual Analytics Methods for Explori...

Wang, Feng.

Linked to FindBook

Google Book

Amazon

博客來

Visual Analytics Methods for Exploring Geographically Networked Phenomena.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Visual Analytics Methods for Exploring Geographically Networked Phenomena./
Author:	Wang, Feng.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	128 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
Contained By:	Dissertation Abstracts International78-09B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10275012
ISBN:	9781369732399

Visual Analytics Methods for Exploring Geographically Networked Phenomena.
Wang, Feng.

Visual Analytics Methods for Exploring Geographically Networked Phenomena. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 128 p.

Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2017.

The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models.

ISBN: 9781369732399Subjects--Topical Terms:

523869
Computer science.

Visual Analytics Methods for Exploring Geographically Networked Phenomena.
LDR:03046nmm a2200289 4500 001 2123631
005 20171003070918.5
008 180830s2017 ||||||||||||||||| ||eng d
020 $a 9781369732399
035 $a (MiAaPQ)AAI10275012
035 $a AAI10275012
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wang, Feng. $3 1037905
245 1 0 $a Visual Analytics Methods for Exploring Geographically Networked Phenomena.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 128 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
500 $a Adviser: Ross Maciejewski.
502 $a Thesis (Ph.D.)--Arizona State University, 2017.
520 $a The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models.
520 $a Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.
590 $a School code: 0010.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a Arizona State University. $b Computer Science. $3 1676136
773 0 $t Dissertation Abstracts International $g 78-09B(E).
790 $a 0010
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10275012