東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Visual Analytics for Spatiotemporal ...

Zhang, Yifan.

FindBook

Google Book

Amazon

博客來

Visual Analytics for Spatiotemporal Cluster Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Visual Analytics for Spatiotemporal Cluster Analysis./
作者:	Zhang, Yifan.
面頁冊數:	133 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-09(E), Section: B.
Contained By:	Dissertation Abstracts International77-09B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10107614
ISBN:	9781339710143

Visual Analytics for Spatiotemporal Cluster Analysis.
Zhang, Yifan.

Visual Analytics for Spatiotemporal Cluster Analysis. - 133 p.

Source: Dissertation Abstracts International, Volume: 77-09(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2016.

Traditionally, visualization is one of the most important and commonly used methods of generating insight into large scale data. Particularly for spatiotemporal data, the translation of such data into a visual form allows users to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena that would not be apparent in tabular form. However, several critical challenges arise when visualizing and exploring these large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex. To simplify the visual representations, analytical methods such as clustering and feature extraction are often applied as part of the classification phase. The automatic classification can then be rendered onto a map; however, one common issue in data classification is that items near a classification boundary are often mislabeled.

ISBN: 9781339710143Subjects--Topical Terms:

523869
Computer science.

Visual Analytics for Spatiotemporal Cluster Analysis.
LDR:02653nmm a2200277 4500 001 2073211
005 20160909082016.5
008 170521s2016 ||||||||||||||||| ||eng d
020 $a 9781339710143
035 $a (MiAaPQ)AAI10107614
035 $a AAI10107614
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Yifan. $3 1902818
245 1 0 $a Visual Analytics for Spatiotemporal Cluster Analysis.
300 $a 133 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-09(E), Section: B.
500 $a Adviser: Ross Maciejewski.
502 $a Thesis (Ph.D.)--Arizona State University, 2016.
520 $a Traditionally, visualization is one of the most important and commonly used methods of generating insight into large scale data. Particularly for spatiotemporal data, the translation of such data into a visual form allows users to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena that would not be apparent in tabular form. However, several critical challenges arise when visualizing and exploring these large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex. To simplify the visual representations, analytical methods such as clustering and feature extraction are often applied as part of the classification phase. The automatic classification can then be rendered onto a map; however, one common issue in data classification is that items near a classification boundary are often mislabeled.
520 $a This thesis explores methods to augment the automated spatial classification by utilizing interactive machine learning as part of the cluster creation step. First, this thesis explores the design space for spatiotemporal analysis through the development of a comprehensive data wrangling and exploratory data analysis platform. Second, this system is augmented with a novel method for evaluating the visual impact of edge cases for multivariate geographic projections. Finally, system features and functionality are demonstrated through a series of case studies, with key features including similarity analysis, multivariate clustering, and novel visual support for cluster comparison.
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 77-09B(E).
790 $a 0010
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10107614