國立虎尾科技大學 |

Context-Preserving Visual Analytics of Multi-Scale Spatial Aggregation.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Context-Preserving Visual Analytics of Multi-Scale Spatial Aggregation./
Author:	Zhang, Jiawei.
Description:	1 online resource (123 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780438018587

Context-Preserving Visual Analytics of Multi-Scale Spatial Aggregation.
Zhang, Jiawei.

Context-Preserving Visual Analytics of Multi-Scale Spatial Aggregation. - 1 online resource (123 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--Purdue University, 2018.

Includes bibliographical references

Spatial datasets (i.e., location-based social media, crime incident reports, and demographic data) often exhibit varied distribution patterns at multiple spatial scales. Examining these patterns across different scales enhances the understanding from global to local perspectives and offers new insights into the nature of various spatial phenomena. Conventional navigation techniques in such multi-scale data-rich spaces are often inefficient, require users to choose between an overview or detailed information, and do not support identifying spatial patterns at varying scales. In this work, we present a context-preserving visual analytics technique that aggregates spatial datasets into hierarchical clusters and visualizes the multi-scale aggregates in a single visual space. We design a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates and explore visual encoding strategies including color, transparency, shading, and shapes, in order to illustrate the hierarchical and statistical patterns of the multi-scale aggregates. We also propose a transparency-based technique that maintains a smooth visual transition as the users navigate across adjacent scales. To further support effective semantic exploration in the multi-scale space, we design a set of text-based encoding and layout methods that draw textual labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each scale, but also indicates the spatial coverage of the aggregates and their hierarchical relationships. We demonstrate the effectiveness of the proposed approaches through real-world application examples and user studies.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438018587Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Context-Preserving Visual Analytics of Multi-Scale Spatial Aggregation.
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500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: David S. Ebert.
502 $a Thesis (Ph.D.)--Purdue University, 2018.
504 $a Includes bibliographical references
520 $a Spatial datasets (i.e., location-based social media, crime incident reports, and demographic data) often exhibit varied distribution patterns at multiple spatial scales. Examining these patterns across different scales enhances the understanding from global to local perspectives and offers new insights into the nature of various spatial phenomena. Conventional navigation techniques in such multi-scale data-rich spaces are often inefficient, require users to choose between an overview or detailed information, and do not support identifying spatial patterns at varying scales. In this work, we present a context-preserving visual analytics technique that aggregates spatial datasets into hierarchical clusters and visualizes the multi-scale aggregates in a single visual space. We design a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates and explore visual encoding strategies including color, transparency, shading, and shapes, in order to illustrate the hierarchical and statistical patterns of the multi-scale aggregates. We also propose a transparency-based technique that maintains a smooth visual transition as the users navigate across adjacent scales. To further support effective semantic exploration in the multi-scale space, we design a set of text-based encoding and layout methods that draw textual labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each scale, but also indicates the spatial coverage of the aggregates and their hierarchical relationships. We demonstrate the effectiveness of the proposed approaches through real-world application examples and user studies.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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