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

FindBook

Google Book

Amazon

博客來

Urban Events Mining from Spatiotemporal Big Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Urban Events Mining from Spatiotemporal Big Data./
作者:	Zhang, Mingyang.
面頁冊數:	1 online resource (152 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-05, Section: A.
Contained By:	Dissertations Abstracts International84-05A.
標題:	Cellular telephones. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29704084click for full text (PQDT)
ISBN:	9798352974827

Urban Events Mining from Spatiotemporal Big Data.
Zhang, Mingyang.

Urban Events Mining from Spatiotemporal Big Data. - 1 online resource (152 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: A.

Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2022.

Includes bibliographical references

Urban events are the sets of real-world occurrences in urban space associated with specific topics. Discovering, understanding, and forecasting various urban events are essential to facilitate smart-city applications and benefit urban life. In recent years, mobile devices and sensors widely located in cities have collected large amounts of data produced in urban space, which form a large-scale, cross-domain, and multi-view data ecosystem. The collected data, termed urban big data, provide an unprecedented opportunity to discover and analyze urban events. In this thesis, we develop data-driven methodologies for urban events mining. We present four of our works on urban anomaly detection, urban location representation learning, road network traffic prediction, and urban events prediction, respectively.First, we introduce an urban dynamic decomposition method to detect urban anomalies. We propose a neural network model to estimate normal urban dynamics using spatiotemporal features and detect abnormal events via residual analysis. We validate the effectiveness of the proposed method with both synthetic and real-world event datasets. Regions are the primary spatial units for urban event analysis. The second work focuses on learning an embedding space from urban big data for urban regions. We propose to construct multi-view relations among urban regions and learn urban region embeddings through a multi-view joint learning model. Experiments on real-world datasets prove the effectiveness of the learned embeddings on downstream applications, including predicting long-term regional event statistics such as crime rates. Traffic prediction is a crucial task for congestion events diagnosis and control. In the third work, we propose a traffic prediction model called Adaptive Spatiotemporal Convolution Network, which models complicated spatiotemporal traffic dependencies adaptively with awareness of the realtime traffic conditions. Experiments on real-world traffic datasets from California and Beijing demonstrate that the proposed model outperforms the state-of-the-art. In the fourth work, we target at location and arrival time prediction of individual urban anomalous events. We design a message passing based mechanism to model the spatiotemporal impacts of historical events. We make joint predictions of event locations and times based on regional states and environmental factors. Our method achieves superior performances on two real-world urban events datasets compared with existing spatiotemporal prediction methods.In the end, we conclude this thesis with future challenges and research directions related to data-driven urban events analysis.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352974827Subjects--Topical Terms:

607843
Cellular telephones.
Index Terms--Genre/Form:

542853
Electronic books.

Urban Events Mining from Spatiotemporal Big Data.
LDR:03931nmm a2200325K 4500 001 2354328
005 20230403071219.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798352974827
035 $a (MiAaPQ)AAI29704084
035 $a (MiAaPQ)HongKongSciTech_991013088959303412
035 $a AAI29704084
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Zhang, Mingyang. $3 3694679
245 1 0 $a Urban Events Mining from Spatiotemporal Big Data.
264 0 $c 2022
300 $a 1 online resource (152 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-05, Section: A.
500 $a Advisor: Hui, Pan; Braud, Tristan Camille.
502 $a Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2022.
504 $a Includes bibliographical references
520 $a Urban events are the sets of real-world occurrences in urban space associated with specific topics. Discovering, understanding, and forecasting various urban events are essential to facilitate smart-city applications and benefit urban life. In recent years, mobile devices and sensors widely located in cities have collected large amounts of data produced in urban space, which form a large-scale, cross-domain, and multi-view data ecosystem. The collected data, termed urban big data, provide an unprecedented opportunity to discover and analyze urban events. In this thesis, we develop data-driven methodologies for urban events mining. We present four of our works on urban anomaly detection, urban location representation learning, road network traffic prediction, and urban events prediction, respectively.First, we introduce an urban dynamic decomposition method to detect urban anomalies. We propose a neural network model to estimate normal urban dynamics using spatiotemporal features and detect abnormal events via residual analysis. We validate the effectiveness of the proposed method with both synthetic and real-world event datasets. Regions are the primary spatial units for urban event analysis. The second work focuses on learning an embedding space from urban big data for urban regions. We propose to construct multi-view relations among urban regions and learn urban region embeddings through a multi-view joint learning model. Experiments on real-world datasets prove the effectiveness of the learned embeddings on downstream applications, including predicting long-term regional event statistics such as crime rates. Traffic prediction is a crucial task for congestion events diagnosis and control. In the third work, we propose a traffic prediction model called Adaptive Spatiotemporal Convolution Network, which models complicated spatiotemporal traffic dependencies adaptively with awareness of the realtime traffic conditions. Experiments on real-world traffic datasets from California and Beijing demonstrate that the proposed model outperforms the state-of-the-art. In the fourth work, we target at location and arrival time prediction of individual urban anomalous events. We design a message passing based mechanism to model the spatiotemporal impacts of historical events. We make joint predictions of event locations and times based on regional states and environmental factors. Our method achieves superior performances on two real-world urban events datasets compared with existing spatiotemporal prediction methods.In the end, we conclude this thesis with future challenges and research directions related to data-driven urban events analysis.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Cellular telephones. $3 607843
650 4 $a Traffic congestion. $3 706812
650 4 $a Sensors. $3 3549539
650 4 $a Transportation. $3 555912
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0709
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Hong Kong University of Science and Technology (Hong Kong). $3 1022235
773 0 $t Dissertations Abstracts International $g 84-05A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29704084 $z click for full text (PQDT)