| LDR | | 02024nam u200373 4500 |
| 001 | | 000000422090 |
| 005 | | 20190215165839 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438136052 |
| 035 | |
▼a (MiAaPQ)AAI10903780 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Wang, Hongjian. |
| 245 | 10 |
▼a Urban Computing with Mobility Data: A Unified Approach. |
| 260 | |
▼a [S.l.]:
▼b The Pennsylvania State University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 142 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 502 | 1 |
▼a Thesis (Ph.D.)--The Pennsylvania State University, 2018. |
| 520 | |
▼a With the advent of the information age, various types of data are collected in the context of urban spaces, including taxi pickups/drop-offs, tweets from users, air quality measure, noise complaints, Point-Of-Interest (POI), and many more. It is |
| 520 | |
▼a This dissertation aims at modeling the complicated interactions of regions in the urban space. Traditionally, due to lack of flow data, interaction is defined only by spatial distance. Recently, the availability of movement data enables us to st |
| 520 | |
▼a In this dissertation, I propose to develop a unified framework to model the mobility-flow-incurred interactions in the urban context. We start with a preliminary study on improving Chicago community-level crime prediction with POI and taxi flow. |
| 590 | |
▼a School code: 0176. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a The Pennsylvania State University.
▼b Information Sciences and Technology. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0176 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000737
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |