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An Integrated CyberGIS and Machine Learning Framework for Data-Intensive Urban Analytics
An Integrated CyberGIS and Machine Learning Framework for Data-Intensive Urban Analytics
Detailed Information
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105654
- ISBN
- 9798265452474
- DDC
- 910
- 저자명
- Lyu, Fangzheng.
- 서명/저자
- An Integrated CyberGIS and Machine Learning Framework for Data-Intensive Urban Analytics
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 130 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: B.
- 주기사항
- Advisor: Wang, Shaowen.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
- 초록/해제
- 요약This thesis introduces a cyberGIS and machine learning framework for data-intensive urban analytics. Due to the rapid urbanization and global changes, it is critical to understand urban environments and the complexity in the urban systems. The framework bridges the gap between heterogeneous geospatial big data and the urban complex system, proposing a novel framework for urban analytics. Applied across three thesis chapters, the framework aims to model, evaluate and predict urban heat with fine spatio temporal granularity, (near) real-time, and high precision using heterogeneous urban big data. The first chapter showcases the integration of cyberGIS and machine learning for predicting Urban Heat Island in Chicago, achieving high spatiotemporal granularity at 1 km spatial resolution and 10 minutes temporal granularity. The second chapter aims to conduct (near) real-time evaluation and mapping of human sentiments of heat exposure using Location-based Social Media data using keywork-based natural language processing algorithm and backend supercomputer. The third chapter introduces a scalable video machine learning framework for urban spatiotemporal analysis, showcasing advantages such as integrated factors, applicability to diverse urban issues, handling of heterogeneous geospatial data, adaptable spatiotemporal granularity, and high precision, which is effectively demonstrated in predicting urban heat dynamics. Overall, these chapters highlight the achievements of the proposed cyberGIS and machine learning framework for data-intensive urban analytics, offering fine spatiotemporal granularity, real-time application, and high accuracy. This innovative urban analytics framework contributes to the understanding of urban heat dynamics and provides effective framework for urban analytics.
- 일반주제명
- Geography
- 일반주제명
- Urban planning
- 키워드
- Machine learning
- 기타저자
- University of Illinois at Urbana-Champaign Geography & GIS
- 기본자료저록
- Dissertations Abstracts International. 87-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798265452474
■035 ▼a(MiAaPQ)AAI32409649
■035 ▼a(MiAaPQ)124515
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a910
■1001 ▼aLyu, Fangzheng.
■24513▼aAn Integrated CyberGIS and Machine Learning Framework for Data-Intensive Urban Analytics
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a130 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: B.
■500 ▼aAdvisor: Wang, Shaowen.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
■520 ▼aThis thesis introduces a cyberGIS and machine learning framework for data-intensive urban analytics. Due to the rapid urbanization and global changes, it is critical to understand urban environments and the complexity in the urban systems. The framework bridges the gap between heterogeneous geospatial big data and the urban complex system, proposing a novel framework for urban analytics. Applied across three thesis chapters, the framework aims to model, evaluate and predict urban heat with fine spatio temporal granularity, (near) real-time, and high precision using heterogeneous urban big data. The first chapter showcases the integration of cyberGIS and machine learning for predicting Urban Heat Island in Chicago, achieving high spatiotemporal granularity at 1 km spatial resolution and 10 minutes temporal granularity. The second chapter aims to conduct (near) real-time evaluation and mapping of human sentiments of heat exposure using Location-based Social Media data using keywork-based natural language processing algorithm and backend supercomputer. The third chapter introduces a scalable video machine learning framework for urban spatiotemporal analysis, showcasing advantages such as integrated factors, applicability to diverse urban issues, handling of heterogeneous geospatial data, adaptable spatiotemporal granularity, and high precision, which is effectively demonstrated in predicting urban heat dynamics. Overall, these chapters highlight the achievements of the proposed cyberGIS and machine learning framework for data-intensive urban analytics, offering fine spatiotemporal granularity, real-time application, and high accuracy. This innovative urban analytics framework contributes to the understanding of urban heat dynamics and provides effective framework for urban analytics.
■590 ▼aSchool code: 0090.
■650 4▼aGeography
■650 4▼aUrban planning
■650 4▼aGeographic information science
■653 ▼aUrban informatics
■653 ▼aGeospatial artificial intelligence
■653 ▼aMachine learning
■653 ▼aData-intensive urban analytics
■690 ▼a0366
■690 ▼a0800
■690 ▼a0999
■690 ▼a0370
■71020▼aUniversity of Illinois at Urbana-Champaign▼bGeography & GIS.
■7730 ▼tDissertations Abstracts International▼g87-06B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361025▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
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