IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v48y2021i3p395-399.html
   My bibliography  Save this article

Advances in urban informatics

Author

Listed:
  • Xintao Liu
  • Wenzhong Shi
  • Anshu Zhang

Abstract

No abstract is available for this item.

Suggested Citation

  • Xintao Liu & Wenzhong Shi & Anshu Zhang, 2021. "Advances in urban informatics," Environment and Planning B, , vol. 48(3), pages 395-399, March.
    • Handle: RePEc:sae:envirb:v:48:y:2021:i:3:p:395-399
    DOI: 10.1177/2399808321998468
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2399808321998468
    Download Restriction: no
    File URL: https://libkey.io/10.1177/2399808321998468?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Shuli Luo & Sylvia Y He, 2021. "Using data mining to explore the spatial and temporal dynamics of perceptions of metro services in China: The case of Shenzhen," Environment and Planning B, , vol. 48(3), pages 449-466, March.
    2. Roberto Ponce Lopez & Joseph Ferreira, 2021. "Identifying spatio-temporal hotspots of human activity that are popular non-work destinations," Environment and Planning B, , vol. 48(3), pages 433-448, March.
    3. Thunyathep Santhanavanich & Volker Coors, 2021. "CityThings: An integration of the dynamic sensor data to the 3D city model," Environment and Planning B, , vol. 48(3), pages 417-432, March.
    4. Mingshu Wang, 2021. "Polycentric urban development and urban amenities: Evidence from Chinese cities," Environment and Planning B, , vol. 48(3), pages 400-416, March.
    5. Xin-Yi Song & Ya Gao & Yubo Peng & Sen Huang & Chao Liu & Zhong-Ren Peng, 2021. "A machine learning approach to modelling the spatial variations in the daily fine particulate matter (PM2.5) and nitrogen dioxide (NO2) of Shanghai, China," Environment and Planning B, , vol. 48(3), pages 467-483, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luo, Shuli & He, Sylvia Y. & Grant-Muller, Susan & Song, Linqi, 2023. "Influential factors in customer satisfaction of transit services: Using crowdsourced data to capture the heterogeneity across individuals, space and time," Transport Policy, Elsevier, vol. 131(C), pages 173-183.
    2. Luo, Shuli & He, Sylvia Y., 2021. "Understanding gender difference in perceptions toward transit services across space and time: A social media mining approach," Transport Policy, Elsevier, vol. 111(C), pages 63-73.
    3. Zhao, Zhiyuan & Yao, Wei & Wu, Sheng & Yang, Xiping & Wu, Qunyong & Fang, Zhixiang, 2023. "Identifying the collaborative scheduling areas between ride-hailing and traditional taxi services based on vehicle trajectory data," Journal of Transport Geography, Elsevier, vol. 107(C).
    4. Adewunmi, Yewande & Chigbu, Uchendu Eugene & Mwando, Sam & Kahireke, Uaurika, 2023. "Entrepreneurship role in the co-production of public services in informal settlements − A scoping review," Land Use Policy, Elsevier, vol. 125(C).
    5. Martin, Miguel & Chong, Adrian & Biljecki, Filip & Miller, Clayton, 2022. "Infrared thermography in the built environment: A multi-scale review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    6. Klaudia Maciąg & Przemysław Leń, 2022. "Assessment of 3D Geoportals of Cities According to CityGML Standard Guidelines," Sustainability, MDPI, vol. 14(23), pages 1-12, November.
    7. Martino Tran & Christina Draeger & Xuerou Wang & Abbas Nikbakht, 2023. "Monitoring the well-being of vulnerable transit riders using machine learning based sentiment analysis and social media: Lessons from COVID-19," Environment and Planning B, , vol. 50(1), pages 60-75, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:48:y:2021:i:3:p:395-399. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.