A Data-Driven Optimisation Approach to Urban Multi-Site Selection for Public Services and Retails
Article No.: 9, Pages 1 - 9
Abstract
Urban lifestyle depends on public services and retails, of which site locations matter to convenience for residents. We introduce a novel approach to the systematic multi-site selection for public services and retails in an urban context. It takes as input a set of data about an urban area and generates an optimal configuration of two-dimensional locations for urban sites on public services and retails. We achieve this goal using data-driven optimisation entangling deep learning. The proposed approach can cost-efficiently generate a multi-site location plan considering representative site selection criteria, including coverage, dispersion and accessibility. It also complies with the local plan and the predicted suitability regarding land-use zoning.
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Information & Contributors
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Published In
November 2019
354 pages
ISBN:9781450370028
DOI:10.1145/3359997
- Conference Chairs:
- Joaquim Jorge,
- June Kim,
- Herman Van Eyken,
- Editor:
- Stephen N. Spencer,
- Program Chairs:
- Mashhuda Glencross,
- Kenny Mitchell,
- Takuji Narumi
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Published: 14 November 2019
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VRCAI '19
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VRCAI '19: The 17th International Conference on Virtual-Reality Continuum and its Applications in Industry
November 14 - 16, 2019
QLD, Brisbane, Australia
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