Publication Type
Editorial
Version
publishedVersion
Publication Date
7-2020
Abstract
Spatial optimization represents a set of powerful spatial analysis techniques that can be used to identify optimal solution(s) and even generate a large number of competitive alternatives. The formulation of such problems involves maximizing or minimizing one or more objectives while satisfying a number of constraints. Solution techniques range from exact models solved with such approaches as linear programming and integer programming, or heuristic algorithms, i.e. Tabu Search, Simulated Annealing, and Genetic Algorithms. Spatial optimization techniques have been utilized in numerous planning applications, such as location-allocation modeling/site selection, land use planning, school districting, regionalization, routing, and urban design. These methods can be seamlessly integrated into the planning process and generate many optimal/near-optimal planning scenarios or solutions, in order to more quantitatively and scientifically support the planning and operation of public and private systems. However, as most spatial optimization problems are non-deterministic polynomial-time-hard (NP-hard) in nature, even a small data set will generate a very complex solution space and therefore tend to be very computationally intensive to solve. In addition, the quantification and modeling of different (spatial) objectives and relevant constraints also remain a challenge, which requires further attention from the scientific community.
Keywords
Big data, land use, optimization
Discipline
Databases and Information Systems | Data Science | Theory and Algorithms | Urban Studies and Planning
Research Areas
Data Science and Engineering
Publication
Environment and Planning B: Urban Analytics and City Science
Volume
47
Issue
6
First Page
941
Last Page
947
ISSN
2399-8083
Identifier
10.1177/2399808320935269
Publisher
SAGE
Citation
CAO, Kai; LI, Wenwen; and CHURCH, Richard.
Big data, spatial optimization, and planning. (2020). Environment and Planning B: Urban Analytics and City Science. 47, (6), 941-947.
Available at: https://ink.library.smu.edu.sg/sis_research/5461
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1177/2399808320935269
Included in
Databases and Information Systems Commons, Data Science Commons, Theory and Algorithms Commons, Urban Studies and Planning Commons