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Big data, spatial optimization, and planning
Environment and Planning B: Urban Analytics and City Science ( IF 2.6 ) Pub Date : 2020-07-16 , DOI: 10.1177/2399808320935269
Kai Cao 1 , Wenwen Li 2 , Richard Church 3
Affiliation  

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.

中文翻译:

大数据,空间优化和计划

空间优化代表了一组强大的空间分析技术,可用于确定最佳解决方案,甚至产生大量竞争性选择。这些问题的提出包括在满足许多约束的同时最大化或最小化一个或多个目标。解决方案技术包括使用线性规划和整数规划或启发式算法(例如禁忌搜索,模拟退火和遗传算法)等方法求解的精确模型。空间优化技术已被用于许多规划应用中,例如位置分配建模/站点选择,土地使用规划,学校分区,区域化,路线和城市设计。这些方法可以无缝地集成到计划过程中,并生成许多最佳/接近最佳的计划方案或解决方案,以便更定量,更科学地支持公共和私有系统的计划和运营。但是,由于大多数空间优化问题本质上都是不确定性的多项式时间难解(NP-hard),因此即使是很小的数据集也将生成非常复杂的解空间,因此解决方案的计算量往往很大。另外,对不同(空间)目标和相关约束的量化和建模也仍然是一个挑战,这需要科学界的进一步关注。由于大多数空间优化问题本质上都是不确定的多项式时间难解(NP-hard),因此即使是很小的数据集也会产生非常复杂的解空间,因此解决起来的计算量也很大。另外,不同(空间)目标和相关约束的量化和建模仍然是一个挑战,这需要科学界的进一步关注。由于大多数空间优化问题本质上都是不确定的多项式时间难解(NP-hard),因此即使是很小的数据集也会产生非常复杂的解空间,因此解决起来的计算量也很大。另外,不同(空间)目标和相关约束的量化和建模仍然是一个挑战,这需要科学界的进一步关注。
更新日期:2020-07-16
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