Spatial Optimization for Land-use Allocation

Abstract

Land use allocation has long been an important area of research in regional science. Land use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction and the protection of the natural environment is at the heart of long-term sustainability. Since land use patterns are spatially explicit in nature, planning and management necessarily must integrate geographical information system (GIS) and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This paper reviews spatial optimization approaches that have been relied upon to support land use planning. Characteristics of sustainable land use, particularly compactness, contiguity and compatibility, are discussed and how spatial optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in spatial optimization approaches are examined.

Quantitative Models of Well-Being to Inform Policy: Problems and Opportunities

“…These frameworks discard sub-optimal (lose-lose) solutions while maintaining a set of non-dominated (win-lose) solutions, which cannot, within the confines of the model, be ranked as objectively better or worse than one another. They are thus the natural choice for unifying quantitative urban models [1][2][3][4]; and with thoughtful presentation also offer valuable opportunities for public engagement [36]. (Multi-criteria optimization is also rightly known in some literature as Pareto optimization; the current paper avoids this term to prevent confusion with the notion of Pareto optimality in economics, which while mathematically related, is a distinct application in a political context not relevant to our discussion).…”

“…Governments and urban planners have long used quantitative models to inform the decision-making process, including optimizing models in transport and spatial models in economics, while academic research experiments more widely with these model types. While most such models are used to compare different options for future developments-with cost-benefit analysis typically used to select options with the highest cost-benefit ratios-a more recent trend primarily confined to academic research is the automatic optimization of a specific outcome measure or multiple measures in a multi-objective trade-off framework [1][2][3][4]. Whether automatically optimized or not, all such models rely on metrics with which to evaluate plans: quantities we aim either to increase or decrease.…”

Synergies or Trade-Offs? Optimizing a Virtual Urban Region to Foster Plant Species Richness, Climate Regulation, and Compactness Under Varying Landscape Composition

“…In urban planning, one of the main strategies for urban development is a compact city (e.g., Cortinovis et al, 2019) to limit negative effects of urban sprawl, such as increased congestion, greenhouse gas emissions and soil sealing (for many: Yao et al, 2017). However, at least three arguments impede straightforward solutions: First, a variety of ecosystem services (i.e., the benefits humans derive from ecosystems, such as carbon sequestration or water provision, Millennium Ecosystem Assessment, 2005) needs to be considered.…”

“…Methods of scalarization integrate multiple targets into a single target and provide one optimal solution [summary of drawbacks by Duh and Brown (2007)]. Recent reviews of spatial optimization techniques for land use allocation by Yao et al (2017) and Kaim et al (2018) provide an overview on such studies and the individual methods available for the two types.…”

“…For trade-off analyses with up to four targets, Kaim et al (2018) suggest Pareto-based algorithms; out of these, genetic algorithms are frequently used. Also, Yao et al (2017) see a lot of potential for heuristics such as genetic algorithms for optimization studies. So-called "multi-objective genetic algorithms" can find optimal landscapes for the targets that have been specified through quantitative models (Lautenbach et al, 2013;Mouchet et al, 2014).…”