Spatial optimization for land use allocation: accounting for sustainability concerns

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 and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article 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

유전 알고리즘을 이용한 다중스케일/다목적 공간계획 최적화모델 구축

학위논문 (박사)-- 서울대학교 대학원 : 환경대학원 협동과정 조경학전공, 2019. 2. 이동근.공간계획 과정에서 다양한 이해관계자와 결부된 목표와 제약 요건을 만족시키는 것은 복잡한 비선형적 문제로서 해결하기 어려운 것으로 알려져 왔다. 그러나 최근 이러한 문제에 유전 알고리즘 (genetic algorithms), 담금질 기법 (simulated annealing), 개미 군집 최적화 (ant colony optimization) 등의 다목적 최적화 알고리즘이 응용되고 있으며, 관련 연구 역시 급증하고 있다. 이 중 유전 알고리즘은 공간 최적화 부문에 가장 빈도 높게 적용된 최적화 알고리즘으로 exploration과 exploitation의 균형으로 합리적인 시간 내에 충분히 좋은 계획안을 제시할 수 있다. 그러나 공간 최적화 연구가 보여준 좋은 성과에도 불구하고 대부분의 연구가 특정 용도 혹은 시설의 배치에 집중되어 있으며, 기후변화 적응, 재해 관리, 그린인프라 계획과 같은 최근의 환경 이슈를 다룬 사례는 매우 미흡하다. 따라서 본 연구에서는 유전 알고리즘과 비지배 정렬 유전 알고리즘 (non-dominated sorting genetic algorithm II)에 기초하여 기후변화 적응, 재해 관리, 도시의 녹지 계획 등과 같은 환경 이슈를 공간계획에 반영할 수 있는 일련의 공간 최적화 모델을 제시하였다. 개별 환경 이슈에 따라 공간 해상도, 목적, 제약요건이 다르게 구성하였으며, 공간적 범위가 좁아지고 공간해상도는 높아지는 순서대로 나열하였다. 논문의 첫번째 장에서는 행정구역 도 규모 (province scale, 해상도 1㎢)에서 미래의 기후변화에 적응하기 위한 토지이용 시나리오를 모의할 수 있는 공간 최적화 모델을 제안하였다. 기후변화가 먼 미래가 아닌, 현재 이미 진행되고 있으며 관련한 다수의 피해가 관찰되고 있기 때문에 공간적 관점에서 기후변화에 대한 적응의 필요성이 지적되어 왔다. 그러나 구체적으로 기후에 대한 회복 탄력성을 향상시키기 위하여 토지이용의 공간적 구성을 어떻게 변화시켜야 할지에 대한 방법론 제시는 미흡하다. 지역계획에서 기후변화 영향을 고려한 토지이용 배분은 매우 유용한, 기본적인 중장기 적응 전략에 해당한다. 본 연구에서는 다목적 유전 알고리즘 (MOGA, multi-objective genetic algorithm)에 기초하여 9,982㎢에 350만의 인구가 거주하는 한국의 충청남도 및 대전광역시 일대를 대상으로 기후변화 적응을 위한 토지이용 시나리오를 제시하였다. 지역적인 기후변화 영향과 경제적 여건을 고려하여 재해 피해 및 전환량의 최소화, 벼 생산량, 종 풍부도 보전, 경제적 가치의 최대화 등 다섯 가지의 목적을 선택하였다. 각 목적 별 가중치를 변화시키며 여섯 가지 가중치 조합에 대한 17개의 파레토 최적 토지이용 시나리오를 생성하였다. 대부분의 시나리오는 정도의 차이는 있으나 현재의 토지이용에 비해 기후변화 적응 부분에서 더 좋은 퍼포먼스를 보였으므로, 기후변화에 대한 회복탄력성이 개선할 수 있을 것으로 판단하였다. 또한 공간 최적화 모델의 유연한 구조를 고려하였을 때, 지역의 실무자 역시 가중치와 같은 모델의 파라미터, 기후변화 영향 평가와 같은 입력자료를 변경함으로써 효율적으로 새로운 시나리오를 생성 및 선택하는 것이 가능할 것으로 예상하였다. 논문의 두 번째 장에서는 행정구역 군 규모 (local scale, 해상도 100m)에서 기후변화에 따른 재해 피해를 관리하기 위한 토지이용 시나리오를 모의할 수 있는 공간 최적화 모델을 제안하였다. 산악지형에서 폭우로 인한 산사태는 인명과 재산에 심각한 피해를 초래할 수 있는 것으로 알려져 있다. 더욱이 기후변화에 따른 강우의 변동성 증가로 이러한 산사태 빈도 및 강도 역시 증대될 것으로 예상된다. 일반적으로 산사태 리스크가 높은 지역을 피해 개발지역을 배치하는 것이 피해를 저감 혹은 회피할 수 있는 가장 효과적인 전략으로 알려져 있으나, 실제공간에서의 계획은 매우 복잡한 비선형의 문제로서 이것을 실현하는 데 어려움이 있다. 따라서 본 연구에서는 비지배 정렬 유전 알고리즘 II에 기초하여 산사태 리스크 및 전환량, 파편화의 최소화 등의 다양한 목적을 만족시키는 종합적인 토지이용 배분 계획을 제안하였다. 대상지는 2018년 동계올림픽 개최지인 한국의 평창군으로서 2006년에 산사태로 인한 대규모의 피해를 경험하였으나, 올림픽 특수 등의 개발압력으로 인한 난개발이 우려되는 지역이다. 최종적으로 한번의 모의를 통해 현재의 토지이용 보다 적어도 한가지 이상의 목적에서 좋은 퍼포먼스를 보이는 100개의 파레토 최적 계획안을 생성하였다. 또한 5개의 대표적인 계획안을 선정하여 산사태리스크 최소화와 전환량 최소화 간에 발생하는 상쇄 효과를 설명하였다. 본 연구결과는 기후변화와 관련된 공간 적응 전략의 수립, 보다 향상된 개발계획을 위한 의사결정을 효과적으로 지원할 수 있을 것으로 예상하였다. 논문의 세 번째 장에서는 블록 규모(neighborhood scale, 2m)에서 도시 내 녹지계획안을 모의할 수 있는 공간 최적화 모델을 제안하였다. 녹지 공간은 도시민의 삶의 질에 결정적인 영향을 미치기 때문에 다양한 도시 재생 및 개발계획에는 녹지와 직 간접적으로 관련된 전략이 포함된다. 녹지 공간은 도시지역 내에서 열섬 현상 완화, 유출량 저감, 생태 네트워크 증진 등 다양한 긍정적 효과가 있음이 알려져 있으나, 공간 계획의 관점에서 이러한 다양한 효과를 종합적, 정량적으로 고려된 사례는 매우 미흡하다. 따라서 본 연구에서는 비지배 정렬 유전 알고리즘 II에 기초하여 녹지의 생태적 연결성 증진, 열섬 효과 완화와 같은 다양한 효과와 설치에 따르는 비용을 종합적으로 고려하여 적절한 녹지의 유형과 위치를 결정한 녹지계획안을 제시하였다. 블록 규모의 가상의 대상지에 본 최적화 모델을 적용함으로써 30개의 파레토 최적 녹지계획안을 생성하였으며, 각 목적 간 퍼포먼스를 비교하여 녹지의 열섬 완화 효과와 생태적 연결성 증진 효과 간의 상승 관계 (synergistic relationship), 이러한 긍정적 효과와 비용 절감 간의 상쇄 효과 (trade-off relationship)를 분석하였다. 또한 다양한 계획안 중 대표적인 특성을 지니는 계획안, 다수의 계획안에서 공통적으로 녹지 설치를 위해 선택된 주요 후보지역 역시 규명하였다. 본 연구에서 제시된 모델은 계획안의 수정에서부터 정량적 평가, 계획안 선택에 이르는 일련의 긍정적인 피드백 과정을 수없이 반복함으로써 기존의 녹지계획 과정을 개선하는 데 기여할 수 있을 뿐만 아니라 모델의 결과 역시 다자간 협력적 디자인 (co-design)을 위한 초안으로서 활용될 수 있을 것으로 예상하였다.The meeting of heterogeneous goals while staying within the constraints of spatial planning is a nonlinear problem that cannot be solved by linear methodologies. Instead, this problem can be solved using multi-objective optimization algorithms such as genetic algorithms (GA), simulated annealing (SA), ant colony optimization (ACO), etc., and research related to this field has been increasing rapidly. GA, in particular, are the most frequently applied spatial optimization algorithms and are known to search for a good solution within a reasonable time period by maintaining a balance between exploration and exploitation. However, despite its good performance and applicability, it has not adequately addressed recent urgent issues such as climate change adaptation, disaster management, and green infrastructure planning. It is criticized for concentrating on only the allocation of specific land use such as urban and protected areas, or on the site selection of a specific facility. Therefore, in this study, a series of spatial optimizations are proposed to address recent urgent issues such as climate change, disaster management, and urban greening by supplementing quantitative assessment methodologies to the spatial planning process based on GA and Non-dominated Sorting Genetic Algorithm II (NSGA II). This optimization model needs to be understood as a tool for providing a draft plan that quantitatively meets the essential requirements so that the stakeholders can collaborate smoothly in the planning process. Three types of spatial planning optimization models are classified according to urgent issues. Spatial resolution, planning objectives, and constraints were also configured differently according to relevant issues. Each spatial planning optimization model was arranged in the order of increasing spatial resolution. In the first chapter, the optimization model was proposed to simulate land use scenarios to adapt to climate change on a provincial scale. As climate change is an ongoing phenomenon, many recent studies have focused on adaptation to climate change from a spatial perspective. However, little is known about how changing the spatial composition of land use could improve resilience to climate change. Consideration of climate change impacts when spatially allocating land use could be a useful and fundamental long-term adaptation strategy, particularly for regional planning. Here climate adaptation scenarios were identified on the basis of existing extents of three land use classes using Multi-objective Genetic Algorithms (MOGA) for a 9,982 km2 region with 3.5 million inhabitants in South Korea. Five objectives were selected for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damageand existing land use conversionmaximization of rice yieldprotection of high-species-richness areasand economic value. The 17 Pareto land use scenarios were generated by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial land use composition for all adaptation objectives, suggesting that some alteration of current land use patterns could increase overall climate resilience. Given the flexible structure of the optimization model, it is expected that regional stakeholders would efficiently generate other scenarios by adjusting the model parameters (weighting combinations) or replacing the input data (impact maps) and selecting a scenario depending on their preference or a number of problem-related factors. In the second chapter, the optimization model was proposed to simulate land use scenarios for managing disaster damage due to climate change on local scale. Extreme landslides triggered by rainfall in hilly regions frequently lead to serious damage, including casualties and property loss. The frequency of landslides may increase under climate change, because of the increased variability of precipitation. Developing urban areas outside landslide risk zones is the most effective method of reducing or preventing damageplanning in real life is, however, a complex and nonlinear problem. For such multi-objective problems, GA may be the most appropriate optimization tool. Therefore, comprehensive land use allocation plans were suggested using the NSGA II to overcome multi-objective problems, including the minimization of landslide risk, minimization of change, and maximization of compactness. The study area is Pyeongchang-gun, the host city of the 2018 Winter Olympics in Korea, where high development pressure has resulted in an urban sprawl into the hazard zone that experienced a large-scale landslide in 2006. We obtained 100 Pareto plans that are better than the actual land use data for at least one objective, with five plans that explain the trade-offs between meeting the first and the second objectives mentioned above. The results can be used by decision makers for better urban planning and for climate change-related spatial adaptation. In the third chapter, the optimization model was proposed to simulate urban greening plans on a neighborhood scale. Green space is fundamental to the good quality of life of residents, and therefore urban planning or improvement projects often include strategies directly or indirectly related to greening. Although green spaces generate positive effects such as cooling and reduction of rainwater runoff, and are an ecological corridor, few studies have examined the comprehensive multiple effects of greening in the urban planning context. To fill this gap in this fields literature, this study seeks to identify a planning model that determines the location and type of green cover based on its multiple effects (e.g., cooling and enhancement of ecological connectivity) and the implementation cost using NSGA II. The 30 Pareto-optimal plans were obtained by applying our model to a hypothetical landscape on a neighborhood scale. The results showed a synergistic relationship between cooling and enhancement of connectivity, as well as a trade-off relationship between greenery effects and implementation cost. It also defined critical lots for urban greening that are commonly selected in various plans. This model is expected to contribute to the improvement of existing planning processes by repeating the positive feedback loop: from plan modification to quantitative evaluation and selection of better plans. These optimal plans can also be considered as options for co-design by related stakeholders.1. INTRODUCTION 2. CHAPTER 1: Modelling Spatial Climate Change Land use Adaptation with Multi-Objective Genetic Algorithms to Improve Resilience for Rice Yield and Species Richness and to Mitigate Disaster Risk 2.1. Introduction 2.2. Study area 2.3. Methods 2.4. Results 2.5. Discussion 2.6. References 2.7. Supplemental material 3. CHAPTER 2: Multi-Objective Land-Use Allocation Considering Landslide Risk under Climate Change: Case Study in Pyeongchang-gun, Korea 3.1. Introduction 3.2. Material and Methods 3.3. Results 3.4. Discussion 3.5. Conclusion 3.6. References 4. CHAPTER 3: Multi-Objective Planning Model for Urban Greening based on Optimization Algorithms 3.1. Introduction 3.2. Methods 3.3. Results 3.4. Discussion 3.5. Conclusion 3.6. References 3.7. Appendix 5. CONCLUSION REFERENCESDocto

Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore

Singapore Management University; Ministry of Education, Singapore under its Academic Research Funding Tier

Artificial Intelligence Applied to Conceptual Design. A Review of Its Use in Architecture

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Location Analytics for Transitioning to Fire Resilient Landscapes

Wildfire risk is significant for forest and vegetative landscapes, particularly in regions where climate change is resulting in prolonged droughts and extended fire seasons that are a fire risk to people and property. An important component of mitigation is restoration programs that transition landscapes to be more fire resilient. A collaborative partnership between the US Forest Service and university researchers is reported that takes advantage of location intelligence. This paper reviews this general planning problem and details location analytic based approaches for informing mitigation efforts. Application of results highlight the ability to optimize goals and objectives while maintaining project area needs and treatment thresholds

A model to support the public administration decisions for the investments selection on historic buildings

The historical buildings can become an instrument for the growth of a territory in connection with the historic and artistic value, the ability to characterize environments and urban, rural and natural landscapesand on the basis of historical and documentary interest. This is confirmed in the numerous legislative measures that deal with urban planning at the international level. Most of the time, however, the interventions on the historical-architectural building heritage do not respond to logic capable of simultaneously ensuring the conservation and valorization. This problem is accentuated when the decision-making process is not supported by operating logical models capable of bringing into account the many effects of aninvestment, which are not only financial, but also social, cultural and environmental nature. The operational research, in particular discrete linear programming crossed with multicriteria analysis, can support the definition of useful models to the selection of investments on historical buildings. Intended for public authorities called to choose the projects to be financed, the model defined and tested in the presentwork can be easily adapted also to the case of resources allocation by private investor. The applicationof the model to a concrete case, concerning the definition of the projects portfolio for the valorization of buildings of historical-architectural value in a Municipality of South Italy, confirms the potential of the instrument in analysis

Spatially optimised sustainable urban development

PhD ThesisTackling urbanisation and climate change requires more sustainable and resilient cities, which in turn will require planners to develop a portfolio of measures to manage climate risks such as flooding, meet energy and greenhouse gas reduction targets, and prioritise development on brownfield sites to preserve greenspace. However, the policies, strategies and measures put in place to meet such objectives can frequently conflict with each other or deliver unintended consequences, hampering long-term sustainability. For example, the densification of cities in order to reduce transport energy use can increase urban heat island effects and surface water flooding from extreme rainfall events. In order to make coherent decisions in the presence of such complex multi-dimensional spatial conflicts, urban planners require sophisticated planning tools to identify and manage potential trade-offs between the spatial strategies necessary to deliver sustainability. To achieve this aim, this research has developed a multi-objective spatial optimisation framework for the spatial planning of new residential development within cities. The implemented framework develops spatial strategies of required new residential development that minimize conflicts between multiple sustainability objectives as a result of planning policy and climate change related hazards. Five key sustainability objectives have been investigated, namely; (i) minimizing risk from heat waves, (ii) minimizing the risk from flood events, (iii) minimizing travel costs in order to reduce transport emissions, (iv) minimizing urban sprawl and (v) preventing development on existing greenspace. A review identified two optimisation algorithms as suitable for this task. Simulated Annealing (SA) is a traditional optimisation algorithm that uses a probabilistic approach to seek out a global optima by iteratively assessing a wide range of spatial configurations against the objectives under consideration. Gradual ‘cooling’, or reducing the probability of jumping to a different region of the objective space, helps the SA to converge on globally optimal spatial patterns. Genetic Algorithms (GA) evolve successive generations of solutions, by both recombining attributes and randomly mutating previous generations of solutions, to search for and converge towards superior spatial strategies. The framework works towards, and outputs, a series of Pareto-optimal spatial plans that outperform all other plans in at least one objective. This approach allows for a range of best trade-off plans for planners to choose from. ii Both SA and GA were evaluated for an initial case study in Middlesbrough, in the North East of England, and were able to identify strategies which significantly improve upon the local authority’s development plan. For example, the GA approach is able to identify a spatial strategy that reduces the travel to work distance between new development and the central business district by 77.5% whilst nullifying the flood risk to the new development. A comparison of the two optimisation approaches for the Middlesbrough case study revealed that the GA is the more effective approach. The GA is more able to escape local optima and on average outperforms the SA by 56% in in the Pareto fronts discovered whilst discovering double the number of multi-objective Pareto-optimal spatial plans. On the basis of the initial Middlesbrough case study the GA approach was applied to the significantly larger, and more computationally complex, problem of optimising spatial development plans for London in the UK – a total area of 1,572km2. The framework identified optimal strategies in less than 400 generations. The analysis showed, for example, strategies that provide the lowest heat risk (compared to the feasible spatial plans found) can be achieved whilst also using 85% brownfield land to locate new development. The framework was further extended to investigate the impact of different development and density regulations. This enabled the identification of optimised strategies, albeit at lower building density, that completely prevent any increase in urban sprawl whilst also improving the heat risk objective by 60% against a business as usual development strategy. Conversely by restricting development to brownfield the ability of the spatial plan to optimise future heat risk is reduced by 55.6% against the business as usual development strategy. The results of both case studies demonstrate the potential of spatial optimisation to provide planners with optimal spatial plans in the presence of conflicting sustainability objectives. The resulting diagnostic information provides an analytical appreciation of the sensitivity between conflicts and therefore the overall robustness of a plan to uncertainty. With the inclusion of further objectives, and qualitative information unsuitable for this type of analysis, spatial optimization can constitute a powerful decision support tool to help planners to identify spatial development strategies that satisfy multiple sustainability objectives and provide an evidence base for better decision making

Combining evolutionary algorithms and agent-based simulation for the development of urbanisation policies

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments. To achieve this goal, this work is underpinned by developing a theoretical framework based on the economic model of Alonso and the associated methodology for modelling spatial and temporal urban growth, in order to better understand the complexity inherent in this kind of system and to generate and improve relevant knowledge for the urban planning community. The model was hybridised with cellular automata and agent-based model and extended to encompass green space planning based on urban cost and satisfaction. Monte Carlo sampling techniques and the use of the urban model as a surrogate tool were the two main elements investigated and applied to overcome the noise and uncertainty derived from dealing with future trends and expectations. Once the evolutionary algorithms were equipped with these mechanisms, the problem under consideration was defined and characterised as a type of adaptive submodular. Afterwards, the performance of a non-adaptive evolutionary approach with a random search and a very smart greedy algorithm was compared and in which way the complexity that is linked with the configuration of the problem modifies the performance of both algorithms was analysed. Later on, the application of very distinct frameworks incorporating evolutionary algorithm approaches for this problem was explored: (i) an ‘offline’ approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation, and (ii) an ‘online’ approach which involves a sequential series of optimizations, each making only a single decision, and starting its simulations from the endpoint of the previous run

Using GIS to Locate Areas for Growing Quality Coffee in Honduras

Abstract Small-scale coffee producers worldwide remain vulnerable to price fluctuations after the 1999-2003 coffee crisis. One way to increase small-scale farmer economic resilience is to produce a more expensive product, such as quality coffee. There is growing demand in coffee-producing and coffee-importing countries for user-friendly tools that facilitate the marketing of quality coffee. The purpose of this study is to develop a prototypical quality coffee marketing tool in the form of a GIS model that identifies regions for producing quality coffee in a country not usually associated with quality coffee, Honduras. Maps of areas for growing quality coffee were produced with information on climate, soils, topography, areas vulnerable to environmental degradation, the location of current quality coffee farms, and infrastructure. The maps depict suitable coffee-growing land in portions of eight western Honduran departments