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

The Spatial Agent-based Competition Model (SpAbCoM)

The paper presents a detailed documentation of the underlying concepts and methods of the Spatial Agent-based Competition Model (SpAbCoM). For instance, SpAbCoM is used to study firms' choices of spatial pricing policy (GRAUBNER et al., 2011a) or pricing and location under a framework of multi-firm spatial competition and two-dimensional markets (GRAUBNER et al., 2011b). While the simulation model is briefly introduced by means of relevant examples within the corresponding papers, the present paper serves two objectives. First, it presents a detailed discussion of the computational concepts that are used, particularly with respect to genetic algorithms (GAs). Second, it documents SpAbCoM and provides an overview of the structure of the simulation model and its dynamics. -- Das vorliegende Papier dokumentiert die zugrundeliegenden Konzepte und Methoden des Räumlichen Agenten-basierten Wettbewerbsmodells (Spatial Agent-based Competition Model) SpAbCoM. Anwendungsbeispiele dieses Simulationsmodells untersuchen die Entscheidung bezüglich der räumlichen Preisstrategie von Unternehmen (GRAUBNER et al., 2011a) oder Preissetzung und Standortwahl im Rahmen eines räumlichen Wettbewerbsmodells, welches mehr als einen Wettbewerber und zweidimensionalen Marktgebiete berücksichtigt. Während das Simulationsmodell in den jeweiligen Arbeiten kurz anhand eines Beispiels eingeführt wird, dient das vorliegende Papier zwei Zielen. Zum Einen sollen die verwendeten computergestützten Konzepte, hier speziell Genetische Algorithmen (GA), detailliert vorgestellt werden. Zum Anderen besteht die Absicht dieser Dokumentation darin, einen Überblick über die Struktur von SpAbCoM und die während einer Simulation ablaufenden Prozesse zu gegeben.Agent-based modelling,genetic algorithms,spatial pricing,location model.,Agent-basierte Modellierung,Genetische Algorithmen,räumliche Preissetzung,Standortmodell.

Spatial Pricing and the Location of Processors in Agricultural Markets

Spatially dispersed production and processing, endemic for most agricultural or renewable resource markets, causes oligopsonistic competition. The possibility and use of spatial price discrimination in these markets is well documented. It is also well known that the location of processors relative to competitors crucially affects the intensity of competition. However, insights regarding the relation between spatial price discrimination and the spatial differentiation of firms are barely present because the simultaneous investigation of these issues is often intractable analytically. We use computational economics to study these problems under a general theoretical framework. For instance, we show whether and under which conditions firms choose to differentiate their locations and/or price strategies. Results are consistent with observations in agricultural markets.spatial price competition, spatial differentiation, price discrimination, computational economics, Agribusiness,

Optimization of multi-objective land use model with genetic algorithm

The first task of the city planner is to effectively locate integrated land use types for various objectives. The Multi Objective Land Use Planning Model developed to achieve this goal, aims to maximize land value and minimize the transportation. The genetic algorithm method developed to find the optimum layout according to the Multi-Objective Land Use Planning Model has been explained, the success and performance of the process has been tested with artificial data, and its usability in real problems has been examined. According to the results of the study, using this method, it is revealed that layout plans that are very close to the maximum efficiency value can be found within 1 day in cities with a population of up to 1,000,000, within 1 week in cities up to 5,000,000, and within 1.5 months in cities close to 16,000,000. By examining the results, the deficiencies of this method are determined and the suggestions for improvement of this method are stated. The problem chosen in this study is a problem that most city planners have to solve and the developed application has been opened to the use of other experts. This makes this work unique as it allows planning experts who are incapable of developing such methods to experiment

Anthropogenic Habitats Facilitate Dispersal of an Early Successional Obligate: Implications for Restoration of an Endangered Ecosystem

Landscape modification and habitat fragmentation disrupt the connectivity of natural landscapes, with major consequences for biodiversity. Species that require patchily distributed habitats, such as those that specialize on early successional ecosystems, must disperse through a landscape matrix with unsuitable habitat types. We evaluated landscape effects on dispersal of an early successional obligate, the New England cottontail (Sylvilagus transitionalis). Using a landscape genetics approach, we identified barriers and facilitators of gene flow and connectivity corridors for a population of cottontails in the northeastern United States. We modeled dispersal in relation to landscape structure and composition and tested hypotheses about the influence of habitat fragmentation on gene flow. Anthropogenic and natural shrubland habitats facilitated gene flow, while the remainder of the matrix, particularly development and forest, impeded gene flow. The relative influence of matrix habitats differed between study areas in relation to a fragmentation gradient. Barrier features had higher explanatory power in the more fragmented site, while facilitating features were important in the less fragmented site. Landscape models that included a simultaneous barrier and facilitating effect of roads had higher explanatory power than models that considered either effect separately, supporting the hypothesis that roads act as both barriers and facilitators at all spatial scales. The inclusion of LiDAR-identified shrubland habitat improved the fit of our facilitator models. Corridor analyses using circuit and least cost path approaches revealed the importance of anthropogenic, linear features for restoring connectivity between the study areas. In fragmented landscapes, human-modified habitats may enhance functional connectivity by providing suitable dispersal conduits for early successional specialists

Multi-Objective Spatial Optimization: Sustainable Land Use Allocation at Sub-Regional Scale

The rational use of territorial resources is a key factor in achieving sustainability. Spatial planning is an important tool that helps decision makers to achieve sustainability in the long term. This work proposes a multi-objective model for sustainable land use allocation known as MAUSS (Spanish acronym for “Modelo de Asignación de Uso Sostenible de Suelo”) The model was applied to the Plains of San Juan, Puebla, Mexico, which is currently undergoing a rapid industrialization process. The main objective of the model is to generate land use allocations that lead to a territorial balance within regions in three main ways by maximizing income, minimizing negative environmental pressure on water and air through specific evaluations of water use and CO2 emissions, and minimizing food deficit. The non-sorting genetic algorithm II (NSGA-II) is the evolutionary optimization algorithm of MAUSS. NSGA-II has been widely modified through a novel and efficient random initializing operator that enables spatial rationale from the initial solutions, a crossover operator designed to streamline the best genetic information transmission as well as diversity, and two geometric operators, geographic dispersion (GDO) and the proportion (PO), which strengthen spatial rationality. MAUSS provided a more sustainable land use allocation compared to the current land use distribution in terms of higher income, 9% lower global negative pressure on the environment and 5.2% lower food deficit simultaneousl

Stepwise investment plan optimization for large scale and multi-zonal transmission system expansion

This paper develops a long term transmission expansion optimization methodology taking the probabilistic nature of generation and demand, spatial aspects of transmission investments and different technologies into account. The developed methodology delivers a stepwise investment plan to achieve the optimal grid expansion for additional transmission capacity between different zones. In this paper, the optimization methodology is applied to the Spanish and French transmission systems for long term optimization of investments in interconnection capacity