A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas

Optimizing land use allocation is a challenging task, as it involves multiple stakeholders with conflicting objectives. In addition, the solution space of the optimization grows exponentially as the size of the region and the resolution increase. This article presents a new ant colony optimization algorithm by incorporating multiple types of ants for solving complex multiple land use allocation problems. A spatial exchange mechanism is used to deal with competition between different types of land use allocation. This multi-type ant colony optimization optimal multiple land allocation (MACO-MLA) model was successfully applied to a case study in Panyu, Guangdong, China, a large region with an area of 1,454,285 cells. The proposed model took only about 25 minutes to find near-optimal solution in terms of overall suitability, compactness, and cost. Comparison indicates that MACO-MLA can yield better performances than the simulated annealing (SA) and the genetic algorithm (GA) methods. It is found that MACO-MLA has an improvement of the total utility value over SA and GA methods by 4.5% and 1.3%, respectively. The computation time of this proposed model amounts to only 2.6% and 12.3%, respectively, of that of the SA and GA methods. The experiments have demonstrated that the proposed model was an efficient and effective optimization technique for generating optimal land use patterns.     

An improved artificial bee colony algorithm for optimal land-use allocation

Land-use allocation is of great importance for rapid urban planning and natural resource management. This article presents an improved artificial bee colony (ABC) algorithm to solve the spatial optimization problem. The new approach consists of a heuristic information-based pseudorandom initialization (HIPI) method for initial solutions and pseudorandom search strategy based on a long-chain (LC) mechanism for neighborhood searches; together, these methods substantially improve the search efficiency and quality when handling spatial data in large areas. We evaluated the approach via a series of land-use allocation experiments and compared it with particle swarm optimization (PSO) and genetic algorithm (GA) methods. The experimental results show that the new approach outperforms the current methods in both computing efficiency and optimization quality.     

Multi-objective spatially constrained clustering for regionalization with particle swarm optimization

Regionalization is an important part of the spatial analysis process, and the solution should be contiguity-constrained in each region. In general, several objectives need to be optimized in practical regionalization, such as the homogeneity of regions and the heterogeneity among regions. Therefore, multi-objective techniques are more suitable for solving regionalization problems. In this paper, we design a multi-objective particle swarm optimization algorithm for solving regionalization problems. Towards this goal, a novel particle representation for regionalization is proposed, which can be expressed in continuous space and has flexible constraints on the number of regions. In the process of optimization, a contiguous-region method is designed that satisfies the constraints and improves the efficiency. The decision solution is selected in the Pareto set based on a trade-off between the objective functions, and the number of regions can be automatically determined. The proposed method outperforms six regionalization algorithms in terms of both the number and the quality of the solutions.     

An explicit method for solving the geomagnetic induction problem

Summary. The problem of inferring the Earth conductivity as a function of depth from surface measurements of the magnetic field is considered. Two versions of a method analogous to that of Bailey are presented. The main feature of the method consists in the fact that the conductivity is given explicitly in terms to two auxiliary sequences of functions which are found by integrating a set of first-order non-linear differential equations.     

An integrated approach for addressing geographic uncertainty in spatial optimization

There exist many facets of error and uncertainty in digital spatial information. As error or uncertainty will not likely ever be completely eliminated, a better understanding of its impacts is necessary. Spatial analytical approaches, in particular, must somehow address data-quality issues. This can range from evaluating impacts of potential data uncertainty in planning processes that make use of methods to devising methods that explicitly account for error/uncertainty. To date, little has been done to structure methods accounting for error. This article develops an integrated approach to address data uncertainty in spatial optimization. We demonstrate that it is possible to characterize uncertainty impacts by constructing and solving a new multi-objective model that explicitly incorporates facets of data uncertainty. Empirical findings indicate that the proposed approaches can be applied to evaluate the impacts of data uncertainty with statistical confidence, which moves beyond popular practices of simulating errors in data.     

An intelligent method to discover transition rules for cellular automata using bee colony optimisation

This paper presents a new, intelligent approach to discover transition rules for geographical cellular automata (CA) based on bee colony optimisation (BCO–CA) that can perform complex tasks through the cooperation and interaction of bees. The artificial bee colony miner algorithm is used to discover transition rules. In BCO–CA, a food source position is defined by its upper and lower thresholds for each attribute, and each bee searches the best upper and lower thresholds in each attribute as a zone. A transition rule is organised when the zone in each attribute is connected to another node by the operator ‘And’ and is linked to a cell status value. The transition rules are expressed by the logical structure statement ‘IF-Then’, which is explicit and easy to understand. Bee colony optimisation could better avoid the tendency to be vulnerable to local optimisation through local and global searching in the iterative process, and it does not require the discretisation of attribute values. Finally, The BCO–CA model is employed to simulate urban development in the Xi’an-Xian Yang urban area in China. Preliminary results suggest that this BCO approach is effective in capturing complex relationships between spatial variables and urban dynamics. Experimental results indicate that the BCO–CA model achieves a higher accuracy than the NULL and ACO–CA models, which demonstrates the feasibility and availability of the model in the simulation of complex urban dynamic change.     

An improved method for the determination of the tectonic stress field from focal mechanism data

All conventional stress inversion methods, when applied to earthquake focal mechanism data, suffer from uncertainty as to which plane is the true fault plane. This paper deals with several problems in stress inversion brought about by this uncertainty. Our analysis shows that the direction of shear stress on the auxiliary plane does not coincide with the hypothetical slip direction unless the B -axis is parallel to one of the three principal stress directions. Based on this simple fact, we propose a new algorithm dealing with the ambiguity in fault/auxiliary plane identification. We also propose a method to handle the inhomogeneity problem of data quality, which is common and unique for focal mechanism data. Different inversion methods and algorithms are applied to two sets of 'focal mechanism' data simulated from field fault-slip measurement data. The inversion results show that, among the four stress parameters inverted, the stress ratio suffers the most from the ambiguity in fault/auxiliary plane identity, whereas the solutions for the principal stress directions are surprisingly good. The errors in inversion solutions resulting from the fault/auxiliary plane ambiguity can be significantly reduced by controlling subjectively the sample variance of the measurement errors. Our results also suggest that the fault plane cannot be distinguished correctly from the auxiliary plane with a high probability on the basis of the stress inversion alone.     

A linear road object matching method for conflation based on optimization and logistic regression

The purpose of object matching in conflation is to identify corresponding objects in different data sets that represent the same real-world entity. This article presents an improved linear object matching approach, named the optimization and iterative logistic regression matching (OILRM) method, which combines the optimization model and logistic regression model to obtain a better matching result by detecting incorrect matches and missed matches that are included in the result obtained from the optimization (Opt) method for object matching in conflation. The implementation of the proposed OILRM method was demonstrated in a comprehensive case study of Shanghai, China. The experimental results showed the following. (1) The Opt method can determine most of the optimal one-to-one matching pairs under the condition of minimizing the total distance of all matching pairs without setting empirical thresholds. However, the matching accuracy and recall need to be further improved. (2) The proposed OILRM method can detect incorrect matches and missed matches and resolve the issues of one-to-many and many-to-many matching relationships with a higher matching recall. (3) In the case where the source data sets become more complicated, the matching accuracy and recall based on the proposed OILRM method are much better than those based on the Opt method.     

An agent-based model for optimal land allocation (AgentLA) with a contiguity constraint

Spatial optimization is complex because it usually involves numerous spatial factors and constraints. The optimization becomes more challenging if a large set of spatial data with fine resolutions are used. This article presents an agent-based model for optimal land allocation (AgentLA) by maximizing the total amount of land-use suitability and the compactness of patterns. The essence of the optimization is based on the collective efforts of agents for formulating the optimal patterns. A local and global search strategy is proposed to inform the agents to select the sites properly. Three sets of hypothetical data were first used to verify the optimization effects. AgentLA was then applied to the solution of the actual land allocation optimization problems in Panyu city in the Pearl River Delta. The study has demonstrated that the proposed method has better performance than the simulated annealing method for solving complex spatial optimization problems. Experiments also indicate that the proposed model can produce patterns that are very close to the global optimums.     

Getting Charlie off the MTA: a multiobjective optimization method to account for cost constraints in public transit accessibility metrics

Most analyses of accessibility by public transit have focused on travel time and not considered the cost of transit fares. It is difficult to include fares in shortest-path algorithms because fares are often path-dependent. When fare policies allow discounted transfers, for example, the fare for a given journey segment depends on characteristics of previous journey segments. Existing methods to characterize tradeoffs between travel time and monetary cost objectives do not scale well to complex networks, or they rely on approximations. Additionally, they often require assumed values of time, which may be problematic for evaluating the equity of service provision. We propose a new method that allows us to find Pareto sets of paths, jointly minimizing fare and travel time. Using a case study in greater Boston, Massachusetts, USA, we test the algorithm’s performance as part of an interactive web application for computing accessibility metrics. Potential extensions for journey planning and route choice models are also discussed.     

Seeking the Pareto front for multiobjective spatial optimization problems

Spatial optimization problems, such as route selection, usually involve multiple, conflicting objectives relevant to locations. An ideal approach to solving such multiobjective optimization problems (MOPs) is to find an evenly distributed set of Pareto‐optimal alternatives, which is capable of representing the possible trade‐off among different objectives. However, these MOPs are commonly solved by combining the multiple objectives into a parametric scalar objective, in the form of a weighted sum function. It has been found that this method fails to produce a set of well spread solutions by disregarding the concave part of the Pareto front. In order to overcome this ill‐behaved nature, a novel adaptive approach has been proposed in this paper. This approach seeks to provide an unbiased approximation of the Pareto front by tuning the search direction in the objective space according to the largest unexplored region until a set of well‐distributed solutions is reached. To validate the proposed methodology, a case study on multiobjective routing has been performed using the Singapore road network with the support of GIS. The experimental results confirm the effectiveness of the approach.     

Optimal spatial land-use allocation for limited development ecological zones based on the geographic information system and a genetic ant colony algorithm

Limited development ecological zones (LDEZs) are often located in poverty-stricken, ecologically vulnerable areas where ethnic minorities reside. Studies on optimal spatial land-use allocation in LDEZs can promote economic and intensive land use, improve soil quality, facilitate local socioeconomic development, and maintain environmental stability. In this study, we optimized spatial land-use allocations in an LDEZ using the geographic information system (GIS) and a genetic ant colony algorithm (GACA). The multi-objective function considers economic benefits and ecological green equivalents, and improves soil erosion. We developed the GACA by integrating a genetic algorithm (GA) with an ant colony algorithm (ACA). This avoids a large number of redundant iterations and the low efficiency of the GA, and the slow convergence speed of the ACA. The study area is located in Pengyang County, Ningxia, China, which is a typical LDEZ. The land-use data were interpreted from remote sensing (RS) images and GIS. We determined the optimal spatial land-use allocations in the LDEZ using the GACA in the GIS environment. We compared the original and optimal spatial schemes in terms of economic benefits, ecological green equivalents, and soil erosion. The results of the GACA were superior to the original allocation, the ACA, and the multi-objective genetic algorithm, in terms of the optimum, time, and robust performance indexes. We also present some suggestions for the reasonable development and protection of LDEZs.     

A stratified optimization method for a multivariate marine environmental monitoring network in the Yangtze River estuary and its adjacent sea

An efficient monitoring network is very important in accessing the marine environmental quality and its protection and management. In an estuary, there are fronts that separate distinctly different water masses and affect material transport, nutrient distribution, pollutant aggregation, and diffusion. This stratified heterogeneous surface neither satisfies the stationary requirements of kriging, nor can be handled adequately by removing a spatially continuous trend. This article presents a stratified optimization method for a multivariate monitoring network. In this method, principal component analysis (PCA) was used to reduce the dimensionality of the correlated targets, and the mean of surface with nonhomogeneity (MSN) method was adopted to produce the best linear unbiased estimator for a spatially stratified heterogeneous surface that failed to satisfy the requirements for a kriging estimate. The existing monitoring network in the Yangtze River estuary and its adjacent sea, which was designed by purposive sampling year ago was optimized as an illustration. The optimization consisted of two steps: reduce the redundant monitoring sites and then optimally add new sites to the remaining sites. After optimization, the inclusion of 51 sites in the monitoring network was found to produce a smaller total estimated error than that of the current network, which has 70 sites; moreover, the use of 55 sites can produce a higher precision of estimation for all three principal components (PCs) than that of the current 70 sites. The results demonstrated that the proposed method is suitable for optimizing environmental monitoring sites that have dominant stratified nonhomogeneity and that involve multiple factors.     

An influencing mechanism for ecological asset gains and losses and its optimization and promotion pathways in China

Journal of Geographical Sciences - Accounting for the gains and losses of ecological assets holds scientific significance in sustaining human well-being. Based on related research on ecological...     

成德绵地区政区位势与行政区经济耦合的时空演化与优化路径

行政区划作为国家治理体系的基础支撑,对区域经济发展具有重要影响。深入探究行政区划与区域经济的时空耦合关系与优化路径,有助于制定区域一体化发展政策。本文以成德绵地区为例,基于政区位势模型、行政区经济模型和位序规模理论,定量识别2005—2018年政区位势与行政区经济的耦合与空间演变,并从体制机制改革、行政区划调整等角度提出优化路径。研究表明：① 成德绵地区政区位势和行政区经济分别经历了“沿交通线轴向扩散”和“中心极化-中部辐射递减-外围塌陷”的演化过程。② 根据政区位势位序规模相对于行政区经济位序规模的耦合关系,将政区耦合类型划分为位势靠前、区划适应型,位势靠前、区划约束型,位势落后、区划适应型,位势落后、区划约束型。③ 研究区耦合类型表现为“位序落后型收缩-区划适应型增加-位势靠前型主导”的演变特征,位势靠前、区划约束型占比最高（45.0%）,市辖区和北部外围政区发展潜力大幅提升的同时,三市交界处行政区经济约束相对较强。成德绵地区作为中国西部地区的重要增长极,未来应结合不同政区类型的实际情况,通过增强政府间合作、充分利用中心城市的辐射带动效应、转变政府职能和审慎稳妥推行行政区划调整工作等方式,促进区域一体化和城市群经济高质量发展。     

An application of problem and product ontologies for the revision of beach nourishments

An ontological approach in GIS serves as a framework for the conceptualization of processes in the real world. In this paper, we examine an application in coastal change in the Netherlands, whereby beaches are subject to artificial nourishment to offset the effect of severe erosion. The use of ontologies helps to define two scenarios: S_I determined by the regulations from the Ministry for Public Works; S_II grounded on the abilities from an existing spatial dataset. A comparison between S_I and S_II shows that 72.8% of the objects suitable and unsuitable for nourishment are correctly classified. A higher overlap is found in areas where actual beach nourishments were carried out. Inaccuracies in attributes influence the determination of the objects. A sensitivity analysis applied to altitude illustrates a significant increase of objects suitable for nourishment for both scenarios, when altitude is decreased within the lower limit of the root mean square error for the 95% confidence interval. Moreover, the sensitivity of altitude shows that artificial boundaries for beach nourishment objects are not reasonable and consequently should be treated as vague objects.