Multi-criteria,personalized route planning using quantifier-guided ordered weighted averaging operators

This paper presents a generic model for using different decision strategies in multi-criteria, personalized route planning. Some researchers have considered user preferences in navigation systems. However, these prior studies typically employed a high tradeoff decision strategy, which used a weighted linear aggregation rule, and neglected other decision strategies. The proposed model integrates a pairwise comparison method and quantifier-guided ordered weighted averaging (OWA) aggregation operators to form a personalized route planning method that incorporates different decision strategies. The model can be used to calculate the impedance of each link regarding user preferences in terms of the route criteria, criteria importance and the selected decision strategy. Regarding the decision strategy, the calculated impedance lies between aggregations that use a logical “and” (which requires all the criteria to be satisfied) and a logical “or” (which requires at least one criterion to be satisfied). The calculated impedance also includes taking the average of the criteria scores. The model results in multiple alternative routes, which apply different decision strategies and provide users with the flexibility to select one of them en-route based on the real world situation. The model also defines the robust personalized route under different decision strategies. The influence of different decision strategies on the results are investigated in an illustrative example. This model is implemented in a web-based geographical information system (GIS) for Isfahan in Iran and verified in a tourist routing scenario. The results demonstrated, in real world situations, the validity of the route planning carried out in the model.     

Methodology and software solutions for multicriteria evaluation of floodplain retention suitability

Extreme flood events often have adverse effects for people living near or within areas at risk. Reactivating morphological river floodplains for flood retention measures can substantially reduce flood wave peaks and the negative flooding consequences. This article accordingly focuses on a methodology for identifying suitable locations for such measures by spatial multicriteria evaluation (MCE). Compromise programming (CP) and the analytic hierarchy process (AHP) are core methodological components. Furthermore, this methodology is based on impact analysis and draws on expert knowledge. This article also deals with software tools that support the operationalization of methodological components. Data harmonization algorithms are implemented as geoprocessing tools. Both CP and AHP are designed as software providing graphical user interfaces (GUIs). While an extension integrates CP into a geoinformation system, AHP is realized as a web application enabling participation of expert practitioners. The methodological components are operationalized through an example on the floodplains of the German river Elbe.     

基于多因素分析决策的关中地区土地适宜性评价

针对传统的土地适宜性评价缺乏从自然、社会和经济综合的角度考虑土地利用问题的缺陷,本文以关中地区为研究区域,探讨了多因素分析决策的土地适宜性评价指标体系、模型以及GIS技术支持下的分析评价方法,为区域土地优化利用提供参考。研究结果表明,关中地区有27.60%的土地最适宜耕作,主要分布在关中平原地区,建设用地最适宜区占区域面积的15.08%,关中地区总面积的29.62%最适宜林地,不适合草地分布的区域占总面积的0.26%。     

Spatial modelling of site suitability assessment for hospitals using geographical information system-based multicriteria approach at Qazvin city,Iran

Due to the population growth and continuous migration of people from rural areas to urban areas, it is important to identify the suitable locations for future development in order to find suitable sites for various kinds of facilities such as schools, hospital and fire stations for new and existing urban areas. Site suitability modelling is a complex process involving various kinds of objectives and issues. Such a complex process includes spatial analysis, use of several decision support tools such as high-spatial resolution remotely sensed data, geographical information system (GIS) and multi criteria analysis (MCA) such as analytical hierarchy process (AHP), and in some cases, prediction techniques like cellular automata (CA) or artificial neural networks (ANN). This paper presents a comparison between the results of AHP and the ordinary least square (OLS) evaluation model, based on various criteria, to select suitable sites for new hospitals in Qazvin city, Iran. Based on the obtained results, proximity to populated areas (0.3) and distance to air polluted areas (0.23–0.26) were the two highest important criteria with high weight value. The results show that these two techniques not only have similarity in size (in m²) for each suitability class but they also have similarity in spatial distribution of each class in the entire study area. Based on calculations of both techniques, 1–2%, 25%, 40–43%, 16–20% and 14% of study areas are assigned as ‘not suitable', ‘less suitable', ‘moderately suitable', ‘suitable' and ‘most suitable' areas for construction of new hospitals. Results revealed that a 75% similarity was found in the distribution of suitability classes in Qazvin city using both techniques. Nineteen per cent (19%) of the study area are assigned as ‘suitable' and ‘most suitable' by both methods, so these areas can be considered as safe or secure areas for clinical purposes. Moreover, almost all (99.8%) suitable areas are located in district 3, because of its higher population, less numbers of existing hospitals and large numbers of barren land plots of acceptable size.     

基于云理论、粗集和模糊神经网络的区域橡胶种植适宜度评估模型

针对橡胶种植适宜性评估,基于云理论、粗集理论和模糊神经网络理论,提出了一种适宜度评估模型。该模型将转化的样本数据进行粗集简约,通过模糊神经网络得出评价因子的隶属函数,计算评价等级。研究结果表明,此模型能够科学、快速、准确地分析出橡胶种植最适宜区、适宜区、次适宜区和不适宜区。     

An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

ABSTRACT

Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.