Enhancing the interpretability of ecological constraint mechanisms of urban sprawl with GIS-integrated ordered weighted averaging

Abstract 

Data aggregation of ecological indicators is the basis of constructing ecological constraint mechanisms for urban development. The aggregation method of geographic-information-system–integrated (GIS-integrated) ordered weighted averaging (OWA) has attracted significant attention because it reflects decision-makers’ risk-taking attitude and controls the trade-off between indicators. However, the OWA algorithm disrupts the ranking information of the indicators in the process of data aggregation, which breaks the direct link between the evaluation results and the input factors. We selected the Golden Triangle of Southern Fujian Province along the southeastern coast of China to carry out a multi-scenario decision-making analysis of ecological space constraints to interpret the GIS-OWA evaluation. We develop an ecological constraint index that considers the importance of ecosystem services and the sensitivity of the ecological environment. This ecological space soft-constraint index uses a regular increasing monotone fuzzy semantic operator to generate the order weights for OWA. On this basis, we propose two indexes of attribute contribution and location contribution to investigate the main factors that affect the GIS-OWA evaluation from pixel scale to local scale. The results show that the factor ratios on the ranked layer differ significantly and undergo spatial clustering. The fractions of the three factors (i) importance of water conservation (IWC), (ii) slope, and (iii) normalized difference vegetation index (NDVI) on the first ranked layer are 40.4%, 15.9%, and 15.7%, respectively. The dispersion in attribute value widens the range of the attribute contribution beyond that of the location contribution. When the exponent is 0.3, the three factors IWC, importance of biodiversity (IB), and NDVI of the cell (row: 1418; column: 951) have attribute contributions of 0.724, 0.133, and 0.070, respectively. A set of key factors exists in the distribution of the attribute contribution at the pixel scale, and the set remains relatively stable for different evaluation strategies. The sets of key factors for cells in different OWA-equivalence areas differ significantly, and an optimal window size exists for the spatial scope of the set under a given threshold. Thus, the contribution index is an appropriate tool for key-factor analysis with the GIS-OWA algorithm.

摘要

生态指标数据聚合是构建城市发展生态约束机制的基础。集成地理信息系统（Geographic Information System, GIS）的有序加权平均（Ordered Weighted Averaging, OWA）聚合方法因能够反映决策者的风险偏好、并实现指标间权衡调控而受到广泛关注。然而，OWA算法在数据聚合过程中会破坏指标的排序信息，切断了评价结果与输入要素之间的直接关联。本研究选取中国东南沿海的闽南金三角作为研究区域，开展生态空间约束多情景决策分析，以对GIS-OWA评价方法进行阐释。本研究构建了兼顾生态系统服务重要性与生态环境敏感性的生态约束指标。该生态空间软约束指标采用正则递增单调模糊语义算子生成OWA的排序权重。在此基础上，本研究提出属性贡献度与位置贡献度两类指标，以从像元尺度到局地尺度分析影响GIS-OWA评价结果的核心要素。研究结果表明，排序层上的要素占比存在显著差异，并呈现空间集聚特征。在排序首位的图层中，水源涵养重要性（Importance of Water Conservation, IWC）、坡度、归一化植被指数（Normalized Difference Vegetation Index, NDVI）三类要素的占比分别为40.4%、15.9%与15.7%。属性值的离散程度使得属性贡献度的波动范围大于位置贡献度。当指数取值为0.3时，像元（行：1418；列：951）对应的IWC、生物多样性重要性（Importance of Biodiversity, IB）以及NDVI的属性贡献度分别为0.724、0.133与0.070。像元尺度的属性贡献度分布存在核心要素集合，且该集合在不同评价策略下保持相对稳定。不同OWA等效区域内像元的核心要素集合存在显著差异，且在给定阈值下，该集合的空间范围存在最优窗口尺度。综上，贡献度指标是适配GIS-OWA算法开展核心要素分析的有效工具。