Directional Change in Polygonal Distributions: Comparing human and computational directional relations in GIS data

Existing methods for calculating directional relations in polygons (i.e. the directional similarity model, the cone-based model, and the modified cone-based model) were compared to human perceptions of change through an online survey. The results from this survey provide the first empirical validation of computational approaches to calculating directional relations in polygonal spatial data. We have found that while the evaluated methods generally agreed with each other, they varied in their alignment with human perceptions of directional relations. Specifically, translation transformations of the target and reference polygons showed greatest discrepancy to human perceptions and across methods. The online survey was developed using Qualtrics Survey Software, and participants were recruited via online messaging on social media (i.e., Twitter) with hashtags related to geographic information science. In total sixty-one individuals responded to the survey. This survey consisted of nine questions. For the first question, participants indicated how many years they have worked with GIS and/or spatial data. For the remaining eight questions, participants ranked pictorial database scenes according to degrees of their match to query scenes. Each of these questions represented a test case that Goyal and Egenhofer (2001) used to empirically evaluate the directional similarity model; participants were randomly presented with four of these questions. The query scenes were created using ArcMap and contained a pair of reference and target polygons. The database scenes were generated by gradually changing the geometry of the target polygon within each query scene. The relations between the target and reference polygon varied by the type of movement, the scaling change of the polygon, and changes in rotation. The scenarios were varied in order to capture a representative range of variability in polygon movements and changes in real world data. The R statistical computing environment was used to determine the similarity value that corresponds with each database scene based on the directional similarity model, the cone-based model, and the modified cone-based model. Using the survey responses, the frequency of first, second, third, etc. ranks were calculated for each database scene. Weight variables were multiplied by the frequencies to create an overall rank based on participant responses. A rank of one was weighted as a five, a rank of two was weighted as a four, and so on. Spearman’s rank-order correlation was used to measure the strength and direction of association between the rank determined using the three models and the rank determined using participant responses.本研究通过线上问卷调查，将现有多边形方向关系计算方法（包括方向相似度模型（directional similarity model）、基于锥型模型（cone-based model）与改进锥型模型（modified cone-based model））与人类对空间变化的感知进行对比。此次问卷调查的结果，首次为多边形空间数据中方向关系计算的各类计算方法提供了实证验证。研究发现，尽管所评估的三种方法整体上彼此一致性较高，但它们与人类对方向关系的感知契合程度存在差异。具体而言，目标多边形与参考多边形的平移变换，在人类感知及方法间均呈现出最大的差异。本问卷调查采用Qualtrics调查软件开发，通过社交媒体（即Twitter）上带有地理信息科学相关话题标签的私信招募参与者。最终共有61名参与者完成了此次问卷，问卷共包含9道题目。第一道题目要求参与者填写其使用地理信息系统（GIS, Geographic Information System）及/或空间数据的工作年限。剩余8道题目中，参与者需根据数据库图像场景与查询场景的匹配程度对其进行排序。每道题目均对应Goyal与Egenhofer于2001年用于实证评估方向相似度模型的测试用例，参与者将随机收到其中4道题目。查询场景通过ArcMap制作，每组场景均包含一对参考多边形与目标多边形。数据库场景则通过逐步修改每组查询场景中目标多边形的几何形状生成。目标与参考多边形间的空间关系会随运动类型、多边形缩放变化及旋转变换而发生改变。研究人员设计了多样的场景，以覆盖真实世界数据中多边形运动与几何变化的典型变异范围。本研究采用R统计计算环境，基于方向相似度模型、基于锥型模型及改进锥型模型，计算得到每组数据库场景对应的相似度数值。基于问卷反馈结果，研究人员统计了每组数据库场景获得第一、第二、第三等各排名的频次。将权重变量与各排名频次相乘，以基于参与者反馈生成综合排名：第一名权重计为5分，第二名计为4分，以此类推。本研究采用斯皮尔曼等级相关系数，分别衡量三种模型生成的排名与参与者反馈生成的排名之间关联的强度与方向。