Journal of Geo-information Science >
Research Progress in Multi-scale Spatial Relations
Received date: 2014-11-15
Request revised date: 2014-12-10
Online published: 2015-02-10
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Modeling spatial relations and their scale changes has been one of the important topics in GIS science. This paper discussed the geometric-based and relational-based representation of geographic information. The first representation aims to store, manage, and analyze geometric data with coordinates, and concentrates on the geometric locations, shapes and distributions of spatial objects, thus it is termed as geometric representation. The latter uses symbols to qualitatively represent, communicate and infer spatial relationships between spatial objects based on people’s cognition and understanding, thus it is termed as relational representation. This paper mainly focused on summarizing the latest progress in theories, methods and applications about the relational representation. First, the above-mentioned two representations were compared and their scale changes were highlighted. It is discovered that the geometric representations of same geographic entities vary at different spatial scales, so do the relational representations vary between geographic entities. This type of changes in relational representations strongly associates with cartographic generalization operators, which affects the changes of shapes, sizes and structures of spatial objects. Second, the influences of the generalization operators, which include shape simplification, merging of areal objects, attribute induction and spatial dimension reduction, on spatial relations were analyzed, and related methods were presented for deriving and modeling the scale changes of topological and directional relations which was caused by the four operators. Third, combined with multi-scale spatial relations, a technological framework for analyzing multi-scale datasets was presented. We also illustrated the concepts and solutions for detecting the consistency of multi-scale data, and tested them practically with case studies to demonstrate their efficiency. Finally, it can be concluded that the generalization operators and modeling methods play important roles in analyzing and understanding multi-scale spatial datasets.
DU Shihong , LUO Liqun , ZHAO Wenzhi , GUO Zhou . Research Progress in Multi-scale Spatial Relations[J]. Journal of Geo-information Science, 2015 , 17(2) : 135 -146 . DOI: 10.3724/SP.J.1047.2015.00135
Fig. 1 Relation representation and spatial scale图1 关系表现与空间尺度 |
Tab. 1 Comparison of geometric representation and relation representation表1 几何表现和关系表现 |
几何表现 | 关系表现 | |
---|---|---|
定位方式 | 笛卡尔坐标系 | 基于关系的定位 |
定位性质 | 绝对定位 | 相对定位 |
定位精度 | 量化、精确 | 定性、近似 |
对象表现 | 几何表现(点、线、面等) | 关系知识表达、推理 |
空间分析 | 几何计算、欧式距离等 | 概念距离、关系距离、场景相似 |
信息度量 | 定量处理(面积、长度) | 定性概念表达 |
Fig. 2 Direction relations图2 方向关系 |
Tab. 2 The correspondences between the eight region-region relations and 19 line-region relations[18]表2 8种面-面与19种线-面拓扑关系的对应性[18] |
8种面-面关系 | 19种线-面关系 | ||||
---|---|---|---|---|---|
矩阵比较法 | 拓扑距离法 | 矩阵合并法 | |||
disjoint | LR1 | LR1 | LR1 | ||
contain | LR14 | LR14 | LR14, LR15, LR16,LR17, LR18, LR19 | ||
inside | LR9 | LR9 | LR9 | ||
equal | LR8 | LR8 | LR8, LR10, LR11,LR12, LR13 | ||
meet | LR3 | LR3 | LR2, LR3, LR5, LR6,LR7 | ||
cover | LR16 | LR16 | LR14, LR15, LR16,LR17, LR18, LR19 | ||
coveredBy | LR10 | LR10 | LR8, LR10, LR11,LR12, LR13 | ||
overlap | 无 | LR16,LR18,LR19 | LR14, LR15, LR16,LR17, LR18, LR19 |
Fig. 3 Topological changes caused by merging regions图3 区域合并引起的拓扑变化 |
Fig. 7 The direction relation change caused by dimension reduction图7 维数退化前后方向关系变化 |
Fig. 8 System interface图8 系统界面 |
Fig. 9 The examples of inconsistent relations at two scales图9 不一致性检测实例 |
The authors have declared that no competing interests exist.
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