Abstract
One of the main steps of acquiring and handling data in a multi-scale database is generation of automatic links between corresponding objects in different scales, which is provided by matching them in the datasets. The basic concept of this process is to detect and measure the spatial similarity between various objects, which differ from one application to another, largely depends on the intrinsic properties of the input data. In fact, spatial similarity index, which is a function of other criteria such as geometric, topological, and semantic ones, is to some extent uncertain. Therefore, the present study aims to provide a matching algorithm based on fuzzy reasoning, while considering human spatial cognition. The proposed algorithm runs on two road datasets of Yazd city in Iran, which are in the scales of 1:5000 and 1:25000. The evaluation results show that matching rate and correctness of the algorithm is 92.7% and 88%, respectively, which validates the appropriate function of the proposed algorithm in matching.
Highlights
Nowadays, Geospatial Information Systems (GISs) play a key role in many location-based managements along with other systems
It can be said that no spatial dataset represents the world by itself completely and correctly; it is beneficial to use the data, obtained from different organizations’ sources, in order to gain access to different features of the objects in various geographical uses (Li, Goodchild 2012)
One of the main challenges, encountered when integrating the existing datasets in Multi-scale Database (MSDB), is to automatically create connections among objects such as geospatial information layers in varied scales, which can happens by means of matching algorithms that detect the corresponding existents among differing geospatial data through spatial similarity indices and by fostering matching relations (Zhonglianga, Jianhuaa 2008)
Summary
Geospatial Information Systems (GISs) play a key role in many location-based managements along with other systems. A suitable solution to manage such spatial data better in terms of reducing parallelisms and increasing the use of miscellaneous aspects of the data in different scales is to establish a Multi-scale Database (MSDB). One of the main challenges, encountered when integrating the existing datasets in MSDB, is to automatically create connections among objects such as geospatial information layers in varied scales, which can happens by means of matching algorithms that detect the corresponding existents among differing geospatial data through spatial similarity indices and by fostering matching relations (Zhonglianga, Jianhuaa 2008). Chen and Walter (2009) proposed a matching method, based on statistics and probability.
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