Integration of sketch maps in community mapping activities

IF 1.6 4区 心理学 Q3 PSYCHOLOGY, EXPERIMENTAL Spatial Cognition and Computation Pub Date : 2020-11-08 DOI:10.1080/13875868.2020.1841202
A. Z. Zardiny, F. Hakimpour
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引用次数: 4

Abstract

ABSTRACT Drawing sketch maps is one of the most widely used tools for observation recording in community mapping. However, because sketches are not to scale and features are not precisely located, they are not spatially accurate. With this in mind, consider an important question. Can the use of sketch maps in a community mapping lead to an acceptable result? This article addresses this question by investigating the sketch maps drawn by children in a simulated community mapping. To make the sketches useful, they must be matched and integrated together. Although much research has been conducted about data matching in sketch maps, the integration of data extracted from sketch maps has been less considered. Therefore, this article focuses on the integration of sketch maps and proposes a solution in order to examine the maps more accurately while revising and customizing the existing matching solutions. The output of the data analysis is an integrated sketch map. The accuracy of the matching between the integrated sketch map and the data extracted from OpenStreetMap (OSM) is about 94.8%. In addition, the output contains features that are not present in the OSM data, which means that this output can be used for descriptive and geometric enrichment of metric maps. These results are the output of a simulated community mapping under some strict conditions. Therefore in a real community mapping, one can expect higher accuracy in using the proposed algorithm for matching and integration of the data in sketch maps.
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草图在社区制图活动中的整合
绘制素描图是社区测绘中应用最广泛的观测记录工具之一。然而,由于草图不是按比例绘制的,特征也不是精确定位的,因此它们在空间上并不准确。考虑到这一点,考虑一个重要的问题。在社区测绘中使用草图能产生可接受的结果吗?本文通过调查儿童在模拟社区测绘中绘制的草图来解决这个问题。为了使草图有用,它们必须匹配并整合在一起。虽然已有很多关于草图数据匹配的研究,但对草图数据的整合研究较少。因此,本文着眼于草图地图的集成,并提出解决方案,以便在修改和定制现有匹配方案的同时更准确地检查地图。数据分析的输出是一个综合的草图。综合素描图与OpenStreetMap (OSM)数据的匹配精度约为94.8%。此外,输出包含OSM数据中不存在的特征,这意味着该输出可用于度量图的描述性和几何丰富性。这些结果是在某些严格条件下模拟社区映射的输出。因此,在实际的社区测绘中,可以期望使用该算法对草图中的数据进行匹配和集成,从而获得更高的精度。
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来源期刊
Spatial Cognition and Computation
Spatial Cognition and Computation PSYCHOLOGY, EXPERIMENTAL-
CiteScore
4.40
自引率
5.30%
发文量
10
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