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Integration of sketch maps in community mapping activities
Spatial Cognition & Computation ( IF 1.6 ) Pub Date : 2020-11-08 , DOI: 10.1080/13875868.2020.1841202
Ali Zare Zardiny 1 , Farshad Hakimpour 2
Affiliation  

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.



中文翻译:

草图在社区制图活动中的整合

摘要

绘制草图图是社区映射中用于观察记录的最广泛使用的工具之一。但是,由于草图未按比例绘制且特征未精确定位,因此它们在空间上不准确。考虑到这一点,请考虑一个重要的问题。在社区地图中使用草图可以带来可接受的结果吗?本文通过研究儿童在模拟社区地图中绘制的草图来解决此问题。为了使草图有用,必须将它们匹配并集成在一起。尽管已经对草图中的数据匹配进行了大量研究,但是从草图中提取的数据的集成却很少被考虑。所以,本文着重于素描图的集成,并提出了一种解决方案,以便在修订和自定义现有匹配解决方案时更准确地检查这些图。数据分析的输出是一个集成的草图。集成草图地图与从OpenStreetMap(OSM)提取的数据之间的匹配精度约为94.8%。另外,输出包含OSM数据中不存在的功能,这意味着该输出可用于度量图的描述性和几何性充实。这些结果是在某些严格条件下模拟社区映射的输出。因此,在实际的社区地图中,可以期望使用提议的算法对草图中的数据进行匹配和集成时具有更高的准确性。数据分析的输出是一个集成的草图。集成草图地图与从OpenStreetMap(OSM)提取的数据之间的匹配精度约为94.8%。另外,输出包含OSM数据中不存在的功能,这意味着该输出可用于度量图的描述性和几何性充实。这些结果是在某些严格条件下模拟社区映射的输出。因此,在实际的社区地图中,可以期望使用提议的算法对草图中的数据进行匹配和集成时具有更高的准确性。数据分析的输出是一个集成的草图。集成草图地图与从OpenStreetMap(OSM)提取的数据之间的匹配精度约为94.8%。另外,输出包含OSM数据中不存在的功能,这意味着该输出可用于度量图的描述性和几何性充实。这些结果是在某些严格条件下模拟社区映射的输出。因此,在实际的社区地图中,可以期望使用提议的算法对草图中的数据进行匹配和集成时具有更高的准确性。输出包含OSM数据中不存在的功能,这意味着该输出可用于度量图的描述性和几何性充实。这些结果是在某些严格条件下模拟社区映射的输出。因此,在实际的社区地图中,可以期望使用提议的算法对草图中的数据进行匹配和集成时具有更高的准确性。输出包含OSM数据中不存在的功能,这意味着该输出可用于度量图的描述性和几何性充实。这些结果是在某些严格条件下模拟社区映射的输出。因此,在实际的社区地图中,可以期望使用提议的算法对草图中的数据进行匹配和集成时具有更高的准确性。

更新日期:2020-11-08
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