A Comparative Study of Geocoder Performance on Unstructured Tweet Locations

Q3 Social Sciences
H. N. Serere, Umut Nefta Kanilmaz, Sruthi Ketineni, Bernd Resch
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引用次数: 0

Abstract

Geocoding is a process of converting human-readable addresses into latitude and longitude points. Whilst most geocoders tend to perform well on structured addresses, their performance drops significantly in the presence of unstructured addresses, such as locations written in informal language. In this paper, we make an extensive comparison of geocoder performance on unstructured location mentions within tweets. Using nine geocoders and a worldwide English-language Twitter dataset, we compare the geocoders’ recall, precision, consensus and bias values. As in previous similar studies, Google Maps showed the highest overall performance. However, with the exception of Google Maps, we found that geocoders which use open data have higher performance than those which do not. The open-data geocoders showed the least per-continent bias and the highest consensus with Google Maps. These results suggest the possibility of improving geocoder performance on unstructured locations by extending or enhancing the quality of openly available datasets.
Geocoder在非结构化Tweet位置上性能的比较研究
地理编码是将人类可读的地址转换为纬度和经度点的过程。虽然大多数地理编码器倾向于在结构化地址上表现良好,但在非结构化地址(例如用非正式语言写的位置)的存在下,它们的性能会显著下降。在本文中,我们对地理编码器在tweet中非结构化位置提及的性能进行了广泛的比较。使用9个地理编码器和一个全球英语Twitter数据集,我们比较了地理编码器的召回率、精度、共识和偏差值。与之前的类似研究一样,谷歌地图显示出最高的整体表现。然而,除了谷歌地图之外,我们发现使用开放数据的地理编码器比不使用开放数据的地理编码器具有更高的性能。开放数据地理编码器显示出最小的大陆偏差,与谷歌地图的一致性最高。这些结果表明,通过扩展或提高公开可用数据集的质量,可以提高地理编码器在非结构化位置上的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
GI_Forum
GI_Forum Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
1.10
自引率
0.00%
发文量
9
审稿时长
23 weeks
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