An effective and efficient approach for manually improving geocoded data
An effective and efficient approach for manually improving geocoded data
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DOI：
10.1186/1476-072x-7-60
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发表时间：
2008-11-26
影响因子：
4.9
通讯作者：
Cockburn, Myles G.
中科院分区：
文献类型：
--
作者：
Goldberg, Daniel W.;Wilson, John P.;Cockburn, Myles G.
背景：地理编码过程产生的输出坐标质量参差不齐。以前的研究表明，简单地从分析中排除具有低质量地理编码的记录可能会引入显著的偏差，但根据不准确的数量和严重程度，它们的包含也可能导致偏差。关于通过手动交互过程纠正地理编码的成本和/或有效性的定量研究很少，因此用于改进地理编码数据的最具成本效益的方法尚不清楚。目前的工作调查所需的时间和精力，以纠正地理编码包含在五个健康相关的数据集，代表的例子中常用的数据在Health GIS.Results：地理编码纠正尝试在五个健康相关的数据集，共包含22,317条记录。这些数据的完整处理耗时11.4周（427小时），平均每条记录的处理时间为69秒。总体而言，与12，280（55%）条记录相关的地理编码得到了成功改进，在所有五个数据集中，每条校正记录的处理时间平均为95秒。地理编码校正将整体匹配率（成功匹配的总数）从79.3%提高到95%。原始成功匹配的地理编码的位置与其校正后的改进对应位置之间的空间偏移平均为9.9公里。经过地理编码校正后，城市和USPS邮政编码精度地理编码的数量分别从10，959和1，031减少到6，284和200，而建筑物质心精度地理编码的数量从0增加到2，261。结果表明，使用web-的交互式方法是提高地理编码数据质量的一种可行且经济有效的方法。所需的工作量因地理编码数据的类型而异。这些结果可用于选择数据改进选项（例如，例如，在一个实施例中，人工干预、伪编码/地理估算、现场GPS读数）。
Background: The process of geocoding produces output coordinates of varying degrees of quality. Previous studies have revealed that simply excluding records with low-quality geocodes from analysis can introduce significant bias, but depending on the number and severity of the inaccuracies, their inclusion may also lead to bias. Little quantitative research has been presented on the cost and/or effectiveness of correcting geocodes through manual interactive processes, so the most cost effective methods for improving geocoded data are unclear. The present work investigates the time and effort required to correct geocodes contained in five health-related datasets that represent examples of data commonly used in Health GIS.Results: Geocode correction was attempted on five health-related datasets containing a total of 22,317 records. The complete processing of these data took 11.4 weeks ( 427 hours), averaging 69 seconds of processing time per record. Overall, the geocodes associated with 12,280 ( 55%) of records were successfully improved, taking 95 seconds of processing time per corrected record on average across all five datasets. Geocode correction improved the overall match rate ( the number of successful matches out of the total attempted) from 79.3 to 95%. The spatial shift between the location of original successfully matched geocodes and their corrected improved counterparts averaged 9.9 km per corrected record. After geocode correction the number of city and USPS ZIP code accuracy geocodes were reduced from 10,959 and 1,031 to 6,284 and 200, respectively, while the number of building centroid accuracy geocodes increased from 0 to 2,261.Conclusion: The results indicate that manual geocode correction using a web-based interactive approach is a feasible and cost effective method for improving the quality of geocoded data. The level of effort required varies depending on the type of data geocoded. These results can be used to choose between data improvement options ( e. g., manual intervention, pseudocoding/geoimputation, field GPS readings).