Modeling the probability distribution of positional errors incurred by residential address geocoding.
Modeling the probability distribution of positional errors incurred by residential address geocoding.
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DOI：
10.1186/1476-072x-6-1
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发表时间：
2007-01-10
影响因子：
4.9
通讯作者：
Rushton G
中科院分区：
文献类型：
--
作者：
Zimmerman DL;Fang X;Mazumdar S;Rushton G
为受试者的住所分配点级地理编码是许多地理公共卫生研究的重要数据同化组成部分。通常，这些分配是通过一种称为自动地理编码的方法进行的，该方法试图将每个主题的地址与街道数据库中地理参考的地址范围街道段进行匹配，然后沿该路段插入地址的位置沿着。不幸的是，该过程导致位置误差。我们的研究试图模拟与自动地理编码和E911地理编码相关的位置误差的概率分布。位置误差确定为卡罗尔县，爱荷华州的1423个农村地址之间的矢量差异，每个100%匹配的自动地理编码和它的真实位置，确定正射影像和包裹信息。还确定了1449个60%匹配的地理编码和2354个E911地理编码的错误。在60%匹配的地理编码错误中出现了巨大的（> 15 km）离群值;其他两种类型的地理编码错误也出现了离群值，但要小得多。E911地理编码比100%匹配的自动地理编码（中位误差长度= 168 m）更准确（中位误差长度= 44 m）。与100%匹配的自动地理编码和E911地理编码相关的位置误差的经验分布呈现出独特的希腊十字形，并且具有许多其他有趣的特征，这些特征无法通过单个二元正态分布或t分布充分拟合。然而，具有两个或三个分量的t分布的混合物非常适合误差。二元t分布与几个组件的混合物似乎是足够灵活，以适应许多位置误差数据集与地理编码，但吝啬不够可行的新生应用的测量误差方法空间流行病学。
The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated geocoding and E911 geocoding. Positional errors were determined for 1423 rural addresses in Carroll County, Iowa as the vector difference between each 100%-matched automated geocode and its true location as determined by orthophoto and parcel information. Errors were also determined for 1449 60%-matched geocodes and 2354 E911 geocodes. Huge (> 15 km) outliers occurred among the 60%-matched geocoding errors; outliers occurred for the other two types of geocoding errors also but were much smaller. E911 geocoding was more accurate (median error length = 44 m) than 100%-matched automated geocoding (median error length = 168 m). The empirical distributions of positional errors associated with 100%-matched automated geocoding and E911 geocoding exhibited a distinctive Greek-cross shape and had many other interesting features that were not capable of being fitted adequately by a single bivariate normal or t distribution. However, mixtures of t distributions with two or three components fit the errors very well. Mixtures of bivariate t distributions with few components appear to be flexible enough to fit many positional error datasets associated with geocoding, yet parsimonious enough to be feasible for nascent applications of measurement-error methodology to spatial epidemiology.