Dasymetric Modeling and Uncertainty.
Dasymetric Modeling and Uncertainty.
复制标题
dasymetric建模和不确定性。
DOI：
10.1080/00045608.2013.843439
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发表时间：
2014-01-01
期刊：
影响因子：
--
通讯作者：
Speilman S
中科院分区：
文献类型：
--
作者：
Nagle NN;Buttenfield BP;Leyk S;Speilman S
非对称模型通过纳入相关的辅助数据层来提高人口数据的空间分辨率。到目前为止，不确定性在非对称建模中的作用还没有完全解决。不确定性通常存在，因为大多数人口数据本身是不确定的，和/或连接人口和辅助数据层的地理过程不是精确已知的。提出了一种新的不对称方法——惩罚最大熵不对称模型（P-MEDM），使这些不确定性源能够被表示和建模。P-MEDM通过模型传播不确定性，并产生具有相关不确定性度量的精细分辨率种群估计。这种方法包含许多其他的理论和实践利益的好处。在非对称建模中，研究人员常常难以确定总体和辅助数据层之间的关系。PEDM模型通过统一包含辅助数据的方式简化了这一步骤。P-MEDM还允许包含丰富的数据数组，这些数据具有不同的空间分辨率、属性分辨率和不确定性。虽然P-MEDM不一定产生比现有方法更精确的估计，但它确实有助于统一数据如何进入非对称模型，它增加了可能使用的数据类型，并且它允许地理学家描述他们的非对称估计的质量。我们提出了一个P-MEDM的应用，该应用包括家庭调查数据和更高空间分辨率的数据，如来自人口普查区、街区组和土地覆盖分类的数据。
Dasymetric models increase the spatial resolution of population data by incorporating related ancillary data layers. The role of uncertainty in dasymetric modeling has not been fully addressed as of yet. Uncertainty is usually present because most population data are themselves uncertain, and/or the geographic processes that connect population and the ancillary data layers are not precisely known. A new dasymetric methodology - the Penalized Maximum Entropy Dasymetric Model (P-MEDM) - is presented that enables these sources of uncertainty to be represented and modeled. The P-MEDM propagates uncertainty through the model and yields fine-resolution population estimates with associated measures of uncertainty. This methodology contains a number of other benefits of theoretical and practical interest. In dasymetric modeling, researchers often struggle with identifying a relationship between population and ancillary data layers. The PEDM model simplifies this step by unifying how ancillary data are included. The P-MEDM also allows a rich array of data to be included, with disparate spatial resolutions, attribute resolutions, and uncertainties. While the P-MEDM does not necessarily produce more precise estimates than do existing approaches, it does help to unify how data enter the dasymetric model, it increases the types of data that may be used, and it allows geographers to characterize the quality of their dasymetric estimates. We present an application of the P-MEDM that includes household-level survey data combined with higher spatial resolution data such as from census tracts, block groups, and land cover classifications.
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影响因子：
1.8
作者：
JOHNSTON, RJ;PATTIE, CJ
通讯作者：
PATTIE, CJ
影响因子：
2.4
作者：
Leyk, Stefan;Buttenfield, Barbara P.;Nagle, Nicholas N.
通讯作者：
Nagle, Nicholas N.
影响因子：
3.6
作者：
Kyriakidis, PC
通讯作者：
Kyriakidis, PC
影响因子：
3.8
作者：
Maantay, Juliana A.;Maroko, Andrew R.;Porter-Morgan, Holly
通讯作者：
Porter-Morgan, Holly
DOI：
10.1080/13658810701492225
发表时间：
2008-01-01
影响因子：
5.7
作者：
Liu, X. H.;Kyriakidis, P. C.;Goodchild, M. F.
通讯作者：
Goodchild, M. F.