Modelling small area counts in the presence of overdispersion and spatial autocorrelation
Modelling small area counts in the presence of overdispersion and spatial autocorrelation
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DOI：
10.1016/j.csda.2008.08.014
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发表时间：
2009-06-15
影响因子：
1.8
通讯作者：
Griffith, Daniel
中科院分区：
文献类型：
--
作者：
Haining, Robert;Law, Jane;Griffith, Daniel
当存在或预期存在过度离散和残余空间自相关时，考虑对小地理区域中观察到的罕见事件进行建模计数时出现的问题。在这种情况下，提出了不同的模型来处理推理。不同的策略是使用英格兰谢菲尔德查点区规模的罪犯计数数据来实施的，并对结果进行比较。选择这个例子是因为之前的研究表明，社会过程和社会构成变量是理解罪犯数量的地理差异的关键，因此，这将显示出在普查区规模和更大范围内聚集的证据。这反过来又导致分析师预测过度分散和空间自相关的存在。描述了诊断措施并实施了不同的建模策略。证据表明，基于使用空间随机效应模型或包含空间滤波器的模型的建模策略似乎效果良好，并为模型推断提供了坚实的基础，但方法论中仍存在差距，需要进一步研究。 (c) 2008 Elsevier B.V. 保留所有权利。
The problems arising when modelling counts of rare events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present or anticipated are considered. Different models are presented for handling inference in this case. The different strategies are implemented using data on offender counts at the enumeration district scale for Sheffield, England and results compared. This example is chosen because previous research suggests that social processes and social composition variables are key to understanding geographical variation in offender counts which will, as a consequence, show evidence of clustering both at the scale of the enumeration district and at larger scales. This in turn leads the analyst to anticipate the presence of overdispersion and spatial autocorrelation. Diagnostic measures are described and different modelling strategies are implemented. The evidence suggests that modelling strategies based on the use of spatial random effects models or models that include spatial filters appear to work well and provide a robust basis for model inference but gaps remain in the methodology that call for further research. (c) 2008 Elsevier B.V. All rights reserved.