Analysis of cancer data, particularly with a small geographic unit, often suffers from the small population (numbers) problem, which causes unstable rate estimates and data suppression in sparsely populated areas. This research proposes a regionalization approach to mitigate the problem by constructing larger areas in Geographic Information Systems (GIS) that are more coherent than geopolitical areas or arbitrary zip code area and census units in terms of attribute and spatial closeness. The method is applied to analysis of late-stage breast cancer risks in Illinois in 2000. Cancer rates in these newly-constructed areas have sufficiently large base population, and are thus more reliable and also conform to a normal distribution. This permits direct mapping, exploratory spatial data analysis, and even simple OLS regression. The method can be used to effectively mitigate the small population problem commonly encountered in analysis of public health data.

中文翻译：

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构建用于癌症数据分析的地理区域：伊利诺伊州晚期乳腺癌风险的案例研究

癌症数据分析，尤其是地理单元较小的癌症数据分析，经常会遇到人口（数量）少的问题，这会导致人口稀少地区的比率估计不稳定和数据抑制。本研究提出了一种区域化方法，通过在地理信息系统 (GIS) 中构建更大的区域来缓解该问题，这些区域在属性和空间接近度方面比地缘政治区域或任意邮政编码区域和人口普查单位更加连贯。将该方法应用于2000年伊利诺伊州晚期乳腺癌风险分析。这些新建地区的癌症发病率基数足够大，因此更可靠，也符合正态分布。这允许直接映射、探索性空间数据分析，甚至简单的 OLS 回归。