Department of Geography, Dartmouth College, 6017 Fairchild, Hanover, NH 03755, USA.
Int J Environ Res Public Health. 2013 Sep 6;10(9):4161-74. doi: 10.3390/ijerph10094161.
Limited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations between disease and environmental factors.
We propose to use a Restricted and Controlled Monte Carlo (RCMC) process to disaggregate polygon-level location data to achieve mapping aggregate data at an approximated individual level. RCMC assigns a random point location to a polygon-level location, in which the randomization is restricted by the polygon and controlled by the background (e.g., population at risk). RCMC allows analytical processes designed for individual data to be applied, and generates high-resolution raster maps.
We applied RCMC to the town-level birth defect data for New Hampshire and generated raster maps at the resolution of 100 m. Besides the map of significance of birth defect risk represented by p-value, the output also includes a map of spatial uncertainty and a map of hot spots.
RCMC is an effective method to disaggregate aggregate data. An RCMC-based disease mapping maximizes the use of available spatial information, and explicitly estimates the spatial uncertainty resulting from aggregation.
受数据可用性的限制，文献中的大多数疾病图谱都是针对相对较大的、主观定义的区域单位，这些图谱存在与多边形地图相关的问题。需要基于客观空间单位的高分辨率图谱，以更精确地检测疾病与环境因素之间的关联。
我们提出使用受限和控制蒙特卡罗（RCMC）过程将多边形级别的位置数据离散化，以实现对近似个体水平的聚合数据进行映射。RCMC 将随机点位置分配给多边形级别的位置，其中随机化受到多边形的限制，并由背景（例如，风险人群）控制。RCMC 允许应用专为个体数据设计的分析过程，并生成高分辨率的栅格地图。
我们将 RCMC 应用于新罕布什尔州的城镇级别出生缺陷数据，并生成了分辨率为 100 米的栅格地图。除了表示 p 值的出生缺陷风险显著程度的地图外，输出还包括空间不确定性地图和热点地图。
RCMC 是离散聚合数据的有效方法。基于 RCMC 的疾病图谱最大限度地利用了可用的空间信息，并明确估计了由于聚合而产生的空间不确定性。
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