Census blocks are administrative units that serve as statistical areas for the decennial Census in the United States. Visible and nonvisible features bound blocks, including roads, railroads, streams, property lines, and city boundaries. The Census Bureau builds blocks using the Master Address File (MAF), which includes field-verified geographic information about the location of housing unit addresses. Unfortunately, there are substantial errors in the counts of housing units at the block level, even with the purported quality checks by the Census Bureau. This paper aims to detail a method of identifying problematic blocks (i.e., ghost blocks) that report the presence of housing units, but no such units exist. Further, we identify the implications of using ghost blocks in location models using the maximal covering location problem (MCLP) in a case study for sensor locations in Los Angeles, California. We discuss policy implications and strategies to address these errors for developing higher-fidelity location models.

中文翻译：

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位置建模的空间数据不确定性：幽灵块及其影响

人口普查区是美国十年一次人口普查的统计区域的行政单位。可见和不可见的特征界定了街区，包括道路、铁路、溪流、地产红线和城市边界。人口普查局使用主地址文件 (MAF) 构建区块，其中包括有关住房单元地址位置的经现场验证的地理信息。不幸的是，即使人口普查局进行了所谓的质量检查，街区层面的住房单元数量也存在重大错误。本文旨在详细介绍一种识别有问题的街区（即幽灵街区）的方法，这些街区报告存在住房单元，但实际上不存在此类单元。此外，我们在加利福尼亚州洛杉矶的传感器位置案例研究中使用最大覆盖位置问题 (MCLP) 确定了在位置模型中使用鬼块的影响。我们讨论了解决这些错误的政策含义和策略，以开发更高保真度的位置模型。