There is a discord between the categorization of mixed–race data in spatial studies, which has become more complex as the mixed–race population increases. We offer an efficient, spatially based method for assigning mixed–race respondents into single–race categories. The present study examined diversity within 25 Metropolitan Statistical Areas in the United States to develop this racial bridging method. We identify prescriptions for each two–race category based on average diversity experiences and similarity scores derived from census tract data. The results show the following category assignments: (1) Black–Asians to Black, (2) White–others to White, (3) Asian–others to Asian, (4) White–Blacks to other, (5) White–Asians to White (if Asian >3.0 percent), (6) White–Asians to Asian (if Asian <3.0 percent), (7) Black–Asians to other (if Black >8.5 percent), and (8) Black–Asians to Black (if Black <8.5 percent). We argue that the proposed method is appropriate for all race–based studies using spatially relevant theoretical constructs such as segregation and gentrification.

中文翻译：

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将多种族数据化简为单种族数据的基于空间的规则

空间研究中混血数据的分类存在不一致，随着混血人口的增加，这变得更加复杂。我们提供了一种有效的、基于空间的方法，用于将混血受访者分配到单一种族类别中。本研究调查了美国 25 个大都市统计区内的多样性，以开发这种种族桥接方法。我们根据从人口普查数据中得出的平均多样性经验和相似性分数为每个双种族类别确定处方。结果显示以下类别分配：(1) 黑人-亚洲人分配给黑人，(2) 白人-其他人分配给白人，(3) 亚洲人-其他人分配给亚洲人，(4) 白人-黑人分配给其他人，(5) 白人-亚洲人白人（如果亚洲人 >3.0%），（6）白人 - 亚洲人到亚洲人（如果亚洲人 <3.0%），(7) 黑人-亚洲人对其他人（如果黑人 >8.5%），以及 (8) 黑人-亚洲人对黑人（如果黑人 <8.5%）。我们认为，所提出的方法适用于所有基于种族的研究，使用空间相关的理论结构，如隔离和绅士化。