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Racial landscapes - a pattern-based, zoneless method for analysis and visualization of racial topography

机译：种族景观 - 一种基于模式，Zoneless的分析和可视化的分析和可视化方法

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Quantifying and effectively communicating the spatio-racial distribution of urban residencies is important for taking the measure of how the multiracial society organizes itself in an urban environment. Most currently used approaches to this problem center around the calculation of segregation metrics; as such, they pertain to only a single pattern's feature and they lack a compelling visualization component. In this paper, we propose a reimagined approach to spatio-racial analysis based on the concept of landscape and landscape analysis. This approach unites quantification and visualization components of the analysis. It also quantifies the entire racial topography, not just segregation. Key novel concepts are the racial landscape (RL) and the exposure matrix. RL is a high-resolution grid in which each cell contains only inhabitants of a single race. The exposure matrix tabulates adjacencies between neighboring cells weighted by the local density of adjacent subpopulations; it provides a concise quantification of the RL pattern. Two information-theoretical metrics, derived from the exposure matrix, quantify diversity, and segregation of the RL. Segregation is quantified from cell adjacencies without the need for subdivision of the region of interest. Thus, the entire region, as well as its arbitrary subregions, are RLs quantified by their diversities and segregations. Coloring cells in RL according to combinations of their race and local densities provides a natural visualization of racial topography which serves as an "observation" that provides check on numerical metrics. The RL method is described and its application is demonstrated on Cook County, IL. An implementation of the RL method in R package accompanies this paper.
机译：量化和有效地沟通城市居民的时空分配对于采取衡量人体社会如何在城市环境中组织的衡量标准是重要的。目前使用的方法围绕分离度量的计算围绕这个问题中心;因此，它们仅涉及一个图案的特征，并且它们缺乏引人注目的可视化组件。在本文中，我们提出了一种基于景观与景观分析的概念的一种重新制成的时空分析方法。该方法单位分析的量化和可视化组件。它还量化了整个种族的地形，而不仅仅是偏离。关键新颖概念是种族景观（RL）和曝光矩阵。 RL是一种高分辨率网格，其中每个细胞仅包含单一种族的居民。曝光矩阵标记由相邻亚群的局部密度加权的相邻小区之间的邻接;它提供了RL模式的简洁量化。源自曝光矩阵，量化分集和RL的分离的两个信息理论度量。偏析量从细胞毗邻量化而无需细分区域的细分。因此，整个区域以及其任意次区域是由它们的多样性和分离量化的RLS。 RL中的着色细胞根据其种族的组合和局部密度提供种族地形的自然可视化，其用作提供关于数值指标检查的“观察”。描述了R1方法，其应用在厨师县IL上进行了证明。本文伴随着R包的RL方法的实现。

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