Superpixels are a promising group of techniques allowing for generalization of spatial information. Among this group, the Simple Linear Iterative Clustering (SLIC) superpixels algorithm proved to be first-rate, both in terms of the quality of the output and the performance. SLIC, however, is limited to detecting homogeneous areas and uses the Euclidean distance only. Here, we propose an extension of SLIC allowing to use any specified distance measure for single or multi-layered spatial raster data. To present our idea, we use the extension to create an over-segmentation of areas with similar proportions of different land cover categories in Ohio. Given a proper distance measure, the proposed extension can also be used for other scenarios, including creating regions of similar temporal patterns or similarly ranked areas. Depending on the use case, the resulting superpixels could be either the result of the analysis or the input for further classification or clustering.

Racial geography in US urban areas is extensively studied with the emphasis on assessing the extent of racial segregation. However, the used methodology has not changed for at least two decades; it relies on calculating ratios of population counts in the entire city and its subdivisions – census aggregation areas. This has a number of limitations; the two most important are: assessment of segregation depends on the subdivisions used, segregation can only be calculated for regions with census subdivisions. Here we present a different conceptualization of racial geography, which leads to a new method called racial landscape (RL). We use block-level census data to construct a high-resolution grid where each cell represents single race inhabitants. The result is a spatial, racial pattern; a degree of spatial autocorrelation of this pattern is a measure of segregation that does not require using subdivisions. We shortly describe the RL method and its application to Cook County, IL. We also describe here its implementation in the R computational environment.