![]() New Spatial Data on Ethnicity: Introducing SIDE. When using this dataset in your research, please include the following reference: Given these limitations, we encourage the use of SIDE for cross-national analyses that require consistent, cross-national data on local ethnic demographies, rather than single-country studies that rely on high-precision data. This depends on the imputation parameters, the local density of the DHS data, and their random displacement of up to 2km (10km) in urban (rural) areas. Third, although the SIDE data are provided as high-resolution rasters, very local variation in the data may not be meaningful. Due to variation in the sampling and coding of the DHS, a substantial share of intertemporal variance in the SIDE data is random noise. Second, even though for many countries SIDE covers multiple years, we caution against relying on this temporal variation for inferential purposes. Although there is no systematic evidence of this, the data might in some cases be affected by local sampling bias. First, DHS sampling may be not always be representative due to social phenomena, such as political violence. Please note the following, important limitations of the data. Please refer to this article for all details. We use methods from geo-statistics and machine learning to estimate the ethnic composition of areas in between these sampling points to produce a continuous map of ethnic compositions for each surveyed country. Many DHS surveys are geo-coded, thus providing a set of spatial sampling points containing local ethnic composition estimates. ![]() These data are a generalization of ethnicity-related information in the geo-coded Demographic and Health Surveys (DHS). ![]() The Spatially Interpolated Data on Ethnicity (SIDE) dataset is a collection of 253 near-continuous maps of local ethno-linguistic, religious, and ethno-religious settlement patterns in 47 low- and middle-income countries. ETH Zurich D-GESS CIS ICR Data SIDE Spatially Interpolated Data on Ethnicity - SIDE Carl Müller-Crepon ![]()
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