clean up
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1 changed files with 2 additions and 5 deletions
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@ -78,7 +78,7 @@ col_sup_politics <- analysis_vars[
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# (c) Infrastructure, mobility, and event counts → col.sup
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# Genuine counts but measuring dwellings, vehicles, farms, animals, or flows
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# Counts but measuring dwellings, vehicles, farms, animals, or flows
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# rather than resident persons. Projecting them as supplementary shows how
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# they relate to the population-composition space without distorting it.
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col_sup_infra <- analysis_vars[
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@ -116,10 +116,7 @@ col_sup_vars <- c(col_sup_edu, col_sup_politics, col_sup_infra)
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# Active: population-composition person-counts
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# All five groups answer "how many residents have attribute X?" and therefore
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# share a common denominator — the resident population. No pre-processing
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# or proportionalization is needed: CA row-profile normalisation already
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# removes the size effect, and chi-square distance directly compares
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# compositional profiles.
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# share a common denominator.
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active_vars <- analysis_vars[
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(str_detect(analysis_vars, "^age_") |
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str_detect(analysis_vars, "^education_level_of_swedish_men_") |
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