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counties cluster near a 1.07 median rate; check the long tail out to 9.17 for unusual counties.","column":"disability_rate","kind":"histogram"},{"caption":"Extreme right skew \u2014 a few mega-counties dwarf the median of ~25K residents and will distort any raw count comparisons.","column":"total_population","kind":"histogram"},{"caption":"Same skewed shape as population; useful to confirm disability counts track population size.","column":"disability_total","kind":"histogram"},{"caption":"The largest single disability category by mean; inspect the outlier tail (366 outliers) before aggregating.","column":"ambulatory_disability","kind":"histogram"},{"caption":"Shows how the 3,222 county rows distribute across the 52 state codes \u2014 useful for spotting state-level coverage imbalances.","column":"state_fips","kind":"bar"}],"model":"anthropic:claude-opus-4-7","narrative":"This dataset contains 2022 US Census disability counts for 3,222 counties, broken out by disability type (ambulatory, cognitive, hearing, vision, self-care, independent living) along with totals, a derived disability rate, and FIPS identifiers. Nearly every count column is heavily right-skewed (skew above 10) with substantial outliers \u2014 total_population alone ranges from 47 to 9.87M with a mean of ~102K but a median of just 25,328, so a handful of large counties dominate the raw counts. The disability_rate field is the most analyst-friendly view: it's bounded, less skewed (skew 2.17), and centers around a median of 1.07 with an IQR of 0.77\u20131.42. Start with disability_rate to compare counties on equal footing, then look at total_population to understand the size distribution before interpreting any raw disability counts.","scope":"dataset","target":"__global__"},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.skew","stats.kurtosis","stats.n_outliers"],"model":"anthropic:claude-opus-4-7","narrative":"This column is the FIPS county code: every one of the 3222 rows is unique and non-null, and the value range (1001 to 72153) matches the standard US state+county FIPS encoding. The distribution is near-symmetric (skew 0.16, kurtosis -0.63) with no outliers, which is expected for an identifier rather than a measured quantity. Treat it as a categorical key, not a number.","role":"identifier","scope":"column","target":"fips","treatment":"Cast to zero-padded string and use as a join key to county-level data; do not feed as a numeric feature."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","top_words","stats.len_mean","stats.word_mean"],"model":"anthropic:claude-opus-4-7","narrative":"Each of the 3,222 rows holds a unique county-and-state string (e.g., '... County, Texas'), averaging 24 characters and roughly 3 words. The token 'county,' appears 2,999 times, so a small minority of entries use a different suffix (parish, borough, census area). Texas (256), Virginia (189), and Georgia (159) lead the state distribution, consistent with a full U.S. county roster.","role":"identifier","scope":"column","target":"county_name","treatment":"Split into county and state fields, then left-join on a FIPS lookup."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.skew","stats.kurtosis","stats.zero_rate"],"model":"anthropic:claude-opus-4-7","narrative":"This is almost certainly the US state FIPS code: 52 unique integer values across 3222 rows, ranging from 1 to 72 with no nulls or zeros. The count of 52 (rather than 50) and a max of 72 indicate inclusion of DC and territories like Puerto Rico. Distribution is roughly uniform (skew 0.16, kurtosis -0.63), consistent with a categorical geographic identifier rather than a measurement.","role":"foreign_key","scope":"column","target":"state_fips","treatment":"Cast to categorical/string and join to a state lookup table; do not treat as a numeric feature."},{"confidence":"high","critiques":[],"evidence_keys":["column","n","n_unique","null_rate","stats.min","stats.max","stats.median","stats.q3","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate"],"model":"anthropic:claude-opus-4-7","narrative":"This is the county-level portion of a FIPS code, stored as an integer from 1 to 840 across 3222 rows with no nulls and only 330 distinct values. The distribution is heavily right-skewed (skew 2.87, kurtosis 11.64) with 178 outliers (5.5%), which is expected since county codes are categorical identifiers and most values cluster low (median 79, Q3 133) while a few counties carry much larger codes. Treating this as a numeric feature would be misleading despite the numeric dtype.","role":"foreign_key","scope":"column","target":"county_fips","treatment":"Cast to categorical and combine with state FIPS to join on full county identifier."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.median","stats.mean","stats.max","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","stats.iqr","stats.q1","stats.q3"],"model":"anthropic:claude-opus-4-7","narrative":"Likely a county- or region-level total population count across 3,222 rows with no nulls and 3,141 unique values. The distribution is extremely right-skewed (skew 13.38, kurtosis 298.69): the median is 25,328 but the mean is 102,232 and the max reaches 9,866,623, with 453 outliers (14.06%) flagged above the IQR fence.","role":"feature","scope":"column","target":"total_population","treatment":"log-transform before regression to tame the heavy right tail."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.std","stats.skew","stats.kurtosis","stats.iqr","stats.q1","stats.q3","stats.n_outliers","stats.outlier_rate","stats.zero_rate"],"model":"anthropic:claude-opus-4-7","narrative":"A heavily right-skewed count of disability cases or claims, ranging from 0 to 69,705 with a median of just 298 but a mean of 1,043. Skew of 10.28 and kurtosis of 166.8 indicate an extreme long tail, with 404 outliers (12.5% of rows) and only 1.7% zeros. The std (2,906) dwarfs the IQR (689), so a small number of very large records dominate the distribution.","role":"feature","scope":"column","target":"disability_total","treatment":"Apply a log1p transform before modelling to tame the extreme skew and outliers."},{"confidence":"high","critiques":[],"evidence_keys":["stats.min","stats.max","stats.median","stats.q1","stats.q3","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","stats.zero_rate","n","n_unique","null_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Numeric disability_rate spanning 0.0 to 9.17 with a median of 1.07 and IQR 0.77-1.42, almost certainly a per-row rate or percentage. The distribution is heavily right-skewed (skew 2.17, kurtosis 15.24) with 117 outliers (3.6%) stretching well beyond the typical range, and 1.7% of rows sit at exactly zero. No nulls across 3,222 rows, and only 305 distinct values suggest rounding to two decimals.","role":"feature","scope":"column","target":"disability_rate","treatment":"Apply a log or winsorising transform before modelling to tame the right tail."},{"confidence":"high","critiques":[],"evidence_keys":["stats.min","stats.max","stats.median","stats.mean","stats.q1","stats.q3","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","stats.zero_rate","null_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Counts of people recorded as having no disability per geographic or administrative unit, ranging from 0 to 2,091,332 with a median of 5,607. The distribution is extremely right-skewed (skew 12.67, kurtosis 259.77) and the mean of 22,872 sits well above Q3 of 14,739, with 442 outliers (13.7%) flagging a long tail of very large units. Only one zero is present and there are no nulls, so the heavy tail\u2014not missingness\u2014is the dominant feature.","role":"feature","scope":"column","target":"no_disability","treatment":"Log-transform (log1p) before modelling to tame the skew and outliers."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","stats.zero_rate"],"model":"anthropic:claude-opus-4-7","narrative":"This column appears to be a count of people with one disability per geographic or administrative unit, ranging from 0 to 44,466 with a median of 217.5. The distribution is severely right-skewed (skew 9.45, kurtosis 139.4), with the mean (755.7) more than triple the median and 408 outliers (12.7% of rows) \u2014 consistent with a few very large units dominating a long tail of small ones. About 2.8% of rows are zero and there are no nulls.","role":"feature","scope":"column","target":"one_disability","treatment":"Apply a log1p transform before modelling to tame the heavy right tail."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.median","stats.mean","stats.q1","stats.q3","stats.skew","stats.kurtosis","stats.outlier_rate","stats.zero_rate"],"model":"anthropic:claude-opus-4-7","narrative":"This column appears to be a count of people (likely per geographic unit) reporting two or more disabilities, ranging from 0 to 25,239 with a median of just 76. The distribution is extremely right-skewed (skew 12.57, kurtosis 253.95), with 11.67% of rows flagged as outliers and ~9% exact zeros, suggesting a few very large jurisdictions dominate while most are small. The mean (287.7) sits well above Q3 (222), confirming the long tail.","role":"feature","scope":"column","target":"two_plus_disabilities","treatment":"Apply a log1p transform before modelling to tame the heavy right tail."},{"confidence":"medium","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","stats.zero_rate","stats.q1","stats.q3"],"model":"anthropic:claude-opus-4-7","narrative":"This appears to be a count or population-style measure related to hearing disability, with all 3222 rows populated and 2314 distinct values ranging from 1 to 296898. The distribution is extremely right-skewed (skew 11.54, kurtosis 226.6) with a median of 1326 well below the mean of 4003, and 391 outliers (12.1%) inflate the tail. The min of 1 and absence of zeros suggest these are aggregated counts rather than individual indicators.","role":"feature","scope":"column","target":"hearing_disability","treatment":"log-transform before regression to tame the heavy right tail."},{"confidence":"high","critiques":[],"evidence_keys":["stats.min","stats.max","stats.median","stats.mean","stats.q1","stats.q3","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","stats.zero_rate","null_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Numeric counts of people with a vision disability per geographic or demographic unit, ranging from 0 to 346,901 with a median of 1,361. The distribution is extremely right-skewed (skew 12.29, kurtosis 254.79) and the mean of 4,246 sits well above Q3 of 3,291, with 380 outliers (11.8%) inflating the upper tail. Near-zero zero_rate (0.03%) and no nulls suggest clean population-style aggregates rather than survey responses.","role":"feature","scope":"column","target":"vision_disability","treatment":"Log-transform before regression to tame the heavy right tail."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","stats.zero_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Likely a count of people with a cognitive disability per geographic or administrative unit, ranging from 0 to 413,990 with a median of 1,623. The distribution is severely right-skewed (skew 12.09, kurtosis 254.7) with 375 outliers (11.6% of rows) and a mean (5,142) more than triple the median, indicating a few very large units dominate. Near-zero null and zero rates suggest the count is reliably populated.","role":"feature","scope":"column","target":"cognitive_disability","treatment":"Apply a log or log1p transform before modelling to tame the heavy right tail."},{"confidence":"high","critiques":[],"evidence_keys":["min","max","median","mean","skew","kurtosis","outlier_rate","n_outliers","std","q1","q3"],"model":"anthropic:claude-opus-4-7","narrative":"Counts of people with ambulatory disability per geographic unit, ranging from 3 to 548,175 with a median of 2,197. The distribution is severely right-skewed (skew 13.0, kurtosis 288.7) and 11.4% of rows are flagged as outliers, indicating a long tail of very large jurisdictions dominating the mean (6,497) versus the median.","role":"feature","scope":"column","target":"ambulatory_disability","treatment":"Log-transform before modelling to tame the heavy right tail."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.mean","stats.median","stats.max","stats.q1","stats.q3","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","stats.zero_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Numeric counts of people with a self-care disability, likely aggregated per geographic or demographic unit given the 3222 rows and 1961 unique values. The distribution is severely right-skewed (skew 16.8, kurtosis 478.7) with a median of 772.5 but a max of 281,611, and 355 outliers (11.0% of rows) sit far above the Q3 of 1948.5. Near-zero null and zero rates suggest the field is consistently populated.","role":"feature","scope":"column","target":"self_care_disability","treatment":"Log-transform before modelling and consider winsorising the long upper tail."},{"confidence":"high","critiques":[],"evidence_keys":["stats.min","stats.max","stats.median","stats.skew","stats.kurtosis","stats.outlier_rate","stats.n_outliers","null_rate","stats.zero_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Counts of people with an independent-living disability per geographic unit, ranging from 2 to 1,417,825 with a median of 3,135. The distribution is severely right-skewed (skew 14.09, kurtosis 329.97) and 13.8% of rows (445) flag as outliers, suggesting the column mixes small areas with very large aggregates. No nulls or zeros are present.","role":"feature","scope":"column","target":"independent_living_disability","treatment":"Log-transform before modelling and consider normalising by area population to control the heavy skew."}],"providers":["anthropic:claude-opus-4-7"],"total_usage":{"completion_tokens":5465,"prompt_tokens":16861,"total_tokens":22326}},"language_counts":{},"meta":{"generated_at":"2026-05-01T17:36:35+00:00","mode":"full","row_count":3222,"sampled_rows":3222,"seed":42,"source":"/home/coolhand/datasets/us-inequality-atlas/disability/census_disability_by_county_2022.csv"},"notes":[],"saturn_version":"0.2.0","schema":{"ambulatory_disability":"numeric","cognitive_disability":"numeric","county_fips":"numeric","county_name":"text","disability_rate":"numeric","disability_total":"numeric","fips":"numeric","hearing_disability":"numeric","independent_living_disability":"numeric","no_disability":"numeric","one_disability":"numeric","self_care_disability":"numeric","state_fips":"numeric","total_population":"numeric","two_plus_disabilities":"numeric","vision_disability":"numeric"}}