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skewed: most counties are small but a few exceed a million renters \u2014 consider a log scale.","column":"total_renters","kind":"histogram"},{"caption":"Roughly symmetric around a median of 37%, showing how widespread moderate rent burden is.","column":"pct_rent_burdened_30plus","kind":"histogram"},{"caption":"Severe rent burden centers near 17%, with a thinner right tail of hardest-hit counties.","column":"pct_rent_burdened_50plus","kind":"histogram"},{"caption":"Even spread across FIPS ranges confirms broad national coverage with no obvious gaps.","column":"fips","kind":"histogram"}],"model":"anthropic:claude-opus-4-7","narrative":"This dataset contains 3,222 rows of U.S. county-level rent burden statistics, with each row identified by a county name and FIPS code and described by total renters and the share of renters paying 30%+ or 50%+ of income on rent. Total renters is extremely skewed (skew 15.8, max 1,810,929 vs. median 2,579.5), so a handful of large urban counties dominate the distribution and warrant separate treatment. Rent-burden percentages are more well-behaved: about 36.4% of renters per county are cost-burdened at the 30%+ threshold and 17.4% at the 50%+ threshold on average, both fairly symmetric. The most useful first look is comparing the two rent-burden distributions and isolating the outlier counties on total_renters.","scope":"dataset","target":"__global__"},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.std","stats.skew"],"model":"anthropic:claude-opus-4-7","narrative":"This is the FIPS county/state code, used as a unique geographic identifier \u2014 every one of the 3,222 rows has a distinct value with no nulls. The range from 1001 to 72153 and the low skew (0.157) reflect the standard FIPS numbering across U.S. states and territories rather than a meaningful numeric distribution. Treating these as numbers (mean 31,377, std 16,299) is misleading; they are categorical codes.","role":"identifier","scope":"column","target":"fips","treatment":"Cast to string and use as a join key on geographic reference tables; do not model as numeric."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","top_words","len_min","len_max","len_mean"],"model":"anthropic:claude-opus-4-7","narrative":"This column holds fully-qualified US county names (e.g., 'X County, State'), with 3222 rows all unique and zero nulls. The token 'county,' appears 2999 times, suggesting ~223 entries don't follow that exact pattern \u2014 likely Louisiana parishes, Alaska boroughs, or independent cities worth checking. State frequencies match expectations, with Texas (256) leading.","role":"identifier","scope":"column","target":"county_name","treatment":"Split into county and state fields and left-join on FIPS rather than this string."},{"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.n_outliers","stats.outlier_rate","stats.zero_rate","stats.q1","stats.q3"],"model":"anthropic:claude-opus-4-7","narrative":"This is a numeric count of renters per record, ranging from 28 to 1,810,929 with a median of 2,579.5 \u2014 likely an aggregate count at some geographic or entity level. The distribution is extremely right-skewed (skew 15.82, kurtosis 398.15) with the mean (13,851) over five times the median, and 449 outliers (13.9%) inflate the std to 55,351. No nulls or zeros, and 2,709 unique values across 3,222 rows suggest minor repetition but largely distinct totals.","role":"feature","scope":"column","target":"total_renters","treatment":"log-transform before modelling 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.q1","stats.q3","stats.std","stats.skew","stats.kurtosis","stats.zero_rate","stats.n_outliers","stats.outlier_rate"],"model":"anthropic:claude-opus-4-7","narrative":"This column captures the percentage of households spending 30%+ of income on rent, reported per row across 3,222 records with no nulls. Values span 0 to 64.96 with a median of 37.36 and IQR of 30.67-43.48, mildly left-skewed (-0.57) and tightly clustered (std 10.0). About 0.25% are exact zeros and 58 rows (1.8%) flag as outliers, but the distribution is otherwise well-behaved and ready to use as-is.","role":"feature","scope":"column","target":"pct_rent_burdened_30plus","treatment":"Use directly as a continuous feature; no transform needed given the near-symmetric, bounded distribution."},{"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.zero_rate","stats.outlier_rate","stats.q1","stats.q3"],"model":"anthropic:claude-opus-4-7","narrative":"Likely the percentage of households spending 50%+ of income on rent at some geographic unit (e.g., county). Values span 0 to 64.96 with mean 17.35 and median 17.62, a near-symmetric distribution (skew 0.05) and modest tails (kurtosis 0.98). Only 0.93% are zero and 1.46% flagged as outliers, so the signal is clean and ready to use.","role":"feature","scope":"column","target":"pct_rent_burdened_50plus","treatment":"Use as-is as a continuous feature; no transform needed given symmetry."}],"providers":["anthropic:claude-opus-4-7"],"total_usage":{"completion_tokens":2064,"prompt_tokens":5873,"total_tokens":7937}},"language_counts":{},"meta":{"generated_at":"2026-05-01T17:00:18+00:00","mode":"full","row_count":3222,"sampled_rows":3222,"seed":42,"source":"/home/coolhand/html/datavis/data_trove/cache/rent_burden.parquet"},"notes":[],"saturn_version":"0.2.0","schema":{"county_name":"text","fips":"numeric","pct_rent_burdened_30plus":"numeric","pct_rent_burdened_50plus":"numeric","total_renters":"numeric"}}