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counts by borough \u2014 Brooklyn and Queens dominate the sample.","column":"county_name","kind":"bar"},{"caption":"Share of renters paying 30%+ of income; median is 50%, showing widespread burden.","column":"pct_rent_burdened","kind":"histogram"},{"caption":"Severe burden (50%+ of income on rent) \u2014 see how thick the right tail runs.","column":"pct_severe_burden","kind":"histogram"},{"caption":"Owner-occupancy share varies widely (IQR 16\u201356%); useful for segmenting tracts.","column":"pct_owner_occupied","kind":"histogram"},{"caption":"Tract size distribution \u2014 right-skewed, so per-capita rates beat raw counts.","column":"total_households","kind":"histogram"}],"model":"anthropic:claude-opus-4-7","narrative":"This dataset covers 2,327 NYC census tracts with 23 columns describing housing tenure, rent burden, income, and rent levels across the five boroughs. The most urgent issue is data hygiene: median_gross_rent and median_household_income both contain a sentinel value of -666666666, which drags their means to roughly -41.5M and -36M respectively despite sensible medians (~$1,735 rent, ~$76,833 income) \u2014 these need to be filtered before any analysis. Beyond that, the substantive story is rent burden: pct_rent_burdened has a median of 50% with an IQR of 40.9\u201358.8, meaning half of NYC tracts have a majority of renters paying 30%+ of income on rent. Brooklyn (Kings) dominates the tract count at 35%, followed by Queens (31%) and the Bronx (15%), so any borough-level comparison should weight accordingly. The state column is constant (all 36, New York) and can be dropped.","scope":"dataset","target":"__global__"},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.skew","stats.kurtosis","stats.zero_rate","stats.n_outliers","stats.outlier_rate"],"model":"anthropic:claude-opus-4-7","narrative":"This column counts renter households per record, ranging from 0 to 8209 with a median of 726 and mean of 946. The distribution is right-skewed (skew 1.59, kurtosis 4.63) with 69 outliers (2.97%) on the high end and 4.38% zero values. No nulls, and 1418 unique values across 2327 rows suggests aggregation at a geographic or administrative unit.","role":"feature","scope":"column","target":"total_renter_households","treatment":"Log-transform before regression to tame the right skew, and decide whether zero-count rows should be modelled separately."},{"confidence":"high","critiques":[],"evidence_keys":["stats.skew","stats.kurtosis","stats.median","stats.mean","stats.max","stats.zero_rate","stats.n_outliers","stats.outlier_rate","stats.q3","stats.min"],"model":"anthropic:claude-opus-4-7","narrative":"Likely a count of households paying 30%-34.9% of income on rent within some geographic unit, given the integer-like values, zero floor, and max of 1205. The distribution is heavily right-skewed (skew 2.76, kurtosis 13.86) with a median of 51 against a mean of 83.05, and 16.2% of rows are exactly zero. 124 outliers (5.33%) extend far above the Q3 of 116, consistent with a few large areas dominating.","role":"feature","scope":"column","target":"rent_30_to_34_9_pct","treatment":"log1p-transform before modelling to tame the skew and zero inflation."},{"confidence":"high","critiques":[],"evidence_keys":["stats.skew","stats.kurtosis","stats.median","stats.max","stats.zero_rate","stats.q1","stats.q3","stats.n_outliers","stats.outlier_rate","null_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Likely a count of households (or housing units) paying 35% to 39.9% of income on rent within some geographic unit. The distribution is heavily right-skewed (skew 2.40, kurtosis 9.27) with a median of 35 but a max of 633, and nearly 20% of rows are zero (zero_rate 0.196), suggesting many small areas have no households in this rent burden bracket. 110 outliers (4.7%) sit well above the Q3 of 83.","role":"feature","scope":"column","target":"rent_35_to_39_9_pct","treatment":"Log1p-transform before regression to tame the right skew and zero inflation."},{"confidence":"high","critiques":[],"evidence_keys":["stats.skew","stats.kurtosis","stats.median","stats.max","stats.n_outliers","stats.outlier_rate","stats.zero_rate","stats.min","stats.q1","stats.q3"],"model":"anthropic:claude-opus-4-7","narrative":"Likely a count of housing units paying rent in the 40-49.9% income bracket per geographic area. The distribution is heavily right-skewed (skew 2.14, kurtosis 7.14) with a median of 49 but a max of 740 and 111 outliers (4.77%), and 15.6% of rows are zero \u2014 consistent with small geographies sitting alongside dense ones.","role":"feature","scope":"column","target":"rent_40_to_49_9_pct","treatment":"log1p-transform before modelling to tame the right skew and zero mass."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.mean","stats.median","stats.min","stats.max","stats.skew","stats.kurtosis","stats.zero_rate","stats.outlier_rate","stats.q1","stats.q3"],"model":"anthropic:claude-opus-4-7","narrative":"Counts of households spending 50% or more of income on rent, aggregated per geographic unit across 2327 rows with no nulls. The distribution is right-skewed (skew 1.60, kurtosis 3.44) with a median of 184 well below the mean of 253.18 and a max of 1918, and 6.27% of rows are zero. About 3.74% of values fall outside the Tukey fence.","role":"feature","scope":"column","target":"rent_50_pct_or_more","treatment":"Log1p-transform before modelling to tame the right skew."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.len_min","stats.len_max","stats.len_median","stats.word_median","top_words","alerts"],"model":"anthropic:claude-opus-4-7","narrative":"This column holds fully-qualified Census Tract names for New York City, every one of 2327 rows unique with zero nulls and tightly bounded length (38-46 chars, median 41). The vocabulary is formulaic: 'new', 'york', 'census', 'tract', 'county;' appear in essentially every row, with the borough split dominated by Kings (805), Queens (725), Bronx (361), and Richmond (126). Because each value is a one-to-one tract label, it functions as a geographic key rather than a modelling feature.","role":"identifier","scope":"column","target":"NAME","treatment":"Treat as a tract-level key; parse out borough or join to a geo table rather than feeding the raw string to a model."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.std","alerts"],"model":"anthropic:claude-opus-4-7","narrative":"The column 'state' is numeric but holds the single value 36 across all 2327 rows, with zero variance and no nulls. It carries no information for modelling and likely encodes a fixed jurisdiction or pipeline stage code that was filtered upstream.","role":"metadata","scope":"column","target":"state","treatment":"Drop, constant column."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.median","stats.skew"],"model":"anthropic:claude-opus-4-7","narrative":"Encoded county identifier stored as a numeric code, with only 5 distinct values across 2327 rows and no nulls. The values (min 5, max 85, median 47) look like sparse categorical codes rather than a continuous measurement, and the negative skew (-0.72) reflects uneven frequency across those 5 codes.","role":"feature","scope":"column","target":"county","treatment":"Cast to categorical and one-hot or target-encode before modelling."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.median","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate"],"model":"anthropic:claude-opus-4-7","narrative":"This is almost certainly a U.S. Census tract code stored as a numeric, with 1530 unique values across 2327 rows and no nulls. The distribution is severely right-skewed (skew 10.14, kurtosis 189.8) with a median of 30100 but a max of 990100, which is the expected pattern for tract codes rather than a true magnitude \u2014 values are categorical identifiers padded into a numeric range. The 63 flagged outliers (2.7%) are likely just tracts in higher-numbered county/state ranges, not data errors.","role":"identifier","scope":"column","target":"tract","treatment":"Treat as a categorical geographic code; cast to zero-padded string and join to tract-level reference data rather than using as a numeric feature."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.cardinality","stats.entropy_ratio","stats.top_rate","stats.top_value","top_values"],"model":"anthropic:claude-opus-4-7","narrative":"This column lists New York City borough/county names across 2327 rows, with exactly 5 unique values and no nulls. Distribution mirrors NYC borough sizes: Brooklyn (Kings) leads at 805 (34.6%), followed by Queens (725), Bronx (361), Manhattan (310), and Staten Island (126). Entropy ratio of 0.90 indicates a fairly balanced spread across the five categories with no extreme concentration.","role":"feature","scope":"column","target":"county_name","treatment":"One-hot or target-encode for modelling."},{"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":"A non-negative integer count named 'moderate_burden', spanning 0 to 1732 with a median of 159 and mean of 216 across 2327 rows, no nulls. The distribution is right-skewed (skew 1.93, kurtosis 6.05) with 86 outliers (3.7%) and 6.4% zeros, suggesting a long tail of high-burden cases over a typical mid-hundreds bulk.","role":"feature","scope":"column","target":"moderate_burden","treatment":"Apply a log1p transform before regression to tame the right-skew and outliers."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.std","stats.q1","stats.q3","stats.iqr","stats.skew","stats.kurtosis","stats.zero_rate","stats.n_outliers","stats.outlier_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Numeric count-like column 'severe_burden' with 2327 rows, no nulls, and 706 unique integer values ranging from 0 to 1918 (median 184, mean 253.18). The distribution is right-skewed (skew 1.60, kurtosis 3.44) with 6.27% zeros and 87 outliers (3.74%) above the upper whisker. The wide IQR (278) and std (236.60) relative to the median suggest substantial dispersion across units.","role":"feature","scope":"column","target":"severe_burden","treatment":"Apply a log1p transform before regression to tame the right skew and outliers."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.mean","stats.median","stats.min","stats.max","stats.q1","stats.q3","stats.iqr","stats.std","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","stats.zero_rate"],"model":"anthropic:claude-opus-4-7","narrative":"This is a percentage feature measuring the share of some population under moderate housing burden, ranging 0\u2013100 with mean 22.74 and median 21.8. The distribution is right-skewed (skew 1.51, kurtosis 6.70) with 59 outliers (2.65%) and a 4.38% null rate. About 2.1% of rows are exact zeros and the IQR is tight at 12.3, so the upper tail past q3=28.2 stretches all the way to 100.","role":"feature","scope":"column","target":"pct_moderate_burden","treatment":"Impute the ~4% nulls and consider a mild transform or winsorization to tame the right tail before modelling."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.mean","stats.median","stats.min","stats.max","stats.std","stats.iqr","stats.skew","stats.kurtosis","stats.outlier_rate","stats.zero_rate","stats.q1","stats.q3"],"model":"anthropic:claude-opus-4-7","narrative":"A percentage metric (0\u2013100 range) capturing the share of some population under severe burden, with a mean of 27.12 and median of 26.2 suggesting a fairly typical right-skewed distribution (skew 0.57). Spread is moderate (std 12.68, IQR 15.9) and only 1.35% of rows are flagged as outliers, though a max of 100.0 alongside a 1.98% zero rate hints at a few extreme records worth inspecting. Note the 4.38% null rate, which will need handling.","role":"feature","scope":"column","target":"pct_severe_burden","treatment":"Impute the 4.4% missing values and use as-is; mild skew does not require transformation."},{"confidence":"medium","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.q1","stats.q3","stats.iqr","stats.std","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","stats.zero_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Likely a count or dollar measure of rent-burdened households (or burden amount) per record, ranging from 0 to 3153 with a median of 358 and mean of 469.26. The distribution is right-skewed (skew 1.49, kurtosis 3.00) with 82 outliers (3.5%) and 4.7% exact zeros, so a long tail dominates the upper end.","role":"feature","scope":"column","target":"rent_burdened","treatment":"Apply a log1p transform before regression to tame the right skew and zero mass."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.std","stats.iqr","stats.q1","stats.q3","stats.skew","stats.kurtosis","stats.zero_rate","stats.n_outliers","stats.outlier_rate"],"model":"anthropic:claude-opus-4-7","narrative":"This is a numeric percentage indicating the share of rent-burdened households per record, ranging from 0 to 100 with a mean of 49.87 and median of 50.0. The distribution is nearly symmetric (skew -0.04) and reasonably tight around the middle (IQR 17.9, std 14.6), with 4.38% nulls and only 0.36% zeros. 62 outliers (2.79%) sit beyond the whiskers, but no severe tail or drift is evident.","role":"feature","scope":"column","target":"pct_rent_burdened","treatment":"Impute the ~4% nulls and use as-is; no transform needed given near-symmetric bounded percentage."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.median","stats.q1","stats.q3","stats.iqr","stats.mean","stats.std","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate"],"model":"anthropic:claude-opus-4-7","narrative":"This is a numeric feature for median gross rent, with 2327 non-null values and 1232 unique levels. The middle of the distribution looks plausible (median 1735, IQR 1441.5\u20132049, max 3501), but the minimum is -666666666 and the mean is -41539608.8 with std 161182638.7, indicating sentinel values masquerading as numbers and producing severe negative skew (-3.62) and 289 outliers (12.4%).","role":"feature","scope":"column","target":"median_gross_rent","treatment":"Replace the -666666666 sentinel with null before any modelling or aggregation."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.mean","stats.median","stats.q1","stats.q3","stats.iqr","stats.max","stats.skew","stats.kurtosis","stats.n_outliers","stats.outlier_rate","alerts"],"model":"anthropic:claude-opus-4-7","narrative":"Median household income in dollars per record, fully populated across 2,327 rows with 2,106 unique values and a sensible median of 76,833 and IQR of 49,117. The mean of -36,017,397 and minimum of -666,666,666 are sentinel-coded missing values masquerading as numbers, which drag skew to -3.94 and kurtosis to 13.53. Roughly 8.9% of rows (208) are flagged as outliers, almost certainly the same sentinel contamination.","role":"feature","scope":"column","target":"median_household_income","treatment":"Replace -666666666 sentinel with null, then consider log-transform or winsorisation before modelling."},{"confidence":"high","critiques":[],"evidence_keys":["min","max","mean","median","q1","q3","iqr","std","skew","kurtosis","n_outliers","outlier_rate","zero_rate","n","n_unique","null_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Counts of households per record, ranging from 0 to 8209 with a median of 1252 and mean of 1410.7. The distribution is right-skewed (skew 1.48, kurtosis 4.38) with 70 outliers (3.0%) on the high end, and 4.1% of rows are zero, which may indicate unpopulated or placeholder areas.","role":"feature","scope":"column","target":"total_households","treatment":"Log-transform or winsorize before modelling and decide whether zero-household rows should be filtered."},{"confidence":"medium","critiques":[],"evidence_keys":["n_unique","min","max","mean","median","skew","kurtosis","n_outliers","outlier_rate","zero_rate","null_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Despite the boolean-sounding name 'owner_occupied', this is a numeric count column with 1001 unique values ranging from 0 to 3052 and a mean of 464.6 \u2014 likely a tally of owner-occupied units per record (e.g., per tract or block). The distribution is right-skewed (skew 1.76, kurtosis 4.25) with 143 outliers (6.1%) and 7.2% zeros. No nulls are present.","role":"feature","scope":"column","target":"owner_occupied","treatment":"Log-transform (log1p to handle the 7% zeros) before modelling to tame the right skew."},{"confidence":"high","critiques":[],"evidence_keys":["min","max","mean","median","skew","kurtosis","n_outliers","outlier_rate","zero_rate","q1","q3","n_unique"],"model":"anthropic:claude-opus-4-7","narrative":"Counts of renter-occupied units per record, ranging from 0 to 8209 with a median of 726 and mean of 946. The distribution is right-skewed (skew 1.59, kurtosis 4.63) with 69 outliers (2.97%) and 4.38% zeros, consistent with area-level housing tallies rather than a per-household flag.","role":"feature","scope":"column","target":"renter_occupied","treatment":"log-transform or scale before regression to tame the right skew."},{"confidence":"high","critiques":[],"evidence_keys":["stats.min","stats.max","stats.mean","stats.median","stats.std","stats.iqr","stats.skew","stats.kurtosis","stats.zero_rate","null_rate","stats.n_outliers"],"model":"anthropic:claude-opus-4-7","narrative":"Percentage of owner-occupied housing per record, ranging the full 0-100 scale with a mean of 37.5 and median of 34.4. The distribution is wide (std 25.7, IQR 39.7) and slightly right-skewed (0.39) with negative kurtosis (-0.85), indicating a flat, near-uniform spread rather than a tight central mass. About 3.2% of rows are exactly zero and 4.1% are null, but no statistical outliers were flagged.","role":"feature","scope":"column","target":"pct_owner_occupied","treatment":"Use as-is as a bounded percentage feature; impute the 4% nulls with the median or a missingness flag."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.std","stats.iqr","stats.skew","stats.kurtosis","stats.n_outliers","stats.zero_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Numeric percentage of renter-occupied units, ranging the full 0\u2013100 with mean 62.5 and median 65.6, suggesting these records skew toward rental-heavy geographies. Spread is wide (std 25.7, IQR 39.7) and the distribution is mildly left-skewed (-0.39) and flat (kurtosis -0.85), so no outliers were flagged. About 4.1% of rows are null and only 0.27% are exact zeros, with 823 distinct values across 2,327 rows.","role":"feature","scope":"column","target":"pct_renter_occupied","treatment":"Use as-is as a bounded percentage feature; impute the 4.1% nulls before modelling."}],"providers":["anthropic:claude-opus-4-7"],"total_usage":{"completion_tokens":7444,"prompt_tokens":23474,"total_tokens":30918}},"language_counts":{},"meta":{"generated_at":"2026-05-01T17:39:37+00:00","mode":"full","row_count":2327,"sampled_rows":2327,"seed":42,"source":"/home/coolhand/html/datavis/data_trove/data/urban/nyc_housing/nyc_housing_metrics_merged.csv"},"notes":[],"saturn_version":"0.2.0","schema":{"NAME":"text","county":"numeric","county_name":"categorical","median_gross_rent":"numeric","median_household_income":"numeric","moderate_burden":"numeric","owner_occupied":"numeric","pct_moderate_burden":"numeric","pct_owner_occupied":"numeric","pct_rent_burdened":"numeric","pct_renter_occupied":"numeric","pct_severe_burden":"numeric","rent_30_to_34_9_pct":"numeric","rent_35_to_39_9_pct":"numeric","rent_40_to_49_9_pct":"numeric","rent_50_pct_or_more":"numeric","rent_burdened":"numeric","renter_occupied":"numeric","severe_burden":"numeric","state":"numeric","total_households":"numeric","total_renter_households":"numeric","tract":"numeric"}}