data trove food desert states summary

This column contains US state names, with all 51 entries being unique (cardinality = 51, n = 51), consistent with a full list of US states plus Washington D.C. or a territory. Entropy ratio is exactly 1.0, meaning perfect uniformity — every value appears exactly once (top_rate = 0.0196, or 1/51). The 'long_tail' alert is technically correct but misleading here: the distribution is not skewed, it is perfectly flat. Treatment: Use as a categorical label or join key for state-level lookups; one-hot encode or ordinal-map if used as a model feature. high · anthropic:default

n

51

nulls

0 (0.0%)

unique

51

top_value

New York

top_rate

0.01961

cardinality

51

entropy

5.672

entropy_ratio

1

This column contains two-letter US state abbreviations, with exactly 51 unique values across 51 rows — covering all 50 states plus one additional entry (likely Washington D.C. or a territory). Every value appears exactly once (top_rate = 0.0196), yielding a perfect entropy ratio of 1.0, meaning this is a fully uniform identifier with zero redundancy. The 'long_tail' alert is a statistical artifact of perfect uniformity, not a genuine concern here. Treatment: Use as a join key or primary identifier for state-level lookups; one-hot encode or map to region groupings if used as a feature. high · anthropic:default

n

51

nulls

0 (0.0%)

unique

51

top_value

NY

top_rate

0.01961

cardinality

51

entropy

5.672

entropy_ratio

1

This column likely represents population counts for 51 distinct geographic or administrative units (e.g., U.S. states or territories), given exactly 51 fully unique, non-null integer values. The distribution is heavily right-skewed (skew = 2.58, kurtosis = 7.61), with a median of 4,372 far below the mean of 6,338 and a maximum of 38,643 — suggesting a small number of very large-population entities pulling the tail; 4 outliers (~7.8% of rows) drive this effect. The std of 7,243 exceeds the mean, confirming high dispersion relative to the central tendency. Treatment: Log-transform before regression or distance-based modelling to reduce skew and outlier influence. medium · anthropic:default

n

51

nulls

0 (0.0%)

unique

51

min

564

max

38,643

mean

6338

median

4,372

std

7243

q1

1770

q3

7,285

iqr

5514

skew

2.583

kurtosis

7.608

n_outliers

4

outlier_rate

0.07843

zero_rate

0

This column likely represents a population count associated with desert regions (e.g., population living in desert areas, possibly by U.S. state or similar unit given n=51). The distribution is severely right-skewed (skew=4.73, kurtosis=25.51): the median is just 21.0 while the mean is 38.27 and the max reaches 449.0, indicating a small number of entities dominate desert population totals. With 6 outliers (≈11.8% of rows) and a standard deviation of 67.39 against a median of 21.0, those extreme values will heavily distort any linear model trained on raw values. Treatment: Log-transform (or apply sqrt) before modelling to reduce skew; investigate and cap or flag the 6 outliers separately. medium · anthropic:default

n

51

nulls

0 (0.0%)

unique

34

min

1

max

449

mean

38.27

median

21

std

67.39

q1

6

q3

35.5

iqr

29.5

skew

4.734

kurtosis

25.51

n_outliers

6

outlier_rate

0.1176

zero_rate

0

This column likely represents a count of people living in poverty, measured per U.S. state (n=51, matching the 50 states plus DC). The distribution is heavily right-skewed (skew=2.53, kurtosis=6.80), with a median of 548 but a mean of 794 and a maximum of 4685, indicating a small number of high-population states pull the mean well above the typical value. Four outliers (~7.8% of rows) are flagged, likely corresponding to the most populous states with the largest absolute poverty populations. The near-uniqueness (49 of 51 distinct values) suggests this is a genuine count variable, not a derived category. Treatment: Log-transform before regression to reduce skew and mitigate outlier influence. high · anthropic:default

n

51

nulls

0 (0.0%)

unique

49

min

60

max

4,685

mean

794

median

548

std

932.9

q1

198

q3

860.5

iqr

662.5

skew

2.526

kurtosis

6.8

n_outliers

4

outlier_rate

0.07843

zero_rate

0

This column likely represents a count of households or individuals without access to a vehicle, aggregated at some geographic unit (e.g., census tract or neighbourhood) across 51 observations. The distribution is severely right-skewed (skew = 4.41, kurtosis = 22.57), with a median of 115 but a mean pulled to 204.9 by a long upper tail reaching 2202. Six outliers (≈11.8% of rows) are driving this extreme shape, suggesting a small number of densely populated or car-deprived areas dominate the upper end while most units cluster between 40 and 203 (IQR = 163). Treatment: Log-transform or apply a robust scaler before modelling; investigate the 6 outlier units for data quality or genuine extreme values. medium · anthropic:default

n

51

nulls

0 (0.0%)

unique

45

min

8

max

2,202

mean

204.9

median

115

std

337.4

q1

40

q3

203

iqr

163

skew

4.408

kurtosis

22.57

n_outliers

6

outlier_rate

0.1176

zero_rate

0

This column represents poverty rate (likely percentage of population below the poverty line) across 51 observations — almost certainly U.S. states plus DC. Values range from 7.33 to 19.2 with a mean of 12.35 and median of 11.91, indicating a modest right skew (skew=0.75) consistent with a handful of higher-poverty states pulling the tail. Three outliers (~5.9% of rows) at the upper end are flagged, likely representing the highest-poverty states; the near-zero kurtosis (0.20) suggests the distribution is otherwise fairly normal. Treatment: Use as-is or apply mild log-transform to reduce right skew before regression; investigate 3 upper outliers for leverage effects. high · anthropic:default

n

51

nulls

0 (0.0%)

unique

50

min

7.33

max

19.2

mean

12.35

median

11.91

std

2.632

q1

10.46

q3

13.57

iqr

3.11

skew

0.753

kurtosis

0.1951

n_outliers

3

outlier_rate

0.05882

zero_rate

0

This column represents the percentage of households without a vehicle, likely a census or survey-derived socioeconomic indicator across 51 geographic units (e.g., states or counties). The distribution is heavily right-skewed (skew=4.53, kurtosis=21.72) with the bulk of values tightly clustered between Q1=2.15% and Q3=3.05%, yet 3 outliers pull the max to 17.37% — more than 5× the median of 2.45%. That extreme upper tail almost certainly reflects a high-density urban area (e.g., New York City) where car-free households are far more common than in typical units. Treatment: Cap or Winsorize at the 95th percentile before modelling, or log-transform to compress the extreme upper tail. high · anthropic:default

n

51

nulls

0 (0.0%)

unique

45

min

1.29

max

17.37

mean

3.092

median

2.45

std

2.484

q1

2.15

q3

3.05

iqr

0.9

skew

4.533

kurtosis

21.72

n_outliers

3

outlier_rate

0.05882

zero_rate

0

This column most likely represents the number of counties per U.S. state (plus D.C.), matching the dataset's 51 rows exactly. The mean of ~62 and median of 62 are consistent with typical state county counts, while the maximum of 254 is almost certainly Texas (which has 254 counties). The distribution is right-skewed (skew 1.44) with high kurtosis (3.91), driven by that single outlier — Texas — which sits far above the rest of the distribution. Treatment: Use as-is for regression/analysis; consider log-transform to reduce skew caused by the Texas outlier. high · anthropic:default

n

51

nulls

0 (0.0%)

unique

46

min

1

max

254

mean

61.65

median

62

std

46.73

q1

23.5

q3

87.5

iqr

64

skew

1.442

kurtosis

3.907

n_outliers

1

outlier_rate

0.01961

zero_rate

0

This column contains latitude coordinates, almost certainly representing the 50 US states plus Washington D.C. (n=51, all unique). The mean of 39.57 and median of 39.55 are tightly aligned, indicating near-symmetric distribution centered on the mid-continental US, though a kurtosis of 3.94 flags heavier tails than normal — driven by the 2 outliers likely corresponding to Alaska (max 64.2) and Hawaii (min 19.9). Treatment: Use as-is or pair with a longitude column for geospatial modelling; consider flagging Alaska and Hawaii as geographic outliers if contiguous-US analysis is intended. high · anthropic:default

n

51

nulls

0 (0.0%)

unique

51

min

19.9

max

64.2

mean

39.57

median

39.55

std

6.418

q1

35.64

q3

43.13

iqr

7.495

skew

0.4074

kurtosis

3.94

n_outliers

2

outlier_rate

0.03922

zero_rate

0

This column contains longitude coordinates, almost certainly representing geographic locations of 51 entities (e.g., US states or cities), all with negative values indicating the Western Hemisphere. The range spans -155.58 to -69.45, consistent with continental US plus Hawaii (≈-155°), and the left skew (skew = -1.27) reflects Hawaii and Alaska pulling the distribution westward. Three duplicate longitude values exist (51 records, 48 unique), and 2 outliers (~3.9%) likely correspond to Hawaii and/or Alaska. Treatment: Use as-is for spatial analysis or pair with latitude for geographic modelling; consider flagging the 2 outlier values (Hawaii/Alaska) if contiguous-US-only analysis is needed. high · anthropic:default

n

51

nulls

0 (0.0%)

unique

48

min

-155.6

max

-69.45

mean

-93.36

median

-89.4

std

19.13

q1

-103.4

q3

-78.84

iqr

24.55

skew

-1.274

kurtosis

1.845

n_outliers

2

outlier_rate

0.03922

zero_rate

0