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--- |
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license: mit |
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task_categories: |
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- text-classification |
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- feature-extraction |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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tags: |
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- geo |
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- geography |
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- US |
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- Location |
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- Geospatial |
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- HNM |
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- Numerical |
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- catageorical |
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- demographic |
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- social |
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- economic |
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--- |
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# US_GeoSpatial_dataset_by_HNM |
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## Dataset Description |
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This dataset contains 56 records with 16 features. |
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### Dataset Summary |
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| Metric | Value | |
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|--------|-------| |
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| Total Rows | 56 | |
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| Total Columns | 16 | |
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| Numeric Columns | 10 | |
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| Categorical Columns | 6 | |
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| Missing Values | 0 (0.00%) | |
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| Duplicate Rows | 0 | |
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| Memory Usage | 19.51 MB | |
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## Dataset Structure |
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### Data Fields |
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| Column | Type | Sample/Range | Unique Values | Missing % | |
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|--------|------|--------------|---------------|----------| |
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| `geo_id` | int64 | Range: [1.00, 78.00] | 56 | 0.0% | |
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| `region_code` | int64 | Range: [1.00, 9.00] | 5 | 0.0% | |
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| `division_code` | int64 | Range: [0.00, 9.00] | 10 | 0.0% | |
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| `state_fips_code` | int64 | Range: [1.00, 78.00] | 56 | 0.0% | |
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| `state_gnis_code` | int64 | Range: [68085.00, 1802710.00] | 56 | 0.0% | |
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| `state` | object | Example: 'GU' | 56 | 0.0% | |
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| `state_name` | object | Example: 'Guam' | 56 | 0.0% | |
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| `lsad_code` | int64 | Range: [0.00, 0.00] | 1 | 0.0% | |
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| `mtfcc_feature_class_code` | object | Example: 'G4000' | 1 | 0.0% | |
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| `functional_status` | object | Example: 'A' | 1 | 0.0% | |
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| `area_land_meters` | int64 | Range: [158340389.00, 1478927050067.00] | 56 | 0.0% | |
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| `area_water_meters` | int64 | Range: [18687196.00, 245394222619.00] | 56 | 0.0% | |
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| `int_point_lat` | float64 | Range: [-14.27, 63.35] | 56 | 0.0% | |
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| `int_point_lon` | float64 | Range: [-170.67, 145.60] | 56 | 0.0% | |
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| `int_point_geom` | object | Example: 'POINT(144.7719021 13.4417451)' | 56 | 0.0% | |
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| `state_geom` | object | Example: 'POLYGON((144.563426 13.448065, 144.56355 13.445248' | 56 | 0.0% | |
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### Data Splits |
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This dataset contains a single split with all 56 examples. |
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This is the **US Census Bureau Geographic Data** - specifically the **state-level geographic identifiers and measurements** dataset. |
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## What This Dataset Contains |
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| Column | Meaning | |
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|--------|---------| |
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| `geo_id`, `state_fips_code` | Federal Information Processing Standard codes for US states/territories | |
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| `region_code`, `division_code` | Census Bureau regional classifications (4 regions, 9 divisions) | |
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| `state_gnis_code` | Geographic Names Information System identifiers | |
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| `area_land_meters`, `area_water_meters` | Land and water area measurements | |
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| `int_point_lat`, `int_point_lon` | Internal centroid coordinates (center point of each state) | |
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## Practical Use Cases |
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**1. Geospatial Analysis & Mapping** |
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- Creating choropleth maps of US states |
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- Calculating distances between state centers |
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- Building location-based services that need state boundaries |
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**2. Data Enrichment & Joins** |
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- Joining with other datasets (population, economic, health data) using FIPS codes |
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- Standardizing geographic identifiers across multiple data sources |
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- Linking Census data with external APIs |
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**3. Regional Analytics** |
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- Comparing metrics across Census regions/divisions (Northeast, Midwest, South, West) |
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- Grouping states for regional market analysis |
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- Understanding geographic distribution patterns |
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**4. Environmental & Land Use Studies** |
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- Calculating land-to-water ratios by state |
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- Analyzing state sizes for resource allocation models |
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- Comparing population density (when combined with population data) |
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**5. Machine Learning Features** |
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- Geographic features for predictive models |
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- Clustering states by location or size |
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- Spatial autocorrelation analysis |
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This dataset is essentially a **foundational geographic reference dataset** - most valuable when joined with demographic, economic, or social datasets for spatial analysis. |
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## Usage |
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### Loading the Dataset |
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```python |
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from datasets import load_dataset |
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# Load from Hugging Face Hub |
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dataset = load_dataset("Omarrran/US_GeoSpatial_Dataset_by_HNM") |
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# Access the data |
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train_data = dataset['train'] |
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print(train_data[0]) |
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``` |
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### Loading as Pandas DataFrame |
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```python |
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import pandas as pd |
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from datasets import load_dataset |
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dataset = load_dataset("Omarrran/US_GeoSpatial_Dataset_by_HNM") |
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df = dataset['train'].to_pandas() |
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``` |
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## Statistical Summary |
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### Numeric Columns |
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``` |
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geo_id region_code division_code state_fips_code state_gnis_code lsad_code area_land_meters area_water_meters int_point_lat int_point_lon |
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count 56.000000 56.000000 56.000000 56.000000 5.600000e+01 56.0 5.600000e+01 5.600000e+01 56.000000 56.000000 |
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mean 32.535714 3.232143 4.660714 32.535714 1.522958e+06 0.0 1.635888e+11 1.245427e+10 36.944973 -85.299146 |
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std 19.075891 2.080132 2.830206 19.075891 4.648599e+05 0.0 2.173973e+11 3.503733e+10 11.055418 49.717199 |
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min 1.000000 1.000000 0.000000 1.000000 6.808500e+04 0.0 1.583404e+08 1.868720e+07 -14.267159 -170.668267 |
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25% 17.750000 2.000000 2.750000 17.750000 1.423460e+06 0.0 2.483234e+10 1.502762e+09 34.376997 -101.722698 |
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50% 31.500000 3.000000 5.000000 31.500000 1.779784e+06 0.0 1.285501e+11 3.706434e+09 38.996207 -87.998035 |
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75% 46.250000 4.000000 7.000000 46.250000 1.779800e+06 0.0 2.007729e+11 8.964466e+09 42.932463 -76.931011 |
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max 78.000000 9.000000 9.000000 78.000000 1.802710e+06 0.0 1.478927e+12 2.453942e+11 63.347356 145.601021 |
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``` |
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## Additional Information |
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### Dataset Creation |
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- **Source**: /content/us_state_boundaries_56_20251126_191744.csv |
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- **Created**: 2025-11-28 |
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- **Format**: CSV |
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### Licensing Information |
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This dataset is released under the MIT License. |
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### Citation Information |
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```bibtex |
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@dataset{us_geospatial_dataset_by_hnm}, |
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title = {US_GeoSpatial_dataset_by_HNM}, |
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year = {2025}, |
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Author = {Haq Nawaz Malik} |
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publisher = {Hugging Face}, |
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Link = {https://huggingface.co/datasets/Omarrran/US_GeoSpatial_Dataset_by_HNM} |
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} |
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``` |
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### Contributions |
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Thanks to the contributors who helped in creating this dataset. |
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--- |
