geodatasets

https://geodatasets.readthedocs.io/
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geodatasets is a Python package that provides an API to fetch, download, and cache spatial data...
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### Install geodatasets from PyPI

Source: https://geodatasets.readthedocs.io/

Install the geodatasets library using pip. This is the standard method for installing Python packages.

```bash
pip install geodatasets

```

--------------------------------

### Install geodatasets Development Version from GitHub

Source: https://geodatasets.readthedocs.io/

Install the latest development version of geodatasets directly from its GitHub repository using pip. This is useful for testing the newest features or contributing to the project.

```bash
pip install git+https://github.com/geopandas/geodatasets.git

```

--------------------------------

### Install geodatasets using Conda

Source: https://geodatasets.readthedocs.io/

Install the geodatasets library from the conda-forge channel using conda or mamba. This is recommended for users within the Anaconda ecosystem.

```bash
conda install geodatasets -c conda-forge

```

--------------------------------

### Get Dataset URL

Source: https://geodatasets.readthedocs.io/

Use the `get_url` function to retrieve the direct download URL for a specific dataset. Provide the dataset name as a string.

```python
In [3]: geodatasets.get_url("geoda airbnb")
Out[3]: 'https://geodacenter.github.io/data-and-lab//data/airbnb.zip'


```

--------------------------------

### Get Dataset Local Path

Source: https://geodatasets.readthedocs.io/

Use the `get_path` function to obtain the local file path for a dataset. If the dataset is not cached, it will be downloaded automatically.

```python
In [4]: geodatasets.get_path('geoda airbnb')
Out[4]: '/Users/martin/Library/Caches/geodatasets/airbnb.zip'


```

--------------------------------

### Import and List Datasets

Source: https://geodatasets.readthedocs.io/

Import the geodatasets library and access the 'data' attribute to view available datasets. This provides a dictionary-like structure containing metadata for each dataset.

```python
In [1]: import geodatasets

In [2]: geodatasets.data
Out[2]:
{'geoda': {'airbnb': {'url': 'https://geodacenter.github.io/data-and-lab//data/airbnb.zip',
   'license': 'NA',
   'attribution': 'Center for Spatial Data Science, University of Chicago',
   'name': 'geoda.airbnb',
   'description': 'Airbnb rentals, socioeconomics, and crime in Chicago',
   'geometry_type': 'Polygon',
   'nrows': 77,
   'ncols': 21,
   'details': 'https://geodacenter.github.io/data-and-lab//airbnb/',
   'hash': 'a2ab1e3f938226d287dd76cde18c00e2d3a260640dd826da7131827d9e76c824',
   'filename': 'airbnb.zip'},
  'atlanta': {'url': 'https://geodacenter.github.io/data-and-lab//data/atlanta_hom.zip',
   'license': 'NA',
   'attribution': 'Center for Spatial Data Science, University of Chicago',
   'name': 'geoda.atlanta',
   'description': 'Atlanta, GA region homicide counts and rates',
   'geometry_type': 'Polygon',
   'nrows': 90,
   'ncols': 24,
   'details': 'https://geodacenter.github.io/data-and-lab//atlanta_old/',
   'hash': 'a33a76e12168fe84361e60c88a9df4856730487305846c559715c89b1a2b5e09',
   'filename': 'atlanta_hom.zip',
   'members': ['atlanta_hom/atl_hom.geojson']},
   ...


```

--------------------------------

### List All Available Dataset Keys

Source: https://geodatasets.readthedocs.io/

Flatten the dataset structure and retrieve a list of all available dataset keys using the `flatten().keys()` method. This provides a comprehensive overview of all datasets managed by the library.

```python
In [7]: geodatasets.data.flatten().keys()
Out[7]: dict_keys(['geoda.airbnb', 'geoda.atlanta', 'geoda.cars', 'geoda.charleston1', 'geoda.charleston2', 'geoda.chicago_health', 'geoda.chicago_commpop', 'geoda.chile_labor', 'geoda.cincinnati', 'geoda.cleveland', 'geoda.columbus', 'geoda.grid100', 'geoda.groceries', 'geoda.guerry', 'geoda.health', 'geoda.health_indicators', 'geoda.hickory1', 'geoda.hickory2', 'geoda.home_sales', 'geoda.houston', 'geoda.juvenile', 'geoda.lansing1', 'geoda.lansing2', 'geoda.lasrosas', 'geoda.liquor_stores', 'geoda.malaria', 'geoda.milwaukee1', 'geoda.milwaukee2', 'geoda.ncovr', 'geoda.natregimes', 'geoda.ndvi', 'geoda.nepal', 'geoda.nyc', 'geoda.nyc_earnings', 'geoda.nyc_education', 'geoda.nyc_neighborhoods', 'geoda.orlando1', 'geoda.orlando2', 'geoda.oz9799', 'geoda.phoenix_acs', 'geoda.police', 'geoda.sacramento1', 'geoda.sacramento2', 'geoda.savannah1', 'geoda.savannah2', 'geoda.seattle1', 'geoda.seattle2', 'geoda.sids', 'geoda.sids2', 'geoda.south', 'geoda.spirals', 'geoda.stlouis', 'geoda.tampa1', 'geoda.us_sdoh', 'ny.bb', 'eea.large_rivers', 'naturalearth.land'])


```

--------------------------------

### Query Dataset by Name

Source: https://geodatasets.readthedocs.io/

Use the `query_name` method to retrieve dataset details using its name. This is an alternative way to access dataset metadata.

```python
In [6]: geodatasets.data.query_name('geoda airbnb')
Out[6]:
{'url': 'https://geodacenter.github.io/data-and-lab//data/airbnb.zip',
 'license': 'NA',
 'attribution': 'Center for Spatial Data Science, University of Chicago',
 'name': 'geoda.airbnb',
 'description': 'Airbnb rentals, socioeconomics, and crime in Chicago',
 'geometry_type': 'Polygon',
 'nrows': 77,
 'ncols': 21,
 'details': 'https://geodacenter.github.io/data-and-lab//airbnb/',
 'hash': 'a2ab1e3f938226d287dd76cde18c00e2d3a260640dd826da7131827d9e76c824',
 'filename': 'airbnb.zip'}


```

--------------------------------

### Access Detailed Dataset Metadata

Source: https://geodatasets.readthedocs.io/

Access detailed metadata for a specific dataset by directly referencing it through the `geodatasets.data` object. This returns a dictionary containing all available information about the dataset.

```python
In [5]: geodatasets.data.geoda.airbnb
Out[5]:
{'url': 'https://geodacenter.github.io/data-and-lab//data/airbnb.zip',
 'license': 'NA',
 'attribution': 'Center for Spatial Data Science, University of Chicago',
 'name': 'geoda.airbnb',
 'description': 'Airbnb rentals, socioeconomics, and crime in Chicago',
 'geometry_type': 'Polygon',
 'nrows': 77,
 'ncols': 21,
 'details': 'https://geodacenter.github.io/data-and-lab//airbnb/',
 'hash': 'a2ab1e3f938226d287dd76cde18c00e2d3a260640dd826da7131827d9e76c824',
 'filename': 'airbnb.zip'}


```
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