| Name | geodatasets JSON |
| Version |
2024.8.0
JSON |
| download |
| home_page | None |
| Summary | Spatial data examples |
| upload_time | 2024-08-30 14:35:31 |
| maintainer | geodatasets contributors |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | BSD 3-Clause |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# geodatasets
Fetch links or download and cache spatial data example files.
The `geodatasets` contains an API on top of a JSON with metadata of externally hosted
datasets containing geospatial information useful for illustrative and educational
purposes.
See the documentation at [geodatasets.readthedocs.io/](https://geodatasets.readthedocs.io/).
## Install
From PyPI:
```sh
pip install geodatasets
```
or using `conda` or `mamba` from conda-forge:
```sh
conda install geodatasets -c conda-forge
```
The development version can be installed using `pip` from GitHub.
```sh
pip install git+https://github.com/geopandas/geodatasets.git
```
## How to use
The package comes with a database of datasets. To see all:
```py
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']},
...
```
There is also a convenient top-level API. One to get only the URL:
```py
In [3]: geodatasets.get_url("geoda airbnb")
Out[3]: 'https://geodacenter.github.io/data-and-lab//data/airbnb.zip'
```
And one to get the local path. If the file is not available in the cache, it will be
downloaded first.
```py
In [4]: geodatasets.get_path('geoda airbnb')
Out[4]: '/Users/martin/Library/Caches/geodatasets/airbnb.zip'
```
You can also get all the details:
```py
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'}
```
Or using the name query:
```py
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'}
```
The whole structure `Bunch` class is based on a dictionary and can be flattened. If you want
to see all available datasets, you can use:
```py
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'])
```
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"description": "# geodatasets\n\nFetch links or download and cache spatial data example files.\n\nThe `geodatasets` contains an API on top of a JSON with metadata of externally hosted\ndatasets containing geospatial information useful for illustrative and educational\npurposes.\n\nSee the documentation at [geodatasets.readthedocs.io/](https://geodatasets.readthedocs.io/).\n\n## Install\n\nFrom PyPI:\n\n```sh\npip install geodatasets\n```\n\nor using `conda` or `mamba` from conda-forge:\n\n```sh\nconda install geodatasets -c conda-forge\n```\n\nThe development version can be installed using `pip` from GitHub.\n\n```sh\npip install git+https://github.com/geopandas/geodatasets.git\n```\n\n## How to use\n\nThe package comes with a database of datasets. To see all:\n\n```py\nIn [1]: import geodatasets\n\nIn [2]: geodatasets.data\nOut[2]:\n{'geoda': {'airbnb': {'url': 'https://geodacenter.github.io/data-and-lab//data/airbnb.zip',\n 'license': 'NA',\n 'attribution': 'Center for Spatial Data Science, University of Chicago',\n 'name': 'geoda.airbnb',\n 'description': 'Airbnb rentals, socioeconomics, and crime in Chicago',\n 'geometry_type': 'Polygon',\n 'nrows': 77,\n 'ncols': 21,\n 'details': 'https://geodacenter.github.io/data-and-lab//airbnb/',\n 'hash': 'a2ab1e3f938226d287dd76cde18c00e2d3a260640dd826da7131827d9e76c824',\n 'filename': 'airbnb.zip'},\n 'atlanta': {'url': 'https://geodacenter.github.io/data-and-lab//data/atlanta_hom.zip',\n 'license': 'NA',\n 'attribution': 'Center for Spatial Data Science, University of Chicago',\n 'name': 'geoda.atlanta',\n 'description': 'Atlanta, GA region homicide counts and rates',\n 'geometry_type': 'Polygon',\n 'nrows': 90,\n 'ncols': 24,\n 'details': 'https://geodacenter.github.io/data-and-lab//atlanta_old/',\n 'hash': 'a33a76e12168fe84361e60c88a9df4856730487305846c559715c89b1a2b5e09',\n 'filename': 'atlanta_hom.zip',\n 'members': ['atlanta_hom/atl_hom.geojson']},\n ...\n```\n\nThere is also a convenient top-level API. One to get only the URL:\n\n```py\nIn [3]: geodatasets.get_url(\"geoda airbnb\")\nOut[3]: 'https://geodacenter.github.io/data-and-lab//data/airbnb.zip'\n```\n\nAnd one to get the local path. If the file is not available in the cache, it will be\ndownloaded first.\n\n```py\nIn [4]: geodatasets.get_path('geoda airbnb')\nOut[4]: '/Users/martin/Library/Caches/geodatasets/airbnb.zip'\n```\n\nYou can also get all the details:\n\n```py\nIn [5]: geodatasets.data.geoda.airbnb\nOut[5]:\n{'url': 'https://geodacenter.github.io/data-and-lab//data/airbnb.zip',\n 'license': 'NA',\n 'attribution': 'Center for Spatial Data Science, University of Chicago',\n 'name': 'geoda.airbnb',\n 'description': 'Airbnb rentals, socioeconomics, and crime in Chicago',\n 'geometry_type': 'Polygon',\n 'nrows': 77,\n 'ncols': 21,\n 'details': 'https://geodacenter.github.io/data-and-lab//airbnb/',\n 'hash': 'a2ab1e3f938226d287dd76cde18c00e2d3a260640dd826da7131827d9e76c824',\n 'filename': 'airbnb.zip'}\n```\n\nOr using the name query:\n\n```py\nIn [6]: geodatasets.data.query_name('geoda airbnb')\nOut[6]:\n{'url': 'https://geodacenter.github.io/data-and-lab//data/airbnb.zip',\n 'license': 'NA',\n 'attribution': 'Center for Spatial Data Science, University of Chicago',\n 'name': 'geoda.airbnb',\n 'description': 'Airbnb rentals, socioeconomics, and crime in Chicago',\n 'geometry_type': 'Polygon',\n 'nrows': 77,\n 'ncols': 21,\n 'details': 'https://geodacenter.github.io/data-and-lab//airbnb/',\n 'hash': 'a2ab1e3f938226d287dd76cde18c00e2d3a260640dd826da7131827d9e76c824',\n 'filename': 'airbnb.zip'}\n```\n\nThe whole structure `Bunch` class is based on a dictionary and can be flattened. If you want\nto see all available datasets, you can use:\n\n```py\nIn [7]: geodatasets.data.flatten().keys()\nOut[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'])\n```\n",
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