Datasets for use with libpysal¶

As of version 4.2, libpysal has refactored the examples package to:

reduce the size of the source installation
allow the use of remote datasets from the Center for Spatial Data Science at the Unversity of Chicago, and other remotes

This notebook highlights the new functionality

Backwards compatibility is maintained¶

If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work.

For example:

from libpysal.examples import get_path

get_path("mexicojoin.dbf")

'/home/runner/micromamba/envs/docs/lib/python3.14/site-packages/libpysal/examples/mexico/mexicojoin.dbf'

An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a builtin dataset.

import libpysal

dbf = libpysal.io.open(get_path("mexicojoin.dbf"))

dbf.header

['POLY_ID',
 'AREA',
 'CODE',
 'NAME',
 'PERIMETER',
 'ACRES',
 'HECTARES',
 'PCGDP1940',
 'PCGDP1950',
 'PCGDP1960',
 'PCGDP1970',
 'PCGDP1980',
 'PCGDP1990',
 'PCGDP2000',
 'HANSON03',
 'HANSON98',
 'ESQUIVEL99',
 'INEGI',
 'INEGI2',
 'MAXP',
 'GR4000',
 'GR5000',
 'GR6000',
 'GR7000',
 'GR8000',
 'GR9000',
 'LPCGDP40',
 'LPCGDP50',
 'LPCGDP60',
 'LPCGDP70',
 'LPCGDP80',
 'LPCGDP90',
 'LPCGDP00',
 'TEST']

The function available is also available but has been updated to return a Pandas DataFrame. In addition to the builtin datasets, available will report on what datasets are available, either as builtin or remotes.

from libpysal.examples import available

df = available()

df.shape

(99, 3)

libpysal.examples.summary()

99 datasets available, 27 installed, 72 remote.

We see that there are 98 total datasets available for use with PySAL. On an initial install (i.e., examples has not been used yet), 27 of these are builtin datasets and 71 are remote. The latter can be downloaded and installed.

Downloading Remote Datasets¶

df.head()

	Name	Description	Installed
0	10740	Albuquerque, New Mexico, Census 2000 Tract Dat...	True
1	AirBnB	Airbnb rentals, socioeconomics, and crime in C...	False
2	Atlanta	Atlanta, GA region homicide counts and rates	False
3	Baltimore	Baltimore house sales prices and hedonics	False
4	Bostonhsg	Boston housing and neighborhood data	False

The remote AirBnB can be installed by calling load_example:

airbnb = libpysal.examples.load_example("AirBnB")

Downloading AirBnB to /home/runner/.local/share/pysal/AirBnB

libpysal.examples.summary()

99 datasets available, 28 installed, 71 remote.

And we see that the number of remotes as declined by one and the number of installed has increased by 1.

Trying to load an example that doesn’t exist will return None and alert the user:

libpysal.examples.load_example("dataset42")

Example not available: dataset42

Getting remote urls¶

If the url, rather than the dataset, is needed this can be obtained on a remote with get_url. As the Baltimore dataset has not yet been downloaded in this example, we can grab it’s url:

balt_url = libpysal.examples.get_url("Baltimore")
balt_url

'https://geodacenter.github.io/data-and-lab//data/baltimore.zip'

Explaining a dataset¶

libpysal.examples.explain("taz")

taz
===

Dataset used for regionalization
--------------------------------

* taz.dbf: attribute data. (k=14)
* taz.shp: Polygon shapefile. (n=4109)
* taz.shx: spatial index.

taz = libpysal.examples.load_example("taz")

Downloading taz to /home/runner/.local/share/pysal/taz

taz.get_file_list()

['/home/runner/.local/share/pysal/taz/taz-master/taz.shp',
 '/home/runner/.local/share/pysal/taz/taz-master/taz.shx',
 '/home/runner/.local/share/pysal/taz/taz-master/taz.dbf',
 '/home/runner/.local/share/pysal/taz/taz-master/README.md']

libpysal.examples.explain("Baltimore")

balt = libpysal.examples.load_example("Baltimore")

Downloading Baltimore to /home/runner/.local/share/pysal/Baltimore

libpysal.examples.available()

	Name	Description	Installed
0	10740	Albuquerque, New Mexico, Census 2000 Tract Dat...	True
1	AirBnB	Airbnb rentals, socioeconomics, and crime in C...	True
2	Atlanta	Atlanta, GA region homicide counts and rates	False
3	Baltimore	Baltimore house sales prices and hedonics	True
4	Bostonhsg	Boston housing and neighborhood data	False
...	...	...	...
94	taz	Traffic Analysis Zones in So. California	True
95	tokyo	Tokyo Mortality data	True
96	us_income	Per-capita income for the lower 48 US states 1...	True
97	virginia	Virginia counties shapefile	True
98	wmat	Datasets used for spatial weights testing	True

99 rows × 3 columns

Working with an example dataset¶

explain will render maps for an example if available

from libpysal.examples import explain

explain("Tampa1")

from libpysal.examples import load_example

tampa1 = load_example("Tampa1")

Downloading Tampa1 to /home/runner/.local/share/pysal/Tampa1

tampa1.installed

True

tampa1.get_file_list()

['/home/runner/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/._TampaMSA',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.xlsx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.gpkg',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.prj',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbn',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mid',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shp',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/gdb',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.gpkg',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mid',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.prj',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shp',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.geojson',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.kml',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.xlsx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sqlite',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.dbf',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.kml',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mif',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.dbf',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbn',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mif',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sqlite',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.geojson']

tampa_counties_shp = tampa1.load("tampa_counties.shp")

tampa_counties_shp

<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7f76621e4ec0>

import geopandas

tampa_df = geopandas.read_file(tampa1.get_path("tampa_counties.shp"))

%matplotlib inline
tampa_df.plot()

<Axes: xlabel='Geodetic longitude [degree]', ylabel='Geodetic latitude [degree]'>

../../_images/82b08ea4f28da6d3f6ae03d60412305686b7fa031d76f4ae58d307de84cbb5d9.png

Other Remotes¶

In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories.

libpysal.examples.explain("Rio Grande do Sul")

Rio_Grande_do_Sul
======================

Cities of the Brazilian State of Rio Grande do Sul
-------------------------------------------------------

* 43MUE250GC_SIR.dbf: attribute data (k=2)
* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)
* 43MUE250GC_SIR.shx: spatial index
* 43MUE250GC_SIR.cpg: encoding file 
* 43MUE250GC_SIR.prj: projection information 
* map_RS_BR.dbf: attribute data (k=3)
* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)
* map_RS_BR.prj: projection information
* map_RS_BR.shx: spatial index



Source: Renan Xavier Cortes 
Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495

Note that the explain function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality.

rio = libpysal.examples.load_example("Rio Grande do Sul")

Downloading Rio Grande do Sul to /home/runner/.local/share/pysal/Rio_Grande_do_Sul

libpysal.examples.remote_datasets.datasets  # a listing of all remotes

{'AirBnB': <libpysal.examples.base.Example at 0x7f7664f416a0>,
 'Atlanta': <libpysal.examples.base.Example at 0x7f7664ef2850>,
 'Baltimore': <libpysal.examples.base.Example at 0x7f7664ef2ad0>,
 'Bostonhsg': <libpysal.examples.base.Example at 0x7f76650a7a80>,
 'Buenosaires': <libpysal.examples.base.Example at 0x7f76650a7bb0>,
 'Charleston1': <libpysal.examples.base.Example at 0x7f7664f3bdf0>,
 'Charleston2': <libpysal.examples.base.Example at 0x7f7664e0c050>,
 'Chicago Health': <libpysal.examples.base.Example at 0x7f76654f2d50>,
 'Chicago commpop': <libpysal.examples.base.Example at 0x7f7664f9fa50>,
 'Chicago parcels': <libpysal.examples.base.Example at 0x7f7664fa1c70>,
 'Chile Labor': <libpysal.examples.base.Example at 0x7f7664fa1d60>,
 'Chile Migration': <libpysal.examples.base.Example at 0x7f7664fa6190>,
 'Cincinnati': <libpysal.examples.base.Example at 0x7f7664fa6350>,
 'Cleveland': <libpysal.examples.base.Example at 0x7f7665500870>,
 'Columbus': <libpysal.examples.base.Example at 0x7f7664f94590>,
 'Elections': <libpysal.examples.base.Example at 0x7f7664f944d0>,
 'Grid100': <libpysal.examples.base.Example at 0x7f76d0b63070>,
 'Groceries': <libpysal.examples.base.Example at 0x7f76666131d0>,
 'Guerry': <libpysal.examples.base.Example at 0x7f7664f52f30>,
 'Health+': <libpysal.examples.base.Example at 0x7f7664f53110>,
 'Health Indicators': <libpysal.examples.base.Example at 0x7f7664f53430>,
 'Hickory1': <libpysal.examples.base.Example at 0x7f7664f53390>,
 'Hickory2': <libpysal.examples.base.Example at 0x7f7664f52c10>,
 'Home Sales': <libpysal.examples.base.Example at 0x7f7664f531b0>,
 'Houston': <libpysal.examples.base.Example at 0x7f7664f532f0>,
 'Juvenile': <libpysal.examples.base.Example at 0x7f7664f53a70>,
 'Lansing1': <libpysal.examples.base.Example at 0x7f7664f53cf0>,
 'Lansing2': <libpysal.examples.base.Example at 0x7f7664f52cb0>,
 'Laozone': <libpysal.examples.base.Example at 0x7f7664f53610>,
 'LasRosas': <libpysal.examples.base.Example at 0x7f7664f53750>,
 'Liquor Stores': <libpysal.examples.base.Example at 0x7f7664f537f0>,
 'Malaria': <libpysal.examples.base.Example at 0x7f7664f53890>,
 'Milwaukee1': <libpysal.examples.base.Example at 0x7f7664f53930>,
 'Milwaukee2': <libpysal.examples.base.Example at 0x7f7664f539d0>,
 'NCOVR': <libpysal.examples.base.Example at 0x7f7664f53b10>,
 'Natregimes': <libpysal.examples.base.Example at 0x7f7664f53bb0>,
 'NDVI': <libpysal.examples.base.Example at 0x7f7664f53c50>,
 'Nepal': <libpysal.examples.base.Example at 0x7f7664f53d90>,
 'NYC': <libpysal.examples.base.Example at 0x7f7664f53e30>,
 'NYC Earnings': <libpysal.examples.base.Example at 0x7f7664f53ed0>,
 'NYC Education': <libpysal.examples.base.Example at 0x7f7664f53f70>,
 'NYC Neighborhoods': <libpysal.examples.base.Example at 0x7f7664e10050>,
 'NYC Socio-Demographics': <libpysal.examples.base.Example at 0x7f7664e100f0>,
 'Ohiolung': <libpysal.examples.base.Example at 0x7f7664e10190>,
 'Orlando1': <libpysal.examples.base.Example at 0x7f7664e10230>,
 'Orlando2': <libpysal.examples.base.Example at 0x7f7664e102d0>,
 'Oz9799': <libpysal.examples.base.Example at 0x7f7664e10370>,
 'Phoenix ACS': <libpysal.examples.base.Example at 0x7f7664e10410>,
 'Pittsburgh': <libpysal.examples.base.Example at 0x7f7664e104b0>,
 'Police': <libpysal.examples.base.Example at 0x7f7664e10550>,
 'Sacramento1': <libpysal.examples.base.Example at 0x7f7664e105f0>,
 'Sacramento2': <libpysal.examples.base.Example at 0x7f7664e10690>,
 'SanFran Crime': <libpysal.examples.base.Example at 0x7f7664e10730>,
 'Savannah1': <libpysal.examples.base.Example at 0x7f7664e107d0>,
 'Savannah2': <libpysal.examples.base.Example at 0x7f7664e10870>,
 'Scotlip': <libpysal.examples.base.Example at 0x7f7664e10910>,
 'Seattle1': <libpysal.examples.base.Example at 0x7f7664e109b0>,
 'Seattle2': <libpysal.examples.base.Example at 0x7f7664e10a50>,
 'SIDS': <libpysal.examples.base.Example at 0x7f7664e10af0>,
 'SIDS2': <libpysal.examples.base.Example at 0x7f7664e10b90>,
 'Snow': <libpysal.examples.base.Example at 0x7f7664e10c30>,
 'South': <libpysal.examples.base.Example at 0x7f7664e10cd0>,
 'Spirals': <libpysal.examples.base.Example at 0x7f7664e10d70>,
 'StLouis': <libpysal.examples.base.Example at 0x7f7664e10e10>,
 'Tampa1': <libpysal.examples.base.Example at 0x7f7664e10eb0>,
 'US SDOH': <libpysal.examples.base.Example at 0x7f7664e10f50>,
 'Rio Grande do Sul': <libpysal.examples.base.Example at 0x7f7664e10ff0>,
 'nyc_bikes': <libpysal.examples.base.Example at 0x7f7664e11090>,
 'taz': <libpysal.examples.base.Example at 0x7f7664e11130>,
 'clearwater': <libpysal.examples.base.Example at 0x7f7664e111d0>,
 'newHaven': <libpysal.examples.base.Example at 0x7f7664e11270>,
 'chicagoSDOH': <libpysal.examples.base.Example at 0x7f7664e11310>}