From 096b620cdc75571397a44f9f06538a91c7c0d9e2 Mon Sep 17 00:00:00 2001 From: peterrrock2 Date: Wed, 15 May 2024 14:45:26 -0600 Subject: [PATCH] Documentation updates to sphinx --- .gitignore | 4 + .readthedocs.yaml | 3 - README.md | 22 +- docs/_static/css/custom.css | 29 ++ .../images}/Providence_blocks_plot.png | Bin .../images}/Providence_new_precincts_plot.png | Bin .../images}/Providence_old_precincts_plot.png | Bin docs/_static/images/output_22_0.png | Bin 0 -> 150735 bytes docs/_static/images/output_23_0.png | Bin 0 -> 171133 bytes docs/_static/images/output_30_0.png | Bin 0 -> 158388 bytes docs/_static/images/output_40_0.png | Bin 0 -> 163397 bytes {examples => docs/_static/images}/plot.png | Bin .../_static/images}/plot_without_gaps.png | Bin .../images}/plot_without_gaps_or_overlaps.png | Bin .../_static/images}/toy_counties.png | Bin .../_static/images}/toy_precincts.png | Bin .../_static/images}/toy_precincts_corner.png | Bin .../images}/toy_precincts_corner_repaired.png | Bin ...recincts_corner_repaired_rook_to_queen.png | Bin .../images}/toy_precincts_repaired.png | Bin .../toy_precincts_repaired_county_aware.png | Bin ...ts_repaired_county_aware_rook_to_queen.png | Bin docs/conf.py | 50 +- docs/full_ref.rst | 100 ++++ docs/index.rst | 65 +-- docs/introduction.rst | 56 +++ docs/requirements.txt | 433 ++---------------- docs/user/getting_started.rst | 176 +++++++ docs/user/prorating.rst | 147 ++++++ docs/user/simple_example.rst | 129 ++++++ docs/user/topological.rst | 133 ++++++ docs/with_gerrychain/islands.rst | 376 +++++++++++++++ docs/with_gerrychain/real-life_plan.rst | 228 +++++++++ maup/intersections.py | 6 +- maup/smart_repair.py | 102 +++-- 35 files changed, 1558 insertions(+), 501 deletions(-) create mode 100644 docs/_static/css/custom.css rename {examples => docs/_static/images}/Providence_blocks_plot.png (100%) rename {examples => docs/_static/images}/Providence_new_precincts_plot.png (100%) rename {examples => docs/_static/images}/Providence_old_precincts_plot.png (100%) create mode 100644 docs/_static/images/output_22_0.png create mode 100644 docs/_static/images/output_23_0.png create mode 100644 docs/_static/images/output_30_0.png create mode 100644 docs/_static/images/output_40_0.png rename {examples => docs/_static/images}/plot.png (100%) rename {examples => docs/_static/images}/plot_without_gaps.png (100%) rename {examples => docs/_static/images}/plot_without_gaps_or_overlaps.png (100%) rename {examples => docs/_static/images}/toy_counties.png (100%) rename {examples => docs/_static/images}/toy_precincts.png (100%) rename {examples => docs/_static/images}/toy_precincts_corner.png (100%) rename {examples => docs/_static/images}/toy_precincts_corner_repaired.png (100%) rename {examples => docs/_static/images}/toy_precincts_corner_repaired_rook_to_queen.png (100%) rename {examples => docs/_static/images}/toy_precincts_repaired.png (100%) rename {examples => docs/_static/images}/toy_precincts_repaired_county_aware.png (100%) rename {examples => docs/_static/images}/toy_precincts_repaired_county_aware_rook_to_queen.png (100%) create mode 100644 docs/full_ref.rst create mode 100644 docs/introduction.rst create mode 100644 docs/user/getting_started.rst create mode 100644 docs/user/prorating.rst create mode 100644 docs/user/simple_example.rst create mode 100644 docs/user/topological.rst create mode 100644 docs/with_gerrychain/islands.rst create mode 100644 docs/with_gerrychain/real-life_plan.rst diff --git a/.gitignore b/.gitignore index a6605c5..fa9b636 100644 --- a/.gitignore +++ b/.gitignore @@ -72,5 +72,9 @@ junit.xml # Editors .vscode/ +# Working environments +.venv +.docs_venv + # Pytest cache .pytest_cache/ diff --git a/.readthedocs.yaml b/.readthedocs.yaml index a02eb77..6a6b6ba 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -17,9 +17,6 @@ sphinx: builder: dirhtml configuration: docs/conf.py -# Optionally build your docs in additional formats such as PDF -formats: - - pdf # Optionally set the version of Python and requirements required to build your docs python: diff --git a/README.md b/README.md index d66e304..6c7a6c0 100644 --- a/README.md +++ b/README.md @@ -253,19 +253,19 @@ So, out of 3,014 total Census blocks, 884 were not assigned to any old precinct >>> blocks.plot() ``` -![Providence blocks](./examples/Providence_blocks_plot.png) +![Providence blocks](../_static/images/Providence_blocks_plot.png) ```python >>> old_precincts.plot() ``` -![Providence old precincts](./examples/Providence_old_precincts_plot.png) +![Providence old precincts](../_static/images/Providence_old_precincts_plot.png) ```python >>> new_precincts.plot() ``` -![Providence new precincts](./examples/Providence_new_precincts_plot.png) +![Providence new precincts](../_static/images/Providence_new_precincts_plot.png) The boundaries of the regions covered by these shapefiles are substantially different---and that doesn't even get into the possibility that the precinct shapefiles may have gaps between precinct polygons that some blocks may fall into. @@ -404,7 +404,7 @@ toy_precincts_df = geopandas.GeoDataFrame(geometry = geopandas.GeoSeries(ppolys) toy_precincts_df.plot(cmap = "tab20", alpha=0.7) ``` -![toy_precincts](./examples/toy_precincts.png) +![toy_precincts](../_static/images/toy_precincts.png) Check for gaps and overlaps: ```python @@ -420,7 +420,7 @@ toy_precincts_repaired_df = maup.smart_repair(toy_precincts_df) toy_precincts_repaired_df.plot(cmap = "tab20", alpha=0.7) ``` -![toy_precincts_repaired](./examples/toy_precincts_repaired.png) +![toy_precincts_repaired](../_static/images/toy_precincts_repaired.png) We can check that the repair succeeded: ```python @@ -440,35 +440,35 @@ toy_counties_df = geopandas.GeoDataFrame(geometry = geopandas.GeoSeries([cpoly1, toy_counties_df.plot(cmap='tab20') ``` -![toy_counties](./examples/toy_counties.png) +![toy_counties](../_static/images/toy_counties.png) We can perform a "county-aware" repair as follows: ```python toy_precincts_repaired_county_aware_df = maup.smart_repair(toy_precincts_df, nest_within_regions = toy_counties_df) toy_precincts_repaired_county_aware_df.plot(cmap = "tab20", alpha=0.7) ``` -![toy_precincts_repaired_county_aware](./examples/toy_precincts_repaired_county_aware.png) +![toy_precincts_repaired_county_aware](../_static/images/toy_precincts_repaired_county_aware.png) Next, suppose that we'd like to get rid of small rook adjacencies at corner points where 4 precincts meet. We might reasonably estimate that these all have length less than $0.1$, so we can accomplish this as follows: ```python toy_precincts_repaired_county_aware_rook_to_queen_df = maup.smart_repair(toy_precincts_df, nest_within_regions = toy_counties_df, min_rook_length = 0.1) toy_precincts_repaired_county_aware_rook_to_queen_df.plot(cmap = "tab20", alpha=0.7) ``` -![toy_precincts_repaired_county_aware_rook_to_queen](./examples/toy_precincts_repaired_county_aware_rook_to_queen.png) +![toy_precincts_repaired_county_aware_rook_to_queen](../_static/images/toy_precincts_repaired_county_aware_rook_to_queen.png) The difference is hard to see, so let's zoom in on gap between the 4 original precincts in the upper left-hand corner. Original precincts: -![toy_precincts_corner](./examples/toy_precincts_corner.png) +![toy_precincts_corner](../_static/images/toy_precincts_corner.png) County-aware repair: -![toy_precincts_corner_repaired](./examples/toy_precincts_corner_repaired.png) +![toy_precincts_corner_repaired](../_static/images/toy_precincts_corner_repaired.png) County-aware repair with rook adjacency converted to queen: -![toy_precincts_corner_repaired_rook_to_queen](./examples/toy_precincts_corner_repaired_rook_to_queen.png) +![toy_precincts_corner_repaired_rook_to_queen](../_static/images/toy_precincts_corner_repaired_rook_to_queen.png) ## Modifiable areal unit problem diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css new file mode 100644 index 0000000..fc755a6 --- /dev/null +++ b/docs/_static/css/custom.css @@ -0,0 +1,29 @@ +.download-badge { + display: inline-block; + padding: 8px 15px; + margin: 10px; + background-color: #0099CD; + color: white; + text-decoration: none; + 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b/docs/_static/images/toy_precincts_repaired.png similarity index 100% rename from examples/toy_precincts_repaired.png rename to docs/_static/images/toy_precincts_repaired.png diff --git a/examples/toy_precincts_repaired_county_aware.png b/docs/_static/images/toy_precincts_repaired_county_aware.png similarity index 100% rename from examples/toy_precincts_repaired_county_aware.png rename to docs/_static/images/toy_precincts_repaired_county_aware.png diff --git a/examples/toy_precincts_repaired_county_aware_rook_to_queen.png b/docs/_static/images/toy_precincts_repaired_county_aware_rook_to_queen.png similarity index 100% rename from examples/toy_precincts_repaired_county_aware_rook_to_queen.png rename to docs/_static/images/toy_precincts_repaired_county_aware_rook_to_queen.png diff --git a/docs/conf.py b/docs/conf.py index a667ce5..ec7fce6 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -13,9 +13,15 @@ import os import sys sys.path.insert(0, os.path.abspath('..')) -print(f"The system path is {sys.path}") + +# Add markdown parser +source_suffix = { + '.rst': 'restructuredtext', + '.md': 'markdown', +} + # -- Project information ----------------------------------------------------- project = 'maup' @@ -32,15 +38,14 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - 'myst_parser', 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', - 'numpydoc', 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', + 'sphinx_copybutton', ] # apidoc @@ -57,23 +62,42 @@ # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] +pygments_style = 'sphinx' # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = "pydata_sphinx_theme" -html_theme_options = { - "github_url": "https://github.com/mggg/maup", - "show_prev_next": False, - "navbar_end": ["search-field.html", "navbar-icon-links.html"], -} -html_sidebars = { - "**": [], -} +html_theme = "sphinx_rtd_theme" + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +html_theme_options = {"style_nav_header_background": "#0099cd"} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -# html_static_path = ['_static'] \ No newline at end of file +html_static_path = ["_static"] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} +html_css_files = [ + 'css/custom.css', +] + + +# -- Extension configuration ------------------------------------------------- + +# Prepend the module name of classes. +add_module_names = True +autodoc_inherit_docstrings = False diff --git a/docs/full_ref.rst b/docs/full_ref.rst new file mode 100644 index 0000000..e6abcd9 --- /dev/null +++ b/docs/full_ref.rst @@ -0,0 +1,100 @@ +Full Package Reference +====================== + +This page contains the full reference for the ``maup`` package, and +is included primarily for the sake of developers trying to hunt down errors +or understand the codebase. + + +.. automodule:: maup + + +Adjacencies +----------- + +.. automodule:: maup.adjacencies + :members: + :undoc-members: + :show-inheritance: + :noindex: + +Assign +------ + +.. automodule:: maup.assign + :members: + :undoc-members: + :show-inheritance: + :noindex: + +CRS +--- + +.. automodule:: maup.crs + :members: + :undoc-members: + :show-inheritance: + :noindex: + +Indexed Geometries +------------------ + +.. automodule:: maup.indexed_geometries + :members: + :undoc-members: + :show-inheritance: + :noindex: + +Indices +------- + +.. automodule:: maup.indices + :members: + :undoc-members: + :show-inheritance: + :noindex: + +Intersections +------------- + +.. automodule:: maup.intersections + :members: + :undoc-members: + :show-inheritance: + :noindex: + +Normalize +--------- + +.. automodule:: maup.normalize + :members: + :undoc-members: + :show-inheritance: + :noindex: + +Progress Bar +------------ + +.. automodule:: maup.progress_bar + :members: + :undoc-members: + :show-inheritance: + :noindex: + +Repair +------ + +.. automodule:: maup.repair + :members: + :undoc-members: + :show-inheritance: + :noindex: + +Smart Repair +------------ + +.. automodule:: maup.smart_repair + :members: + :undoc-members: + :show-inheritance: + :noindex: diff --git a/docs/index.rst b/docs/index.rst index 699a789..a132bdf 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -3,49 +3,58 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -Welcome to maup's API documentation! +Welcome to MAUP's documentation! ==================================== + +.. quick tip on reamaking the readme +.. pandoc --from=markdown --to=rst --output=introduction.rst ../README.md + +.. include:: introduction.rst + + .. toctree:: :maxdepth: 2 - :caption: Contents: + :caption: Tutorials -``maup`` is the geospatial toolkit for redistricting data. The package streamlines -the basic workflows that arise when working with blocks, precincts, and -districts, such as + user/getting_started + user/prorating + user/topological + user/simple_example -- Assigning precincts to districts -- Aggregating block data to precincts -- Disaggregating data from precincts down to blocks -- Prorating data when units do not nest neatly -- Fixing topological issues, overlaps, and gaps +.. toctree:: + :maxdepth: 2 + :caption: Working with GerryChain -Quick help: + with_gerrychain/real-life_plan + with_gerrychain/islands -- `Example usage `_ -- `Best practice guidelines `_ -- `API reference `_ +.. Installation +.. ------------ +.. Install ``maup`` by running -Installation ------------- +.. pip install maup -Install ``maup`` by running:: - pip install maup +.. Contribute +.. ---------- +.. All contributions are welcome! `maup` is licensed under the MIT license. -Contribute ----------- +.. - Issue Tracker: https://github.com/mggg/maup/issues +.. - Source Code: https://github.com/mggg/maup -All contributions are welcome! `maup` is licensed under the MIT license. +.. Indices and tables +.. ================== -- Issue Tracker: https://github.com/mggg/maup/issues -- Source Code: https://github.com/mggg/maup +.. * :ref:`genindex` +.. * :ref:`modindex` +.. * :ref:`search` -Indices and tables -================== -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` +.. toctree:: + :caption: Index + :maxdepth: 4 + + full_ref \ No newline at end of file diff --git a/docs/introduction.rst b/docs/introduction.rst new file mode 100644 index 0000000..81df38c --- /dev/null +++ b/docs/introduction.rst @@ -0,0 +1,56 @@ +|maup tests| |codecov| |PyPI| + +``maup`` is the geospatial toolkit for redistricting data. The package +streamlines the basic workflows that arise when working with blocks, +precincts, and districts, such as + +- `Assigning precincts to + districts `_, +- `Aggregating block data to + precincts `__, +- `Disaggregating data from precincts down to + blocks `__, +- `Prorating data when units do not nest + neatly `__, and +- `Fixing topological issues, overlaps, and + gaps `__ + +The project's priorities are to be efficient by using spatial indices +whenever possible and to integrate well with the existing ecosystem +around `pandas `__, +`geopandas `__ and +`shapely `__. The package is +distributed under the MIT License. + +Installation +------------ + +To install ``maup`` from PyPI, run + +.. code:: console + + pip install maup + +from the terminal. + + +Modifiable areal unit problem +----------------------------- + +The name of this package comes from the `modifiable areal unit problem +(MAUP) `__: +the same spatial data will look different depending on how you divide up +the space. Since ``maup`` is all about changing the way your data is +aggregated and partitioned, we have named it after the MAUP to encourage +users to use the toolkit thoughtfully and responsibly. + +.. |maup tests| image:: https://github.com/mggg/maup/actions/workflows/tests.yaml/badge.svg + :target: https://github.com/mggg/maup/actions/workflows/tests.yaml +.. |codecov| image:: https://codecov.io/gh/mggg/maup/branch/master/graph/badge.svg + :target: https://codecov.io/gh/mggg/maup +.. |PyPI| image:: https://img.shields.io/pypi/v/maup.svg?color=%23 + :target: https://pypi.org/project/maup/ + + + + diff --git a/docs/requirements.txt b/docs/requirements.txt index 0ae0551..9818834 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,394 +1,39 @@ -alabaster==0.7.12; python_version >= "3.6" \ - --hash=sha256:446438bdcca0e05bd45ea2de1668c1d9b032e1a9154c2c259092d77031ddd359 \ - --hash=sha256:a661d72d58e6ea8a57f7a86e37d86716863ee5e92788398526d58b26a4e4dc02 -atomicwrites==1.4.0; python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6") or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.4.0" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6") \ - 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--hash=sha256:753a0374df26658f99d826cfe40394a686d05985786d946fbe4165b5148f5a7c \ - --hash=sha256:a7acd0977125325f516bda9735fa7142b909a8d01e8b2e4c8108d0984e6e0098 -zipp==3.4.1; python_version < "3.8" and python_version >= "3.6" \ - --hash=sha256:51cb66cc54621609dd593d1787f286ee42a5c0adbb4b29abea5a63edc3e03098 \ - --hash=sha256:3607921face881ba3e026887d8150cca609d517579abe052ac81fc5aeffdbd76 +alabaster==0.7.16 +attrs==23.2.0 +Babel==2.15.0 +certifi==2024.2.2 +charset-normalizer==3.3.2 +click==8.1.7 +click-plugins==1.1.1 +cligj==0.7.2 +docutils==0.20.1 +fiona==1.9.6 +geopandas==0.14.4 +idna==3.7 +imagesize==1.4.1 +Jinja2==3.1.4 +MarkupSafe==2.1.5 +numpy==1.26.4 +packaging==24.0 +pandas==2.2.2 +Pygments==2.18.0 +pyproj==3.6.1 +python-dateutil==2.9.0.post0 +pytz==2024.1 +requests==2.31.0 +shapely==2.0.4 +six==1.16.0 +snowballstemmer==2.2.0 +Sphinx==7.3.7 +sphinx-copybutton==0.5.2 +sphinx-rtd-theme==2.0.0 +sphinxcontrib-applehelp==1.0.8 +sphinxcontrib-devhelp==1.0.6 +sphinxcontrib-htmlhelp==2.0.5 +sphinxcontrib-jquery==4.1 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==1.0.7 +sphinxcontrib-serializinghtml==1.1.10 +tqdm==4.66.4 +tzdata==2024.1 +urllib3==2.2.1 diff --git a/docs/user/getting_started.rst b/docs/user/getting_started.rst new file mode 100644 index 0000000..25c0696 --- /dev/null +++ b/docs/user/getting_started.rst @@ -0,0 +1,176 @@ +Getting started with MAUP +========================= + +Here are some basic situations where you might find ``maup`` helpful. +For these examples, we use test data from Providence, Rhode Island, +which you can find in our `Rhode Island shapefiles +repo `__, or in the +``examples`` folder of this repo, reprojected to a non-geographic +coordinate reference system (CRS) optimized for Rhode Island. + +.. note:: + + Many of maup's functions behave badly in geographic projections + (i.e., lat/long coordinates), which are the default for shapefiles from + the U.S. Census bureau. In order to find an appropriate CRS for a + particular shapefile, consult the database at https://epsg.org. + +.. code:: python + + >>> import geopandas + >>> import pandas + >>> + >>> blocks = geopandas.read_file("zip://./examples/blocks.zip").to_crs(32030) + >>> precincts = geopandas.read_file("zip://./examples/precincts.zip").to_crs(32030) + >>> districts = geopandas.read_file("zip://./examples/districts.zip").to_crs(32030) + +.. _assigning-precincts-to-districts: + +Assigning precincts to districts +-------------------------------- + +The ``assign`` function in ``maup`` takes two sets of geometries called +``sources`` and ``targets`` and returns a pandas ``Series``. The Series +maps each geometry in ``sources`` to the geometry in ``targets`` that +covers it. (Here, geometry *A* *covers* geometry *B* if every point of +*A* and its boundary lies in *B* or its boundary.) If a source geometry +is not covered by one single target geometry, it is assigned to the +target geometry that covers the largest portion of its area. + +.. code:: python + + >>> import maup + >>> + >>> precinct_to_district_assignment = maup.assign(precincts, districts) + >>> # Add the assigned districts as a column of the `precincts` GeoDataFrame: + >>> precincts["DISTRICT"] = precinct_to_district_assignment + >>> precinct_to_district_assignment.head() + 0 7 + 1 5 + 2 13 + 3 6 + 4 1 + dtype: int64 + +As an aside, you can use that ``precinct_to_district_assignment`` object +to create a +`gerrychain `__ +``Partition`` representing this districting plan. + +.. _aggregating-block-data-to-precincts: + +Aggregating block data to precincts +----------------------------------- + +Precinct shapefiles usually come with election data, but not demographic +data. In order to study their demographics, we need to aggregate +demographic data from census blocks up to the precinct level. We can do +this by assigning blocks to precincts and then aggregating the data with +a Pandas +`groupby `_ +operation: + +.. code:: python + + >>> variables = ["TOTPOP", "NH_BLACK", "NH_WHITE"] + >>> + >>> blocks_to_precincts_assignment = maup.assign(blocks, precincts) + >>> precincts[variables] = blocks[variables].groupby(blocks_to_precincts_assignment).sum() + >>> precincts[variables].head() + TOTPOP NH_BLACK NH_WHITE + 0 5907 886 380 + 1 5636 924 1301 + 2 6549 584 4699 + 3 6009 435 1053 + 4 4962 156 3713 + +If you want to move data from one set of geometries to another but your +source geometries do not nest cleanly into your target geometries, see +`Prorating data when units do not nest +neatly <#prorating-data-when-units-do-not-nest-neatly>`__. + +.. _disaggregating-data-when-units-do-not-nest-neatly: + +Disaggregating data from precincts down to blocks +------------------------------------------------- + +It's common to have data at a coarser scale that you want to attach to +finer-scale geometries. For instance, this may happen when vote totals +for a certain election are only reported at the county level, and we +want to attach that data to precinct geometries. + +Let's say we want to prorate the vote totals in the columns +``"PRES16D"``, ``"PRES16R"`` from our ``precincts`` GeoDataFrame down to +our ``blocks`` GeoDataFrame. The first crucial step is to decide how we +want to distribute a precinct's data to the blocks within it. Since +we're prorating election data, it makes sense to use a block's total +population or voting-age population. Here's how we might prorate by +population (``"TOTPOP"``): + +.. code:: python + + >>> election_columns = ["PRES16D", "PRES16R"] + >>> blocks_to_precincts_assignment = maup.assign(blocks, precincts) + >>> + >>> # We prorate the vote totals according to each block's share of the overall + >>> # precinct population: + >>> weights = blocks.TOTPOP / blocks_to_precincts_assignment.map(blocks.TOTPOP.groupby(blocks_to_precincts_assignment).sum()) + >>> prorated = maup.prorate(blocks_to_precincts_assignment, precincts[election_columns], weights) + >>> + >>> # Add the prorated vote totals as columns on the `blocks` GeoDataFrame: + >>> blocks[election_columns] = prorated + >>> + >>> # We'll call .round(2) to round the values for display purposes, but note that the + >>> # actual values should NOT be rounded in order to avoid accumulation of rounding + >>> # errors. + >>> blocks[election_columns].round(2).head() + PRES16D PRES16R + 0 0.00 0.00 + 1 12.26 1.70 + 2 15.20 2.62 + 3 15.50 2.67 + 4 3.28 0.45 + + +.. warning:: + + (1) Many states contain Census blocks and precincts that have zero population. In the example above, a zero-population precinct leads to division by zero in the definition of the weights, which results in NaN values for some entries. + + Although it is not strictly necessary to resolve this in the example + above, sometimes this creates issues down the line. One option is to + replace NaN values with zeros, using + + .. code:: python + + >>> weights = weights.fillna(0) + + (2) In some cases, zero-population precincts may have a small nonzero number of recorded votes in some elections. The procedure outlined above will lose these votes in the proration process due to the zero (or NaN) values for the weights corresponding to all the blocks in those precincts. If it is crucial to keep vote totals perfectly accurate, these votes will need to be assigned to the new units manually. + +Progress bars +------------- + +For long-running operations, the user might want to see a progress bar +to estimate how much longer a task will take (and whether to abandon it +altogether). + +``maup`` provides an optional progress bar for this purpose. To +temporarily activate a progress bar for a certain operation, use +``with maup.progress():``: + +.. code:: python + + with maup.progress(): + assignment = maup.assign(precincts, districts) + +To turn on progress bars for all applicable operations (e.g. for an +entire script), set ``maup.progress.enabled = True``: + +.. code:: python + + maup.progress.enabled = True + # Now a progress bar will display while this function runs: + assignment = maup.assign(precincts, districts) + # And this one too: + pieces = maup.intersections(old_precincts, new_precincts, area_cutoff=0) + + diff --git a/docs/user/prorating.rst b/docs/user/prorating.rst new file mode 100644 index 0000000..2c237d6 --- /dev/null +++ b/docs/user/prorating.rst @@ -0,0 +1,147 @@ +Prorating data when units do not nest neatly +============================================ + +.. warning:: + + **We strongly urge you not to prorate by area!** The area of a census + block is **not** a good predictor of its population. In fact, the + correlation goes in the other direction: larger census blocks are *less* + populous than smaller ones. + +Suppose you have a shapefile of precincts with some election results +data and you want to join that data onto a different, more recent +precincts shapefile. The two sets of precincts will have overlaps, and +will not nest neatly like the blocks and precincts did in the above +examples. (Not that blocks and precincts always nest neatly—in fact, +they usually don't!) + +In most cases, election data should be prorated from each old precinct +to the new precincts with weights proportional to the population of the +intersections between the old precinct and each new precinct. The most +straightforward way to accomplish this is to first disaggregate the data +from the old precincts to Census blocks as in the example above, and +then reaggregate from blocks to the new precincts. + +.. code:: python + + >>> old_precincts = precincts + >>> new_precincts = geopandas.read_file("zip://./examples/new_precincts.zip").to_crs(32030) + >>> + >>> election_columns = ["SEN18D", "SEN18R"] + >>> + >>> blocks_to_old_precincts_assignment = maup.assign(blocks, old_precincts) + >>> blocks_to_new_precincts_assignment = maup.assign(blocks, new_precincts) + >>> + >>> # We prorate the vote totals according to each block's share of the overall + >>> # old precinct population: + >>> weights = blocks.TOTPOP / blocks_to_old_precincts_assignment.map(blocks.TOTPOP.groupby(blocks_to_old_precincts_assignment).sum()).fillna(0) + >>> prorated = maup.prorate(blocks_to_old_precincts_assignment, precincts[election_columns], weights) + >>> + >>> # Add the prorated vote totals as columns on the `blocks` GeoDataFrame: + >>> blocks[election_columns] = prorated + >>> + >>> new_precincts[election_columns] = blocks[election_columns].groupby(blocks_to_new_precincts_assignment).sum() + >>> new_precincts[election_columns].round(2).head() + SEN18D SEN18R + 0 728.17 49.38 + 1 370.00 21.00 + 2 97.00 17.00 + 3 91.16 5.55 + 4 246.00 20.00 + +As a sanity check, let's make sure that no votes were lost in either +step. Total votes in the old precincts, blocks, and new precincts: + +.. code:: python + + >>> old_precincts[election_columns].sum() + SEN18D 23401 + SEN18R 3302 + dtype: float64 + >>> + >>> blocks[election_columns].sum() + SEN18D 23401.0 + SEN18R 3302.0 + dtype: float64 + >>> + >>> new_precincts[election_columns].sum() + SEN18D 20565.656675 + SEN18R 2947.046857 + dtype: float64 + +Oh no - what happened??? All votes were successfully disaggregated to +blocks, but a significant percentage were lost when reaggregating to new +precincts. + +It turns out that when blocks were assigned to both old and new +precincts, many blocks were not assigned to any precincts. We can count +how many blocks were unassigned in each case: + +.. code:: python + + print(len(blocks)) + print(blocks_to_old_precincts_assignment.isna().sum()) + print(blocks_to_new_precincts_assignment.isna().sum()) + 3014 + 884 + 1227 + +So, out of 3,014 total Census blocks, 884 were not assigned to any old +precinct and 1,227 were not assigned to any new precinct. If we plot the +GeoDataFrames, we can see why: + +.. code:: python + + >>> blocks.plot() + +.. figure:: ../_static/images/Providence_blocks_plot.png + :alt: Providence blocks + + +.. code:: python + + >>> old_precincts.plot() + +.. figure:: ../_static/images/Providence_old_precincts_plot.png + :alt: Providence old precincts + + +.. code:: python + + >>> new_precincts.plot() + +.. figure:: ../_static/images/Providence_new_precincts_plot.png + :alt: Providence new precincts + + +The boundaries of the regions covered by these shapefiles are +substantially different—and that doesn't even get into the possibility +that the precinct shapefiles may have gaps between precinct polygons +that some blocks may fall into. + +Once we know to look for this issue, we can see that it affected the +previous example as well: + +.. code:: python + + >>> blocks[variables].sum() + TOTPOP 178040 + NH_BLACK 23398 + NH_WHITE 66909 + dtype: int64 + >>> + >>> precincts[variables].sum() + TOTPOP 140332 + NH_BLACK 19345 + NH_WHITE 46667 + dtype: int64 + + +.. admonition:: Moral: Precinct shapefiles often have *terrible* topological issues! + :class: tip + + These issues should be diagnosed and repaired to the greatest extent + possible before moving data around between shapefiles; see `Fixing + topological issues, overlaps, and + gaps <#fixing-topological-issues-overlaps-and-gaps>`__ below for details + about how maup can help with this. diff --git a/docs/user/simple_example.rst b/docs/user/simple_example.rst new file mode 100644 index 0000000..b47f53a --- /dev/null +++ b/docs/user/simple_example.rst @@ -0,0 +1,129 @@ + +.. _examples-1: + +Example +~~~~~~~ + +First, we'll use ``shapely`` and ``geopandas`` to create a GeoDataFrame of “toy precincts” from scratch. + +.. code:: python + + import random + import geopandas + import maup + from shapely.geometry import Polygon + + random.seed(2023) # For reproducibility + + ppolys = [] + for i in range(4): + for j in range(4): + poly = Polygon( + [(0.5*i + 0.1*k, 0.5*j + (random.random() - 0.5)/12) for k in range(6)] + + [(0.5*(i+1) + (random.random() - 0.5)/12, 0.5*j + 0.1*k) for k in range(1,6)] + + [(0.5*(i+1) - 0.1*k, 0.5*(j+1) + (random.random() - 0.5)/12) for k in range(1,6)] + + [(0.5*i + (random.random() - 0.5)/12, 0.5*(j+1) - 0.1*k) for k in range(1,5)] + ) + ppolys.append(poly) + + toy_precincts_df = geopandas.GeoDataFrame(geometry = geopandas.GeoSeries(ppolys)) + toy_precincts_df.plot(cmap = "tab20", alpha=0.7) + +.. figure:: ../_static/images/toy_precincts.png + :alt: toy_precincts + + +Check for gaps and overlaps: + +.. code:: python + + >>> maup.doctor(old_precincts) + There are 28 overlaps. + There are 23 holes. + False + +All the gaps between geometries in this example are below the default +threshold, so a basic application of ``smart_repair`` will resolve all +overlaps and fill all gaps: + +.. code:: python + + toy_precincts_repaired_df = maup.smart_repair(toy_precincts_df) + toy_precincts_repaired_df.plot(cmap = "tab20", alpha=0.7) + +.. figure:: ../_static/images/toy_precincts_repaired.png + :alt: toy_precincts_repaired + + +We can check that the repair succeeded: + +.. code:: python + + >>> maup.doctor(old_precincts) + True + +Now suppose that the precincts are intended to nest cleanly into the +following “toy counties:” + +.. code:: python + + cpoly1 = Polygon([(0,0), (1,0), (1,1), (0,1)]) + cpoly2 = Polygon([(1,0), (2,0), (2,1), (1,1)]) + cpoly3 = Polygon([(0,1), (1,1), (1,2), (0,2)]) + cpoly4 = Polygon([(1,1), (2,1), (2,2), (1,2)]) + + toy_counties_df = geopandas.GeoDataFrame(geometry = geopandas.GeoSeries([cpoly1, cpoly2, cpoly3, cpoly4])) + + toy_counties_df.plot(cmap='tab20') + +.. figure:: ../_static/images/toy_counties.png + :alt: toy_counties + + +We can perform a “county-aware” repair as follows: + +.. code:: python + + toy_precincts_repaired_county_aware_df = maup.smart_repair(toy_precincts_df, nest_within_regions = toy_counties_df) + toy_precincts_repaired_county_aware_df.plot(cmap = "tab20", alpha=0.7) + +.. figure:: + ../_static/images/toy_precincts_repaired_county_aware.png + :alt: toy_precincts_repaired_county_aware + + +Next, suppose that we'd like to get rid of small rook adjacencies at +corner points where 4 precincts meet. We might reasonably estimate that +these all have length less than :math:`0.1`, so we can accomplish this +as follows: + +.. code:: python + + toy_precincts_repaired_county_aware_rook_to_queen_df = maup.smart_repair(toy_precincts_df, nest_within_regions = toy_counties_df, min_rook_length = 0.1) + toy_precincts_repaired_county_aware_rook_to_queen_df.plot(cmap = "tab20", alpha=0.7) + +.. figure:: + ../_static/images/toy_precincts_repaired_county_aware_rook_to_queen.png + :alt: toy_precincts_repaired_county_aware_rook_to_queen + + +The difference is hard to see, so let's zoom in on gap between the 4 +original precincts in the upper left-hand corner. + +Original precincts: + +.. figure:: ../_static/images/toy_precincts_corner.png + :alt: toy_precincts_corner + + +County-aware repair: + +.. figure:: ../_static/images/toy_precincts_corner_repaired.png + :alt: toy_precincts_corner_repaired + + +County-aware repair with rook adjacency converted to queen: + +.. figure:: + ../_static/images/toy_precincts_corner_repaired_rook_to_queen.png + :alt: toy_precincts_corner_repaired_rook_to_queen diff --git a/docs/user/topological.rst b/docs/user/topological.rst new file mode 100644 index 0000000..84a7c7c --- /dev/null +++ b/docs/user/topological.rst @@ -0,0 +1,133 @@ + +Fixing topological issues, overlaps, and gaps +--------------------------------------------- + +Precinct shapefiles are often created by stitching together collections +of precinct geometries sourced from different counties or different +years. As a result, the shapefile often has gaps or overlaps between +precincts where the different sources disagree about the boundaries. +(And by “often,” we mean “for almost every shapefile that isn't produced +by the U.S. Census Burueau.”) As we saw in the examples above, these +issues can pose problems when moving data between shapefiles. + +Even when working with a single shapefile, gaps and overlaps may cause +problems if you are interested in working with the adjacency graph of +the precincts. This adjacency information is especially important when +studying redistricting, because districts are almost always expected to +be contiguous. + +Before doing anything else, it is wise to understand the current status +of a shapefile with regard to topological issues. ``maup`` provides a +``doctor`` function to diagnose gaps, overlaps, and invalid geometries. +If a shapefile has none of these issues, ``maup.doctor`` returns a value +of ``True``; otherwise it returns ``False`` along with a brief summary +of the problems that it found. + +The blocks shapefile, like most shapefiles from the Census Bureau, is +clean: + +.. code:: python + + >>> maup.doctor(blocks) + True + +The old precincts shapefile, however, has some minor issues: + +.. code:: python + + >>> maup.doctor(old_precincts) + There are 2 overlaps. + There are 3 holes. + False + +As of version 2.0.0, ``maup`` provides two repair functions with a +variety of options for fixing these issues: + +1. ``quick_repair`` is the new name for the ``autorepair`` function from + version 1.x (and ``autorepair`` still works as a synonym). This + function makes fairly simplistic repairs to gaps and overlaps: + + - Any polygon :math:`Q` created by the overlapping intersection of + two geometries :math:`P_1` and :math:`P_2` is removed from both + polygons and reassigned to the one with which it shares the + greatest perimeter. + - Any polygon :math:`Q` representing a gap between geometries + :math:`P_1,\ldots, P_n` is assigned to the one with which it + shares the greatest perimeter. + + This function is probably sufficient when gaps and overlaps are all + very small in area relative to the areas of the geometries, **AND** + when the repaired file will only be used for operations like + aggregating and prorating data. But it should **NOT** be relied upon + when it is important for the repaired file to accurately represent + adjacency relations between neighboring geometries, such as when a + precinct shapefile is used as a basis for creating districting plans + with contiguous districts. + + For instance, when a gap adjoins many geometries (which happens + frequently along county boundaries in precinct shapefiles!), + whichever geometry the gap is adjoined to becomes “adjacent” to + **all** the other geometries adjoining the gap, which can lead to the + creation of discontiguous districts in plans based on the repaired + shapefile. + +2. ``smart_repair`` is a more sophisticated repair function designed to + reproduce the “true” adjacency relations between geometries as + accurately as possible. In the case of gaps that adjoin several + geometries, this is accomplished by an algorithm that divides the gap + into pieces to be assigned to different geometries instead of + assigning the entire gap to a single geometry. + + In addition to repairing gaps and overlaps, ``smart_repair`` includes + two optional features: + + - In many cases, the shapefile geometries are intended to nest + cleanly into some larger units; e.g., in many states, precincts + should nest cleanly into counties. ``smart_repair`` allows the + user to optionally specify a second shapefile—e.g., a shapefile of + county boundaries within a state—and then performs the repair + process so that the repaired geometries nest cleanly into the + units in the second shapefile. + - Whether as a result of inaccurate boundaries in the original map + or as an artifact of the repair algorithm, it may happen that some + units share boundaries with very short perimeter but should + actually be considered “queen adjacent”—i.e., intersecting at only + a single point—rather than “rook adjacent”—i.e., intersecting + along a boundary of positive length. ``smart_repair`` includes an + optional step in which all rook adjacencies of length below a + user-specified parameter are converted to queen adjacencies. + +``smart_repair`` can accept either a GeoSeries or GeoDataFrame as input, +and the output type will be the same as the input type. The input must +be projected to a non-geographic coordinate reference system (CRS)—i.e., +**not** lat/long coordinates—in order to have sufficient precision for +the repair. One option is to reproject a GeoDataFrame called ``gdf`` to +a suitable UTM (Universal Transverse Mercator) projection via + +.. code:: python + + gdf = gdf.to_crs(gdf.estimate_utm_crs()) + +At a minimum, all overlaps will be repaired in the output. Optional +arguments include: \* ``snapped`` (default value ``True``): If ``True``, +all polygon vertices are snapped to a grid of size no more than +:math:`10^{-10}` times the maximum of width/height of the entire +shapefile extent. **HIGHLY RECOMMENDED** to avoid topological exceptions +due to rounding errors. \* ``fill_gaps`` (default value ``True``): If +``True``, all simply connected gaps with area less than +``fill_gaps_threshold`` times the largest area of all geometries +adjoining the gap are filled. Default threshold is :math:`0.1`; setting +``fill_gaps_threshold = None`` will fill all simply connected gaps. \* +``nest_within_regions`` (default value ``None``): If +``nest_within_regions`` is a secondary GeoSeries or GeoDataFrame of +region boundaries (e.g., counties within a state) then the repair will +be performed so that repaired geometries nest cleanly into the region +boundaries; specifically, each repaired geometry will be contained in +the region with which the original geometry has the largest area of +intersection. Note that the CRS for the region GeoSeries/GeoDataFrame +must be the same as that for the primary input. \* ``min_rook_length`` +(default value ``None``): If ``min_rook_length`` is given a numerical +value, all rook adjacencies with length below this value will be +replaced with queen adjacencies. Note that this is an absolute value and +not a relative value, so make sure that the value provided is in the +correct units with respect to the input GeoSeries/GeoDataFrame's CRS. diff --git a/docs/with_gerrychain/islands.rst b/docs/with_gerrychain/islands.rst new file mode 100644 index 0000000..9e94ce0 --- /dev/null +++ b/docs/with_gerrychain/islands.rst @@ -0,0 +1,376 @@ +What to do about islands and connectivity +========================================= + +When you create or load a graph, you may have see a warning about +islands, like this one for Massachusetts's 2016 voting precincts. First +we download the shapefile into a GeoDataFrame called “precincts_df” and +then build the Graph object “precincts_graph” from the GeoDataFrame. + +.. code:: python + + >>> from gerrychain import Graph + >>> import geopandas# # + >>> precincts_df = geopandas.read_file("https://github.com/mggg-states/MA-shapefiles/raw/master/MA_precincts_12_16.zip") + >>> precincts_graph = Graph.from_geodataframe(precincts_df) + graph.py:388: UserWarning: Found islands (degree-0 nodes). Indices of islands: {836, 1278} + warnings.warn( + +This warning is telling you that there are a couple of nodes (836, 1278) +that have no neighbors—that is, they are completely disconnected from +the rest of the graph. In addition to these nodes without neighbors, we +can also have a whole cluster of nodes that is disconnected from the +rest of the graph. + +These are a problem because we want the districting plans we generate to +have contiguous districts, and any district with one of these islands +will not be contiguous. In addition, spanning tree-based methods like +the ReCom proposal or the ``recursive_tree_part`` seed-creation method +require that the graph be connected—starting with a disconnected graph +can lead to mysterious ``KeyError``\ s and infinite loops. + +These islands and disconnected components show up when there are actual +geographic islands, or (for example) when there is a unit that is +connected to the mainland by a bridge but whose official boundary in the +shapefile does not touch the mainland's boundary. If we plot the +shapefile, we can see the problem: + +.. code:: python + + >>> import matplotlib.pyplot as plt + >>> + >>> # Change the projection so we can plot it: + >>> precincts_df.to_crs(precincts_df.estimate_utm_crs(), inplace=True) + >>> + >>> precincts_df.plot(figsize=(10, 10)) + >>> plt.axis('off') + >>> plt.show() + +.. figure:: + ../_static/images/output_22_0.png + :alt: MA + + +This notebook will show you how to diagnose these contiguity problems, +and will give you a few different strategies for dealing with islands +and disconnected components so that you can get your graph to a workable +state for generating ensembles of districting plans. + +Figuring out what is going on +----------------------------- + +The first step in the process is to figure out what is going on. To see +the node IDs of the islands that GerryChain warned us about, we can +inspect ``precincts_graph.islands``: + +.. code:: python + + >>> islands = precincts_graph.islands + >>> islands + {836, 1278} + +GerryChain warns you when there are islands, but we'll also want to see +if there are whole disconnected components of the graph. We can use the +`NetworkX `__ function +``connected_components`` to do this. The ``connected_components`` +function yields the set of node indices in each connected component of +the graph. We'll put these sets into a list, and then print out their +lengths to see how bad the contiguity situation is: + +.. code:: python + + >>> from networkx import is_connected, connected_components + >>> components = list(connected_components(precincts_graph)) + >>> [len(c) for c in components] + [2143, 6, 1, 1] + +We have one big component with 2143 nodes. The two components with 1 +node are the islands that GerryChain warned us about. Then we also have +another disconnected component with 6 nodes. + +To fix these problems, we have two options. The first strategy is to +just delete these disconnected components, and do our analysis on the +big connected component. This might be the right way to go if you are +just trying to get started—you can always go back and make a better fix +later when you want to make sure your results are sound. + +The second strategy is to manually add edges to the graph to connect all +of the disconnected components. This is more involved but will give you +a much better graph for your analysis. + +Strategy 1: Delete the problem components +----------------------------------------- + +For the quick and very dirty route, we can just delete all of the nodes +in components that are causing us problems: + +.. code:: python + + >>> biggest_component_size = max(len(c) for c in components) + >>> problem_components = [c for c in components if len(c) != biggest_component_size] + >>> for component in problem_components: + ... for node in component: + ... precincts_graph.remove_node(node) + +We can verify that our graph is now connected: + +.. code:: python + + >>> is_connected(precincts_graph) + True + +And now we're ready to run a chain! Let's save the graph in a new +``.json`` file so that we don't have to do this fix every time we want +to run a chain. + +.. code:: python + + >>> precincts_graph.to_json("./my_graph_with_islands_deleted.json") + +Strategy 2: Connect the components manually +------------------------------------------- + +Deleting the problem components will make it easier for your code to +run, but we should not use this method when we actually care about the +results we are getting. Deleting nodes from the graph also deletes all +the people who live in those nodes, which is strictly not OK, morally +speaking. For a better, more laborious solution, we will: + +- Use QGIS to open the shapefile that our graph was made from +- Examine the disconnected components in QGIS, and +- Judiciously add edges to the graph to make it connected. + +This requires making some human judgements about which units are “near” +to each other, for the purposes of district contiguity. This is an art +more than a science, with a lot of questions with no single right +answer. For instance, should all of the Hawaiian islands be connected to +one another, or connected in a chain from east to west? Should the upper +peninsula of Michigan connect to the lower pensinsula only at the +Mackinac bridge, or all along the coast? + +Inspecting the disconnected components +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The next step in the process is to open the precinct shapefile in QGIS +so we can see what's going on. Here is the precinct map again: + +.. code:: python + + >>> precincts_df.plot(figsize=(10, 10)) + >>> plt.axis('off') + >>> plt.show() + +.. figure:: + ../_static/images/output_22_0.png + :alt: MA + + +It's not immediately clear from this plot where the disconnected +components are. But we can use our adjacency graph to get the +identifiers (often located in a column called something like “GEOID”, +but in this example located in a column called “NAME”) of the +disconnected nodes, which we can then highlight using QGIS (or +geopandas). + +First we'll collect the NAMEs of all of the nodes and use them to +highlight the precincts that we need to connect. + +.. code:: python + + >>> components = list(connected_components(precincts_graph)) + >>> [len(c) for c in components] + [2143, 6, 1, 1] + >>> biggest_component_size = max(len(c) for c in components) + >>> problem_components = [c for c in components if len(c) != biggest_component_size] + >>> problem_nodes = [node for component in problem_components for node in component] + >>> problem_ids = [precincts_graph.nodes[node]["NAME"] for node in problem_nodes] + >>> problem_ids + ['Edgartown Town Precinct 1', + 'Chilmark Town Precinct 1', + 'Oak Bluffs Town Precinct 1', + 'Tisbury Town Precinct 1', + 'Aquinnah Town Precinct 1', + 'West Tisbury Town Precinct 1', + 'Gosnold Town Precinct 1', + 'Nantucket Precinct 1'] + >>> is_a_problem = precincts_df["NAME"].isin(problem_ids) + >>> precincts_df.plot(column=is_a_problem, figsize=(10, 10), cmap='viridis') + >>> plt.axis('off') + >>> plt.show() + +.. figure:: + ../_static/images/output_30_0.png + :alt: MA islands + + +We can do the same thing in QGIS by using the “Select by Expression” +feature with an expression like this: + +:: + + "NAME" in ( + 'Edgartown Town Precinct 1', + 'Chilmark Town Precinct 1', + 'Oak Bluffs Town Precinct 1', + 'Tisbury Town Precinct 1', + 'Aquinnah Town Precinct 1', + 'West Tisbury Town Precinct 1', + 'Gosnold Town Precinct 1', + 'Nantucket Precinct 1' + ) + +where we've copied and pasted the NAMEs from our ``problem_ids`` +variable into the ``...`` part between the parentheses. + +Then click “Select features” and you should see these same features +highlighted on the map. + +Adding edges to the graph +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now comes the fun part! We want to identify some edges that we can add +to make the graph connected. To do this, we want to: + +- Zoom into the areas where these disconnected precincts (highlighted + in yellow) are close to block groups in the main connected component + (in purple) +- Use the “Identify features” tool to inspect the attributes of the + yellow and purple block groups that we want to connect +- Copy the ``"NAME"`` attribute values for both of the units we want to + connect + +Once we have these GEOIDs, we can find the corresponding nodes in the +graph and add that edge. For the Massachusetts example, here are two +GEOIDs of nodes that I want to connect that I found in QGIS: + +.. code:: python + + >>> purple_nameid = "Falmouth Town Precinct 1" + >>> yellow_nameid = "Gosnold Town Precinct 1" + +Next, we find the corresponding node using this NAME. Let's write a +function to do this to save some mental space: + +.. code:: python + + >>> def find_node_by_nameid(nameid, graph=precincts_graph): + >>> for node in graph: + ... if graph.nodes[node]["NAME"] == nameid: + ... return node + +.. code:: python + + >>> purple_node = find_node_by_nameid(purple_nameid) + >>> yellow_node = find_node_by_nameid(yellow_nameid) + >>> purple_node, yellow_node + (755, 836) + +And now we can finally add the edge ``(755, 836)`` to connect these +nodes: + +.. code:: python + + >>> precincts_graph.add_edge(purple_node, yellow_node) + +Let's use ``connected_components`` to see if we've made our graph more +connected. + +.. code:: python + + >>> [len(c) for c in connected_components(precincts_graph)] + [2144, 6, 1] + +Yay! We've connected one of the islands that GerryChain warned us about. +Now we'll repeat this process of adding edges until the graph only has +one connected component. + +Here are the remaining pairs of NAMEs that I found that I wanted to +connect: + +.. code:: python + + >>> nameids_i_found = [ + ... ("Nantucket Precinct 1", "Edgartown Town Precinct 1"), + ... ("Aquinnah Town Precinct 1", "Gosnold Town Precinct 1") + ... ] + +Let's add them: + +.. code:: python + + >>> edges_to_add = [(find_node_by_nameid(u), find_node_by_nameid(v)) for u, v in nameids_i_found] + >>> for u, v in edges_to_add: + ... precincts_graph.add_edge(u, v) + +And let's verify that the graph is connected: + +.. code:: python + + >>> assert len(list(connected_components(precincts_graph))) == 1 + >>> is_connected(precincts_graph) + True + +Hooray! 🎉🎉🎉 The graph is connected now! 🎉🎉🎉 + +Now we can run a proper Markov chain, without deleting any people from +the graph. + +Discontiguous plans +------------------- + +In addition to connectivity problems in the actual graph, you may also +need to think about discontiguities in districting plans. That is, if we +want to use a real-life plan as our initial state in GerryChain, we will +want it to be contiguous, so we need to make sure that our graph +structure has the right edges in place for that to be true. + +The process for fixing discontiguous plans is similar to the above +process. The only difference is in how we identify the problematic +nodes. GerryChain provides a function ``contiguous_components`` that +takes a Partition and returns the contiguous components of each +district. + +Here's how we can find those components, using a random example plan +with 2 districts for Massachusetts, just to see what a (very!) +discontiguous plan looks like: + +.. code:: python + + >>> from gerrychain.constraints.contiguity import contiguous_components, contiguous + >>> from gerrychain import Partition + >>> import random + >>> random.seed(0) + >>> assignment = {} + >>> for node in precincts_graph: + ... if random.random() < 0.5: + ... assignment[node] = 0 + ... else: + ... assignment[node] = 1 + >>> discontiguous_plan = Partition(precincts_graph, assignment) + +Now we'll verify that the plan does not pass our contiguity test, and +examine the two contiguous components: + +.. code:: python + + >>> contiguous(discontiguous_plan) + False + >>> for key in contiguous_components(discontiguous_plan).keys(): + ... print(key, len(contiguous_components(discontiguous_plan)[key])) + 0 90 + 1 97 + >>> discontiguous_plan.plot(precincts_df, figsize=(10, 10), cmap="tab20") + >>> plt.axis('off') + >>> plt.show() + +.. figure:: + ../_static/images/output_40_0.png + :alt: MA discontiguous plan + + +For any district with more than one contiguous component, you'll want to +do the exact same process that we did with the overall graph above: add +edges until there is only one contiguous component. + +If the starting plan is not important to you, then you might want to use +a function like ``recursive_tree_part`` to generate a starting plan from +scratch. diff --git a/docs/with_gerrychain/real-life_plan.rst b/docs/with_gerrychain/real-life_plan.rst new file mode 100644 index 0000000..7c23196 --- /dev/null +++ b/docs/with_gerrychain/real-life_plan.rst @@ -0,0 +1,228 @@ +Using ``maup`` to import a real-life plan in GerryChain +======================================================= + +To generate an ensemble of districting plans using GerryChain, we need a +starting point for our Markov chain. GerryChain gives you functions like +``recursive_tree_part`` to generate such plans from scratch, but you may +want to use an actual districting plan from the real world instead. You +also may want to compare a real-life plan to your ensemble. + +`MAUP `_ is MGGG's package for doing +common spatial data operations with redistricting data. We'll use this +package to assign our basic geographic units to the districts in the +plan we're interested in. Our steps are: + +- Download shapefiles of the districting plan we're interested in and + the basic units that we want to use, +- Use ``maup`` to assign the basic units to the districts in that plan, + and then +- Create a GerryChain Partition using that assignment. + +.. code:: python + + >>> import maup + >>> import geopandas + >>> import matplotlib.pyplot as plt + +Downloading shapefiles +---------------------- + +For this guide, we'll assign Massachusetts's 2020 block groups to the +2022 Massachusetts State Senate districting plan. We can use +`geopandas `__ to download the shapefiles that we +need straight from the TIGER/Line files on the U.S. Census Bureau's +website and load them into Python as GeoDataFrames: + +.. code:: python + + >>> districts = geopandas.read_file("https://www2.census.gov/geo/tiger/TIGER2022/SLDU/tl_2022_25_sldu.zip") + >>> units = geopandas.read_file("https://www2.census.gov/geo/tiger/TIGER2022/BG/tl_2022_25_bg.zip") + +.. note:: + + These shapefiles and the GeoDataFrames that we have + created from them contain only basic information about the geographies + of the underlying units. It is often important to incorporate additional + information about population, election data, etc. See + `this page <../user/getting_started.html>`_ for more info. + +Getting the assignment +---------------------- + +Now we can use ``maup`` to assign our units to districts. + +.. code:: python + + >>> assignment = maup.assign(units, districts) + +Note that this produces a warning message something like the following: + +.. code:: python + + --------------------------------------------------------------------------- + + + Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation. + + df = df[df.area > area_cutoff].reset_index(drop=True) + UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation. + + geometries = geometries[geometries.area > area_cutoff] + Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation. + + return assign_to_max(intersections(sources, targets, area_cutoff=0).area) + +This is because shapefiles from the Census Bureau all use a “geographic” +coordinate projection, using latitude/longitude coordinates to represent +the locations of points. These coordinates are too coarse for many +geometric operations (e.g., accurate computations of lengths and areas), +so it is recommended (but not required!) to convert to a +“non-geographic” coordinate projection better suited for such +computations. One option is to reproject to a suitable UTM (Universal +Transverse Mercator) projection via + +.. code:: python + + >>> districts.to_crs(districts.estimate_utm_crs(), inplace=True) + >>> units.to_crs(units.estimate_utm_crs()), inplace=True) + +Now let's repeat the ``maup.assign`` command: + +.. code:: python + + >>> assignment = maup.assign(units, districts) + +And now it runs without producing the warning message! 🎉 + +We'll save the assignment in a column of our ``units`` GeoDataFrame: + +.. code:: python + + >>> units["SENDIST"] = assignment + +Let's use the `pandas `__ ``.isna()`` method +to see if we have any units that could not be assigned to districts: + +.. code:: python + + >>> assignment.isna().sum() + 0 + +This means that every unit was successfully assigned. If our basic units +were too large to get a meaningful assignment, or if the districts did +not cover all of our units (e.g. if our units included parts of the +Atlantic Ocean but the districts did not), then we would have units with +NA assignments that we would need to make decisions about. + + +Creating a ``Partition`` with the real-life assignment +------------------------------------------------------ + +Now we are ready to use this assignment in GerryChain. We'll start by +building the GerryChain Graph object that records adjacency information +between our basic units + +.. code:: python + + >>> from gerrychain import Graph, Partition + >>> graph = Graph.from_geodataframe(units) + +For larger GeoDataFrames, building this Graph object can take awhile! +Once you have built it, you may want to save it as a .json file: + +.. code:: python + + >>> graph.to_json("./BG_graph.json") + +Then you can read it in again later using: + +.. code:: python + + >>> graph = Graph.from_json("./BG_graph.json") + +Each node of this graph represents one basic unit, and the node contains +all the information about that unit that was contained in the +GeoDataFrame for the units. For example, here is the first node in the +graph: + +.. code:: python + + >>> graph.nodes[0] + {'boundary_node': False, + 'area': 1054008.1312115968, + 'STATEFP': '25', + 'COUNTYFP': '027', + 'TRACTCE': '728200', + 'BLKGRPCE': '3', + 'GEOID': '250277282003', + 'NAMELSAD': 'Block Group 3', + 'MTFCC': 'G5030', + 'FUNCSTAT': 'S', + 'ALAND': 1053449, + 'AWATER': 0, + 'INTPTLAT': '+42.3265093', + 'INTPTLON': '-071.8222284', + 'SENDIST': 33} + +.. note:: + If we happened to have a preexisting unit graph and we + needed to add the district assignments to the nodes of the graph, we + would need to match the nodes of our graph to the geometries in + ``units`` by their GEOIDs. We could use the Graph object's ``.join()`` + method to do this matching automatically: + + .. code:: python + + >>> graph.join(units, columns=["SENDIST"], left_index="GEOID", right_index="GEOID") + + The ``left_index`` and ``right_index`` arguments tell the ``.join()`` + method to use the ``GEOID`` node attribute and the ``GEOID`` column of + ``units`` to match the records in ``units`` to the nodes of our + graph. + +Now we use the Graph node attribute called ``SENDIST`` to create a +Partition: + +.. code:: python + + >>> real_life_plan = Partition(graph, "SENDIST") + +To check our work, let's plot the ``real_life_plan`` on the units using +the Partition's ``.plot()`` method: + +.. code:: python + + >>> real_life_plan.plot(units, figsize=(10, 10), cmap="tab20") + >>> plt.axis('off') + >>> plt.show() + +.. figure:: + ../_static/images/output_23_0.png + :alt: MA real-life plan + + +That looks like a districting plan! Woohoo! 🎉🎉🎉 + +Troubleshooting possible issues +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If the plan looked like random noise or confetti, then we might suspect +that something had gone wrong. The two places we would want to look for +problems would be: + +- the ``graph.join`` call, which would go wrong if the GEOIDs did not + match up correctly, or +- the final ``real_life_plan.plot`` call, which would go wrong if the + GeoDataFrame's index did not match the node IDs of our graph in the + right way. + +We could inspect both issues by making sure that the records with +matching IDs actually referred to the same block groups. + +You also might run into problems when you go to run a Markov chain using +the partition we made. If the districts are not contiguous with respect +to your underlying graph, you would need to add edges (within reason) to +make the graph agree with the notion of contiguity that the real-life +plan uses. See `What to do about islands and +connectivity `__ +for a guide to handling those types of issues. diff --git a/maup/intersections.py b/maup/intersections.py index 894e745..af3953e 100644 --- a/maup/intersections.py +++ b/maup/intersections.py @@ -9,7 +9,7 @@ @require_same_crs def intersections(sources, targets, output_type="geoseries", area_cutoff=None): """Computes all of the nonempty intersections between two sets of geometries. - By default, the returned `~geopandas.GeoSeries` will have a MultiIndex, where the + By default, the returned :meth:`~geopandas.GeoSeries` will have a MultiIndex, where the geometry at index *(i, j)* is the intersection of ``sources[i]`` and ``targets[j]`` (if it is not empty). If output_type == "geodataframe", the return type is a range-indexed GeoDataFrame @@ -20,8 +20,8 @@ def intersections(sources, targets, output_type="geoseries", area_cutoff=None): :param targets: geometries :type targets: :class:`~geopandas.GeoSeries` or :class:`~geopandas.GeoDataFrame` :rtype: :class:`~geopandas.GeoSeries` - :param area_cutoff: (optional) if provided, only return intersections with - area greater than ``area_cutoff`` + :param area_cutoff: (optional) if provided, only return intersections with area + greater than ``area_cutoff`` :type area_cutoff: Number or None """ diff --git a/maup/smart_repair.py b/maup/smart_repair.py index 7d4e0d2..f0517c9 100644 --- a/maup/smart_repair.py +++ b/maup/smart_repair.py @@ -47,35 +47,36 @@ def smart_repair(geometries_df, snapped=True, snap_precision=10, fill_gaps=True, GeoDataFrame or GeoSeries, with an emphasis on preserving intended adjacency relations between geometries as closely as possible. - Specifically, the algorithm - (1) Applies shapely.make_valid to all polygon geometries. - (2) If snapped = True (default), snaps all polygon vertices to a grid of size no - more than 10^(-snap_precision) times the max of width/height of the entire - extent of the input. HIGHLY RECOMMENDED to avoid topological exceptions due to - rounding errors. Default value for snap_precision is 10; if topological - exceptions still occur, try reducing snap_precision (which must be integer- - valued) to 9 or 8. - (3) Resolves all overlaps. - (4) If fill_gaps = True (default), closes all simply connected gaps with area - less than fill_gaps_threshold times the largest area of all geometries adjoining - the gap. Default threshold is 10%; if fill_gaps_threshold = None then all - simply connected gaps will be filled. - (5) If nest_within_regions is a secondary GeoDataFrame/GeoSeries of region boundaries - (e.g., counties in a state) then all of the above will be performed so that - repaired geometries nest cleanly into the region boundaries; each repaired - geometrywill be contained in the region with which the original geometry has the - largest area of intersection. Default value is None. - (6) If min_rook_length is given a numerical value, replaces all rook adjacencies - with length below this value with queen adjacencies. Note that this is an - absolute value and not a relative value, so make sure that the value provided - is in the correct units with respect to the input's CRS. - Default value is None. - (7) Sometimes the repair process creates tiny fragments that are disconnected from - the district that they are assigned to. A final cleanup step assigns any such - fragments to a neighboring geometry if their area is less than - disconnection_threshold times the area of the largest connected component of - their assigned geometry. Default threshold is 0.01%, and this seems to work - well in practice. + Specifically, the algorithm: + + 1. Applies shapely.make_valid to all polygon geometries. + 2. If snapped = True (default), snaps all polygon vertices to a grid of size no + more than 10^(-snap_precision) times the max of width/height of the entire + extent of the input. HIGHLY RECOMMENDED to avoid topological exceptions due to + rounding errors. Default value for snap_precision is 10; if topological + exceptions still occur, try reducing snap_precision (which must be integer- + valued) to 9 or 8. + 3. Resolves all overlaps. + 4. If fill_gaps = True (default), closes all simply connected gaps with area + less than fill_gaps_threshold times the largest area of all geometries adjoining + the gap. Default threshold is 10%; if fill_gaps_threshold = None then all + simply connected gaps will be filled. + 5. If nest_within_regions is a secondary GeoDataFrame/GeoSeries of region boundaries + (e.g., counties in a state) then all of the above will be performed so that + repaired geometries nest cleanly into the region boundaries; each repaired + geometry will be contained in the region with which the original geometry has the + largest area of intersection. Default value is None. + 6. If min_rook_length is given a numerical value, replaces all rook adjacencies + with length below this value with queen adjacencies. Note that this is an + absolute value and not a relative value, so make sure that the value provided + is in the correct units with respect to the input's CRS. + Default value is None. + 7. Sometimes the repair process creates tiny fragments that are disconnected from + the district that they are assigned to. A final cleanup step assigns any such + fragments to a neighboring geometry if their area is less than + disconnection_threshold times the area of the largest connected component of + their assigned geometry. Default threshold is 0.01%, and this seems to work + well in practice. """ # Keep a copy of the original input for comparisons later! @@ -613,21 +614,22 @@ def drop_bad_holes(reconstructed_df, holes_df, fill_gaps_threshold): def smart_close_gaps(geometries_df, holes_df): """ Fill simply connected gaps; general procedure is roughly as follows: - (1) Fill in gaps that only intersect one non-exterior geometry in the - obvious way. - (2) For remaining gaps, partially fill by "convexifying" boundaries with each - non-exterior geometry. This will have the effect of completely filling - gaps that only intersect 2 geometries and no exterior boundaries. - (3) For any gap that intersects 4 or more geometries nontrivially (including - exterior boundaries), find the non-adjacent pair with the shortest distance - between them and try to connect the pair by adding a "triangle" to each of the - non-exterior geometries in the pair. (Keep trying until this succeeds for - some pair.) This reduces the gap to 1 or 2 smaller gaps, each intersecting - strictly fewer geometries than the original. Put the smaller gaps back in the - queue for the next round. - (4) For any gap that intersects exactly 3 geometries (including exterior boundaries) - nontrivially, fill by a process that gives a portion of the gap to each of - the non-exterior geometries that it intersects. + + 1. Fill in gaps that only intersect one non-exterior geometry in the + obvious way. + 2. For remaining gaps, partially fill by "convexifying" boundaries with each + non-exterior geometry. This will have the effect of completely filling + gaps that only intersect 2 geometries and no exterior boundaries. + 3. For any gap that intersects 4 or more geometries nontrivially (including + exterior boundaries), find the non-adjacent pair with the shortest distance + between them and try to connect the pair by adding a "triangle" to each of the + non-exterior geometries in the pair. (Keep trying until this succeeds for + some pair.) This reduces the gap to 1 or 2 smaller gaps, each intersecting + strictly fewer geometries than the original. Put the smaller gaps back in the + queue for the next round. + 4. For any gap that intersects exactly 3 geometries (including exterior boundaries) + nontrivially, fill by a process that gives a portion of the gap to each of + the non-exterior geometries that it intersects. """ geometries_df = geometries_df.copy() holes_df = holes_df.copy() @@ -1507,11 +1509,13 @@ def shortest_path_in_polygon(polygon, start, end, full_triangulation=None): def convexify_hole_boundaries(geometries_df, holes_df): """ Partially fill gaps as follows: - (1) Assign any gap that only adjoins 1 geometry to that geometry. - (2) For each gap that adjoins at least 2 geometries, "convexify" the geometries - surrounding the gap by replacing the gap's boundary with each geometry by the - shortest path within the gap between its endpoints and "filling in" the - geometry up to the new boundary. (Exterior boundaries, if any, are left alone.) + + 1. Assign any gap that only adjoins 1 geometry to that geometry. + 2. For each gap that adjoins at least 2 geometries, "convexify" the geometries + surrounding the gap by replacing the gap's boundary with each geometry by the + shortest path within the gap between its endpoints and "filling in" the + geometry up to the new boundary. (Exterior boundaries, if any, are left alone.) + If there are only 2 non-exterior (and no exterior) geometries intersecting the gap, this will fill the gap completely; otherwise it will usually leave one or more smaller gaps remaining. The convexity of the geometry boundaries will simplify