maup
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
We recommend installing maup from conda-forge using conda:
conda install -c conda-forge maup
You can get conda by installing Miniconda, a free Python distribution made especially for data science and scientific computing. You might also consider Anaconda, which includes many data science packages that you might find useful.
To install maup from PyPI, run pip install maup from your terminal.
Examples
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.
>>> import geopandas
>>> import pandas
>>>
>>> blocks = geopandas.read_file("zip://./examples/blocks.zip")
>>> precincts = geopandas.read_file("zip://./examples/precincts.zip")
>>> districts = geopandas.read_file("zip://./examples/districts.zip")
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.
>>> import maup
>>>
>>> assignment = maup.assign(precincts, districts)
>>> # Add the assigned districts as a column of the `precincts` GeoDataFrame:
>>> precincts["DISTRICT"] = assignment
>>> assignment.head()
0 7
1 5
2 13
3 6
4 1
dtype: int64
As an aside, you can use that assignment object to create a gerrychain Partition representing this districting plan.
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:
>>> variables = ["TOTPOP", "NH_BLACK", "NH_WHITE"]
>>>
>>> assignment = maup.assign(blocks, precincts)
>>> precincts[variables] = blocks[variables].groupby(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 and target geometries do not nest neatly (i.e. have overlaps), see Prorating 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-scaled geometries. Usually this happens 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"):
>>> election_columns = ["PRES16D", "PRES16R"]
>>> assignment = maup.assign(blocks, precincts)
>>>
>>> # We prorate the vote totals according to each block's share of the overall
>>> # precinct population:
>>> weights = blocks.TOTPOP / assignment.map(precincts.TOTPOP)
>>> prorated = maup.prorate(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.
>>> 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 about areal interpolation
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.
Prorating data when units do not nest neatly
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...)
We can use maup.intersections to break the two sets of precincts into pieces that nest neatly into both sets. Then we can disaggregate from the old precincts onto these pieces, and aggregate up from the pieces to the new precincts. This move is a bit complicated, so maup provides a function called prorate that does just that.
We'll use our same blocks GeoDataFrame to estimate the populations of the pieces for the purposes of proration.
For our "new precincts" shapefile, we'll use the VTD shapefile for Rhode Island that the U.S. Census Bureau produced as part of their 2018 test run of for the 2020 Census.
>>> old_precincts = precincts
>>> new_precincts = geopandas.read_file("zip://./examples/new_precincts.zip")
>>>
>>> columns = ["SEN18D", "SEN18R"]
>>>
>>> # Include area_cutoff=0 to ignore any intersections with no area,
>>> # like boundary intersections, which we do not want to include in
>>> # our proration.
>>> pieces = maup.intersections(old_precincts, new_precincts, area_cutoff=0)
>>>
>>> # Weight by prorated population from blocks
>>> weights = blocks["TOTPOP"].groupby(maup.assign(blocks, pieces)).sum()
>>> # Normalize the weights so that votes are allocated according to their
>>> # share of population in the old_precincts
>>> weights = maup.normalize(weights, level=0)
>>>
>>> # Use blocks to estimate population of each piece
>>> new_precincts[columns] = maup.prorate(
... pieces,
... old_precincts[columns],
... weights=weights
... )
>>> new_precincts[columns].head()
SEN18D SEN18R
0 752.0 51.0
1 370.0 21.0
2 97.0 17.0
3 585.0 74.0
4 246.0 20.0
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()::
>>> 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:
>>> 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)
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. These gaps and overlaps pose problems when you are interested in working with the adjacency graph of the precincts, and not just in mapping the precincts. This adjacency information is especially important when studying redistricting, because districts are almost always expected to be contiguous.
maup provides functions for closing gaps and resolving overlaps in a collection of geometries. As an example, we'll apply both functions to these geometries, which have both an overlap and a gap:
Usually the gaps and overlaps in real shapefiles are tiny and easy to miss, but this exaggerated example will help illustrate the functionality.
First, we'll use shapely to create the polygons from scratch:
from shapely.geometry import Polygon
geometries = geopandas.GeoSeries([
Polygon([(0, 0), (2, 0), (2, 1), (1, 1), (1, 2), (0, 2)]),
Polygon([(2, 0), (4, 0), (4, 2), (2, 2)]),
Polygon([(0, 2), (2, 2), (2, 4), (0, 4)]),
Polygon([(2, 1), (4, 1), (4, 4), (2, 4)]),
])
Now we'll close the gap:
without_gaps = maup.close_gaps(geometries)
The without_gaps geometries look like this:
And then resolve the overlaps:
without_overlaps_or_gaps = maup.resolve_overlaps(without_gaps)
The without_overlaps_or_gaps geometries look like this:
Alternatively, there is also a convenience maup.autorepair() function provided that attempts to resolve topological issues as well as close gaps and resolve overlaps:
without_overlaps_or_gaps = maup.autorepair(geometries)
The functions resolve_overlaps, close_gaps, and autorepair accept a relative_threshold argument. This threshold controls how large of a gap or overlap the function will attempt to fix. The default value of relative_threshold is 0.1, which means that the functions will leave alone any gap/overlap whose area is more than 10% of the area of the geometries that might absorb that gap/overlap. In the above example, we set relative_threshold=None to ensure that no gaps or overlaps were ignored.
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.
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