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geocrosswalk.py
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geocrosswalk.py
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# This file is part of the Minnesota Population Center's NHGISXWALK.
# For copyright and licensing information, see the NOTICE and LICENSE files
# in this project's top-level directory, and also on-line at:
# https://github.com/ipums/nhgisxwalk
"""IPUMS/NHGIS Census Crosswalk and Atom Generator
"""
import os
import pickle
import shutil
import numpy
import pandas
from .id_codes import (
bg_gj,
bgp_gj,
blk_gj,
co_gj,
code_cols,
generate_atom_id,
generate_geoid,
gj_code_components,
id_from,
tr_gj,
)
# used to fetch/vectorize ID generation functions
id_generator_funcs = [blk_gj, bgp_gj, bg_gj, tr_gj, co_gj]
id_generators = {f.__name__: f for f in id_generator_funcs}
# sorting parameters -- all crosswalks are sorted accordingly
SORT_PARAMS = {
"ascending": True,
"na_position": "last",
"ignore_index": True,
"inplace": True,
}
# sort columns in this order
SORT_BYS = {
"nhgis_blk1990_blk2010_ge": ["GEOID90", "GEOID10"],
"nhgis_blk1990_blk2010_gj": ["GJOIN1990", "GJOIN2010"],
"nhgis_blk2000_blk2010_ge": ["GEOID00", "GEOID10"],
"nhgis_blk2000_blk2010_gj": ["GJOIN2000", "GJOIN2010"],
}
# All blk-blk crosswalk ID columns
ID_COLS = ["GJOIN1990", "GJOIN2000", "GJOIN2010", "GEOID90", "GEOID00", "GEOID10"]
# special README.txt name for block group parts
BGP_README = "nhgis_bgp_crosswalk"
# extensions
CSV = "csv"
ZIP = "zip"
TXT = "txt"
# NaN string
NaN = "nan"
class GeoCrossWalk:
"""Generate a temporal crosswalk for census geography data
built from the smallest intersecting units (atoms). Each row in
a crosswalk represents a single atom and is comprised of a source
ID (geo+year), a target ID (geo+year), and at least one column
of weights. The weights are the interpolated proportions of source
attributes that are are calculated as being within the target units.
For a description of the algorithmic workflow see the
General Crosswalk Construction Framework [https://github.com/ipums/nhgisxwalk/
blob/master/resources/frameworks/general-crosswalk-construction-framework.pdf].
For a description of the algorithmic workflow
in the 1990 "no data" scenarios see
Handling 1990 No-Data Blocks in Crosswalks [https://github.com/ipums/nhgisxwalk/
blob/master/resources/frameworks/handling-1990-no-data-blocks-in-crosswalks.pdf].
For more information of the base crosswalks see their
technical details [https://www.nhgis.org/user-resources/frameworks/
geographic-crosswalks#details] here.
For further description see:
* Schroeder, J. P. 2007. Target-density weighting interpolation
and uncertainty evaluation for temporal analysis of census
data. Geographical Analysis 39 (3):311–335.
Parameters
----------
base : pandas.DataFrame
The base-level crosswalk containing composite atoms of smallest units
to build larger crosswalk atoms of larger source and target units.
source_year : str
The census source units year.
target_year : str
The census target units year.
source_geo : str
The census source geographic units.
target_geo : str
The census target geographic units.
base_source_geo : str
The base-level crosswalk's source geographic units.
base_source_table : str
The path to the source year's base tabular data.
input_var : str or iterable
Demographic or housing census variables. For currently available
variables call ``nhgisxwalk.desc_code_YYYY()`` where YYYY is the
census year.
weight_var : str or iterable
The column tags to use for the atomic interpolated variables.
weight_prefix : str
Optional prefix to add to the weights columns. Default is "wt_".
base_weight : str
Name for the weight column in the base crosswalk. Default is "WEIGHT".
base_parea : str
Name for the area column in the base crosswalk. Default is "PAREA".
Note: 1990 is "PAREA_VIA_BLK00".
stfips : str
If a state-level subset is desired, set the state FIPS code.
add_geoid : bool
Add in the corresponding Census GEOID (``True``). Default is ``True``.
This options is not available for "bgp" (block group parts).
The associated method is ``generate_geoids()``.
keep_base : bool
Keep the base crosswalk when building of the atomic crosswalk
is complete (``True``). Default is ``False``.
drop_base_cols : bool
Flag to remove unnecessary columns from the base crosswalk.
Default is ``True``.
vectorized : bool
Vectorize the ``id_codes.id_from()`` function for (potential)
speedups (``True``). Default is ``True``.
supp_source_table : str
The path to the source year's base supplementary
tabular data. Default is ``None``.
drop_supp_col : bool
Drop the supplementary containing ID generated with the 1990 "no data" process.
Default is ``True``.
weights_precision : int
Round the resultant crosswalk weights to this many decimals. Default is 10.
Attributes
----------
source : str
The combination of the source census geographic unit and census year.
target : str
The combination of the target census geographic unit and census year.
xwalk_name : str
The combination of ``source` and ``target``.
xwalk : pandas.DataFrame
The actual crosswalk generated between ``source`` and ``target``.
wt : str
See ``weight`` parameter.
source_id_components : pandas.DataFrame
The ``source`` result from ``id_codes.gj_code_components()``.
target_id_components : pandas.DataFrame
The ``target`` result from ``id_codes.gj_code_components()``.
base_tab_df : pandas.DataFrame
Summary file tabular for associated base crosswalk.
all_base_ids : numpy.array
All source IDs from the base crosswalk. Declared in ``handle_1990_no_data``.
pop_base_ids : numpy.array
Source IDs associated with some population/housing value
from the base crosswalk. Declared in ``handle_1990_no_data``.
nopop_base_ids : numpy.array
Source IDs associated with no population/housing value
from the base crosswalk. Declared in ``handle_1990_no_data``.
nopop_base : pandas.DataFrame
The base-level crosswalk associated with no population/housing value containing
composite atoms of smallest units to build larger crosswalk atoms of larger
source and target units. Declared in ``handle_1990_no_data``.
nod_xwalk : pandas.DataFrame
The actual crosswalk associated with no population/housing value
generated between ``source`` and ``target``.
Declared in ``handle_1990_no_data``.
weight_var : list
See ``weight_var`` parameter.
weight_col : str or iterable
Full weight column names (including prefixes).
Declared in ``handle_1990_no_data``.
supp_geo : str
Type of geographic unit needed to determine unpopulated units. Currently
this can only be 1990 block groups ('bg') for determining unpopulated
1990 NHGIS block group parts ('bgp').
supp_source : str or None
The source supplementary unit.
supp_target : str or None
The target supplementary unit.
src_unacc : numpy.array
Unaccounted for / potential source IDs.
Declared in ``handle_1990_no_data`` or ``accounting``.
trg_unacc : numpy.array
Unaccounted for / potential target IDs. Declared in ``accounting``.
Notes
-----
For more information see the ``nhgisxwalk`` FAQ
`page <https://github.com/ipums/nhgisxwalk/wiki/FAQ-&-Resources>`_.
Examples
--------
**Ex. 1:** Instantiate the example data and calculate an atomic crosswalk.
>>> import nhgisxwalk
>>> df = nhgisxwalk.example_crosswalk_data()
>>> df
bgp1990 blk1990 blk2010 tr2010 wt pop_1990 hh_1990
0 A A.1 X.1 X 1.0 60.0 25.0
1 A A.2 X.2 X 0.3 100.0 40.0
2 A A.2 Y.1 Y 0.7 100.0 40.0
3 B B.1 X.3 X 1.0 50.0 20.0
4 B B.2 Y.2 Y 1.0 80.0 30.0
This synthetic data is comprised of 1990 and 2010 census blocks (``blk1990``
and ``blk2010``, respectively); the base atomic crosswalk. Since the
boundaries of census blocks are subject to change over time, the 1990
blocks don't nest perfectly in the 2010 blocks. The magnitude of this
imperfect nesting is represented in the weight column (``wt``), which
records the areal portion of the 1990 block that intersects with the 2010
blocks. Further, the population and household counts for the 1990 blocks
are available through the ``pop_1990`` and ``hh_1990`` columns.
Finally, the associated 1990 census block group parts and the 2010 census
tracts are also represented with ``bgp1990`` and ``tr2010``. With this
information it is possible to create a (synthetic) 1990 block group part
to 2010 tract crosswalk.
>>> atoms = nhgisxwalk.calculate_atoms(
... df,
... weight="wt",
... input_var=["pop_1990", "hh_1990"],
... weight_var=["pop", "hh"],
... weight_prefix="wt_",
... source_id="bgp1990",
... groupby_cols=["bgp1990", "tr2010"]
... )
>>> atoms
bgp1990 tr2010 wt_pop wt_hh
0 A X 0.562500 0.569231
1 A Y 0.437500 0.430769
2 B X 0.384615 0.400000
3 B Y 0.615385 0.600000
The result is four atomic intersections between the synthetic 1990 census
block group parts and the 2010 census tracts with varying population
and household proportional weights.
**Ex. 2:** Generate an empirical crosswalk between block group parts from
the 2000 Decennial Census and tracts from the 2010 Decennial Census.
>>> import nhgisxwalk
Set the source and target years to 2000 and 2010, respectively.
>>> source_year, target_year = "2000", "2010"
Read in the base unit crosswalk. This is the crosswalk that is used
to build up the source and and target units from the source and and target
years. Currently supported base crosswalks are 1990-2010 blocks and
2000-2010 blocks, which can be downloaded from
NHGIS [https://github.com/ipums/nhgisxwalk/wiki/FAQ-&-Resources
#where-can-i-download-the-base-geographic-crosswalks>].
The versions found within ``nhgisxwalk`` (see
`./testing_data_subsets/` [https://github.com/ipums/
nhgisxwalk/tree/master/testing_data_subsets])
are single state subsets (Delaware) for testing and demonstration purposes.
>>> subset_data_dir = "./testing_data_subsets/"
>>> base_xwalk_name = "nhgis_blk%s_blk%s_gj" % (source_year, target_year)
>>> data_types = nhgisxwalk.str_types(
... ["GJOIN%s"%source_year, "GJOIN%s"%target_year]
... )
>>> from_csv = {"path": subset_data_dir, "archived": True, "remove_unpacked": True}
>>> read_csv = {"dtype": data_types}
>>> base_xwalk = nhgisxwalk.xwalk_df_from_csv(
... base_xwalk_name, **from_csv, **read_csv
... )
>>> base_xwalk.head()
GJOIN2000 GJOIN2010 WEIGHT PAREA
0 G10000100401001000 G10000100401001000 1.000000 1.000000
1 G10000100401001001 G10000100401001001 0.999981 0.999988
2 G10000100401001001 G10000100401001003 0.000019 0.000012
3 G10000100401001002 G10000100401001002 1.000000 1.000000
4 G10000100401001003 G10000100401001003 1.000000 1.000000
This base unit crosswalk shows the areal portion (``WEIGHT``) of the
source units (``GJOIN2000``) in the target units (``GJOIN2010``).
For example, the vast majority of 2000 block ``G10000100401001001``
intersects with 2010 block ``G10000100401001001``, but a minute portion
intersects with 2010 block ``G10000100401001003``.
Next, use the shorthand lookup tool for geography abbreviations and set
the source and target geographies to ``bgp`` and ``tr``, respectively.
>>> shorthand = nhgisxwalk.valid_geo_shorthand(shorthand_name=False)
>>> for k,v in shorthand.items():
... print(f"{k}: {v}")
block: blk
block group part: bgp
block group: bg
tract: tr
county: co
>>> source_geog, target_geog = "bgp", "tr"
Select the Persons and Families variables with the lookup tool
for the 2000 Summary File 1b (``desc_code_2000_SF1b``), and set
column tags for the weights to be interpolated.
>>> input_vars = [
... nhgisxwalk.desc_code_2000_SF1b["Persons"]["Total"],
... nhgisxwalk.desc_code_2000_SF1b["Families"]["Total"],
... ]
>>> input_vars
['FXS001', 'F2V001']
>>> input_var_tags = ["pop", "fam"]
At this point an ``nhgisxwalk.GeoCrossWalk`` object can be instantiated,
which will be a state-level crosswalk for Delaware (state FIPS code 10).
>>> subset_state = "10"
>>> bgp2000_to_tr2010 = nhgisxwalk.GeoCrossWalk(
... base_xwalk,
... source_year=source_year,
... target_year=target_year,
... source_geo=source_geog,
... target_geo=target_geog,
... base_source_table=subset_data_dir+"/2000_block.csv.zip",
... input_var=input_vars,
... weight_var=input_var_tags,
... add_geoid=True,
... stfips=subset_state
... )
>>> cols = ["bgp2000gj", "tr2010gj", "wt_pop", "wt_fam"]
>>> bgp2000_to_tr2010.xwalk[1020:1031][cols]
bgp2000gj tr2010gj wt_pop wt_fam
1020 G10000509355299999051302R1 G1000050051302 1.000000 1.000000
1021 G10000509355299999051302R2 G1000050051302 1.000000 1.000000
1022 G10000509355299999051302U1 G1000050051302 1.000000 1.000000
1023 G10000509355299999051303R1 G1000050051303 1.000000 1.000000
1024 G10000509355299999051303U1 G1000050051303 1.000000 1.000000
1025 G10000509355299999051304R1 G1000050051305 0.680605 0.633909
1026 G10000509355299999051304R1 G1000050051306 0.319167 0.365782
1027 G10000509355299999051304R1 G1000050051400 0.000227 0.000309
1028 G10000509355299999051304R2 G1000050051305 0.802661 0.817568
1029 G10000509355299999051304R2 G1000050051306 0.197339 0.182432
1030 G10000509355299999051304U2 G1000050051305 0.530658 0.557464
The above slice of the generated crosswalk provides two key insights.
First, the initial 6 atoms show that the corresponding 2000 block group
parts nest entirely within the intersecting 2010 tracts. However, the
following 5 atoms partially intersect to varying degrees. Second,
the proportional weight for each variable will likely differ based on
the counts used for interpolation. This is the reason why a single
weighted portion can't be used for all variables.
The corresponding census-assigned GEOIDs are also available within the
crosswalk. The block group parts have no corresponding GEOIDs because
they are a direct product of the NHGIS.
>>> bgp2000_to_tr2010.xwalk[1020:1031][["bgp2000gj", "tr2010gj", "tr2010ge"]]
bgp2000gj tr2010gj tr2010ge
1020 G10000509355299999051302R1 G1000050051302 10005051302
1021 G10000509355299999051302R2 G1000050051302 10005051302
1022 G10000509355299999051302U1 G1000050051302 10005051302
1023 G10000509355299999051303R1 G1000050051303 10005051303
1024 G10000509355299999051303U1 G1000050051303 10005051303
1025 G10000509355299999051304R1 G1000050051305 10005051305
1026 G10000509355299999051304R1 G1000050051306 10005051306
1027 G10000509355299999051304R1 G1000050051400 10005051400
1028 G10000509355299999051304R2 G1000050051305 10005051305
1029 G10000509355299999051304R2 G1000050051306 10005051306
1030 G10000509355299999051304U2 G1000050051305 10005051305
"""
def __init__(
self,
base,
source_year=None,
target_year=None,
source_geo=None,
target_geo=None,
base_source_table=None,
input_var=None,
weight_var=None,
stfips=None,
base_source_geo="blk",
base_weight="WEIGHT",
base_parea="PAREA",
weight_prefix="wt_",
add_geoid=True,
keep_base=False,
drop_base_cols=True,
vectorized=True,
supp_source_table=None,
drop_supp_col=True,
weights_precision=10,
):
# Set class attributes -------------------------------------------------
# source and target class attributes
self.source_year, self.target_year = source_year, target_year
self.source_geo, self.target_geo = source_geo, target_geo
# check that supplemental table is declared if 1990 block group parts
if (
self.source_year == "1990"
and self.source_geo == "bgp"
and not supp_source_table
):
msg = (
"The 'supp_source_table' parameter must be declared (and valid) "
"when creating a crosswalk sourced from 1990 blocks/block group "
f"parts. The current value is '{supp_source_table}'."
)
raise RuntimeError(msg)
# set for gj (nhgis ID)
self.code_type, self.code_label = "gj", "GJOIN"
self.tabular_code_label = "GISJOIN"
# source and target names
self.source = self.source_geo + self.source_year + self.code_type
self.target = self.target_geo + self.target_year + self.code_type
self.xwalk_name = f"nhgis_{self.source[:-2]}_{self.target[:-2]}"
self.stfips = stfips
# input, summed, and weight variable names
self.input_var, self.weight_var = input_var, weight_var
self.base_weight = base_weight
self.base_parea = base_parea
self.wt = weight_prefix
# source geographies within the base crosswalk
self.base_source_geo = base_source_geo
# columns within the base crosswalk
self.base_source_col = self.code_label + self.source_year
self.base_target_col = self.code_label + self.target_year
# path to the tabular data for the base source units
self.base_source_table = base_source_table
# Prepare base for output crosswalk ------------------------------------
self.base = base
# fetch all components of that constitute various geographic IDs -------
self.fetch_gj_code_components()
# join the (base) source tabular data to the base crosswalk ------------
self.join_source_base_tabular()
# add source geographic unit ID to the base crosswalk ------------------
self.generate_ids("source", vectorized)
# add target geographic unit ID to the base crosswalk ------------------
self.generate_ids("target", vectorized)
# Create atomic crosswalk ----------------------------------------------
# calculate the source to target atom values
self.xwalk = calculate_atoms(
self.base,
weight=self.base_weight,
input_var=self.input_var,
weight_var=self.weight_var,
weight_prefix=self.wt,
source_id=self.source,
groupby_cols=[self.source, self.target],
overwrite_attrs=self,
)
"""-------------------------------------------------------------------------
Special case for handling 1990 data, where blocks without population/housing
where excluded from the publicly-released summary files
-------------------------------------------------------------------------"""
if self.source_year == "1990" or self.target_year == "1990":
if self.source_geo == "bgp" or self.target_geo == "bgp":
# block group IDs are needed to determine
# populated blocks in 1990
self.supp_geo = "bg"
if self.source_geo == "bgp":
__supp_src = self.supp_geo + self.source_year + self.code_type
self.supp_source = __supp_src
self.supp_target = None
else:
self.supp_source = None
__supp_tgt = self.supp_geo + self.target_year + self.code_type
self.supp_target = __supp_tgt
# call special function
handle_1990_no_data(self, vectorized, supp_source_table, drop_supp_col)
else:
raise RuntimeError("Only 'bgp' is currently functional.")
# keep only the necessary base columns ---------------------------------
if drop_base_cols:
self._drop_base_cols()
# Step 9 from the General Workflow -------------------------------------
self.accounting()
# discard building base if not needed ----------------------------------
if not keep_base:
del self.base
# add column(s) for the original Census GEOID --------------------------
# -- this options is not available for "bgp" (block group parts)
if add_geoid:
for xdir in [self.source, self.target]:
if xdir.startswith("bgp"):
continue
else:
# col = "tr%s" % target_year
self.xwalk[xdir[:-2] + "ge"] = self.xwalk[xdir].map(
lambda x: generate_geoid(x)
)
# reorder columns
_id_cols = [c for c in self.xwalk.columns if c not in self.weight_col]
self.xwalk = self.xwalk[_id_cols + self.weight_col]
# extract a subset of national resultant crosswalk to target state (if desired)
if self.stfips:
self.xwalk = extract_state(
self.xwalk, self.stfips, self.xwalk_name, self.target
)
self.xwalk_name += "_" + self.stfips
# round the weights in the resultant crosswalk (if desired)
if weights_precision:
self.xwalk = round_weights(self.xwalk, decimals=weights_precision)
# sort the resultant values
self.xwalk.sort_values(by=[self.source, self.target], **SORT_PARAMS)
def _drop_base_cols(self):
"""Retain only ID columns and original weights in the base crosswalk."""
retain = [
self.source,
self.base_source_col,
self.base_target_col,
self.target,
self.base_weight,
self.base_parea,
]
order = [c for r in retain for c in self.base.columns if c.startswith(r)]
self.base = self.base[order]
def fetch_gj_code_components(self):
"""Fetch dataframe that describes each component of a geographic unit ID."""
self.base_source_gj_components = gj_code_components(
self.source_year, self.base_source_geo
)
self.source_gj_components = gj_code_components(
self.source_year, self.source_geo
)
self.target_gj_components = gj_code_components(
self.target_year, self.target_geo
)
def join_source_base_tabular(self):
"""Join tabular attributes to base crosswalk."""
# read in national tabular data
data_types = str_types(self.base_source_gj_components["Variable"])
self.base_tab_df = pandas.read_csv(self.base_source_table, dtype=data_types)
# Special case for 2000 blocks (of 2000 bgp)-- needs Urban/Rural code
# For more details see:
# https://gist.github.com/ipums/36c7640af1f228cdc8a691505262e543
# and
# nhgisxwalk/notebooks/build_subset.ipynb
# do left merge
self.base = pandas.merge(
left=self.base,
right=self.base_tab_df,
how="left",
left_on=self.base_source_col,
right_on=self.tabular_code_label,
validate="many_to_many",
)
def generate_ids(self, id_type, vect, supp=False, supp_base=None, return_df=False):
"""Add source or target geographic unit ID to the base crosswalk.
Parameters
----------
id_type : str
Either ``source`` or ``target``.
vect : bool
See the ``vectorized`` parameter in ``GeoCrossWalk``.
supp : bool
Use the supplementary (unpopulated) base crosswalk. Default is ``False``.
supp_base : pandas.DataFrame
The supplementary (unpopulated) base crosswalk. See ``self.nopop_base``.
Default is ``None``.
return_df : bool
Set to ``True`` to return ``df`` instead of updating ``self.base``.
Returns
-------
df : pandas.DataFrame
The updated crosswalk dataframe.
"""
# set the crosswalk to be used
df = supp_base if return_df else self.base
# declare id type-specific variables
if id_type == "source":
cname = self.source if not supp else self.supp_source
year, geog, base_col = (
self.source_year,
self.source_geo,
self.base_source_col,
)
else:
cname = self.target if not supp else self.supp_target
year, geog, base_col = (
self.target_year,
self.target_geo,
self.base_target_col,
)
# flag supplemental block group parts scenario
supp_bgp = supp and geog == "bgp"
# generate IDS
if not supp and geog == "bgp":
cols, func = code_cols(geog, year), bgp_gj
args = df, cols
df = func(*args, cname=cname)
elif id_type == "target" or supp_bgp:
if id_type == "source" and geog == "blk":
raise AttributeError()
func = id_generators[
"%s_gj" % geog if not supp else "%s_gj" % self.supp_geo
]
df[cname] = id_from(func, year, df[base_col], vect)
else:
msg = "(id_type: %s, supp: %s, cname: %s, year: %s, geog: %s)"
msg = msg % (id_type, supp, cname, year, geog)
msg = "Error in generate_ids params: " + msg
raise RuntimeError(msg)
# return the dataframe for supplementary scenarios
if return_df:
return df
def accounting(self):
"""Step 9 in the General Workflow."""
# Isolate unaccounted for source geographies
if not hasattr(self, "src_unacc"):
self.src_unacc = numpy.setdiff1d(
self.base[self.source].tolist(),
self.xwalk[self.source].tolist(),
)
# Isolate unaccounted for target geographies
self.trg_unacc = numpy.setdiff1d(
self.base[self.target].tolist(),
self.xwalk[self.target].tolist(),
)
# Append unaccounted source and target atoms
# start with the last index of the resultant crosswalk
endex = self.xwalk.index[-1]
# dict for source and target 'unaccounted for' ids
unaccounted = {self.source_geo: self.src_unacc}
unaccounted.update({self.target_geo: self.trg_unacc})
# check for these character lengths in column names for each record append
lsrc, ltrg = len(self.source_geo), len(self.target_geo)
# confirm variable data types
if not hasattr(self, "weight_col"):
self.weight_var = _check_vars(self.weight_var)
self.weight_col = _weight_columns(
self.wt if self.wt else "", self.weight_var
)
# iterate over {geography_type: unaccounted_for_ids}
for geo, unaccs in unaccounted.items():
# move to the geography type if there are no missing IDs
if unaccs.size == 0:
continue
# iterate over each unaccounted for id}
for idx, unacc in enumerate(unaccs, 1):
endex += idx
# append one record to the dataframe
self.xwalk.loc[endex] = [
unacc
if (c.split("_")[0][:lsrc] == geo or c.split("_")[0][:ltrg] == geo)
else 0.0
if c in self.weight_col
else numpy.nan
for c in self.xwalk.columns
]
def xwalk_to_pickle(self, path="", fext=".pkl"):
"""Write the produced ``GeoCrossWalk`` object."""
with open(path + self.xwalk_name + fext, "wb") as pkl_xwalk:
pickle.dump(self, pkl_xwalk, protocol=2)
@staticmethod
def xwalk_from_pickle(fname, fext=".pkl"):
"""Read in a produced crosswalk from a pickled ``GeoCrossWalk``."""
with open(fname + fext, "rb") as pkl_xwalk:
self = pickle.load(pkl_xwalk)
return self
def extract_state(in_xwalk, stfips, xwalk_name, endpoint, code="gj", sort_by=None):
"""Subset a national crosswalk to state-level (within target year).
Parameters
----------
in_xwalk : pandas.DataFrame
The original full crosswalk. See the ``xwalk`` attribute or ``base``
parameter in ``GeoCrossWalk``.
stfips : str
See the ``stfips`` parameter in ``GeoCrossWalk``.
Set to 'nan' to extract geographies with no associated state.
xwalk_name : str
See the ``xwalk_name`` parameter in ``GeoCrossWalk``.
endpoint : str
Column name to extract from.
code : str
The code type used in indexing unique states. Default is ``'gj'``.
sort_by : list
Columns to sort by. This is used along with ``SORT_PARAMS``.
Default is ``None``.
Returns
-------
out_xwalk : pandas.DataFrame
A state-level (target) crosswalk.
"""
# make sure the crosswalk isn't already an extracted state or overwritten
check_state_label = xwalk_name.split("_")[-1]
if check_state_label.isnumeric() or check_state_label == NaN:
msg = "This crosswalk may already be a state subset. "
msg += "Check the name/attributes.\n"
msg += f"\txwalk_name: '{xwalk_name}', stfips: {stfips}'"
raise RuntimeError(msg)
# set NaN (null) extraction condition
nan = stfips.lower() == NaN
# set extraction condition
extract_lambda = (
lambda x: nan if str(x) == NaN else _state(x, stfips=stfips, code=code)
)
condition = in_xwalk[endpoint].map(extract_lambda)
out_xwalk = in_xwalk[condition].copy()
if sort_by:
out_xwalk.sort_values(by=sort_by, **SORT_PARAMS)
return out_xwalk
def extract_unique_stfips(cls=None, df=None, endpoint="target", code="gj"):
"""Return a set of unique state FIPS codes.
Parameters
----------
cls : nhgisxwalk.GeoCrossWalk
Instance of a crosswalk object. When this parameter is used, the
``df`` parameter should not be used. Default is ``None``.
df : pandas.DataFrame
A crosswalk of spatio-temporal census geographies. Default is ``None``.
When the ``cls`` parameter is not used, this parameter should be used.
endpoint : str
The column from which unique states should extracted. Default is ``'target'``,
which represents the ``target`` attribute of an ``nhgisxwalk.GeoCrossWalk``.
code : str
The code type used in indexing unique states. Default is ``'gj'``.
Returns
-------
unique_stfips : set
All unique states from the specified column.
"""
if cls:
endpoint = getattr(cls, endpoint.lower())
df = cls.xwalk
unique_stfips = set(df[endpoint].map(lambda x: _state(x, code=code)))
return unique_stfips
def xwalk_df_to_csv(cls=None, dfkwds=dict(), path=""):
"""Write the produced crosswalk to ``.csv``.
Parameters
----------
cls : nhgisxwalk.GeoCrossWalk
Instance of a crosswalk object. When this parameter is used, the
``dfkwds`` parameter should not be used. Default is ``None``.
dfkwds : dict()
When the ``cls`` parameter is not used, this parameter should be used.
It should contain three keys in the form:
``{"df":<pandas.DataFrame>, "stfips": <str>, "xwalk_name": <str>}``.
Default is ``dict()``.
path : str
Directory path without the file name. Default is ``''``.
"""
if cls:
stfips = cls.stfips
xwalk_name = cls.xwalk_name
xwalk = cls.xwalk
else:
try:
stfips = dfkwds["stfips"]
except KeyError:
stfips = None
xwalk_name = dfkwds["xwalk_name"]
xwalk = dfkwds["df"]
if stfips and xwalk_name.split("_")[-1] != stfips:
xwalk_name += "_" + stfips
file_name = f"{xwalk_name}.{CSV}"
file_name = os.path.join(path, file_name)
xwalk.to_csv(file_name, index=False)
def xwalk_df_from_csv(
fname, path="", archived=False, remove_unpacked=False, **read_csv
):
"""Read in a produced crosswalk from an archived ``.zip`` or ``.csv``.
Pass in ``pandas.read_csv()`` keyword arguments with ``**kwargs``.
Parameters
----------
fname : str
Crosswalk (file) name.
path : str
Directory path without the file name. Default is ``''``.
archived : bool
``True`` if the crosswalk is coming from an archived
directory, otherwise ``False``. Default is ``False``.
remove_unpacked : bool
Delete the unzipped directory after (``True``), otherwise ``False``.
Default is ``False``.
read_csv : dict
Pass in ``pandas.read_csv()`` keyword arguments with this parameter.
Returns
-------
xwalk : pandas.DataFrame
The crosswalk dataframe.
"""
def _unzip_directory():
"""Initialize and decompress a directory."""
if not os.path.exists(archive_path):
os.mkdir(archive_path + "/")
shutil.unpack_archive(f"{archive_path}.{ZIP}", extract_dir=archive_path)
archive_path = None
if archived:
archive_path = f"{path}{fname}"
# make the temporary directory to store the unzipped archive and unpack
_unzip_directory()
# update file path+name
fname = f"{fname}/{fname}"
file_path = f"{path}{fname}.{CSV}"
xwalk = pandas.read_csv(file_path, **read_csv)
if remove_unpacked and archive_path:
shutil.rmtree(archive_path)
return xwalk
def calculate_atoms(
df,
weight=None,
input_var=None,
weight_var=None,
weight_prefix=None,
source_id=None,
groupby_cols=None,
overwrite_attrs=None,
):
"""Calculate the atoms (intersecting parts) of census geographies
and interpolate a proportional weight of the source attribute that
lies within the target geography.
Parameters
----------
df : pandas.DataFrame
The input data. See ``GeoCrossWalk.base``.
weight : str
The weight colum name(s).
input_var : str or iterable
The input variable column name(s).
weight_var : str or iterable
The groupby and summed variable column name(s).
weight_prefix : str
Prepend this prefix to the the ``weight_var`` column name.
source_id : str
The source ID column name.
groupby_cols : list
The dataframe columns on which to perform groupby.
overwrite_attrs : None or GeoCrossWalk
Setting this parameter to a ``GeoCrossWalk`` object overwrites the
``input_var`` and ``weight_var`` attributes. Default is ``None``.
Returns
-------