Integration of AISD School Catchments and Census Tract Data Workflow

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The purpose of this repository is to merge census tract and AISD shapefile data, in order to approximate the number of elementary, middle, and high school students from each census tract in a given school catchment. We want to do this for every 5 counties in Austin-Round Rock-San Marcos MSA:

  • Williamson

  • Bastrop

  • Travis

  • Hays

  • Caldwell

Kelly P. provided a demonstration of the workflow:

SABS_1516_SchoolLevels.zip notes

  • This directory contains the school catchments for almost every school district in Texas at all grade levels (e.g. elementary, middle, high school) for the 2015-16 school year.

  • Two districts relevant to the Austin Granular Model are missing: Burnet ISD and Austin ISD.

aisd_shapefiles/ notes

  • This directory contains the shapefiles for AISD elementary, middle and high schools.

  • These data are from the 2019-20 school year

2020_texas_census_tracts/

  • This directory contains the shapefile for all census tracts as defined in 2020

catchment_census_tract_overlap.ipynb

  • This short notebook contains an example of overlapping AISD elementary school catchments with the 2020 census tracts for Texas

Goals:

  1. Merge the AISD school catchments into the SABS school catchments by grade level to produce three shapefiles: one each for elementary, middle and high schools across Texas.

  2. Overlap the merged catchment shapefiles with the census shapefile and calculate the percentage overlap as ( overlap area / census tract area ). This will give us the percentage, by area, of the census tract assigned to each school.

  3. Write the results in CSV format (following the example in the notebook)

Authors

  • Ethan Ho (@eho-tacc)

  • Kelly Pierce

Snakemake Usage

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) repository and, if available, its DOI (see above).

Step 1: Obtain a copy of this workflow

  1. Create a new github repository using this workflow as a template .

  2. Clone the newly created repository to your local system, into the place where you want to perform the data analysis.

Step 2: Configure workflow

Configure the workflow according to your needs via editing the files in the config/ folder. Adjust config.yaml to configure the workflow execution, and samples.tsv to specify your sample setup.

Step 3: Install Snakemake

Install Snakemake using conda : 

conda create -c bioconda -c conda-forge -n snakemake snakemake

For installation details, see the instructions in the Snakemake documentation .

Step 4: Execute workflow

Activate the conda environment: 

conda activate snakemake

Test your configuration by performing a dry-run via 

snakemake --use-conda -n

Execute the workflow locally via 

snakemake --use-conda --cores $N

using $N cores or run it in a cluster environment via 

snakemake --use-conda --cluster qsub --jobs 100

or 

snakemake --use-conda --drmaa --jobs 100

If you not only want to fix the software stack but also the underlying OS, use 

snakemake --use-conda --use-singularity

in combination with any of the modes above. See the Snakemake documentation for further details.

Step 5: Investigate results

After successful execution, you can create a self-contained interactive HTML report with all results via: 

snakemake --report report.html

This report can, e.g., be forwarded to your collaborators. An example (using some trivial test data) can be seen here .

Step 6: Commit changes

Whenever you change something, don't forget to commit the changes back to your github copy of the repository: 

git commit -a
git push

Step 7: Obtain updates from upstream

Whenever you want to synchronize your workflow copy with new developments from upstream, do the following.

  1. Once, register the upstream repository in your local copy: git remote add -f upstream [email protected]:snakemake-workflows/aisd_shapefile_integration.git or git remote add -f upstream https://github.com/snakemake-workflows/aisd_shapefile_integration.git if you do not have setup ssh keys.

  2. Update the upstream version: git fetch upstream .

  3. Create a diff with the current version: git diff HEAD upstream/master workflow > upstream-changes.diff .

  4. Investigate the changes: vim upstream-changes.diff .

  5. Apply the modified diff via: git apply upstream-changes.diff .

  6. Carefully check whether you need to update the config files: git diff HEAD upstream/master config . If so, do it manually, and only where necessary, since you would otherwise likely overwrite your settings and samples.

Step 8: Contribute back

In case you have also changed or added steps, please consider contributing them back to the original repository:

  1. Fork the original repo to a personal or lab account.

  2. Clone the fork to your local system, to a different place than where you ran your analysis.

  3. Copy the modified files from your analysis to the clone of your fork, e.g., cp -r workflow path/to/fork . Make sure to not accidentally copy config file contents or sample sheets. Instead, manually update the example config files if necessary.

  4. Commit and push your changes to your fork.

  5. Create a pull request against the original repository.

Testing

Test cases are in the subfolder .test . They are automatically executed via continuous integration with Github Actions .

Code Snippets

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import geopandas as gpd
import descartes
import pandas as pd
import os
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import datetime
import numpy as np
import contextily as ctx
SoS Notebook Pandas numpy matplotlib geopandas From line 2 of notebooks/20210330_sandbox.ipynb 
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data_dir = '../../data/'
atx_ct_shp = gpd.read_file(os.path.join(data_dir, '2020_texas_census_tracts/2020_texas_census_tracts.shp'))
es_shp = gpd.read_file(os.path.join(data_dir, 'aisd_shapefiles/20_21_elem_updt_v2.shp'))
sabs_shp = gpd.read_file(os.path.join(data_dir, 'SABS_1516_SchoolLevels/SABS_1516_Primary.shp'))
SoS Notebook From line 14 of notebooks/20210330_sandbox.ipynb 
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sabs_shp = sabs_shp[sabs_shp['stAbbrev'] == 'TX']
sabs_shp.shape
SoS Notebook From line 21 of notebooks/20210330_sandbox.ipynb 
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print(atx_ct_shp.crs.name)
print(es_shp.crs.name)
print(sabs_shp.crs.name)
SoS Notebook From line 26 of notebooks/20210330_sandbox.ipynb 
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atx_shp_osm = atx_ct_shp.to_crs({'init': 'EPSG:3857'})
es_shp_osm = es_shp.to_crs({'init': 'EPSG:3857'})
sabs_shp_osm = sabs_shp
SoS Notebook From line 32 of notebooks/20210330_sandbox.ipynb 
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assert atx_shp_osm.crs == es_shp_osm.crs == sabs_shp_osm.crs
SoS Notebook From line 38 of notebooks/20210330_sandbox.ipynb 
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sabs_shp_osm.head()
SoS Notebook From line 42 of notebooks/20210330_sandbox.ipynb 
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es_shp_osm.head()
SoS Notebook From line 46 of notebooks/20210330_sandbox.ipynb 
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atx_shp_osm.head()
SoS Notebook From line 50 of notebooks/20210330_sandbox.ipynb 
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es_shp_osm.plot(figsize=(10, 7))
SoS Notebook From line 54 of notebooks/20210330_sandbox.ipynb 
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es_shp_osm['es_area'] = es_shp_osm.area
atx_shp_osm['cbg_area'] = atx_shp_osm.area
SoS Notebook From line 58 of notebooks/20210330_sandbox.ipynb 
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es_atx = gpd.overlay(atx_shp_osm, es_shp_osm, how='intersection')
SoS Notebook From line 63 of notebooks/20210330_sandbox.ipynb 
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es_atx['overlap_area'] = es_atx.area
# what percent of an elementary catchment corresponds to the underlying CBG?
es_atx['pct_overlap'] = (es_atx['overlap_area'] / es_atx['cbg_area']) * 100
SoS Notebook From line 67 of notebooks/20210330_sandbox.ipynb 
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overlaps = es_atx[['GEOID', 'ISD', 'CAMPUS', 'SCHL_YEAR', 'pct_overlap']]
SoS Notebook From line 73 of notebooks/20210330_sandbox.ipynb 
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overlaps['level'] = 'elementary'
SoS Notebook From line 77 of notebooks/20210330_sandbox.ipynb 
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overlaps.to_csv('AISD_elem_census_tract_overlaps.csv')
SoS Notebook From line 81 of notebooks/20210330_sandbox.ipynb 
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cd /Users/TASethanho/tacc/projects/meyers/covid/aisd_shapefile_integration
SoS Notebook From line 10 of notebooks/20210407_get_msas_term.ipynb 
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ls data/2018_msas/
SoS Notebook From line 22 of notebooks/20210407_get_msas_term.ipynb 
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from glob import glob as g
SoS Notebook From line 26 of notebooks/20210407_get_msas_term.ipynb 
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msa_shps = g('./data/2018_msas/*.shp')
SoS Notebook From line 30 of notebooks/20210407_get_msas_term.ipynb 
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msas = [gpd.read_file(fp) for fp in msa_shps]
SoS Notebook From line 38 of notebooks/20210407_get_msas_term.ipynb 
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gpd.concat(msas)
SoS Notebook From line 50 of notebooks/20210407_get_msas_term.ipynb 
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pd.concat(msas)
SoS Notebook From line 58 of notebooks/20210407_get_msas_term.ipynb 
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gpd.GeoDataFrame(d.concat(msas))
SoS Notebook From line 62 of notebooks/20210407_get_msas_term.ipynb 
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gpd.GeoDataFrame(pd.concat(msas))
SoS Notebook From line 66 of notebooks/20210407_get_msas_term.ipynb 
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gpd.GeoDataFrame(pd.concat(msas)).plot()
SoS Notebook From line 70 of notebooks/20210407_get_msas_term.ipynb 
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gpd.GeoDataFrame(pd.concat(msas)).plot(figsize=(20, 10))
SoS Notebook From line 74 of notebooks/20210407_get_msas_term.ipynb 
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tx_msas = gpd.GeoDataFrame(pd.concat(msas))
SoS Notebook From line 78 of notebooks/20210407_get_msas_term.ipynb 
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tx_msas = tx_msas[tx_msas['NAME'].str.contains(', TX', case=False)]
SoS Notebook From line 86 of notebooks/20210407_get_msas_term.ipynb 
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tx_msas.plot(figsize=(20, 10))
SoS Notebook From line 90 of notebooks/20210407_get_msas_term.ipynb 
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tx_msas[tx_msas['NAME'].str.contains('Austin')]
SoS Notebook From line 98 of notebooks/20210407_get_msas_term.ipynb 
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tx_msas[tx_msas['NAME'].str.contains('Austin')].plot(figsize=(20, 10))
SoS Notebook From line 102 of notebooks/20210407_get_msas_term.ipynb 
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import geopandas as gpd
import descartes
import pandas as pd
import os
from glob import glob
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import datetime
import numpy as np
import contextily as ctx
SoS Notebook Pandas numpy matplotlib geopandas From line 2 of notebooks/20210407.ipynb 
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data_dir = '../../data/'
SoS Notebook From line 15 of notebooks/20210407.ipynb 
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def find_fp_with_atx(query: str) -> gpd.GeoDataFrame:
    """Find the census shp that contains 'large' MSAs like Austin.
    Should be 25 in TX"""
    for fp in glob(query):
        gdf = gpd.read_file(fp)
        # Austin MSA
        has_atx = gdf['NAME'].str.contains('Austin', case=False)
        if has_atx.any():
            print(fp)
            return gdf[has_atx]
    assert 0
    # assert gdfs, "query returned null"
    # return gpd.GeoDataFrame(pd.concat(gdfs))
SoS Notebook From line 19 of notebooks/20210407.ipynb 
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find_fp_with_atx(os.path.join(data_dir, '2018_msas/*.shp'))
SoS Notebook From line 35 of notebooks/20210407.ipynb 
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atx_ct_shp = gpd.read_file(os.path.join(data_dir, '2020_texas_census_tracts/2020_texas_census_tracts.shp'))
es_shp = gpd.read_file(os.path.join(data_dir, 'aisd_shapefiles/20_21_elem_updt_v2.shp'))
sabs_shp = gpd.read_file(os.path.join(data_dir, 'SABS_1516_SchoolLevels/SABS_1516_Primary.shp'))
SoS Notebook From line 39 of notebooks/20210407.ipynb 
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msa_query = os.path.join(data_dir, '2018_msas/*cbsa*.shp')
SoS Notebook From line 45 of notebooks/20210407.ipynb 
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def get_msas(query: str) -> gpd.GeoDataFrame:
    """Concatenates all GDFs in glob `query`"""
    gdfs = [gpd.read_file(fp) for fp in glob(query)]
    assert gdfs, "query returned null"
    return gpd.GeoDataFrame(pd.concat(gdfs))
SoS Notebook From line 49 of notebooks/20210407.ipynb 
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msas_shp = get_msas(msa_query)
SoS Notebook From line 57 of notebooks/20210407.ipynb 
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sabs_shp = sabs_shp[sabs_shp['stAbbrev'] == 'TX']
tx_msas = msas_shp[msas_shp['NAME'].str.contains('TX', case=False)]
print(f"shape of MSAs GDF: {tx_msas.shape}")
SoS Notebook From line 61 of notebooks/20210407.ipynb 
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print(
    tx_msas['NAME'].unique(),
    len(tx_msas['NAME'].unique()))
SoS Notebook From line 67 of notebooks/20210407.ipynb 
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atx_msas = tx_msas[tx_msas['NAME'].str.contains('Austin', case=False)]
atx_msas
SoS Notebook From line 73 of notebooks/20210407.ipynb 
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print(atx_ct_shp.crs.name)
print(es_shp.crs.name)
print(sabs_shp.crs.name)
print(atx_msas.crs.name)
SoS Notebook From line 78 of notebooks/20210407.ipynb 
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atx_shp_osm = atx_ct_shp.to_crs({'init': 'EPSG:3857'})
es_shp_osm = es_shp.to_crs({'init': 'EPSG:3857'})
msa_shp_osm = atx_msas.to_crs({'init': 'EPSG:3857'})
sabs_shp_osm = sabs_shp
SoS Notebook From line 85 of notebooks/20210407.ipynb 
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assert atx_shp_osm.crs == es_shp_osm.crs == sabs_shp_osm.crs == msa_shp_osm.crs
SoS Notebook From line 92 of notebooks/20210407.ipynb 
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sabs_shp_osm.head()
SoS Notebook From line 96 of notebooks/20210407.ipynb 
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es_shp_osm.head()
SoS Notebook From line 100 of notebooks/20210407.ipynb 
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atx_shp_osm.head()
SoS Notebook From line 104 of notebooks/20210407.ipynb 
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es_shp_osm.plot(figsize=(10, 7))
SoS Notebook From line 108 of notebooks/20210407.ipynb 
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gpd.overlay(es_shp_osm, msa_shp_osm, how="intersection").plot()
SoS Notebook From line 112 of notebooks/20210407.ipynb 
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sabs_shp_osm.shape
SoS Notebook From line 116 of notebooks/20210407.ipynb 
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sabs_shp_osm['ncessch'].unique().shape
SoS Notebook From line 120 of notebooks/20210407.ipynb 
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ix_keys = gpd.overlay(sabs_shp_osm, msa_shp_osm, how="intersection")['ncessch'].unique()
ix_keys
SoS Notebook From line 124 of notebooks/20210407.ipynb 
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sabs_shp_osm[sabs_shp_osm['ncessch'].isin(ix_keys)].plot()
SoS Notebook From line 129 of notebooks/20210407.ipynb 
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msa_shp_osm.plot()
SoS Notebook From line 133 of notebooks/20210407.ipynb 
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gpd.overlay(msa_shp_osm, sabs_shp_osm, how="intersection").plot()
SoS Notebook From line 137 of notebooks/20210407.ipynb 
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gpd.overlay(
    sabs_shp_osm[sabs_shp_osm['ncessch'].isin(ix_keys)],
    msa_shp_osm, how="intersection").plot()
SoS Notebook From line 141 of notebooks/20210407.ipynb 
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gpd.overlay(
    msa_shp_osm,
    sabs_shp_osm[sabs_shp_osm['ncessch'].isin(ix_keys)],
    # how="symmetric_difference"
    how="difference"
).plot()
SoS Notebook From line 147 of notebooks/20210407.ipynb 
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es_shp_osm['es_area'] = es_shp_osm.area
atx_shp_osm['cbg_area'] = atx_shp_osm.area
SoS Notebook From line 156 of notebooks/20210407.ipynb 
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gpd.overlay(
    gpd.overlay(
        sabs_shp_osm[sabs_shp_osm['ncessch'].isin(ix_keys)],
        msa_shp_osm, how="intersection"),
    es_atx,
    how="union"
).plot()
SoS Notebook From line 161 of notebooks/20210407.ipynb 
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es_atx = gpd.overlay(atx_shp_osm, es_shp_osm, how='intersection')
SoS Notebook From line 171 of notebooks/20210407.ipynb 
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es_atx.plot()
SoS Notebook From line 175 of notebooks/20210407.ipynb 
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es_atx['overlap_area'] = es_atx.area
# what percent of an elementary catchment corresponds to the underlying CBG?
es_atx['pct_overlap'] = (es_atx['overlap_area'] / es_atx['cbg_area']) * 100
SoS Notebook From line 179 of notebooks/20210407.ipynb 
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overlaps = es_atx[['GEOID', 'ISD', 'CAMPUS', 'SCHL_YEAR', 'pct_overlap']]
SoS Notebook From line 185 of notebooks/20210407.ipynb 
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overlaps['level'] = 'elementary'
SoS Notebook From line 189 of notebooks/20210407.ipynb 
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overlaps.to_csv('AISD_elem_census_tract_overlaps.csv')
SoS Notebook From line 193 of notebooks/20210407.ipynb 
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import geopandas as gpd
import pandas as pd
import os
from glob import glob
SoS Notebook Pandas geopandas From line 2 of notebooks/20210409_missing_isd.ipynb 
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data_dir = '../../data/'
sabs_shp_usa = gpd.read_file(os.path.join(data_dir, 'SABS_1516_SchoolLevels/SABS_1516_Primary.shp'))
SoS Notebook From line 9 of notebooks/20210409_missing_isd.ipynb 
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msa_query = os.path.join(data_dir, '2018_msas/*cbsa*.shp')
SoS Notebook From line 14 of notebooks/20210409_missing_isd.ipynb 
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def get_msas(query: str) -> gpd.GeoDataFrame:
    """Concatenates all GDFs in glob `query`"""
    gdfs = [gpd.read_file(fp) for fp in glob(query)]
    assert gdfs, "query returned null"
    return gpd.GeoDataFrame(pd.concat(gdfs))
SoS Notebook From line 18 of notebooks/20210409_missing_isd.ipynb 
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msas_shp = get_msas(msa_query)
SoS Notebook From line 26 of notebooks/20210409_missing_isd.ipynb 
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atx_msas = msas_shp[msas_shp['NAME'].str.contains('Austin-Round', case=False)]
atx_msas.head()
SoS Notebook From line 30 of notebooks/20210409_missing_isd.ipynb 
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sabs_shp = sabs_shp_usa[sabs_shp_usa['stAbbrev'] == 'TX']
SoS Notebook From line 35 of notebooks/20210409_missing_isd.ipynb 
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print(sabs_shp.crs.name)
print(atx_msas.crs.name)
SoS Notebook From line 39 of notebooks/20210409_missing_isd.ipynb 
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atx_msas_osm = atx_msas.to_crs({'init': 'EPSG:3857'})
sabs_osm = sabs_shp
SoS Notebook From line 44 of notebooks/20210409_missing_isd.ipynb 
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assert atx_msas_osm.crs == sabs_osm.crs
SoS Notebook From line 49 of notebooks/20210409_missing_isd.ipynb 
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atx_msas_osm.plot()
SoS Notebook From line 53 of notebooks/20210409_missing_isd.ipynb 
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# uncomment to view all SABS data in TX
# sabs_osm.plot()
SoS Notebook From line 57 of notebooks/20210409_missing_isd.ipynb 
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olap_sabs_msa = gpd.overlay(atx_msas_osm, sabs_osm, how="intersection")
olap_sabs_msa.head()
SoS Notebook From line 62 of notebooks/20210409_missing_isd.ipynb 
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olap_sabs_msa.plot()
SoS Notebook From line 67 of notebooks/20210409_missing_isd.ipynb 
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atx_msas_osm.plot()
SoS Notebook From line 71 of notebooks/20210409_missing_isd.ipynb 
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aisd_shp_osm = (
    gpd
    .read_file(os.path.join(data_dir, 'aisd_shapefiles/20_21_elem_updt_v2.shp'))
    .to_crs({'init': 'EPSG:3857'})
)
assert atx_msas_osm.crs == sabs_osm.crs == aisd_shp_osm.crs
aisd_shp_osm.plot()
SoS Notebook From line 75 of notebooks/20210409_missing_isd.ipynb 
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with_aisd = gpd.overlay(olap_sabs_msa, aisd_shp_osm, how="union")
with_aisd.plot()
SoS Notebook From line 85 of notebooks/20210409_missing_isd.ipynb
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Created: 1yr ago
Updated: 1yr ago
Maitainers: public
URL: https://github.com/eho-tacc/aisd_shapefile_integration
Name: aisd_shapefile_integration
Version: 1
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dna-seq-gatk-variant-calling
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This Snakemake pipeline implements the GATK best-practices workflow
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This Snakemake pipeline implements the GATK best-practices workflow for calling small germline variants. The usage of thi...
VCF raw sequence reads Variant calling genetic variants gatk rust-bio-tools snakemake-wrapper-utils tabix BCFtools BWA FastQC MultiQC Pandas Picard SAMtools Snakemake Trimmomatic Variant Effect Predictor (VEP) common matplotlib numpy seaborn DNA
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