School Data

import geopandas as gpd
from matplotlib import pyplot as plt
from fuzzywuzzy import fuzz

School Location and Catchment Area Analysis

# school locations as points
school_pnts = gpd.read_file('./data/Schools.geojson').to_crs(3857)

# school catchment areas for the Philadelphia Public School District
school_ct_es = gpd.read_file('./data/psd_catchments/SchoolDist_Catchments_ES.geojson').to_crs(3857)
school_ct_ms = gpd.read_file('./data/psd_catchments/SchoolDist_Catchments_MS.geojson').to_crs(3857)
school_ct_hs = gpd.read_file('./data/psd_catchments/SchoolDist_Catchments_HS.geojson').to_crs(3857)

# Philadelphia city boundary from PA school district shapefile
phl_boundary = gpd.read_file('./data/PaSchoolDistricts2024_03.geojson').query('CTY_NAME == "PHILADELPHIA"').to_crs(3857) 

The city of Philadelphia provides the locations of most schools within the boundaries of Philadelphia County. Also available are catchment areas - regions of the city where residents’ children get priority admission status and do not need apply to attend their associated schools - as well as boundaries for school districts within Pennsylvania, of which the School District of Philadelphia was extracted as an external boundary for this analysis.

# plot school locations by grade level using geopandas with philly boundary overlaid
m = school_pnts.explore(
    column='grade_level',
    cmap='gist_rainbow',
    marker_kwds={'radius': 3},
    legend=True,
    tiles='CartoDB Positron',
    tooltip=True,
    legend_kwds={'caption': 'Grade Level'}
)
phl_boundary.explore(
    m=m,
    color='grey',
    style_kwds={'fillOpacity': 0, 'weight': 2},
    tooltip=False, highlight=False
)

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# create a table of the count of each school type
school_type_counts = school_pnts['type_specific'].value_counts()
print("\nSchool Type Counts:\n", school_type_counts, sep="")
schools_pnts_district = school_pnts.query('type_specific == "DISTRICT"')

School Type Counts:
DISTRICT       235
CHARTER        102
PRIVATE         99
ARCHDIOCESE     40
CONTRACTED      19
Name: type_specific, dtype: int64

There are 235 schools in the Philadelphia School Points dataset that are classified as “District,” meaning that they are within the Philadelphia School District. However, these are further divied up by District and Alternative Education schools, the latter of while is not specified in the dataset (SDP, 2025). The next step will be to confirm that they can be spatially joined with the school catchment areas dataset in order to confirm that the datasets correspond (i.e. 1 school per catchment area, as indicated in the ‘ES_/MS_/HS_ID’ values). While both datasets are relatively current, the points dataset provides a more accurate dataset of which schools are currently in operation. No duplicated ID values exist in any of the catchment area datasets, so theoretically the district school points should only join one-to-one.

# confirm length of the elementary catchment zone before joining to confirm success
len(school_ct_es)

160

# check which school catchment areas have missing elementary school assignments by spatially joining school points filtered to just elementaries
school_ct_es_sj = gpd.sjoin(school_ct_es, schools_pnts_district[schools_pnts_district['grade_level'].str.contains("elementary", case=False)][['school_name', 'location_id', 'geometry']], how='left', predicate='contains', rsuffix='pnt')

# check for duplicated schools
print("Number of Duplicate Rows Post-Join:", len(school_ct_es_sj) - len(school_ct_es))
dupes_esid = school_ct_es_sj[school_ct_es_sj['ES_NAME'].duplicated(keep=False)]
dupes_esid

Number of Duplicate Rows Post-Join: 10

	OBJECTID	ES_ID	ES_NAME	MS_ID	MS_NAME	HS_ID	HS_NAME	GR_ID_K	GR_ID_01	GR_ID_02	...	GR_ID_12	ES_GRADE	MS_GRADE	HS_GRADE	Shape__Area	Shape__Length	geometry	index_pnt	school_name	location_id
42	43	1470	Locke, Alain	1470	Locke, Alain	1020	West Philadelphia HS	1470	1470	1470	...	1020	K-8	K-8	9-12	2.699519e+06	8661.940577	POLYGON ((-8371731.267 4860788.060, -8371834.5...	170.0	PHILADELPHIA JUVENILE JUSTICE SERVICES CENTER	2530
42	43	1470	Locke, Alain	1470	Locke, Alain	1020	West Philadelphia HS	1470	1470	1470	...	1020	K-8	K-8	9-12	2.699519e+06	8661.940577	POLYGON ((-8371731.267 4860788.060, -8371834.5...	299.0	LOCKE, ALAIN SCHOOL	1470
50	51	2490	Waring, Laura W	2490	Waring, Laura W	2010	Franklin, Benjamin HS	2490	2490	2490	...	2010	K-8	K-8	9-12	2.642568e+06	7631.090463	POLYGON ((-8367280.204 4861703.700, -8367364.3...	481.0	STUDENT TRANSITION CENTER	NaN
50	51	2490	Waring, Laura W	2490	Waring, Laura W	2010	Franklin, Benjamin HS	2490	2490	2490	...	2010	K-8	K-8	9-12	2.642568e+06	7631.090463	POLYGON ((-8367280.204 4861703.700, -8367364.3...	202.0	PHILADELPHIA VIRTUAL ACADEMY	8780
50	51	2490	Waring, Laura W	2490	Waring, Laura W	2010	Franklin, Benjamin HS	2490	2490	2490	...	2010	K-8	K-8	9-12	2.642568e+06	7631.090463	POLYGON ((-8367280.204 4861703.700, -8367364.3...	383.0	WARING, LAURA W. SCHOOL	2490
54	55	7200	Barton, Clara	7500	Feltonville School of Arts and Sciences	7060	Olney High School	7200	7200	7200	...	7060	K-2	6-8	9-12	5.283003e+06	13091.581638	POLYGON ((-8361520.256 4869850.387, -8361524.1...	438.0	CROSSROADS @ HUNTING PARK	3190
54	55	7200	Barton, Clara	7500	Feltonville School of Arts and Sciences	7060	Olney High School	7200	7200	7200	...	7060	K-2	6-8	9-12	5.283003e+06	13091.581638	POLYGON ((-8361520.256 4869850.387, -8361524.1...	132.0	FELTONVILLE INTERMEDIATE	7310
54	55	7200	Barton, Clara	7500	Feltonville School of Arts and Sciences	7060	Olney High School	7200	7200	7200	...	7060	K-2	6-8	9-12	5.283003e+06	13091.581638	POLYGON ((-8361520.256 4869850.387, -8361524.1...	165.0	BARTON SCHOOL	7200
60	61	4240	Cassidy, Lewis C	4240	Cassidy, Lewis C	4020	Overbrook High	4240	4240	4240	...	4020	K-8	K-8	9-12	3.027245e+06	8085.200227	POLYGON ((-8376356.755 4862790.797, -8376503.6...	303.0	OVERBROOK EDUCATIONAL CENTER	4480
60	61	4240	Cassidy, Lewis C	4240	Cassidy, Lewis C	4020	Overbrook High	4240	4240	4240	...	4020	K-8	K-8	9-12	3.027245e+06	8085.200227	POLYGON ((-8376356.755 4862790.797, -8376503.6...	179.0	CASSIDY,LEWIS C ACADEMICS PLUS	4240
62	63	4270	Dick, William	4270	Dick, William	4140	Strawberry Mansion HS	4270	4270	4270	...	4140	K-8	K-8	9-12	1.038674e+06	5326.762372	POLYGON ((-8367452.044 4863904.448, -8367414.7...	116.0	DICK, WILLIAM SCHOOL	4270
62	63	4270	Dick, William	4270	Dick, William	4140	Strawberry Mansion HS	4270	4270	4270	...	4140	K-8	K-8	9-12	1.038674e+06	5326.762372	POLYGON ((-8367452.044 4863904.448, -8367414.7...	493.0	(TEMPORARY) DICK, WILLIAM SCHOOL	4270
119	120	6340	Pennell, Joseph	7130	Wagner, General Louis	6060	King, Martin Luther	6340	6340	6340	...	6060	K-5	6-8	9-12	2.168672e+06	7422.991708	POLYGON ((-8365266.834 4872662.887, -8365383.8...	320.0	WIDENER MEMORIAL SCHOOL	6400
119	120	6340	Pennell, Joseph	7130	Wagner, General Louis	6060	King, Martin Luther	6340	6340	6340	...	6060	K-5	6-8	9-12	2.168672e+06	7422.991708	POLYGON ((-8365266.834 4872662.887, -8365383.8...	353.0	PENNELL, JOSEPH ELEMENTARY	6340
153	154	6470	Kelly, John B	6360	Roosevelt, Theodore	6060	King, Martin Luther	6470	6470	6470	...	6060	K-5	K-8	9-12	4.803549e+06	8820.115823	POLYGON ((-8368326.224 4871059.161, -8368412.3...	336.0	KELLY, JOHN B. SCHOOL	6470
153	154	6470	Kelly, John B	6360	Roosevelt, Theodore	6060	King, Martin Luther	6470	6470	6470	...	6060	K-5	K-8	9-12	4.803549e+06	8820.115823	POLYGON ((-8368326.224 4871059.161, -8368412.3...	134.0	FITLER ACADEMICS PLUS	6230
157	158	8130	Northeast Community Propel Academy	8130	Northeast Community Propel Academy	8010	Lincoln, Abraham	8130	8130	8130	...	8010	K-8	K-8	9-12	6.065923e+06	14488.884903	POLYGON ((-8352877.171 4872634.423, -8352918.8...	468.0	NORTHEAST COMMUNITY PROPEL ACADEMY\n	8130\n
157	158	8130	Northeast Community Propel Academy	8130	Northeast Community Propel Academy	8010	Lincoln, Abraham	8130	8130	8130	...	8010	K-8	K-8	9-12	6.065923e+06	14488.884903	POLYGON ((-8352877.171 4872634.423, -8352918.8...	492.0	(TEMPORARY) HOLME, THOMAS SCHOOL	8270

18 rows × 29 columns

There are 10 duplicate rows that resulted from this join, which indicates that there are 10 elementary schools labeled as “District” that fall within the catchment area of a district school, yet they are not associated with the ES_ID value of that catchment area. While having multiple schools within a catchment zone does not necessarily detract from the analysis - catchment IDs can be manually associated with each point through a spatial join - it cannot be guaranteed that the majority of students traveling to that school will be coming from that catchment area. Therefore, these 10 duplicate rows need to be identified and removed from the points dataset.

# function to compute fuzzy name match ratios between two columns and return a list of ratio
def name_match(list1, list2):
    # create empty list to store results
    results = []
    
    # iterate through the lists (same length)
    for i in range(len(list1)):
        
        # compute fuzzy match ratio
        ratio = fuzz.ratio(list1[i], list2[i])
        
        # append ratio to results list
        results.append(ratio)
    return results

# function that identifies duplicate school catchment areas based on spatial join to school points 
def exclude_schools(df, sch_level, catchment_name):
    
    # spatially join gdf to school points
    sch_ctchmt_join = gpd.sjoin(df, schools_pnts_district[schools_pnts_district['grade_level'].str.contains(sch_level, case=False)][['school_name', 'geometry']], how='left', predicate='contains', rsuffix='pnt')

    # identify duplicates based on catchment_name
    dupes = sch_ctchmt_join[sch_ctchmt_join[catchment_name].duplicated(keep=False)]

    # create copy to avoid warnings
    dupes_cpy = dupes.copy()

    list1 = dupes[catchment_name].tolist()
    list2 = dupes['school_name'].tolist()
    
    # apply name matching function to lists of catchment names and school names in the duplicates dataframe
    dupes_cpy['name_match'] = name_match(list1, list2)
    
    # sort by descending name_match score and drop the first duplicate catchment_name, keeping only the poorer matches
    dupes_cpy_sort = dupes_cpy.sort_values(by='name_match', ascending=False)
    
    # identify duplicate rows to drop
    dupes_to_drop = dupes_cpy_sort[dupes_cpy_sort.groupby(catchment_name).cumcount() != 0]
    
    # return list of point indices to drop
    pnt_ind_to_drop = dupes_to_drop['index_pnt']

    # filter original schools points gdf to exclude duplicates
    result = schools_pnts_district[schools_pnts_district['grade_level'].str.contains(sch_level, case=False)].drop(index=pnt_ind_to_drop)
    
    return result

# confirm which strings to use for filtering schools to spatially join to catchment areas
school_pnts['grade_level'].unique()

array(['ELEMENTARY/MIDDLE', 'PRE-K/KINDERGARTEN', 'ELEMENTARY SCHOOL',
       'MIDDLE/HIGH', 'MIDDLE SCHOOL', 'HIGH SCHOOL',
       'ELEMENTARY/MIDDLE/HIGH', 'UNGRADED', 'SPECIAL CENTER',
       'ELEMENTARY?MIDDLE/HIGH', nan, 'PRE-K/ELEMENTARY', 'ELEMENTARY'],
      dtype=object)

# identify the schools that need to be excluded from each catchment area type
es_exclude = exclude_schools(school_ct_es, 'elementary', 'ES_NAME')
ms_exclude = exclude_schools(school_ct_ms, 'middle', 'MS_NAME')
hs_exclude = exclude_schools(school_ct_hs, 'high', 'HS_NAME')

# Sanity Check: compare original counts, filtered counts, and catchment zone counts
school_info = {
    "Elementary": {"exclude": es_exclude, "catchment": school_ct_es},
    "Middle": {"exclude": ms_exclude, "catchment": school_ct_ms},
    "High": {"exclude": hs_exclude, "catchment": school_ct_hs}
}

for level, info in school_info.items():
    original_count = len(schools_pnts_district[
        schools_pnts_district['grade_level'].str.contains(level, case=False)
    ])
    filtered_count = len(info['exclude'])
    catchment_count = len(info['catchment'])
    
    print(f"{level} School Counts:")
    print(f"  Original: {original_count}")
    print(f"  Filtered: {filtered_count}")
    print(f"  Catchment Zones: {catchment_count}\n")

Elementary School Counts:
  Original: 156
  Filtered: 146
  Catchment Zones: 160

Middle School Counts:
  Original: 134
  Filtered: 115
  Catchment Zones: 126

High School Counts:
  Original: 66
  Filtered: 20
  Catchment Zones: 22

Individually, approximately 91% of each category of catchment area is represented by district school points in the dataset that are served by those catchment areas. While the reason for this is unclear, this analysis will only concern catchment areas that have an associated school point.

# spatially filter catchment areas to only those with matched schools
school_ct_es_filt = school_ct_es.sjoin(es_exclude[['location_id', 'geometry']], how='inner', predicate='contains')
school_ct_ms_filt = school_ct_ms.sjoin(ms_exclude[['location_id', 'geometry']], how='inner', predicate='contains')
school_ct_hs_filt = school_ct_hs.sjoin(hs_exclude[['location_id', 'geometry']], how='inner', predicate='contains')

# faceted plot of the catchment areas with their respective filtered schools
fig, axes = plt.subplots(1, 3, figsize=(18, 6))

# Elementary Schools
school_ct_es_filt.plot(ax=axes[0], color='honeydew', edgecolor='grey')
es_exclude.plot(ax=axes[0], color='forestgreen', markersize=10)
axes[0].set_title('Elementary School Catchment Areas')

# Middle Schools
school_ct_ms_filt.plot(ax=axes[1], color='lightcyan', edgecolor='grey')
ms_exclude.plot(ax=axes[1], color='steelblue', markersize=10)
axes[1].set_title('Middle School Catchment Areas')

# High Schools
school_ct_hs_filt.plot(ax=axes[2], color='mistyrose', edgecolor='grey')
hs_exclude.plot(ax=axes[2], color='maroon', markersize=10)
axes[2].set_title('High School Catchment Areas')

# edit plot elements
for ax in axes:
    ax.axis('off')
plt.tight_layout()
plt.show()