# School zones and places Georgia Civic Data publishes school attendance zones: the areas that assign a home to its zoned elementary, middle and high school. This article maps one district's zones, checks which schools have no zone and how precise the zones are, and finds the places a location falls in. The geometry functions return geopandas `GeoDataFrame`s, so install the `geo` extra (`pip install georgiacivicdata[geo]`) to map them. For plain tables, use `geometry=False`. ```python import matplotlib.pyplot as plt import pandas as pd import georgiacivicdata as gcd ``` ## Map one district's zones ```python zones = gcd.school_zones(district="Atlanta Public Schools") zones.groupby(["zone_level", "method", "accuracy_class"]).size() ``` ```text zone_level method accuracy_class elementary official_gis authoritative_vector 45 high official_gis authoritative_vector 9 middle official_gis authoritative_vector 13 dtype: int64 ``` ```python fig, axes = plt.subplots(1, 3, figsize=(9, 3.4)) for ax, level in zip(axes, ["elementary", "middle", "high"]): zones[zones["zone_level"] == level].plot( ax=ax, column="school_name", cmap="tab20", edgecolor="white", linewidth=0.2 ) ax.set_title(level) ax.set_axis_off() fig.suptitle("Atlanta Public Schools attendance zones, 2026-27") plt.show() ``` ![image](../_images/bd8fd819ecb492b12491cf8401e64f2afc76a46d4600925705f3a0e7ac241642.png) Each colour is a school. The default `resolution="display"` returns simplified polygons for maps. `resolution="full"` returns the source polygons, which are much larger; filter to a district before asking for them. ## Schools without a zone Charter, magnet, alternative and virtual schools have no attendance zone. `gcd.school_zone_status()` says which schools have one and why the others don't: ```python status = gcd.school_zone_status(district="Atlanta Public Schools") status["zone_status"].value_counts() ``` ```text zone_status zoned 64 no_zone 23 Name: count, dtype: int64[pyarrow] ``` The schools with no zone, by reason: ```python status.loc[status["zone_status"] == "no_zone", "no_zone_category"].value_counts().head() ``` ```text no_zone_category charter 16 magnet_or_choice 3 closed_or_consolidated 2 residential_or_treatment 1 alternative 1 Name: count, dtype: int64[pyarrow] ``` ```python print(gcd.meta(status)["note"]) ``` ```text Whether each school has an attendance zone on this platform and, if not, why. zoned says only that a zone is published here, not that it covers every grade the school teaches. Not an official enrollment determination: confirm with the school district. ``` ## How precise are the zones? Zones come from very different sources. Some are the district's official GIS layers; others are digitized from PDF maps, rebuilt from street lists, or are the whole district outline (in districts with one school per grade). Every zone says how it was built (`method`) and how exact it is (`accuracy_class`); treat them accordingly. ```python coverage = gcd.school_zone_coverage() pd.crosstab(coverage["level_status"], coverage["zone_level"]) ``` ```text zone_level elementary high middle level_status not_available 29 5 10 one_school_per_grade 82 130 115 partial 10 6 9 served_by_other_district 0 1 0 zoned 59 38 46 ``` `gcd.school_zone_coverage()` gives one row per district and level, with the share of the district's land and 2020 population its zones cover, and the reason for anything short of complete: ```python coverage.loc[ coverage["district_name"] == "Appling County", ["zone_level", "level_status", "pct_population", "methods", "reason"], ] ``` ```text zone_level level_status pct_population methods \ 0 elementary not_available NaN district_outline 1 middle one_school_per_grade 1.0 district_outline 2 high one_school_per_grade 1.0 district_outline reason 0 Elementary: The district has separate elementary attenda... 1 Elementary: The district has separate elementary attenda... 2 Elementary: The district has separate elementary attenda... ``` ## Locate a place `gcd.locate()` takes points (`lat`, `lon`) or street addresses and returns the county, Census tract and block group, school district and zoned schools each falls in, one row per place. ```python landmarks = pd.DataFrame( { "landmark": [ "Georgia State Capitol", "Stone Mountain Park", "Okefenokee Swamp Park", "Appling County Courthouse", ], "lat": [33.7490, 33.8053, 31.0558, 31.7782], "lon": [-84.3880, -84.1455, -82.2701, -82.3482], } ) places = gcd.locate(lat=landmarks["lat"], lon=landmarks["lon"], id=landmarks["landmark"]) places[["id", "geographic_type", "geographic_id", "geographic_name", "grades"]].head() ``` ```text GCDAdvisory: A zone is the default assignment, not the only option: a Georgia student may attend another public school by transfer, or a charter, magnet or other choice school. See attendance_rules for the main ways. ``` ```text id geographic_type geographic_id \ 0 Georgia State Capitol county 13121 1 Georgia State Capitol tract 13121003500 2 Georgia State Capitol block_group 131210035001 3 Georgia State Capitol school_district 761 4 Georgia State Capitol elementary_school 761-0199 geographic_name grades 0 Fulton 1 Census Tract 35 2 Block Group 1, Census Tract 35, Fulton County 3 Atlanta Public Schools 4 Centennial Academy (Charter) K-5 ``` The output is long (`head()` shows its first five rows): filter it to the rows you need, such as `places[places["geographic_type"] == "high_school"]`. A school's `geographic_id` is its `"DISTRICT-SCHOOL"` code, the form `gcd.rows()` takes as `school_id`. The `note` column explains any row that is missing or needs care, such as a level with no zone collected yet: ```python noted = places[places["note"].notna()] noted[["id", "geographic_type", "geographic_name"]] ``` ```text id geographic_type geographic_name 25 Appling County Courthouse elementary_school ``` ```python import textwrap for note in noted["note"]: print(textwrap.fill(note, width=76), end="\n\n") ``` ```text No elementary school zone has been collected for this district yet. Elementary: The district has separate elementary attendance zones (the Appling County Elementary Complex in Baxley, which is Appling County Primary for pre-K to grade 3 and Appling County Elementary for grades 4-5; Altamaha Elementary; and Fourth District Elementary in Surrency), but no map, street list or online lookup of them is published. ``` ### Addresses Addresses are geocoded on your computer with the U.S. Census batch geocoder; only the resulting coordinates are sent to Georgia Civic Data. Give one-line addresses or a data frame with `street`, `city`, `state` and `zip`: ```python capitol = gcd.locate(address="206 Washington St SW, Atlanta, GA 30334") capitol[["match_status", "matched_address", "geographic_type", "geographic_name", "grades"]].head() ``` ```text match_status matched_address geographic_type \ 0 match 206 WASHINGTON ST SW, ATLANTA, GA, 30334 county 1 match 206 WASHINGTON ST SW, ATLANTA, GA, 30334 tract 2 match 206 WASHINGTON ST SW, ATLANTA, GA, 30334 block_group 3 match 206 WASHINGTON ST SW, ATLANTA, GA, 30334 school_district 4 match 206 WASHINGTON ST SW, ATLANTA, GA, 30334 elementary_school geographic_name grades 0 Fulton 1 Census Tract 35 2 Block Group 1, Census Tract 35, Fulton County 3 Atlanta Public Schools 4 Centennial Academy (Charter) K-5 ``` The same lookup for several buildings, from a data frame with one column per address part. `id` carries each building's name into the output: ```python buildings = pd.DataFrame( { "building": ["Georgia State Capitol", "Savannah City Hall"], "street": ["206 Washington St SW", "2 E Bay St"], "city": ["Atlanta", "Savannah"], "state": ["GA", "GA"], "zip": ["30334", "31401"], } ) located = gcd.locate(address=buildings, id=buildings["building"], quiet=True) located.loc[ located["geographic_type"] == "high_school", ["id", "match_status", "geographic_name", "grades"], ] ``` ```text id match_status geographic_name grades 6 Georgia State Capitol match Midtown High School 9-12 13 Savannah City Hall match The School Of Liberal Studies At Savannah High 9-12 ``` ### Overlapping school zones Some residences are zoned to one elementary school for the early grades and another for the later ones. In this case, a level has more than one school row and `grades` tells them apart. City Schools of Decatur, for example, splits elementary schools between K-2 and 3-5, so some homes in the district show two elementary schools: ```python oakhurst = gcd.dimension( "schools", district_code="773", q="Oakhurst", active=True, columns=["school_name", "latitude", "longitude"], ) oakhurst ``` ```text school_name latitude longitude 0 Oakhurst Elementary School 33.7637 -84.306 ``` ```python split = gcd.locate(lat=oakhurst["latitude"], lon=oakhurst["longitude"], quiet=True) split.loc[ split["geographic_type"].str.endswith("_school"), ["geographic_type", "geographic_id", "geographic_name", "grades"], ] ``` ```text geographic_type geographic_id geographic_name grades 4 elementary_school 773-0105 Oakhurst Elementary School K-2 5 elementary_school 773-0112 Fifth Avenue Upper Elementary School 3-5 6 middle_school 773-1552 Beacon Hill Middle School 6-8 7 high_school 773-3050 Decatur High School 9-12 ``` `grade` keeps only the school for one grade: ```python one_grade = gcd.locate(lat=oakhurst["latitude"], lon=oakhurst["longitude"], grade=4, quiet=True) one_grade.loc[ one_grade["geographic_type"].str.endswith("_school"), ["geographic_type", "geographic_name", "grades"], ] ``` ```text geographic_type geographic_name grades 4 elementary_school Fifth Avenue Upper Elementary School 3-5 ``` ### The default assignment A zone is the default assignment, not the only option: ```python print(gcd.meta(capitol)["assignment_note"]) ``` ```text A zone is the default assignment, not the only option: a Georgia student may attend another public school by transfer, or a charter, magnet or other choice school. See attendance_rules for the main ways. ``` Students can attend another public school by transfer, or a charter, magnet, or other choice school option. See Georgia Civic Data's article [School Zones and School Choice in Georgia](https://georgiacivicdata.org/stories/school-attendance-rules) for more information.