# NYC School Data
# Copyright (C) 2022. Matthew X. Curinga
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU AFFERO GENERAL PUBLIC LICENSE (the "License") as
# published by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the License for more details.
#
# You should have received a copy of the License along with this program.
# If not, see <http://www.gnu.org/licenses/>.
# ==============================================================================
import pandas as pd
import geopandas as gpd
import folium
import os
import os.path
from . import config
urls = config.urls
school_location_file = os.path.join(config.data_dir, "school_locations.geojson")
[docs]def load_zipcodes():
"""Load the NYC zip code boundaries as a GeoDataFrame from data_dir.
Zip codes are compiled from the NYC Data Portal via the US Post Office"""
df = gpd.read_file(os.path.join(config.data_dir,urls["zipcodes"].filename))
return df
[docs]def load_school_locations():
"""Returns a GeoDataFrame with the school locations and location meta-data"""
try:
df = gpd.read_file(school_location_file)
return df
except Exception as e: # geopandas throws DriveError, but I don't know where to import it to catch it
if e.type != "<class 'fiona.errors.DriverError'>":
raise e
return get_and_save_locations()
[docs]def load_school_geo_points():
"""Load only the school location points as a GeoDataFrame"""
df = load_school_locations()
return df[["dbn", "x", "y", "geometry"]]
[docs]def get_and_save_locations(filename=school_location_file):
points = get_points()
locations = get_locations()
points = points.merge(locations, on="dbn", how="left")
df = gpd.GeoDataFrame(points)
df.to_file(filename, driver="GeoJSON")
return df
[docs]def get_points(geojsonurl=urls["school_geo"].url):
"""Read the school location points and zipcodes from an Open Data Portal GeoJSON URL"""
# this is the API feed for the location points
# it's the best place to get zip codes
df = gpd.read_file(geojsonurl)
df = df.rename(columns={"xcoordinat":"x","ycoordinat":"y",})
df.x = pd.to_numeric(df.x, errors='coerce')
df.y = pd.to_numeric(df.y, errors='coerce')
df = df[df.x > 0]
df["dbn"] = df.ats_code
df["district"] = df.adimindist.astype(int)
df = df.rename(columns={"geodistric":"geo_district"})
cols = ['dbn', 'zip', 'geo_district', 'district', 'x','y', 'geometry']
df = df[cols]
df.zip = df.zip.astype("string")
return df
[docs]def get_locations(url=urls["school_locations"].url):
"""
Read school level data with many location-related columns: school x,y
coords, and data about the school locations including NYS BEDS ids,
census tract, and police precinct.
"""
locations = pd.read_csv(url)
locations["dbn"] = locations.system_code
cols = [
'dbn',
'administrative_district_code',
'administrative_district_name',
'beds',
'borough_block_lot',
'census_tract',
'community_district',
'community_school_sup_name',
'council_district',
'fax_number',
'fiscal_year',
'geographical_district_code',
'grades_final_text',
'grades_text',
'highschool_network',
'highschool_network_location',
'highschool_network_name',
'latitude',
'location_category_description',
'location_code',
'location_name',
'location_type_description',
'longitude',
'managed_by_name',
'nta',
'nta_name',
'open_date',
'police_precinct',
'primary_building_code',
'principal_name',
'principal_phone_number',
'principal_title',
'state_code',
'status_descriptions']
locations = locations[cols]
locations.beds = locations.beds.astype("string")
locations.open_date = locations.open_date.astype("datetime64[ns]").dt.year
# locations.beds = locations.beds.astype("string")
return locations
[docs]def load_districts(url=urls["district_geo"].url):
"""Get geo shape file for NYC school districts, indexed by district number."""
districts = gpd.read_file(url)
# rename the columns
districts.columns = ['district', 'area', 'length', 'geometry']
districts.district = pd.to_numeric(districts.district, downcast='integer', errors='coerce')
districts = districts.to_crs(epsg=4326)
return districts
[docs]def add_labels(ax, df, col, fontsize=14):
def label(row):
xy=row.geometry.centroid.coords[0]
ax.annotate(row[col], xy=xy, ha='center', fontsize=fontsize)
df.apply(label, axis=1)
