GeoAnalytics Engine Code Samples¶

from geoanalytics.sql import functions as fns_ga
from pyspark.sql import SparkSession
from pyspark.sql import functions as fns

pqt_pth = "/path/to/parquet"
coord_x_col = "longitude"
coord_y_col = "latitude"
geom_col = "geometry"

# get the active spark session - requires there to be one
spark = SparkSession.getActiveSession()

# read in the data frame
df = (spark.read.parquet(pqt_pth)
    .withColumn(
        colName=geom_col, 
        col=fns_ga.point(
            x=coord_x_col, 
            y=coord_y_col,
            sr=4326
        )
    )
    .st.set_geometry_field(geom_col)
)

from geoanalytics.sql import functions as fns_ga
from pyspark.sql import functions as fns

df_pnts = df_pnts.join(
    other=df_poly,
    on=fns_ga.intersects(
        geometry1=df_pnts["geometry"], 
        geomery2=df_poly["overlay_geometry"]
    ),
    how="left",
)

from geoanalytics import auth_info
from geoanalytics.tools import Geocode
from geoanalytics.sql import functions as fns_ga
from pyspark.sql import functions as fns

# variables for urls and paths
data_url = r"https://services1.arcgis.com/Ua5sjt3LWTPigjyD/arcgis/rest/services/Public_School_Location_201819/FeatureServer/0"
loc_pth = r"/home/geoanalytics/United_States.mmpk"

# Create a public schools DataFrame
df = (spark.read.format("feature-service").load(data_url) 
      .filter(fns.col('STATE') == 'WA')
      .select("NAME", "STREET", "CITY", "STATE", "ZIP")
     )

# Use Geocode to convert the public school addresses into actual locations
geocoder = (Geocode()
            .setLocator(loc_pth)
            .setAddressFields("NAME", "STREET", "CITY", "STATE", "ZIP")
            .setMinScore(80)
            .setOutFields("all")
            .setCountryCode("USA")
           )

# run the geocoder
df_addr = geocoder.run(df)