Once you've got a feel for the query format and output, transition over to your API tool of choice. Make sure to point to the Places API URL. We've provided a few examples below to quickly kick things off in some common languages.
API Endpoint
https://api.safegraph.com/v2/graphql
Sample Requests
{your-api-key}
Remember to input your unique API key where {your-api-key} is denoted in the below samples.
Get Core Info on a Specific Starbucks
Submitting the individual placekey with the Lookup query. This is the same query as the original one used in the above GraphiQL Explorer.
query {
lookup(placekey: "[email protected]") {
placekey
safegraph_core {
location_name
street_address
city
region
postal_code
iso_country_code
}
}
}
curl --location --request POST 'https://api.safegraph.com/v2/graphql' \
--header 'apikey: {your-api-key}' \
--header 'content-type: application/json' \
--data-raw '{"query":"query {\r\n lookup(placekey: \"[email protected]\") { \r\n\tplacekey \r\n safegraph_core {\r\n location_name\r\n street_address\r\n\t city\r\n region\r\n postal_code\r\n iso_country_code\r\n }\r\n }\r\n}","variables":{}}'
# pip install safegraphQL
import safegraphql.client as sgql
sgql_client = sgql.HTTP_Client(apikey = '{your-api-key}')
pk = '[email protected]'
cols = [
'location_name',
'street_address',
'city',
'region',
'postal_code',
'iso_country_code'
]
sgql_client.lookup(product = 'core', placekeys = pk, columns = cols)
import os
import requests
import json
headers = {
'Content-Type': 'application/json',
'apikey': '{your-api-key}'
}
query = """
query {
lookup(placekey: "[email protected]") {
placekey
safegraph_core {
location_name
street_address
city
region
postal_code
iso_country_code
}
}
}
"""
req = requests.post(
'https://api.safegraph.com/v2/graphql',
json={
'query': query,
},
headers=headers
)
r_json = req.json()
print (json.dumps(r_json, indent=4, sort_keys=True))
{
"data": {
"lookup": {
"placekey": "[email protected]",
"safegraph_core": {
"location_name": "Starbucks",
"street_address": "2020 Market St",
"city": "San Francisco",
"region": "CA",
"postal_code": "94114",
"iso_country_code": "US"
}
}
},
"extensions": {
"row_count": 1,
"version_date": "1627739802__2021_07"
}
}
Search For Geometry Data On Starbucks in Denver, CO
Search query using brand and address fields. Note this search is limited to 5 responses per the first argument. To filter through a longer list, you'd need to expand that limit and ultimately paginate through the responses using the after argument.
query{
search(first: 5 filter:{
brand:"Starbucks"
address: {
city:"Denver"
region: "CO"
}
}) {
places {
edges {
node {
placekey
safegraph_geometry{
location_name
street_address
polygon_wkt
polygon_class
includes_parking_lot
is_synthetic
}
}
}
}
}
}
curl --location --request POST 'https://api.safegraph.com/v1/graphql' \
--header 'apikey: {your-api-key}' \
--header 'content-type: application/json' \
--data-raw '{"query":"query{\n search( first:5 filter:{\n brand:\"Starbucks\"\n address: {\n city:\"Denver\"\n region: \"CO\"\n }\n }) {\n placekey\n safegraph_geometry{\n location_name\n street_address\n polygon_wkt\n polygon_class\n includes_parking_lot\n is_synthetic\n }\n }\n}","variables":{}}'
# pip install safegraphQL
import safegraphql.client as sgql
sgql_client = sgql.HTTP_Client(apikey = 'your-api-key')
brand = 'Starbucks'
city = 'Denver'
region = 'CO'
cols = [
'location_name',
'street_address',
'polygon_wkt',
'polygon_class',
'includes_parking_lot',
'is_synthetic'
]
sgql_client.search(product = 'geometry', brand = brand, city = city, region = region, columns = cols, max_results = 5)
import os
import requests
import json
headers = {
'Content-Type': 'application/json',
'apikey': '{your-api-key}'
}
query = """
query{
search(first: 5 filter:{
brand:"Starbucks"
address: {
city:"Denver"
region: "CO"
}
}) {
places {
edges {
node {
placekey
safegraph_geometry{
location_name
street_address
polygon_wkt
polygon_class
includes_parking_lot
is_synthetic
}
}
}
}
}
}
"""
req = requests.post(
'https://api.safegraph.com/v2/graphql',
json={
'query': query,
},
headers=headers
)
r_json = req.json()
print (json.dumps(r_json, indent=4, sort_keys=True))
{
"data": {
"search": {
"places": {
"edges": [
{
"node": {
"placekey": "[email protected]",
"safegraph_geometry": {
"location_name": "Starbucks",
"street_address": "7777 E Hampden Ave",
"polygon_wkt": "POLYGON ((-104.8999285697937 39.654614050795956, -104.89980518817902 39.654403415686595, -104.90022361278534 39.65430016294756, -104.90033090114594 39.65450253817094, -104.8999285697937 39.654614050795956))",
"polygon_class": "OWNED_POLYGON",
"includes_parking_lot": null,
"is_synthetic": false
}
}
},
{
"node": {
"placekey": "[email protected]",
"safegraph_geometry": {
"location_name": "Starbucks",
"street_address": "9925 E Hampden Ave",
"polygon_wkt": "POLYGON ((-104.87311438967487 39.6532728683004, -104.873114079457 39.65342099105103, -104.87297193088692 39.65342047478039, -104.87297223259708 39.65327261016452, -104.87311438967487 39.6532728683004))",
"polygon_class": "OWNED_POLYGON",
"includes_parking_lot": false,
"is_synthetic": false
}
}
},
{
"node": {
"placekey": "[email protected]",
"safegraph_geometry": {
"location_name": "Starbucks",
"street_address": "6395 E Hampden Ave",
"polygon_wkt": "POLYGON ((-104.9159077747457 39.6536787217925, -104.91587089437175 39.653682851942165, -104.91586888271499 39.65370453522379, -104.91581054466891 39.65370453522379, -104.91581188577342 39.65368594955424, -104.91564894157577 39.6536849170169, -104.91564894157577 39.65358166320465, -104.91590911585021 39.653584760821275, -104.9159077747457 39.6536787217925))",
"polygon_class": "OWNED_POLYGON",
"includes_parking_lot": false,
"is_synthetic": false
}
}
},
{
"node": {
"placekey": "[email protected]",
"safegraph_geometry": {
"location_name": "Starbucks",
"street_address": "4505 Peoria St",
"polygon_wkt": "POLYGON ((-104.84757173931939 39.778288818440984, -104.84771121418817 39.77828153047684, -104.84770450866563 39.778370443586574, -104.847681709889 39.778370443586574, -104.847681709889 39.77842437443324, -104.84766360497821 39.77842632330247, -104.84766427553046 39.778501449818364, -104.8475744215284 39.778497253888624, -104.84757173931939 39.778288818440984))",
"polygon_class": "OWNED_POLYGON",
"includes_parking_lot": null,
"is_synthetic": false
}
}
},
{
"node": {
"placekey": "[email protected]",
"safegraph_geometry": {
"location_name": "Starbucks",
"street_address": "4005 Chambers Rd",
"polygon_wkt": "POLYGON ((-104.81088279280812 39.773426435665314, -104.81070576701313 39.77339963625025, -104.81071381364018 39.77333676065848, -104.81069369707257 39.77333160691892, -104.81070442590863 39.77328006950195, -104.81073258910328 39.77328728474266, -104.81074331793934 39.77324296253787, -104.81092034373432 39.773268731265055, -104.81088279280812 39.773426435665314))",
"polygon_class": "OWNED_POLYGON",
"includes_parking_lot": null,
"is_synthetic": false
}
}
}
]
}
}
},
"extensions": {
"row_count": 5,
"version_date": "1635494405__2021_10"
}
}