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Geo Spatial Tutorial

The GeoJSON functionalities in GDN are based on Google’s S2 geospatial index. We support indexing on a subset of the GeoJSON standard, as well as simple latitude-longitude pairs (Non-GeoJSON mode).

Calculating e.g. the distance between two coordinate tuples or checking whether a coordinate pair is located inside a polygon was possible, but those functions could not benefit by using the geo index optimizations. Those operations need to be as fast as possible to prevent them from being a show stopper.

Of course, speed is not everything, so we also want to provide a broader set of geo functionality by integrating full GeoJSON support including Polygons, Multi-Polygons and other geometry primitives.

With these functionalities, one can do more complex queries and build e.g. location-aware recommendation engines by combining the graph data model with geo-location aspects or use multiple data models.

Pre-requisite

Let's assume your

SDK download

  pyC8 requires Python 3.5+. Python 3.6 or higher is recommended

To install pyC8, simply run

$ pip3 install pyC8

or, if you prefer to use conda:

conda install -c conda-forge pyC8

or pipenv:

pipenv install --pre pyC8

Once the installation process is finished, you can begin developing applications in Python.

Code Sample

""" This file is a demo of using Geo Locations on collections """
import pprint
from c8 import C8Client

# Variables - Connection
URL = "play.paas.macrometa.io"
API_KEY = "my API key" #Change to my API key
GEO_FABRIC = "_system"


# Variables - DB
collection_transit = "transit"
collection_shops = "shops"

# Variable - Requests locations
latitude2 = 37.3972452334285 # Sunnyvale Elementary School
longitude2 = -121.99073796514953 # Sunnyvale Elementary School

# Variables - Data
shops_data = [
{ "shop": "Walgreens", "address": "San Jose, CA", "coordinate": [ 37.2333253, -121.6846349 ] },
{ "shop": "Costco", "address": "San Jose, CA", "coordinate": [ 37.4282724, -121.9066238 ] },
{ "shop":"Walmart", "address": "San Jose, CA", "coordinate":
[ 37.3361905, -121.8905833 ] },
{ "shop": "Bed Bath & Beyond", "address": "Mountain View, CA", "coordinate":
[ 37.4443293, -122.1598465] },
{ "shop":"Best Buy", "address": "Mountain View, CA", "coordinate":
[ 37.35992431640625,-122.06830596923828 ] },
{ "shop": "Target", "address": "Mountain View, CA", "coordinate":
[ 37.4443293, -122.1598465] },
{ "shop": "Walgreens", "address": "Fremont, CA", "coordinate":
[ 37.48849288005677, -121.94342916254486 ] },
{ "shop": "Costco", "address": "Almeda, CA", "coordinate":
[ 37.766039418486855, -122.2430211806723 ] },
{ "shop": "Sweet Greens", "address": "San Francisco, CA", "coordinate":
[ 38.621725, -92.571271 ] }
]

transit_data = [
{ "bus": "CA123", "stop": "Santa Clara, CA", "coordinate": [ 37.2333253, -121.6846349 ] },
{ "bus": "CA233", "stop": "Milpitas, CA", "coordinate": [ 37.4282724, -121.9066238 ] },
{ "bus": "CA433", "stop": "San Jose, CA", "coordinate": [ 37.3361905, -121.8905833 ] },
{ "bus": "CA323", "stop": "Palo Alto, CA", "coordinate": [ 37.4443293, -122.1598465] },
{ "bus": "CA523", "stop": "Mountain View, CA", "coordinate":
[ 37.35992431640625,-122.06830596923828 ] },
{ "bus": "CA143", "stop": "Fremont, CA", "coordinate":
[ 37.48849288005677, -121.94342916254486 ] },
{ "bus": "CA423", "stop": "Almeda, CA", "coordinate":
[ 37.766039418486855, -122.2430211806723 ] },
{ "bus": "CA129", "stop": "San Francisco, CA", "coordinate": [ 38.621725, -92.571271 ] }
]

pp = pprint.PrettyPrinter(indent=4)

# Step 1: Open connection to GDN. You will be routed to the closest region.
print(f"\n1. CONNECT: federation: {URL}, user: {API_KEY}")
client = C8Client(protocol='https', host=URL, port=443, apikey = API_KEY, geofabric=GEO_FABRIC)

# Step 2: Create a collection and geo index if not exists.
print(f"\n2. CREATE_COLLECTION: region: {URL}, collection: {collection_shops}")
if client.has_collection(collection_shops):
shopCol = client.collection(collection_shops)
else:
shopCol = client.create_collection(collection_shops)
client.add_geo_index(collection_shops, fields=["coordinate"], ordered=False)

# Step 3: Insert data into collection.
print(f"\n3. INSERT_SHOP_DATA: in region: {URL}")
#shopCol.insert_many(shops_data)
client.insert_document(collection_name=collection_shops, document=shops_data)

# Step 3a: Find shops closer to given location.
lat1 = 37.43007055731992 # McCarthy Blvd, Milpitas
long1 = -121.92344167914754 # McCarthy Blvd, Milpitas
radius = 10 * 1000 # meters
print(f"\n3a. FIND_NEAR_BY_SHOPS for request: ({lat1}, {long1})")
within_query = (f"FOR loc IN WITHIN ({collection_shops}, {lat1}, {long1}, {radius})"
f"SORT loc._distance ASC RETURN loc")
cursor = client.execute_query(within_query)
docs = [document for document in cursor]
pp.pprint(docs)

# Step 3b: Find shop closest to given location.
print(f"\n3b. FIND_NEAREST_SHOP for request: ({lat1}, {long1})")
near_query = f"FOR loc IN NEAR ({collection_shops}, {lat1}, {long1}, 1) RETURN loc"
cursor = client.execute_query(near_query)
docs = [document for document in cursor]
pp.pprint(docs)

#-------------------------------------------------------------------#

# Step 4: Create a collection and geoindex if none exists.
print(f"\n4. CREATE_COLLECTION: region: {URL}, collection: {collection_transit}")
if client.has_collection(collection_transit):
transitCol = client.collection(collection_transit)
else:
transitCol = client.create_collection(collection_transit)
client.add_geo_index(collection_transit, fields=["coordinate"], ordered=False)

# Step 5: Insert data into collection.
print(f"\n5. INSERT_TRANSIT_DATA: in region: {URL}")
client.insert_document(collection_name=collection_transit, document=transit_data)

# Step 6a: Find buses currently within given rectangular fence.
lat1 = 37.38905593900322
long1 = -122.14426630984782
lat2 = 37.332401582858324
long2 = -121.80235913612003

print(f"\n6a. FIND_BUSES available within RECTANGULAR fence: ({lat1}, {long1}, {lat2}, {long2})")
within_query = (f"FOR loc IN WITHIN_RECTANGLE("
f"{collection_transit}, {lat1}, {long1}, {lat2}, {long2}) RETURN loc")
cursor = client.execute_query(within_query)
docs = [document for document in cursor]
pp.pprint(docs)

# Step 6b: Find shop closest to given location.
print(f"\n6b. FIND_NEAREST_BUS for given location: ({lat1}, {long1})")
near_query = f"FOR loc IN NEAR ({collection_transit}, {lat1}, {long1}, 1) RETURN loc"
cursor = client.execute_query(near_query)
docs = [document for document in cursor]
pp.pprint(docs)

#-------------------------------------------------------------------#

# Step 7: Delete data.
print(f"\n7. DELETE_DATA: region: {URL}, collections: {collection_shops}, {collection_transit}")
shopCol.truncate()
transitCol.truncate()
client.delete_collection(collection_transit)
client.delete_collection(collection_shops)