Part 3 - Finding Point of Interests

In addition to geocoding addresses, the geocoding service can also be used to find points of interest (POI) of several different kinds. In Part 3, we will explain briefly about category parameter of geocode() function and its applications.

Note: The World Geocoding Service gets updated constantly to add new data sources and to improve the geocoding quality. With these updates, the results returned from geocoding operations may vary.

Getting started

First, Let's look at some Quickstart examples, which show how the category attributes change based on the search string.

Search for administrative place names

The geocode() method supports single field and multi-field searches for administrative place names. This includes searches for neighborhoods, cities, counties, states, provinces, or countries. If a search for a city name results in multiple matches with the same name, the World Geocoding Service will sort the candidates in order of their relative importance to each other (as indicated by the value of the Rank output field), with priority generally based on population and capital status.

For example, there are many cities in the world named Liverpool, so a search for "Liverpool" results in several equivalent matches; Liverpool, UK will always be the top candidate since it has the greatest population.

from arcgis.geocoding import geocode
from arcgis.gis import GIS
from arcgis.map import Map
from arcgis.map.popups import PopupInfo

gis = GIS(profile="your_enterprise_profile")

address = "Liverpool"

map1 = Map("United Kingdom")
map1

map1.zoom = 6

liverpool = geocode(address)[0]

liverpool['location'].update({"spatialReference" : {"wkid" : 4326}})

map1.content.draw(liverpool['location'])

However, rank alone is not always enough to distinguish between administrative places. Also, you may not necessarily want to find the highest-ranked feature for a particular search. It may be necessary to remove ambiguity by refining searches with additional information. For example, a search for Liverpool returns Liverpool, UK as the top candidate based on rank. If you instead want to find the town of Liverpool, New York, it is necessary to add the state information to the search.

address = {
    "Address" : "Liverpool",
    "Region" : "NY"
}

map2 = Map("Onondaga County, New York, United States")
map2

liverpool = geocode(address)[0]
liverpool['location'].update({"spatialReference" : {"wkid" : 4326}})
map2.content.draw(liverpool['location'])

Example of finding landmarks

The geocode() method can be used to find famous landmarks. The example below geocodes and maps Mt. Everest in Asia, Eiffel Tower in Europe, and the Statue of Liberty in North America:

landmarks = ["Mt. Everest", "Eiffel Tower", "Statue of Liberty"]

map3 = Map()
map3

for lm in landmarks:
    lm_res = geocode(lm)[0]
    lm_res['location'].update({"spatialReference" : {"wkid" : 4326}})
    map3.content.draw(lm_res['location'])

Search for postal codes

The geocode() method supports searches for postal codes and postal code extensions. When searching for postal codes, it is important to note that the same code can be valid in more than one country. For the best results, it may be necessary to include additional information with the postal code, such as city or country.

address = {
    "Postal" : 110001,
    "CountryCode" : "India"
}

map4 = Map("New Delhi, India")
map4

pincode = geocode(address)[0]
pincode['location'].update({"spatialReference" : {"wkid" : 4326}})
map4.content.draw(pincode['location'])

The matched address contains several attributes that provide values for the various output fields supported by the geocoder, as listed below:

pincode['attributes']

{'Loc_name': 'World',
 'Status': 'M',
 'Score': 100,
 'Match_addr': '110001, New Delhi, Delhi',
 'LongLabel': '110001, New Delhi, Delhi, IND',
 'ShortLabel': '110001',
 'Addr_type': 'Postal',
 'Type': '',
 'PlaceName': '110001',
 'Place_addr': 'Connaught Place, New Delhi, Delhi, 110001',
 'Phone': '',
 'URL': '',
 'Rank': 5,
 'AddBldg': '',
 'AddNum': '',
 'AddNumFrom': '',
 'AddNumTo': '',
 'AddRange': '',
 'Side': '',
 'StPreDir': '',
 'StPreType': '',
 'StName': '',
 'StType': '',
 'StDir': '',
 'BldgType': '',
 'BldgName': '',
 'LevelType': '',
 'LevelName': '',
 'UnitType': '',
 'UnitName': '',
 'SubAddr': '',
 'StAddr': '',
 'Block': '',
 'Sector': 'Chelmsford Lane',
 'Nbrhd': '',
 'District': 'Connaught Place',
 'City': 'New Delhi',
 'MetroArea': 'New Delhi',
 'Subregion': 'New Delhi',
 'Region': 'Delhi',
 'RegionAbbr': 'DL',
 'Territory': '',
 'Zone': '',
 'Postal': '110001',
 'PostalExt': '',
 'Country': 'IND',
 'CntryName': 'India',
 'LangCode': 'ENG',
 'Distance': 0,
 'X': 77.218791,
 'Y': 28.6324252,
 'DisplayX': 77.218791,
 'DisplayY': 28.6324252,
 'Xmin': 77.195791,
 'Xmax': 77.241791,
 'Ymin': 28.6094252,
 'Ymax': 28.6554252,
 'ExInfo': ''}

Note that if users want to get exact administrative boundaries, they can do so by using geoenrichment as shown here.

Example of finding multiple categories

In the example below, we search for Indian and Thai Food in Los Angeles, and plot their locations using different symbols based on the Type attribute:

categories = "Indian Food, Thai Food"

map5 = Map("Downtown, Los Angeles, CA")
map5

dtla = geocode("Downtown, Los Angeles, CA")[0]

# find and plot up to 100 Indian and Thai restaurants in DTLA
restaurants = geocode(None, dtla['extent'], category=categories, max_locations=100)

from arcgis.map.symbols import SimpleMarkerSymbolEsriSMS, SimpleMarkerSymbolStyle, SimpleLineSymbolEsriSLS

thai_symbol = SimpleMarkerSymbolEsriSMS(
    style=SimpleMarkerSymbolStyle.esri_sms_square,
    color=[76,115,0,255],
    size=8,
    angle=0,
    xoffset=0,
    yoffset=0,
    outline=SimpleLineSymbolEsriSLS(
        color=[152,230,0,255],
        width=1
    )
)

indian_symbol = SimpleMarkerSymbolEsriSMS(
    style=SimpleMarkerSymbolStyle.esri_sms_circle,
    color=[115,0,76,255],
    size=8,
    angle=0,
    xoffset=0,
    yoffset=0,
    outline=SimpleLineSymbolEsriSLS(
        color=[152,230,0,255],
        width=1
    )
)

for restaurant in restaurants:
    popup = PopupInfo(**{ 
    "title" : restaurant['address'], 
    "description" : "Phone: " + restaurant['attributes']['Phone']
        })
    restaurant['location'].update({"spatialReference" : {"wkid" : 4326}})
    if restaurant['attributes']['Type'] == 'Thai Food':
        map5.content.draw(restaurant['location'], popup, thai_symbol) # use a green square symbol for Thai food
    else:
        map5.content.draw(restaurant['location'], popup, indian_symbol)

Example of finding hospitals within 10 mile buffer around Esri HQ and distance to each

Next, let's walk through the example of finding hospitals within a 10-mile buffer around Esri Headquarter (HQ) and computing distances from Esri to each hospital. The steps of implementation in this section would include:

Creating a Point object for Esri HQ, use the geometry module to build a buffer, and then applying it as search_extent parameter.
Getting geocode() results as a FeatureSet, iterating through each feature within, and using distance() to compute the distance from the original Point to the feature.
Presenting results as a DataFrame showing different columns along with distance, and plotting the distance as a bar chart.
Plotting the FeatureSet on the map with appropriate symbols (using the an url location) or programmatically create these symbols).

Step 1. Create a Point object for Esri HQ, and build a buffer

esrihq_fset = geocode("Esri", as_featureset=True)
esrihq_fset

<FeatureSet> 20 features

esri_geom = esrihq_fset.features[0]
esri_geom.geometry.JSON

'{"x": -117.195695233, "y": 34.0560864, "spatialReference": {"wkid": 4326, "latestWkid": 4326}}'

from arcgis.features import Feature, FeatureSet
from arcgis.geometry import buffer

esri_buffer = buffer([esri_geom.geometry], 
                     in_sr = 102100, buffer_sr=102100,
                     distances=0.1, unit=9001)[0]

esri_buffer_f = Feature(geometry=esri_buffer)
esri_buffer_fset = FeatureSet([esri_buffer_f])
esri_buffer_fset

<FeatureSet> 1 features

# need to change the `type` from `MultiPolygon` to `Polygon`
esri_buffer_f_geom_dict = {"type": "Polygon", 
                           "coordinates":  esri_buffer_f.geometry.coordinates().tolist()}

Step 2. Geocode and compute distance

map6 = Map("Redlands, CA")
map6

esri_buffer_geom = Geometry(esri_buffer_f_geom_dict)
esri_buffer_geom.extent

(-117.295695233, 33.9560864, -117.09569523299999, 34.1560864)

from arcgis.map.symbols import SimpleFillSymbolEsriSFS, SimpleLineSymbolEsriSLS, SimpleFillSymbolStyle, PictureMarkerSymbolEsriPMS

fill_symbol = SimpleFillSymbolEsriSFS(
    color=[255,0,0,100],
    style=SimpleFillSymbolStyle.esri_sfs_null,
    outline=SimpleLineSymbolEsriSLS(
        color=[0,0,0,255],
        width=1
    ))

map6.content.draw(esri_buffer_geom, symbol=fill_symbol)

star_symbol = PictureMarkerSymbolEsriPMS(
    angle=0,
    xoffset=0,
    yoffset=0,
    url="http://static.arcgis.com/images/Symbols/Shapes/RedStarLargeB.png",
    contentType="image/png",
    width=24,
    height=24
)

map6.content.draw(esri_geom.geometry, symbol=star_symbol)

search_area_extent = { 'xmin': esri_buffer_geom.extent[0],
                       'ymin': esri_buffer_geom.extent[1],
                       'xmax': esri_buffer_geom.extent[2],
                       'ymax': esri_buffer_geom.extent[3],
                       'spatialReference': {'latestWkid': 4326, 'wkid': 102100}}

hospitals = geocode('hospital', search_extent=search_area_extent, max_locations=50)
len(hospitals)

hospital_symbol = PictureMarkerSymbolEsriPMS(
    angle=0,
    xoffset=0,
    yoffset=0,
    url="http://static.arcgis.com/images/Symbols/SafetyHealth/Hospital.png",
    contentType="image/png",
    width=24,
    height=24
)

neighborhood_data_dict = {}
neighborhood_data_dict['hospitals'] = []

for place in hospitals:
    place['location'].update({"spatialReference" : {"wkid" : 4326}})
    popup=PopupInfo(**{"title" : place['attributes']['PlaceName'], 
    "description" : place['attributes']['Place_addr']})
    map6.content.draw(place['location'], symbol=hospital_symbol, popup=popup)
    neighborhood_data_dict['hospitals'].append(place['attributes']['PlaceName'])

for place in hospitals:
    print(place["location"])

{'x': -117.196766177888, 'y': 34.108191331266, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.194836431779, 'y': 34.135694787075, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.194836479629, 'y': 34.135694757465, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.195432903971, 'y': 34.121581106134, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.220569945935, 'y': 34.048283738392, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.25241592665, 'y': 34.050235512268, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.252356430406, 'y': 34.047383376877, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.243677086826, 'y': 34.063384148104, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.261122200373, 'y': 34.049504416919, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.26567905278, 'y': 34.050524217791, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.261122141553, 'y': 34.049369351607, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.205019353286, 'y': 34.037157571828, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.214470871989, 'y': 34.048694352034, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.217167206134, 'y': 34.061966615667, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.255487610658, 'y': 34.104516812661, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.278761323128, 'y': 34.134917592562, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.278760385857, 'y': 34.134472458815, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.278465929599, 'y': 34.13489096724, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.27616503598, 'y': 34.1359064912, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.277012707438, 'y': 34.135915708772, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.285191602008, 'y': 34.067766367944, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.285188057476, 'y': 34.068724, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.278795482457, 'y': 34.078829, 'spatialReference': {'wkid': 4326}, 'type': 'point'}
{'x': -117.19435582209, 'y': 34.135812141583, 'spatialReference': {'wkid': 4326}, 'type': 'point'}

from arcgis.geometry import Point, distance

neighborhood_data_dict['distance'] = []

for place in hospitals:
    dis = distance(      
        spatial_ref=4326, 
        geometry1=Point(place["location"]), 
        geometry2=esri_geom.geometry, 
        geodesic=False, 
        gis=gis)
    neighborhood_data_dict['distance'].append(dis['distance'])

Step 3. Present results in tables and bar charts

Note: The distance column is to displayed in units of 100 miles.

import pandas as pd
neighborhood_df = pd.DataFrame.from_dict(neighborhood_data_dict, orient='index')
neighborhood_df = neighborhood_df.transpose()
neighborhood_df

	hospitals	distance
0	Lindora Clinic	0.052116
1	Wound Healing Center - St. Bernardine Medical ...	0.079613
2	Dignity Health Urgent Care - Highland	0.079613
3	Dignity Health Medical Group - Inland Empire	0.065495
4	Loma Linda Surgical Hospital	0.02607
5	VA Loma Linda Medical Center	0.057022
6	Loma Linda University Medical Center East Camp...	0.057326
7	Select Physical Therapy	0.048534
8	Loma Linda University Medical Center	0.065757
9	Loma Linda University Health	0.070205
10	Loma Linda University Children's Hospital	0.065771
11	Redlands Community Hospital	0.021101
12	Loma Linda University Behavioral Medical Center	0.020178
13	Aegis Treatment Centers	0.022263
14	Mfi Recovery Center	0.076946
15	Dignity Health Medical Group - Inland Empire	0.114518
16	St Bernardine Medical Center	0.114211
17	Emergency Services - St. Bernardine Medical Ce...	0.114285
18	Dignity Health Medical Group - Inland Empire	0.113343
19	Baby And Family Center at St. Bernardine Medic...	0.113953
20	Planned Parenthood - San Bernardino Health Center	0.090255
21	Inland Pain Medicine	0.090381
22	Sygnus Desendant	0.086156
23	Dignity Health Medical Group - Inland Empire	0.079737

The bar plot below displays the real address on the x-axis for each entry of hospitals on the x-axis.

import matplotlib.pyplot as plt 
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
addrs = neighborhood_data_dict["hospitals"]
scores = neighborhood_data_dict["distance"]
ax.bar(addrs,scores)
plt.xticks(rotation=90)
plt.show()

Finding POIs using category filtering

We have seen selective examples with the category parameter being used in the geocode() function above. The steps below show the users how to get the list of all categories the geocoder knows and some usage examples:

Get a list of available categories and sub-categories with the current geocoder

from arcgis.geocoding import get_geocoders
geocoder = get_geocoders(gis)[0]

def list_categories(obj, depth = 0):
    for category in obj['categories']:
        print('\t'*depth  + category['name'])
        if 'categories' in category:
            list_categories(category, depth + 1)
            
list_categories(geocoder.properties)

Address
	Subaddress
	Point Address
	Street Address
	Distance Marker
	Intersection
	Street Midblock
	Street Between
	Street Name
Postal
	Primary Postal
	Postal Locality
	Postal Extension
Coordinate System
	LatLong
	XY
	YX
	MGRS
	USNG
Populated Place
	Block
	Sector
	Neighborhood
	District
	City
	Metro Area
	Subregion
	Region
	Territory
	Country
	Zone
POI
	Arts and Entertainment
		Amusement Park
		Aquarium
		Art Gallery
		Art Museum
		Billiards
		Bowling Alley
		Casino
		Cinema
		Historical Monument
		History Museum
		Indoor Sports
		Jazz Club
		Landmark
		Library
		Live Music
		Museum
		Other Arts and Entertainment
		Performing Arts
		Ruin
		Science Museum
		Tourist Attraction
		Wild Animal Park
		Zoo
	Education
		College
		Fine Arts School
		Other Education
		School
		Vocational School
	Food
		African Food
		American Food
		Argentinean Food
		Australian Food
		Austrian Food
		Bakery
		Balkan Food
		BBQ and Southern Food
		Belgian Food
		Bistro
		Brazilian Food
		Breakfast
		Brewpub
		British Isles Food
		Burgers
		Cajun and Creole Food
		Californian Food
		Caribbean Food
		Chicken Restaurant
		Chilean Food
		Chinese Food
		Coffee Shop
		Continental Food
		Creperie
		East European Food
		Fast Food
		Filipino Food
		Fondue
		French Food
		Fusion Food
		German Food
		Greek Food
		Grill
		Hawaiian Food
		Ice Cream Shop
		Indian Food
		Indonesian Food
		International Food
		Irish Food
		Italian Food
		Japanese Food
		Korean Food
		Kosher Food
		Latin American Food
		Malaysian Food
		Mexican Food
		Middle Eastern Food
		Moroccan Food
		Other Restaurant
		Pastries
		Pizza
		Polish Food
		Portuguese Food
		Restaurant
		Russian Food
		Sandwich Shop
		Scandinavian Food
		Seafood
		Snacks
		South American Food
		Southeast Asian Food
		Southwestern Food
		Spanish Food
		Steak House
		Sushi
		Swiss Food
		Tapas
		Thai Food
		Turkish Food
		Vegetarian Food
		Vietnamese Food
		Winery
	Land Features
		Atoll
		Basin
		Butte
		Canyon
		Cape
		Cave
		Cliff
		Continent
		Desert
		Dune
		Flat
		Forest
		Glacier
		Grassland
		Hill
		Island
		Isthmus
		Lava
		Marsh
		Meadow
		Mesa
		Mountain
		Mountain Range
		Oasis
		Other Land Feature
		Peninsula
		Plain
		Plateau
		Point
		Ravine
		Ridge
		Rock
		Scrubland
		Swamp
		Valley
		Volcano
		Wetland
	Nightlife Spot
		Bar or Pub
		Dancing
		Karaoke
		Night Club
		Nightlife
	Parks and Outdoors
		Basketball
		Beach
		Campground
		Diving Center
		Fishing
		Garden
		Golf Course
		Golf Driving Range
		Hockey
		Ice Skating Rink
		Nature Reserve
		Other Parks and Outdoors
		Park
		Racetrack
		Scenic Overlook
		Shooting Range
		Ski Lift
		Ski Resort
		Soccer
		Sports Center
		Sports Field
		Swimming Pool
		Tennis Court
		Trail
		Wildlife Reserve
	Professional and Other Places
		Ashram
		Banquet Hall
		Border Crossing
		Building
		Business Facility
		Cemetery
		Church
		City Hall
		Civic Center
		Convention Center
		Court House
		Dentist
		Doctor
		Embassy
		Factory
		Farm
		Fire Station
		Government Office
		Gurdwara
		Hospital
		Industrial Zone
		Insurance
		Livestock
		Medical Clinic
		Military Base
		Mine
		Mosque
		Observatory
		Oil Facility
		Orchard
		Other Professional Place
		Other Religious Place
		Pagoda
		Place of Worship
		Plantation
		Police Station
		Post Office
		Power Station
		Prison
		Public Restroom
		Radio Station
		Ranch
		Recreation Facility
		Religious Center
		Scientific Research
		Shrine
		Storage
		Synagogue
		Telecom
		Temple
		Tower
		Veterinarian
		Vineyard
		Warehouse
		Water Tank
		Water Treatment
	Residence
		Estate
		House
		Nursing Home
		Residential Area
	Shops and Service
		ATM
		Auto Dealership
		Auto Maintenance
		Auto Parts
		Bank
		Beauty Salon
		Beauty Supplies
		Bookstore
		Butcher
		Candy Store
		Car Wash
		Childrens Apparel
		Clothing Store
		Consumer Electronics Store
		Convenience Store
		Delivery Service
		Department Store
		Electrical
		Fitness Center
		Flea Market
		Food and Beverage Shop
		Footwear
		Furniture Store
		Gas Station
		Grocery
		Home Improvement Store
		Jewelry
		Laundry
		Market
		Mens Apparel
		Mobile Phone Shop
		Motorcycle Shop
		Office Supplies Store
		Optical
		Other Shops and Service
		Pet Store
		Pharmacy
		Plumbing
		Repair Services
		Shopping Center
		Spa
		Specialty Store
		Sporting Goods Store
		Tire Store
		Toy Store
		Used Car Dealership
		Wholesale Warehouse
		Wine and Liquor
		Womens Apparel
		Yoga Studio
	Travel and Transport
		Airport
		Bed and Breakfast
		Bridge
		Bus Station
		Bus Stop
		Cargo Center
		Dock
		EV Charging Station
		Ferry
		Heliport
		Highway Exit
		Hostel
		Hotel
		Marina
		Metro Station
		Motel
		Other Travel
		Parking
		Pier
		Port
		Railyard
		Rental Cars
		Resort
		Rest Area
		Taxi
		Tollbooth
		Tourist Information
		Train Station
		Transportation Service
		Travel Agency
		Truck Stop
		Tunnel
		Weigh Station
	Water Features
		Abyssal Plain
		Bay
		Canal
		Channel
		Continental Rise
		Continental Shelf
		Continental Slope
		Cove
		Dam
		Delta
		Estuary
		Fjord
		Fracture Zone
		Gulf
		Harbor
		Hot Spring
		Irrigation
		Jetty
		Lagoon
		Lake
		Ocean
		Ocean Bank
		Oceanic Basin
		Oceanic Plateau
		Oceanic Ridge
		Other Water Feature
		Reef
		Reservoir
		Sea
		Seamount
		Shoal
		Sound
		Spring
		Strait
		Stream
		Submarine Canyon
		Submarine Cliff
		Submarine Fan
		Submarine Hill
		Submarine Terrace
		Submarine Valley
		Trench
		Undersea Feature
		Waterfall
		Well
		Wharf

An example of finding restaurants around a given location

Now let us find restaurants near Time Square (with a maximum of returned results set to 100, since there are way too many restaurants in the specified location).

time_square = geocode("Time Square, NYC")[0]

map8 = Map("Time Square, NYC")
map8

map8.center = [40.7575 , -73.9855]

restaurants = geocode(None, time_square['extent'], category="Food", max_locations=100)
for restaurant in restaurants:
    popup = PopupInfo(**{ 
                "title" : restaurant['address'], 
                "description" : "Phone: " + restaurant['attributes']['Phone']
            })
    restaurant['location'].update({"spatialReference" : {"wkid" : 4326}})
    map8.content.draw(restaurant['location'], popup)

How to refine the results by sub-category

Still using the last request to search for restaurants near Time Square, we will further refine the search by sub-categories, e.g. Indian, Chinese, Burgers, Thai.. etc.

The category parameter is used to specify a place or address type which can be used to filter results. The parameter supports input of single category values or multiple comma-separated values. Its usage (shown below) is applicable for all other categories that have sub-categories, besides food. For instance, Education would include College, Fine Arts School, Other Education, School and Vocational School. For more categories and sub-categories, please refer to API Reference.

map9 = Map("Time Square, NYC")
map9

categories = "Indian Food, Chinese Food, Burgers, Thai Food"

chinese_symbol = SimpleMarkerSymbolEsriSMS(
    style=SimpleMarkerSymbolStyle.esri_sms_square,
    color=[115,100,76,55],
    size=8,
    angle=0,
    xoffset=0,
    yoffset=0,
    outline=SimpleLineSymbolEsriSLS(
        color=[152,230,0,255],
        width=1
    ))

burgers_symbol = SimpleMarkerSymbolEsriSMS(
    style=SimpleMarkerSymbolStyle.esri_sms_circle,
    color=[15,0,176,255],
    size=8,
    angle=0,
    xoffset=0,
    yoffset=0,
    outline=SimpleLineSymbolEsriSLS(
        color=[152,230,0,255],
        width=1
    ))

restaurants = geocode(None, time_square['extent'], category=categories, max_locations=100)

for restaurant in restaurants:
    restaurant['location'].update({"spatialReference" : {"wkid" : 4326}})
    popup = PopupInfo(**{ 
                "title" : restaurant['address'], 
                "content" : "Phone: " + restaurant['attributes']['Phone']
            })
    if restaurant['attributes']['Type'] == 'Thai Food':
        map9.content.draw(restaurant['location'], popup, thai_symbol) # green square
    elif restaurant['attributes']['Type'] == 'Indian Food':
        map9.content.draw(restaurant['location'], popup, indian_symbol) # dark red circle
    elif restaurant['attributes']['Type'] == 'Chinese Food':
        map9.content.draw(restaurant['location'], popup, chinese_symbol) # mint square
    else:
        map9.content.draw(restaurant['location'], popup, burgers_symbol) # blue circle

Example of finding gas stations, bars, and other facilities near a given location

The example below showcases how to find gas stations, bars, libraries, schools, parks, and grocery stores around a given location, based on the previous sample in which we look for hospitals near Esri Headquarter.

Step 1. Create symbols for facilities

symbols = {"groceries": PictureMarkerSymbolEsriPMS(**{"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS",
                         "url":"http://static.arcgis.com/images/Symbols/PeoplePlaces/Shopping.png",
                         "contentType":"image/png","width":12,"height":12}),
          "coffee": PictureMarkerSymbolEsriPMS(**{"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS",
                     "url":"http://static.arcgis.com/images/Symbols/PeoplePlaces/Coffee.png",
                     "contentType":"image/png","width":12,"height":12}),
          "restaurant": PictureMarkerSymbolEsriPMS(**{"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS",
                         "url":"http://static.arcgis.com/images/Symbols/PeoplePlaces/Dining.png",
                         "contentType":"image/png","width":12,"height":12}),
          "bar": PictureMarkerSymbolEsriPMS(**{"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS",
                  "url":"http://static.arcgis.com/images/Symbols/PeoplePlaces/Bar.png",
                  "contentType":"image/png","width":12,"height":12}),
          "gas": PictureMarkerSymbolEsriPMS(**{"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS",
                  "url":"http://static.arcgis.com/images/Symbols/Transportation/esriBusinessMarker_72.png",
                  "contentType":"image/png","width":12,"height":12}),
          "park": PictureMarkerSymbolEsriPMS(**{"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS",
                      "url":"http://static.arcgis.com/images/Symbols/OutdoorRecreation/RestArea.png",
                      "contentType":"image/png","width":10,"height":10}),
          "school": PictureMarkerSymbolEsriPMS(**{"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS",
                     "url":"http://static.arcgis.com/images/Symbols/PeoplePlaces/Note.png",
                     "contentType":"image/png","width":10,"height":10}),
          "library": PictureMarkerSymbolEsriPMS(**{"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS",
                      "url":"http://static.arcgis.com/images/Symbols/PeoplePlaces/LiveShow.png",
                      "contentType":"image/png","width":12,"height":12})}

list(symbols.keys())

['groceries',
 'coffee',
 'restaurant',
 'bar',
 'gas',
 'park',
 'school',
 'library']

Step 2. Define your own geocode function

Next, let's define a function to use the ArcGIS Geocoding service in search of facilities around the Esri Headquarter, based on the kind of facilities (e.g. groceries) you are looking for:

def search_and_map(in_map, kind="groceries"):
    per_kind = geocode(kind, search_extent=search_area_extent, 
                       max_locations=20, as_featureset=True)
    neighborhood_data_dict[kind] = []

    for place in per_kind:
        popup=PopupInfo(**{"title" : place.attributes['PlaceName'], 
               "description" : place.attributes['Place_addr']})
        in_map.content.draw(place.geometry, symbol=symbols[kind], popup=popup)
        neighborhood_data_dict[kind].append(place.attributes['PlaceName'])

Now, we are ready to loop through the list of facility types and perform geocoding for each kind, then map the results with the customized symbols:

map7 = Map("Redlands, CA")
map7

map7.zoom = 13

for kind in list(symbols.keys()) :
    search_and_map(map7, kind)

Step 3. Tabularize the results

Last but not least, let's present the results in a table:

neighborhood_df = pd.DataFrame.from_dict(neighborhood_data_dict, orient='index')
neighborhood_df = neighborhood_df.transpose()
neighborhood_df

	hospitals	distance	groceries	coffee	restaurant	bar	gas	park	school	library
0	Lindora Clinic	0.052116	Stater Bros. Markets	Starbucks	Rock Coffee	Third Street Tavern	C A R Enterprises	Shadow Mountain Park	Vista Heights Middle School	Highland Sam J. Racadio Library & Environmenta...
1	Wound Healing Center - St. Bernardine Medical ...	0.079613	Stater Bros. Markets	Starbucks	Keke's Taco Stand	The Green Frog	Shell Oil	Gateway Park	Sugar Hill Elementary School	San Bernardino County Library - Loma Linda
2	Dignity Health Urgent Care - Highland	0.079613	ALDI	Starbucks	Steves Burger	The Belle	Pilot Flying J	Box Springs Regional Park	Mentone Elementary School	Loma Linda Branch Public Library
3	Dignity Health Medical Group - Inland Empire	0.065495	Food 4 Less	Starbucks	Armandos Sunnymead	Joy's Lounge	Food'n Fuel	Rich Dauer Pine Park	Arrowhead Christian Academy	Loma Linda City Library
4	Loma Linda Surgical Hospital	0.02607	F & T Market	Starbucks	Saucedo Asadero Campestre	Lobby Bar	ARCO	Leonard Bailey Park	Cope Middle School	Llu Del E Webb Memorial Library
5	VA Loma Linda Medical Center	0.057022	Albertsons	Boba Tea House	Maria's Cafe Mexican Restaurant	The 909	Chevron Extra Mile	Hulda Crooks Park	Kingsbury Elementary School	Mentone Library & Senior Center
6	Loma Linda University Medical Center East Camp...	0.057326	Stater Bros. Markets	Dutch Bros Coffee	El Mushasho Alegre Mariscos	George Lopez's Chingon Kitchen	Shell Oil	Loma Linda Community Park	Serrano Middle School	University of Redlands - George & Verda Armaco...
7	Select Physical Therapy	0.048534	Smart & Final	Starbucks	Funnel Cake Friday's	The Beach House	76	Mission Creek Park	Lankershim Elementary School	Armacost Library
8	Loma Linda University Medical Center	0.065757	Clark's Nutritional Center Inc	Starbucks	Cheliz Restaurant	The Blue Bar	Propane Refill	Redlands Community Park	Warm Springs Elementary School	Ak Smiley Public Library
9	Loma Linda University Health	0.070205	Dhaka Sweets Spices	The Coffee Bean & Tea Leaf	Denny's	Diamond Jim's Saloon	Mobil	Sylvan Park	Bryn Mawr Elementary School	River Christian Reformed Church Library
10	Loma Linda University Children's Hospital	0.065771	Stater Bros. Markets	Starbucks	American Legion	The Boiler Room	Chevron	Texonia Park	Crafton Elementary School	Howard M. Rowe Branch Library
11	Redlands Community Hospital	0.021101	Trader Joe's	Starbucks	Pepper Steak Restaurant	Vapor Squad Smoke Shop	Valero Energy	The Terrace	Moore Middle School	Inland Library System
12	Loma Linda University Behavioral Medical Center	0.020178	Vons	Starbucks	The Burger Boys	The Pink Flamingo	ARCO	Ford Park	Kimberly Elementary School	Law Library For San Bernardino County
13	Aegis Treatment Centers	0.022263	Sprouts Farmers Market	Starbucks	Bear Springs Bistro & Lounge	Absent 9	Valero Energy	Caroline Park	Aquinas High School	San Bernardino Symphony Guthrie Library
14	Mfi Recovery Center	0.076946	ALDI	Starbucks	Armando's Mexican Food	Club Evo	Chevron	Ed Hales Park	Sierra High School	Argosy University - San Bernardino Library
15	Dignity Health Medical Group - Inland Empire	0.114518	Stater Bros. Markets	Starbucks	Del Taco	Empire Bowl	Chevron Extra Mile	Smiley Park	Childrens Depot	Crafton Hills College Library
16	St Bernardine Medical Center	0.114211	IGA	Starbucks	Oishii Sushi & Teriyaki	Black Antler Book Club	7-Eleven Fuel	Brookside Park	Harris Family WeeCare	Highland Branch San Bernardino County Library
17	Emergency Services - St. Bernardine Medical Ce...	0.114285	Stater Bros. Markets	Starbucks	Jack in the Box	The Vault	ARCO	Mission Gables	Adams Family Daycare & Preschl	Loma Linda Branch San Bernardino County Library
18	Dignity Health Medical Group - Inland Empire	0.113343	Albertsons	Starbucks	The Cupcake & Espresso Bar	Vault Martini Bar & Grill	Valero Energy	Prospect Park	Hidden Springs Elementary School	Del E Webb Memorial Library
19	Baby And Family Center at St. Bernardine Medic...	0.113953	Stater Bros. Markets	Starbucks	Deserved Sweets By	Karaoke Kubo	Chevron	Meadowbrook Park	Country Day School	Mentone Branch San Bernardino County Library
20	Planned Parenthood - San Bernardino Health Center	0.090255	None	None	None	None	None	None	None	None
21	Inland Pain Medicine	0.090381	None	None	None	None	None	None	None	None
22	Sygnus Desendant	0.086156	None	None	None	None	None	None	None	None
23	Dignity Health Medical Group - Inland Empire	0.079737	None	None	None	None	None	None	None	None

neighborhood_df.count().plot(kind='bar')
plt.title('Facilities within 10 miles of Esri')

Text(0.5, 1.0, 'Facilities within 10 miles of Esri')

Conclusions

In Part 3, we have walked through different user scenarios using the category parameter within the geocode() function to search and filter geocoded results. In the last scenario, because home buyers often look for access to facilities, such as groceries, restaurants, schools, emergency, and health care, near prospective neighborhoods when shortlisting properties, we used the geocoding module to search for these facilities, build a table for each property, and map the deliverables.