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.

Input

from arcgis.geocoding import geocode
from arcgis.gis import GIS

Input

gis = GIS("portal url", "username", "password")

Input

address = "Liverpool"

Input

map1 = gis.map("United Kingdom", 6)
map1

Input

liverpool = geocode(address)[0]

Input

map1.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.

Input

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

Input

map2 = gis.map("Onondaga County, New York, United States")
map2

Input

liverpool = geocode(address)[0]
map2.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:

Input

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

Input

map4 = gis.map()
map4

Input

for lm in landmarks:
    lm_res = geocode(lm)[0]
    map4.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.

Input

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

Input

map3 = gis.map("New Delhi, India")
map3

Input

pincode = geocode(address)[0]
map3.draw(pincode['location'])

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

Input

pincode['attributes']

Output

{'Loc_name': 'World',
 'Status': 'M',
 'Score': 100,
 'Match_addr': '110001',
 'LongLabel': '110001, New Delhi, Delhi, IND',
 'ShortLabel': '110001',
 'Addr_type': 'Postal',
 'Type': '',
 'PlaceName': '110001',
 'Place_addr': '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': '',
 'Nbrhd': '',
 'District': '',
 'City': 'New Delhi',
 'MetroArea': '',
 'Subregion': 'New Delhi',
 'Region': 'Delhi',
 'RegionAbbr': 'DL',
 'Territory': '',
 'Zone': '',
 'Postal': '110001',
 'PostalExt': '',
 'Country': 'IND',
 'LangCode': 'ENG',
 'Distance': 0,
 'X': 77.22255833300005,
 'Y': 28.623410000000035,
 'DisplayX': 77.22255833300005,
 'DisplayY': 28.623410000000035,
 'Xmin': 77.19555833300005,
 'Xmax': 77.24955833300005,
 'Ymin': 28.596410000000034,
 'Ymax': 28.650410000000036,
 '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:

Input

categories = "Indian Food, Thai Food"

Input

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

map5 = gis.map(dtla)
map5

Input

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

Input

thai_symbol = {
"type": "esriSMS",
 "style": "esriSMSSquare",
 "color": [76,115,0,255],
 "size": 8,
 "angle": 0,
 "xoffset": 0,
 "yoffset": 0,
 "outline": 
  {
  "color": [152,230,0,255],
   "width": 1
  }
}

indian_symbol = {
"type": "esriSMS",
 "style": "esriSMSCircle",
 "color": [115,0,76,255],
 "size": 8,
 "angle": 0,
 "xoffset": 0,
 "yoffset": 0,
 "outline": 
  {
  "color": [152,230,0,255],
   "width": 1
  }
}

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

Example of finding restaurants within a polygon drawn on map¶

In the example above, we have learned to search for restaurants in downtown LA. Next we will design a call-back method that could get the geometry of the drawn sketch entered by user and use it as the customized search_extent for the geocode() method.

Input

map6 = gis.map()
map6.extent = {  'spatialReference': {'latestWkid': 3857, 'wkid': 102100},
                 'xmin': -8235706.664189668,
                 'ymin': 4977993.551288029,
                 'xmax': -8233351.448255569,
                 'ymax': 4978949.014141619}
map6

Searching within the area of drawn polygon...
{'xmin': -8237203.157884028, 'ymin': 4975541.833547482, 'xmax': -8234069.23972434, 'ymax': 4977544.483455284, 'spatialReference': {'latestWkid': 3857, 'wkid': 102100}}
Paris Baguette
Melt Bakery
Farmstand
Liquiteria
Sushi Express
Playa Bowls
Ovenly
Starbucks
Toby's Estate Coffee
Sushi On Jones

Input

from arcgis.geometry import Polygon, project, Geometry

Input

# Define the callback function that search within the area.
drawn_polygon = None

def find_restaurants(map1, g):
    global drawn_polygon
    drawn_polygon = g
    
    print("Searching within the area of drawn polygon...")
    search_area = Polygon(g)
    search_area_extent = { 'xmin': search_area.extent[0],
                           'ymin': search_area.extent[1],
                           'xmax': search_area.extent[2],
                           'ymax': search_area.extent[3],
                           'spatialReference': {'latestWkid': 3857, 'wkid': 102100}}
    print(search_area_extent)
    
    restaurants = geocode(None, search_extent=search_area_extent, category="Food", max_locations=10)
    for restaurant in restaurants:
        popup = { 
                    "title" : restaurant['address'], 
                    "content" : "Phone: " + restaurant['attributes']['Phone']
                }
        map1.draw(restaurant['location'], popup)
        print(restaurant['address'])

# Set as the callback function to be invoked when a polygon is drawn on the map
map6.on_draw_end(find_restaurants)

Input

# Either use the geometry defined below
"""
search_area_dict = {'type': 'Polygon', 'coordinates': [[[-73.95949251594702, 40.763214654993785], 
                                                    [-73.97567231593547, 40.74551709428493], 
                                                    [-74.00064117977167, 40.76103030464491],
                                                    [-73.95949251594702, 40.763214654993785]]]}

search_area = Geometry(search_area_dict)
"""
# Or draw your own polygon
map6.draw("polygon")

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 Python symbol picker for this, or programmatically create these symbols).

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

Input

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

Output

<FeatureSet> 20 features

Input

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

Output

'{"type": "Point", "coordinates": [-117.19569523299998, 34.05608640000003]}'

Input

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

Input

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

Output

<FeatureSet> 1 features

Input

# 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¶

Input

map7 = gis.map("Redlands, CA")
map7.basemap='gray'
map7

Input

esri_buffer_geom = Geometry(esri_buffer_f_geom_dict)
esri_buffer_geom.extent

Output

(-117.29569523299998,
 33.956086400000025,
 -117.09569523299999,
 34.15608640000003)

Input

fill_symbol = {"type": "esriSFS",
               "style": "esriSFSNull",
               "outline":{"color": [0,0,0,255]}}
map7.draw(esri_buffer_geom, symbol=fill_symbol)

Input

house_symbol = {"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS",
                "url":"http://static.arcgis.com/images/Symbols/Shapes/RedStarLargeB.png",
                "contentType":"image/png","width":24,"height":24}
map7.draw(esri_geom.geometry, symbol=house_symbol)

Input

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)

Output

Input

hospital_symbol = {"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS",
                   "url":"http://static.arcgis.com/images/Symbols/SafetyHealth/Hospital.png",
                   "contentType":"image/png","width":24,"height":24}

Input

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

for place in hospitals:
    popup={"title" : place['attributes']['PlaceName'], 
    "content" : place['attributes']['Place_addr']}
    map7.draw(place['location'], symbol=hospital_symbol, popup=popup)
    neighborhood_data_dict['hospitals'].append(place['attributes']['PlaceName'])

Input

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

{'x': -117.27639997720607, 'y': 34.13462000447212, 'type': 'point', 'spatialReference': {'wkid': 4326}}
{'x': -117.27727999322025, 'y': 34.13429000894418, 'type': 'point', 'spatialReference': {'wkid': 4326}}
{'x': -117.20487003505623, 'y': 34.036959959461214, 'type': 'point', 'spatialReference': {'wkid': 4326}}
{'x': -117.2044000617453, 'y': 34.03624003779764, 'type': 'point', 'spatialReference': {'wkid': 4326}}
{'x': -117.2632400167344, 'y': 34.049760036643725, 'type': 'point', 'spatialReference': {'wkid': 4326}}
{'x': -117.26472994994006, 'y': 34.04951998167821, 'type': 'point', 'spatialReference': {'wkid': 4326}}

Input

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.

Input

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

Output

	hospitals	distance
0	Dignity Health St Bernardine Medical Center	0.112609
1	Dignity Health St Bernardine Medical Center-ER	0.113013
2	Redlands Community Hospital	0.0212131
3	Redlands Community Hospital-ER	0.0216715
4	Loma Linda University Health Loma Linda Univer...	0.0678404
5	Loma Linda University Health Loma Linda Univer...	0.0693463

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

Input

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¶

Input

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

Input

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 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
		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
		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
		Harbor
		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
		Library
		Livestock
		Medical Clinic
		Military Base
		Mine
		Mosque
		Observatory
		Oil Facility
		Orchard
		Other Professional Place
		Other Religious Place
		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
		Bookstore
		Butcher
		Candy Store
		Car Wash
		Childrens Apparel
		Clothing Store
		Consumer Electronics Store
		Convenience Store
		Department Store
		Electrical
		Fitness Center
		Flea Market
		Food and Beverage Shop
		Footwear
		Furniture Store
		Gas Station
		Grocery
		Jewelry
		Laundry
		Home Improvement Store
		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
	Travel and Transport
		Airport
		Bed and Breakfast
		Bridge
		Bus Station
		Cargo Center
		Dock
		Ferry
		Heliport
		Highway Exit
		Hostel
		Hotel
		Marina
		Metro Station
		Motel
		Other Travel
		Parking
		Pier
		Port
		Rental Cars
		Railyard
		Resort
		Rest Area
		Taxi
		Tollbooth
		Tourist Information
		Train Station
		Transportation Service
		Truck Stop
		Tunnel
		Weigh Station
	Water Features
		Bay
		Canal
		Channel
		Cove
		Dam
		Delta
		Estuary
		Fjord
		Gulf
		Hot Spring
		Irrigation
		Jetty
		Lagoon
		Lake
		Ocean
		Other Water Feature
		Reef
		Reservoir
		Sea
		Sound
		Spring
		Strait
		Stream
		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).

Input

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

map8 = gis.map(time_square)
map8

Input

restaurants = geocode(None, time_square['extent'], category="Food", max_locations=100)
for restaurant in restaurants:
    popup = { 
                "title" : restaurant['address'], 
                "content" : "Phone: " + restaurant['attributes']['Phone']
            }
    map8.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.

Input

map9 = gis.map(time_square)
map9

Input

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

Input

chinese_symbol = {
"type": "esriSMS",
 "style": "esriSMSSquare",
 "color": [115,100,76,55],
 "size": 8,
 "angle": 0,
 "xoffset": 0,
 "yoffset": 0,
 "outline": 
  {
  "color": [152,230,0,255],
   "width": 1
  }
}

burgers_symbol = {
"type": "esriSMS",
 "style": "esriSMSCircle",
 "color": [15,0,176,255],
 "size": 8,
 "angle": 0,
 "xoffset": 0,
 "yoffset": 0,
 "outline": 
  {
  "color": [152,230,0,255],
   "width": 1
  }
}

Input

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

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

(You can get your symbols using this online tool.)

Input

symbols = {"groceries": {"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": {"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": {"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": {"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": {"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": {"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": {"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": {"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}}

Input

list(symbols.keys())

Output

['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:

Input

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={"title" : place.attributes['PlaceName'], 
               "content" : place.attributes['Place_addr']}
        in_map.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:

Input

map7.zoom = 13
map7

Input

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:

Input

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

Output

	hospitals	groceries	coffee	restaurant	bar	gas	park	school	library
0	Dignity Health St Bernardine Medical Center	Greenspot Market	Starbucks	Antonious Pizza	None	Circle K	Victoria Park	Mentone Elementary School	AK Smiley Public Library
1	Dignity Health St Bernardine Medical Center-ER	Trader Joe's	Starbucks	Grasshopper Express	None	ARCO	Meadowbrook Park	Valley Star High School	Howard Rowe Public Library
2	Redlands Community Hospital	Cookie's Market	Starbucks	Huicho's Tacos	None	Valero	San Bernardino Soccer Complex	Redlands East Valley High School	Country Law Library
3	Redlands Community Hospital-ER	Todd's Market	Starbucks	La Costa Mariscos	None	76	Seccombe Lake	Holy Trinity School	San Bernardino County Library
4	Loma Linda University Health Loma Linda Univer...	Los Altos Meat Market	Chaca Tea Bar	New York Pizza	None	Valero	Meadowbrook Fields	Monterey Elementary School	Coddington Library
5	Loma Linda University Health Loma Linda Univer...	Jane's Natural Foods & Spices	Starbucks	Saverino's Italian Deli & Market	None	Valero	Palm Park	Public Safety Academy	Norman Feldheym Central Library
6	None	La Canasta Clearance Center	Starbucks	Breakfast Shack	None	ARCO	Perris Hill Park	Public Safety Academy	San Bernardino Public Library
7	None	Rocky Market	MR. Taco	Mu Restaurant	None	Valero	Mill Park	Victoria Elementary School	Highland Branch Library
8	None	Del Rosa Market	Post Cafe	Dhat Island	None	Chevron	Redlands Sports Park	San Gorgonio High School	Loma Linda Branch Library
9	None	Stater Bros	Stell Coffee & Tea Co	Pizza Hut	None	Valero	Sylvan Park	Rodriguez Prep Academy	None
10	None	Super Mini Mart	C Cafe	El Pollo Loco	None	Shell	Community Park	Emmerton Elementary School	None
11	None	Rio Ranch Market	Starbucks	Original Mona Lisa	None	Exxon	Israel Beal Park	Barton Elementary School	None
12	None	LFC & Family Fish Market	Starbucks	El Burrito	None	Valero	Crafton Park	Hunt Elementary School	None
13	None	Fresh & Easy Neighborhood Market	The Coffee Bean & Tea Leaf	Baker's	None	7-Eleven	Redlands Dog Park	Ready Set Grow School	None
14	None	Hiep Thanh Supermarket	Augie's Coffee House	COCO'S	None	Circle K	Ford Park	Aquinas High School	None
15	None	96 Cents Discount Store	Greater Good Coffee	Carl's Jr	None	Mobil	Prospect Park	Pacific High School	None
16	None	Grace's Market	Wild Goose Roasters	Arby's	None	Shell	Smiley Park	Fairfax Elementary School	None
17	None	Shop Rite 3 Market	Starbucks	Taco Bell	None	Mobil	Caroline Park	Options for Youth	None
18	None	Genus Home Care Inc of SB	Starbucks	Papa John's	None	Mobil	Brookside Park	Curtis Middle School	None
19	None	Stater Bros	DCH Roasters	SUBWAY	None	76	Jennie Davis Park	Indian Springs High School	None

Input

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

Output

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.

ArcGIS API for Python

Part 3 - Finding Point of Interests

Was this page helpful?

Was this page helpful?