Feature engineering - quantifying access to facilities¶

Often, when shortlisting facilities buyers look for access to facilities such as groceries, restaurants, schools, emergency and health care in thier neighborhood. In this notebook, we use the geocoding module to search for such facilities and build a table for each property.

In [1]:

import pandas as pd
import matplotlib.pyplot as plt
from pprint import pprint
%matplotlib inline

from arcgis.gis import GIS
from arcgis.geocoding import geocode
from arcgis.features import Feature, FeatureLayer, FeatureSet, GeoAccessor, GeoSeriesAccessor
from arcgis.features import SpatialDataFrame
from arcgis.geometry import Geometry, Point
from arcgis.geometry.functions import buffer
from arcgis.network import RouteLayer

import pandas as pd import matplotlib.pyplot as plt from pprint import pprint %matplotlib inline from arcgis.gis import GIS from arcgis.geocoding import geocode from arcgis.features import Feature, FeatureLayer, FeatureSet, GeoAccessor, GeoSeriesAccessor from arcgis.features import SpatialDataFrame from arcgis.geometry import Geometry, Point from arcgis.geometry.functions import buffer from arcgis.network import RouteLayer

Connect to GIS

In [2]:

gis = GIS(profile='')

gis = GIS(profile='')

Read one of the shortlisted properties¶

In [3]:

prop_list_df = pd.read_csv('resources/houses_for_sale_att_filtered.csv')
prop_list_df.shape

prop_list_df = pd.read_csv('resources/houses_for_sale_att_filtered.csv') prop_list_df.shape

Out[3]:

(331, 24)

In [4]:

prop_list_df = pd.DataFrame.spatial.from_xy(prop_list_df, 'LONGITUDE','LATITUDE')
type(prop_list_df)

prop_list_df = pd.DataFrame.spatial.from_xy(prop_list_df, 'LONGITUDE','LATITUDE') type(prop_list_df)

Out[4]:

pandas.core.frame.DataFrame

In [5]:

prop1 = prop_list_df[prop_list_df['MLS']==18389440]
prop1

prop1 = prop_list_df[prop_list_df['MLS']==18389440] prop1

Out[5]:

	Unnamed: 0	SALE TYPE	PROPERTY TYPE	ADDRESS	CITY	STATE	ZIP	PRICE	BEDS	BATHS	...	DAYS ON MARKET	PRICE PER SQFT	HOA PER MONTH	STATUS	URL	SOURCE	MLS	LATITUDE	LONGITUDE	SHAPE
203	2328	MLS Listing	Single Family Residential	3775 NW Hilton Head Ter	Portland	OR	97229.0	649900.0	4.0	2.5	...	21.0	221.0	25.0	Active	http://www.redfin.com/OR/Portland/3775-NW-Hilt...	RMLS	18389440	45.546644	-122.815658	{"x": -122.8156584, "y": 45.546644, "spatialRe...

1 rows × 24 columns

Create symbols for facilities¶

Get your symbols using this online tool: http://esri.github.io/arcgis-python-api/tools/symbol.html

In [6]:

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}
grocery_symbol = symbol = {"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}
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}
coffee_symbol = {"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_symbol = {"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_symbol = {"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_symbol = {"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}
shops_service_symbol={"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS","url":"http://static.arcgis.com/images/Symbols/PeoplePlaces/esriBusinessMarker_58_Red.png","contentType":"image/png","width":10,"height":10}
transport_symbol = {"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS","url":"http://static.arcgis.com/images/Symbols/Transportation/esriDefaultMarker_195_White.png","contentType":"image/png","width":15,"height":15}
professional_symbol = {"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS","url":"http://static.arcgis.com/images/Symbols/PeoplePlaces/esriBusinessMarker_64_Yellow.png","contentType":"image/png","width":10,"height":10}
parks_symbol = {"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}
education_symbol = {"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}
arts_symbol = {"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}
destination_symbol = {"angle":0,"xoffset":0,"yoffset":12,"type":"esriPMS","url":"http://static.arcgis.com/images/Symbols/Basic/RedStickpin.png","contentType":"image/png","width":24,"height":24}
fill_symbol = {"type": "esriSFS","style": "esriSFSNull",
               "outline":{"color": [255,0,0,255]}}

fill_symbol2 = {"type": "esriSFS","style": "esriSFSNull",
               "outline":{"color": [0,0,0,255]}}

route_symbol = {"type": "esriSLS","style": "esriSLSSolid",
                "color": [0, 120, 255, 255],"width": 1.5}

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} grocery_symbol = symbol = {"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} 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} coffee_symbol = {"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_symbol = {"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_symbol = {"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_symbol = {"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} shops_service_symbol={"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS","url":"http://static.arcgis.com/images/Symbols/PeoplePlaces/esriBusinessMarker_58_Red.png","contentType":"image/png","width":10,"height":10} transport_symbol = {"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS","url":"http://static.arcgis.com/images/Symbols/Transportation/esriDefaultMarker_195_White.png","contentType":"image/png","width":15,"height":15} professional_symbol = {"angle":0,"xoffset":0,"yoffset":0,"type":"esriPMS","url":"http://static.arcgis.com/images/Symbols/PeoplePlaces/esriBusinessMarker_64_Yellow.png","contentType":"image/png","width":10,"height":10} parks_symbol = {"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} education_symbol = {"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} arts_symbol = {"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} destination_symbol = {"angle":0,"xoffset":0,"yoffset":12,"type":"esriPMS","url":"http://static.arcgis.com/images/Symbols/Basic/RedStickpin.png","contentType":"image/png","width":24,"height":24} fill_symbol = {"type": "esriSFS","style": "esriSFSNull", "outline":{"color": [255,0,0,255]}} fill_symbol2 = {"type": "esriSFS","style": "esriSFSNull", "outline":{"color": [0,0,0,255]}} route_symbol = {"type": "esriSLS","style": "esriSLSSolid", "color": [0, 120, 255, 255],"width": 1.5}

Get 5 mile extent around the property of interest¶

In [7]:

paddress = prop1.ADDRESS + ", " + prop1.CITY + ", " + prop1.STATE
prop_geom_fset = geocode(paddress.values[0], as_featureset=True)

paddress = prop1.ADDRESS + ", " + prop1.CITY + ", " + prop1.STATE prop_geom_fset = geocode(paddress.values[0], as_featureset=True)

Create an envelope around the property using its extent

In [8]:

prop_geom = prop_geom_fset.features[0]
prop_geom.geometry

prop_geom = prop_geom_fset.features[0] prop_geom.geometry

Out[8]:

{'x': -122.81532305026238,
 'y': 45.54674878544642,
 'spatialReference': {'wkid': 4326, 'latestWkid': 4326}}

In [9]:

prop_geom = prop_geom_fset.features[0]
prop_buffer = buffer([prop_geom.geometry], 
                     in_sr = 102100, buffer_sr=102100,
                     distances=0.05, unit=9001)[0]

prop_buffer_f = Feature(geometry=prop_buffer)
prop_buffer_fset = FeatureSet([prop_buffer_f])

prop_geom = prop_geom_fset.features[0] prop_buffer = buffer([prop_geom.geometry], in_sr = 102100, buffer_sr=102100, distances=0.05, unit=9001)[0] prop_buffer_f = Feature(geometry=prop_buffer) prop_buffer_fset = FeatureSet([prop_buffer_f])

Plot house and buffer on map¶

In [10]:

pdx_map = gis.map('Portland, OR')
pdx_map.basemap='gray'
pdx_map

pdx_map = gis.map('Portland, OR') pdx_map.basemap='gray' pdx_map

MapView(basemap='gray', layout=Layout(height='400px', width='100%'))

In [11]:

pdx_map.draw(prop_buffer_fset, symbol=fill_symbol2)
pdx_map.draw(prop_geom_fset, symbol=house_symbol)

pdx_map.draw(prop_buffer_fset, symbol=fill_symbol2) pdx_map.draw(prop_geom_fset, symbol=house_symbol)

Geocode for facilities¶

We use the ArcGIS Geocoding service to search for facilities around this house

Groceries¶

In [15]:

neighborhood_data_dict = {}

neighborhood_data_dict = {}

In [16]:

groceries = geocode('groceries', search_extent=prop_buffer.extent, 
                    max_locations=20, as_featureset=True)
neighborhood_data_dict['groceries'] = []

for place in groceries:
    popup={"title" : place.attributes['PlaceName'], 
    "content" : place.attributes['Place_addr']}
    pdx_map.draw(place.geometry, symbol=grocery_symbol, popup=popup)
    neighborhood_data_dict['groceries'].append(place.attributes['PlaceName'])

groceries = geocode('groceries', search_extent=prop_buffer.extent, max_locations=20, as_featureset=True) neighborhood_data_dict['groceries'] = [] for place in groceries: popup={"title" : place.attributes['PlaceName'], "content" : place.attributes['Place_addr']} pdx_map.draw(place.geometry, symbol=grocery_symbol, popup=popup) neighborhood_data_dict['groceries'].append(place.attributes['PlaceName'])

We will geocode for the following facilities within the said 5 mile buffer.

Groceries
Restaurants
Hospitals
Coffee shops
Bars
Gas stations
Shops and service
Travel and transport
Parks and outdoors
Education

Restaurants¶

In [48]:

pdx_map2 = gis.map('Portland, OR')
pdx_map2.basemap='gray'
pdx_map2

pdx_map2 = gis.map('Portland, OR') pdx_map2.basemap='gray' pdx_map2

MapView(basemap='gray', layout=Layout(height='400px', width='100%'))

In [59]:

pdx_map2.draw(prop_buffer_fset, symbol=fill_symbol2)
pdx_map2.draw(prop_geom_fset, symbol=house_symbol)

pdx_map2.draw(prop_buffer_fset, symbol=fill_symbol2) pdx_map2.draw(prop_geom_fset, symbol=house_symbol)

In [21]:

restaurants = geocode('restaurant', search_extent=prop_buffer.extent, max_locations=200)
neighborhood_data_dict['restauruants'] = []

for place in restaurants:
    popup={"title" : place['attributes']['PlaceName'], 
    "content" : place['attributes']['Place_addr']}
    pdx_map2.draw(place['location'], symbol=restaurant_symbol, popup=popup)
    neighborhood_data_dict['restauruants'].append(place['attributes']['PlaceName'])

restaurants = geocode('restaurant', search_extent=prop_buffer.extent, max_locations=200) neighborhood_data_dict['restauruants'] = [] for place in restaurants: popup={"title" : place['attributes']['PlaceName'], "content" : place['attributes']['Place_addr']} pdx_map2.draw(place['location'], symbol=restaurant_symbol, popup=popup) neighborhood_data_dict['restauruants'].append(place['attributes']['PlaceName'])

Hospitals¶

In [51]:

hospitals = geocode('hospital', search_extent=prop_buffer.extent, max_locations=50)
neighborhood_data_dict['hospitals'] = []

for place in hospitals:
    popup={"title" : place['attributes']['PlaceName'], 
    "content" : place['attributes']['Place_addr']}
    pdx_map2.draw(place['location'], symbol=hospital_symbol, popup=popup)
    neighborhood_data_dict['hospitals'].append(place['attributes']['PlaceName'])

hospitals = geocode('hospital', search_extent=prop_buffer.extent, max_locations=50) neighborhood_data_dict['hospitals'] = [] for place in hospitals: popup={"title" : place['attributes']['PlaceName'], "content" : place['attributes']['Place_addr']} pdx_map2.draw(place['location'], symbol=hospital_symbol, popup=popup) neighborhood_data_dict['hospitals'].append(place['attributes']['PlaceName'])

Coffee shops¶

In [52]:

coffees = geocode('coffee', search_extent=prop_buffer.extent, max_locations=50)
neighborhood_data_dict['coffees'] = []

for place in coffees:
    popup={"title" : place['attributes']['PlaceName'], 
    "content" : place['attributes']['Place_addr']}
    pdx_map2.draw(place['location'], symbol=coffee_symbol, popup=popup)
    neighborhood_data_dict['coffees'].append(place['attributes']['PlaceName'])

coffees = geocode('coffee', search_extent=prop_buffer.extent, max_locations=50) neighborhood_data_dict['coffees'] = [] for place in coffees: popup={"title" : place['attributes']['PlaceName'], "content" : place['attributes']['Place_addr']} pdx_map2.draw(place['location'], symbol=coffee_symbol, popup=popup) neighborhood_data_dict['coffees'].append(place['attributes']['PlaceName'])

Bars¶

In [53]:

bars = geocode('bar', search_extent=prop_buffer.extent, max_locations=50)
neighborhood_data_dict['bars'] = []

for place in bars:
    popup={"title" : place['attributes']['PlaceName'], 
    "content" : place['attributes']['Place_addr']}
    pdx_map2.draw(place['location'], symbol=bar_symbol, popup=popup)
    neighborhood_data_dict['bars'].append(place['attributes']['PlaceName'])

bars = geocode('bar', search_extent=prop_buffer.extent, max_locations=50) neighborhood_data_dict['bars'] = [] for place in bars: popup={"title" : place['attributes']['PlaceName'], "content" : place['attributes']['Place_addr']} pdx_map2.draw(place['location'], symbol=bar_symbol, popup=popup) neighborhood_data_dict['bars'].append(place['attributes']['PlaceName'])

Gas stations¶

In [54]:

gas = geocode('gas station', search_extent=prop_buffer.extent, max_locations=50)
neighborhood_data_dict['gas'] = []

for place in gas:
    popup={"title" : place['attributes']['PlaceName'], 
    "content" : place['attributes']['Place_addr']}
    pdx_map2.draw(place['location'], symbol=gas_symbol, popup=popup)
    neighborhood_data_dict['gas'].append(place['attributes']['PlaceName'])

gas = geocode('gas station', search_extent=prop_buffer.extent, max_locations=50) neighborhood_data_dict['gas'] = [] for place in gas: popup={"title" : place['attributes']['PlaceName'], "content" : place['attributes']['Place_addr']} pdx_map2.draw(place['location'], symbol=gas_symbol, popup=popup) neighborhood_data_dict['gas'].append(place['attributes']['PlaceName'])

Shops and service¶

In [55]:

shops_service = geocode("",category='shops and service', search_extent=prop_buffer.extent, max_locations=50)
neighborhood_data_dict['shops'] = []

for place in shops_service:
    popup={"title" : place['attributes']['PlaceName'], 
    "content" : place['attributes']['Place_addr']}
    pdx_map2.draw(place['location'], symbol=shops_service_symbol, popup=popup)
    neighborhood_data_dict['shops'].append(place['attributes']['PlaceName'])

shops_service = geocode("",category='shops and service', search_extent=prop_buffer.extent, max_locations=50) neighborhood_data_dict['shops'] = [] for place in shops_service: popup={"title" : place['attributes']['PlaceName'], "content" : place['attributes']['Place_addr']} pdx_map2.draw(place['location'], symbol=shops_service_symbol, popup=popup) neighborhood_data_dict['shops'].append(place['attributes']['PlaceName'])

Travel and transport¶

In [56]:

transport = geocode("",category='travel and transport', search_extent=prop_buffer.extent, max_locations=50)
neighborhood_data_dict['transport'] = []

for place in transport:
    popup={"title" : place['attributes']['PlaceName'], 
    "content" : place['attributes']['Place_addr']}
    pdx_map2.draw(place['location'], symbol=transport_symbol, popup=popup)
    neighborhood_data_dict['transport'].append(place['attributes']['PlaceName'])

transport = geocode("",category='travel and transport', search_extent=prop_buffer.extent, max_locations=50) neighborhood_data_dict['transport'] = [] for place in transport: popup={"title" : place['attributes']['PlaceName'], "content" : place['attributes']['Place_addr']} pdx_map2.draw(place['location'], symbol=transport_symbol, popup=popup) neighborhood_data_dict['transport'].append(place['attributes']['PlaceName'])

Parks and outdoors¶

In [57]:

parks = geocode("",category='parks and outdoors', search_extent=prop_buffer.extent, max_locations=50)
neighborhood_data_dict['parks'] = []

for place in parks:
    popup={"title" : place['attributes']['PlaceName'], 
    "content" : place['attributes']['Place_addr']}
    pdx_map2.draw(place['location'], symbol=parks_symbol, popup=popup)
    neighborhood_data_dict['parks'].append(place['attributes']['PlaceName'])

parks = geocode("",category='parks and outdoors', search_extent=prop_buffer.extent, max_locations=50) neighborhood_data_dict['parks'] = [] for place in parks: popup={"title" : place['attributes']['PlaceName'], "content" : place['attributes']['Place_addr']} pdx_map2.draw(place['location'], symbol=parks_symbol, popup=popup) neighborhood_data_dict['parks'].append(place['attributes']['PlaceName'])

Education¶

In [58]:

education = geocode("",category='education', search_extent=prop_buffer.extent, max_locations=50)
neighborhood_data_dict['education'] = []

for place in education:
    popup={"title" : place['attributes']['PlaceName'], 
    "content" : place['attributes']['Place_addr']}
    pdx_map2.draw(place['location'], symbol=education_symbol, popup=popup)
    neighborhood_data_dict['education'].append(place['attributes']['PlaceName'])

education = geocode("",category='education', search_extent=prop_buffer.extent, max_locations=50) neighborhood_data_dict['education'] = [] for place in education: popup={"title" : place['attributes']['PlaceName'], "content" : place['attributes']['Place_addr']} pdx_map2.draw(place['location'], symbol=education_symbol, popup=popup) neighborhood_data_dict['education'].append(place['attributes']['PlaceName'])

Present the results in a table¶

In [75]:

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

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

Out[75]:

	groceries	restauruants	hospitals	coffees	bars	gas	shops	transport	parks	education
0	Bales Market Place	Coffee. Cup	Providence St Vincent Medical Center-ER	Coffee. Cup	None	Shell	Anderson Towing & Recovery	MAX-Elmonica & SW 170th Ave	Jqay House Park	Portland Community College-Rock Creek
1	Safeway	Papa Murphy's	Providence St Vincent Medical Center	Starbucks	None	ARCO	Bassitt Auto Co	Powder Lodging	Jackie Husen Park	Cedar Mill Elementary School
2	QFC	Tilly's Gelato	None	Starbucks	None	76	Cottman Transmission	Homestead Studio Suites-Beaverton	The Bluffs	Goddard School
3	Dinihanian's Farm Market	Oak Hills Brew Pub	None	Poppa's Haven	None	Costco	Powell Paint Center	MAX-Sunset TC	Bonny Slope Park	St Pius X Elementary School
4	Walmart Neighborhood Market	Starbucks	None	Tazza Cafe	None	76	Retied	MAX-Merlo & SW 158th Ave	Burton Park	Terra Linda Elementary School
5	India Supermarket	Starbucks	None	Laughing Planet Cafe	None	Fred Meyer Fuel Center	Chrisman's Picture Frame & Gallery	MAX-Beaverton Creek	Lost Park	Montessori School of Beaverton
6	Apna Bazaar	Chiam	None	Coffee Renaissance	None	Chevron	Team Uniforms	Rodeway Inn & Suites-Portland	Terra Linda Park	A Childs Way Kindergarten
7	Fred Meyer	Bandito Taco	None	Starbucks	None	Chevron	T-Mobile	Doubletree-Beaverton	Jordan Park	Christ United Methodist Preschool
8	Albertsons	Chipotle	None	Bowl & Berry	None	76	Holistic Pet	Hilton Garden Inn-Beaverton	Cedar Mill Woods Park	Cornell Children's Village Day Sch
9	WinCo Foods	Bollywood Bites	None	Taiwan Eats	None	Shell	Shell	Fairfield Inn & Suites-Beaverton	Cedar Mill Park	Catlin Gabel School
10	Plaid Pantry	Pizza Schmizza	None	Starbucks	None	76	Mike's Auto Parts	Homewood Suites-Hillsboro Beaverton	Roger Tilbury Memorial Park	West Tualatin View Elementary Sch
11	Target	Wan Q Restaurant	None	Bethany Public House	None	Shell	Kaady Car Wash	Poehler Airport	Merritt Orchard Park	Beaver Acres Elementary School
12	None	Shari's	None	Starbucks	None	76	7-Eleven	Park & Ride-Elmonica/SW 170th Ave	Commonwealth Park	Ridgewood Elementary School
13	None	Ct Bistro	None	Starbucks	None	None	Motor Sports International	Park & Ride-NW Cornell	Pioneer Park	William Walker Elementary School
14	None	McDonald's	None	Creatures of Habit Espresso & Deli	None	None	Bank of America	Park & Ride-Sunset TRANSIT Center	Peppertree Park	Meadow Park Middle School
15	None	Pizzicato Westside	None	Starbucks	None	None	Bales Market Place	Park & Ride-Beaverton Creek	Claremont Golf Course	Pasitos Spanish School
16	None	Lejoi Cafe	None	Coffee Rush	None	None	US Bank	Park & Ride-Cedar Hills Church	Kaiser Woods Park	Holy Trinity Elementary School
17	None	Ichiban Japanese	None	Starbucks	None	None	Dufresne's Auto Service	Dock	Bethany Meadows Park	Barnes Elementary School
18	None	Mac's Market & Deli	None	None	None	None	US Bank	Dock	Kaiser Ridge Park	Shiny Sparkles Montessori
19	None	SUBWAY	None	None	None	None	Bank of America	Fred Meyer	Morgans Run Park	Stoller Middle School
20	None	Salars Mediterranean Grill	None	None	None	None	Ace Hardware	Dock	West Union Estates Park	Findley Elementary
21	None	Poppa's Haven	None	None	None	None	Du Fresne's Auto Repair	Dock	College Park	Kindercare Learning Center
22	None	Papa John's	None	None	None	None	Jiffy Lube	Dock	Northeast Neighborhood Park	Westview Senior High School
23	None	Tazza Cafe	None	None	None	None	Wells Fargo	Dock	Ben Graf Park	Beaverton School
24	None	Teriyaki Beef Bowl	None	None	None	None	Walgreens	1-1	Spyglass Park	Sweet Peas Kidzone
25	None	China Rim	None	None	None	None	Wells Fargo	Exit 67/Murray Blvd/E	Stoller Farms Park	La Petite Academy
26	None	SUBWAY	None	None	None	None	Safeway	Exit 65/Bethany Blvd/E	Emerald Estates Park	Oak Hills Elementary School
27	None	Dairy Queen	None	None	None	None	Team Uniforms	Exit 67/Murray Blvd/W	Serah Lindsay Estates Park	Bethany Elementary School
28	None	Si Senor Mexican Restaurant	None	None	None	None	Xpressolube	Exit 69B/Park Way/W	Quarry Park	Kids of the Kingdom
29	None	Laughing Planet Cafe	None	None	None	None	Mike's Auto Parts	Exit 68/Cedar Hills Blvd/W	Springville Meadows Park	Elmonica Elementary School
30	None	Pizza Schmizza	None	None	None	None	SHERWIN-WILLIAMS	Tillamook/N	Skyview Park	Touchstone School
31	None	Bleachers	None	None	None	None	Pet Barn	Exit 65/Cornell Rd/W	John Marty Park	Five Oaks Middle School
32	None	Tazza Cafe	None	None	None	None	Rock It Resell	Exit 69B/Cedar Hills/E	George W Otten Park	Pacific Academy
33	None	Bowl & Berry	None	None	None	None	Baby & Me	Exit 68/Cedar Hills Blvd/E	Somerset Meadows Park	Merlo Station High School
34	None	Sweet Lemon Vegetarian Bistro	None	None	None	None	Cedar Mill Home Theater	Exit 1/Walker Rd/S	Oak Hills Park	Kinder Prep Private Preschool
35	None	Juan Colorado	None	None	None	None	Dollar Tree	Exit 64/185th Ave/W	Bronson Creek Park	Prince of Peace Lutheran Preschool
36	None	Taiwan Eats	None	None	None	None	Sunset Science Park Federal CU	None	Autumn Ridge Park	Sunset High School
37	None	Taiwan Eats	None	None	None	None	Sunset Science Park Federal CU	None	Apollo Ridge Park	Cedar Hills Kindercare
38	None	Cackalack's Hot Chicken Shack	None	None	None	None	Dennis Market	None	Willow Creek Nature Park	Northwest Montessori School
39	None	Chen's Dynasty Restaurant	None	None	None	None	Laughing Planet Cafe	None	Dwight S Parr Jr Park	Agia Sophia Academy
40	None	Bethany Sushi	None	None	None	None	None	None	Moshofsky Woods Park	Learning Years Day School
41	None	SUBWAY	None	None	None	None	None	None	Stonegate at Willow Creek	ABC Children's Academy
42	None	Starbucks	None	None	None	None	None	None	Waterhouse Park	Arco Iris Spanish Immersion School
43	None	Bethany Public House	None	None	None	None	None	None	Sunset Park	Forest Park Elementary School
44	None	Bethany Public House	None	None	None	None	None	None	Foege Park	None
45	None	Bliss Bake Shoppe	None	None	None	None	None	None	Peterkort Village Park	None
46	None	Bliss Bake Shoppe	None	None	None	None	None	None	Wanda L Peck Memorial Park	None
47	None	Biscuits Cafe	None	None	None	None	None	None	Howard M Terpenning Complex	None
48	None	Koi Fusion	None	None	None	None	None	None	Forest Heights Park	None
49	None	Starbucks	None	None	None	None	None	None	Forest Heights City Park	None

In [76]:

neighborhood_df.count().plot(kind='bar')
plt.title('Facilities within 5 miles of {}'.format(prop1.ADDRESS.values[0]))

neighborhood_df.count().plot(kind='bar') plt.title('Facilities within 5 miles of {}'.format(prop1.ADDRESS.values[0]))

Out[76]:

Text(0.5,1,'Facilities within 5 miles of 3775 NW Hilton Head Ter')

Find duration to commute to work¶

In [32]:

route_service_url = gis.properties.helperServices.route.url
route_service = RouteLayer(route_service_url, gis=gis)

route_service_url = gis.properties.helperServices.route.url route_service = RouteLayer(route_service_url, gis=gis)

In [33]:

stops = [paddress.values[0], '309 SW 6th Ave #600, Portland, OR 97204']
from arcgis.geocoding import geocode, batch_geocode
stops_geocoded = batch_geocode(stops)

stops_geocoded = [item['location'] for item in stops_geocoded]
stops_geocoded2 = '{},{};{},{}'.format(stops_geocoded[0]['x'],stops_geocoded[0]['y'],
                                       stops_geocoded[1]['x'],stops_geocoded[1]['y'])
stops_geocoded2

stops = [paddress.values[0], '309 SW 6th Ave #600, Portland, OR 97204'] from arcgis.geocoding import geocode, batch_geocode stops_geocoded = batch_geocode(stops) stops_geocoded = [item['location'] for item in stops_geocoded] stops_geocoded2 = '{},{};{},{}'.format(stops_geocoded[0]['x'],stops_geocoded[0]['y'], stops_geocoded[1]['x'],stops_geocoded[1]['y']) stops_geocoded2

Out[33]:

'-122.81532304999996,45.546748785000034;-122.67727209699996,45.52153932300007'

In [34]:

modes = route_service.retrieve_travel_modes()['supportedTravelModes']
for mode in modes:
    print(mode['name'])

modes = route_service.retrieve_travel_modes()['supportedTravelModes'] for mode in modes: print(mode['name'])

Walking Time
Rural Driving Distance
Driving Time
Driving Distance
Walking Distance
Rural Driving Time
Trucking Time
Trucking Distance

In [35]:

route_service.properties.impedance

route_service.properties.impedance

Out[35]:

'TravelTime'

Calculate time it takes to get to work. Set start time as 8:00 AM on Mondays. ArcGIS routing service will use historic averages, so we provide this time as 8:00 AM, Monday, June 4 1990 in Unix epoch time. Read more about this here

In [36]:

route_result = route_service.solve(stops_geocoded2, return_routes=True, 
                             return_stops=True, return_directions=True,
                             impedance_attribute_name='TravelTime',
                             start_time=644511600000,
                             return_barriers=False, return_polygon_barriers=False,
                             return_polyline_barriers=False)

route_result = route_service.solve(stops_geocoded2, return_routes=True, return_stops=True, return_directions=True, impedance_attribute_name='TravelTime', start_time=644511600000, return_barriers=False, return_polygon_barriers=False, return_polyline_barriers=False)

In [37]:

route_length = route_result['directions'][0]['summary']['totalLength']
route_duration = route_result['directions'][0]['summary']['totalTime']
route_duration_str = "{}m, {}s".format(int(route_duration), 
                                       round((route_duration %1)*60,2))
print("route length: {} miles, route duration: {}".format(round(route_length,3),
                                                         route_duration_str))

route_length = route_result['directions'][0]['summary']['totalLength'] route_duration = route_result['directions'][0]['summary']['totalTime'] route_duration_str = "{}m, {}s".format(int(route_duration), round((route_duration %1)*60,2)) print("route length: {} miles, route duration: {}".format(round(route_length,3), route_duration_str))

route length: 10.273 miles, route duration: 27m, 48.39s

In [49]:

route_features = route_result['routes']['features']
route_fset = FeatureSet(route_features)
stop_features = route_result['stops']['features']
stop_fset = FeatureSet(stop_features)

route_pop_up = {'title':'Name',
               'content':'Total_Miles'}
pdx_map2.draw(route_fset, symbol=route_symbol, popup=route_pop_up)

route_features = route_result['routes']['features'] route_fset = FeatureSet(route_features) stop_features = route_result['stops']['features'] stop_fset = FeatureSet(stop_features) route_pop_up = {'title':'Name', 'content':'Total_Miles'} pdx_map2.draw(route_fset, symbol=route_symbol, popup=route_pop_up)

In [50]:

pdx_map2.draw(stop_fset, symbol=destination_symbol)

pdx_map2.draw(stop_fset, symbol=destination_symbol)