Enrich Using Point Geometries¶

Starting off, we import a few required Python resources. While there are quite a few in there, the import of note is from arcgis.geoenrichment import Country, get_countries. We are going to use this object and method to discover and perform our analysis.

[1]:

import os
from pathlib import Path

from arcgis.features import GeoAccessor
from arcgis.geoenrichment import Country
from arcgis.geometry import Geometry
from arcgis.gis import GIS
from dotenv import load_dotenv, find_dotenv
import pandas as pd

Next, we need some test data to work with. For the sake of simplicity, we are going to create two point geometries on the fly and build a Spatially Enabled Data Frame with these two points. We are going to investigate how to get very similar results by inputting just the list of point Geometry objects and also these geometries as part of a Spatially Enabled Data Frame.

[2]:

nm_lst = ['Bayview', "Ralph's Thriftway"]
coord_lst = [
    (-122.9074835, 47.0450249),  # Bayview Grocery Store
    (-122.8749600, 47.0464031)   # Ralph's Thriftway Grocery Store
]

# create a list of point geometry objects from the coordinate tuples - what we are going to use first
geom_lst = [Geometry({'x': pt[0], 'y': pt[1], 'spatialReference': {'wkid': 4326}}) for pt in coord_lst]

# create a spatially enabled dataframe - what we are going to use later
grocery_df = pd.DataFrame(zip(nm_lst, geom_lst), columns=['store_name', 'SHAPE'])
grocery_df.spatial.set_geometry('SHAPE')
grocery_df

[2]:

	store_name	SHAPE
0	Bayview	{"x": -122.9074835, "y": 47.0450249, "spatialR...
1	Ralph's Thriftway	{"x": -122.87496, "y": 47.0464031, "spatialRef...

Now, we are going to need a connection to ArcGIS Online to demonstrate the abiliy to use ArcGIS Online for geoenrichment. This is accomplished by instantiating a GIS object instance with valid credentials read from environment variables.

[3]:

gis_agol = GIS(
    url=os.getenv('ESRI_GIS_URL'),
    username=os.getenv('ESRI_GIS_USERNAME'),
    password=os.getenv('ESRI_GIS_PASSWORD')
)

gis_agol

[3]:

GIS @ https://www.arcgis.com

Point List Using Defaults¶

To enrich, we start by creating a Country object instance. As part of the constructor, we need to tell the object what Business Analyst source to use in the gis parameter. In this case, we are telling the object to use a local instance of ArcGIS Pro with Business Analyst and the United States data pack.

[4]:

usa_local = Country('usa', gis=GIS('pro'))

usa_local

[4]:

<Country - United States 2021 ('local')>

Next, we need to get some enrich variables to use. We can discover what is available using the enrich_variables property of the country object to retrieve a Pandas Data Frame of variables available for the country.

[5]:

ev = usa_local.enrich_variables

ev

[5]:

	name	alias	data_collection	enrich_name	enrich_field_name
0	CHILD_CY	2021 Child Population	AgeDependency	AgeDependency.CHILD_CY	AgeDependency_CHILD_CY
1	WORKAGE_CY	2021 Working-Age Population	AgeDependency	AgeDependency.WORKAGE_CY	AgeDependency_WORKAGE_CY
2	SENIOR_CY	2021 Senior Population	AgeDependency	AgeDependency.SENIOR_CY	AgeDependency_SENIOR_CY
3	CHLDDEP_CY	2021 Child Dependency Ratio	AgeDependency	AgeDependency.CHLDDEP_CY	AgeDependency_CHLDDEP_CY
4	AGEDEP_CY	2021 Age Dependency Ratio	AgeDependency	AgeDependency.AGEDEP_CY	AgeDependency_AGEDEP_CY
...	...	...	...	...	...
17958	MOEMEDYRMV	2019 Median Year Householder Moved In MOE (ACS...	yearmovedin	yearmovedin.MOEMEDYRMV	yearmovedin_MOEMEDYRMV
17959	RELMEDYRMV	2019 Median Year Householder Moved In REL (ACS...	yearmovedin	yearmovedin.RELMEDYRMV	yearmovedin_RELMEDYRMV
17960	ACSOWNER	2019 Owner Households (ACS 5-Yr)	yearmovedin	yearmovedin.ACSOWNER	yearmovedin_ACSOWNER
17961	MOEOWNER	2019 Owner Households MOE (ACS 5-Yr)	yearmovedin	yearmovedin.MOEOWNER	yearmovedin_MOEOWNER
17962	RELOWNER	2019 Owner Households REL (ACS 5-Yr)	yearmovedin	yearmovedin.RELOWNER	yearmovedin_RELOWNER

17963 rows × 5 columns

Tens of thousands of variables is just a few too many to deal with, so we can parse this down a bit using some Pandas Data Frame filtering to get just key United States variables for the current year.

[6]:

kv = ev[
    (ev.data_collection.str.lower().str.contains('key'))
    & (ev.name.str.lower().str.endswith('cy'))
].reset_index(drop=True)

kv

[6]:

	name	alias	data_collection	enrich_name	enrich_field_name
0	TOTPOP_CY	2021 Total Population	KeyUSFacts	KeyUSFacts.TOTPOP_CY	KeyUSFacts_TOTPOP_CY
1	GQPOP_CY	2021 Group Quarters Population	KeyUSFacts	KeyUSFacts.GQPOP_CY	KeyUSFacts_GQPOP_CY
2	DIVINDX_CY	2021 Diversity Index	KeyUSFacts	KeyUSFacts.DIVINDX_CY	KeyUSFacts_DIVINDX_CY
3	TOTHH_CY	2021 Total Households	KeyUSFacts	KeyUSFacts.TOTHH_CY	KeyUSFacts_TOTHH_CY
4	AVGHHSZ_CY	2021 Average Household Size	KeyUSFacts	KeyUSFacts.AVGHHSZ_CY	KeyUSFacts_AVGHHSZ_CY
5	MEDHINC_CY	2021 Median Household Income	KeyUSFacts	KeyUSFacts.MEDHINC_CY	KeyUSFacts_MEDHINC_CY
6	AVGHINC_CY	2021 Average Household Income	KeyUSFacts	KeyUSFacts.AVGHINC_CY	KeyUSFacts_AVGHINC_CY
7	PCI_CY	2021 Per Capita Income	KeyUSFacts	KeyUSFacts.PCI_CY	KeyUSFacts_PCI_CY
8	TOTHU_CY	2021 Total Housing Units	KeyUSFacts	KeyUSFacts.TOTHU_CY	KeyUSFacts_TOTHU_CY
9	OWNER_CY	2021 Owner Occupied HUs	KeyUSFacts	KeyUSFacts.OWNER_CY	KeyUSFacts_OWNER_CY
10	RENTER_CY	2021 Renter Occupied HUs	KeyUSFacts	KeyUSFacts.RENTER_CY	KeyUSFacts_RENTER_CY
11	VACANT_CY	2021 Vacant Housing Units	KeyUSFacts	KeyUSFacts.VACANT_CY	KeyUSFacts_VACANT_CY
12	MEDVAL_CY	2021 Median Home Value	KeyUSFacts	KeyUSFacts.MEDVAL_CY	KeyUSFacts_MEDVAL_CY
13	AVGVAL_CY	2021 Average Home Value	KeyUSFacts	KeyUSFacts.AVGVAL_CY	KeyUSFacts_AVGVAL_CY
14	POPGRW10CY	2010-2021 Growth Rate: Population	KeyUSFacts	KeyUSFacts.POPGRW10CY	KeyUSFacts_POPGRW10CY
15	HHGRW10CY	2010-2021 Growth Rate: Households	KeyUSFacts	KeyUSFacts.HHGRW10CY	KeyUSFacts_HHGRW10CY
16	FAMGRW10CY	2010-2021 Growth Rate: Families	KeyUSFacts	KeyUSFacts.FAMGRW10CY	KeyUSFacts_FAMGRW10CY
17	DPOP_CY	2021 Total Daytime Population	KeyUSFacts	KeyUSFacts.DPOP_CY	KeyUSFacts_DPOP_CY
18	DPOPWRK_CY	2021 Daytime Pop: Workers	KeyUSFacts	KeyUSFacts.DPOPWRK_CY	KeyUSFacts_DPOPWRK_CY
19	DPOPRES_CY	2021 Daytime Pop: Residents	KeyUSFacts	KeyUSFacts.DPOPRES_CY	KeyUSFacts_DPOPRES_CY

Finally, we can enrich using the points and variables collected above. Please notice, we are not specifying the area around the input points, so we the proximity defaults are being used - a straight line distance of one kilometer around the points. This circular area is then used to apportion data to the locations specified by the point geometries.

[7]:

pt1_enrich_df = usa_local.enrich(
    geographies=geom_lst,
    enrich_variables=kv
)

pt1_enrich_df.info()
pt1_enrich_df.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 27 columns):
 #   Column                 Non-Null Count  Dtype
---  ------                 --------------  -----
 0   has_data               2 non-null      int64
 1   area_type              2 non-null      object
 2   buffer_units           2 non-null      object
 3   buffer_unit            2 non-null      object
 4   buffer_radii           2 non-null      float64
 5   aggregation_method     2 non-null      object
 6   keyusfacts_totpop_cy   2 non-null      float64
 7   keyusfacts_gqpop_cy    2 non-null      float64
 8   keyusfacts_divindx_cy  2 non-null      float64
 9   keyusfacts_tothh_cy    2 non-null      float64
 10  keyusfacts_avghhsz_cy  2 non-null      float64
 11  keyusfacts_medhinc_cy  2 non-null      float64
 12  keyusfacts_avghinc_cy  2 non-null      float64
 13  keyusfacts_pci_cy      2 non-null      float64
 14  keyusfacts_tothu_cy    2 non-null      float64
 15  keyusfacts_owner_cy    2 non-null      float64
 16  keyusfacts_renter_cy   2 non-null      float64
 17  keyusfacts_vacant_cy   2 non-null      float64
 18  keyusfacts_medval_cy   2 non-null      float64
 19  keyusfacts_avgval_cy   2 non-null      float64
 20  keyusfacts_popgrw10cy  2 non-null      float64
 21  keyusfacts_hhgrw10cy   2 non-null      float64
 22  keyusfacts_famgrw10cy  2 non-null      float64
 23  keyusfacts_dpop_cy     2 non-null      float64
 24  keyusfacts_dpopwrk_cy  2 non-null      float64
 25  keyusfacts_dpopres_cy  2 non-null      float64
 26  SHAPE                  2 non-null      geometry
dtypes: float64(21), geometry(1), int64(1), object(4)
memory usage: 560.0+ bytes

[7]:

	has_data	area_type	buffer_units	buffer_unit	buffer_radii	aggregation_method	keyusfacts_totpop_cy	keyusfacts_gqpop_cy	keyusfacts_divindx_cy	keyusfacts_tothh_cy	...	keyusfacts_vacant_cy	keyusfacts_medval_cy	keyusfacts_avgval_cy	keyusfacts_popgrw10cy	keyusfacts_hhgrw10cy	keyusfacts_famgrw10cy	keyusfacts_dpop_cy	keyusfacts_dpopwrk_cy	keyusfacts_dpopres_cy	SHAPE
0	1	RingBuffer	esriKilometers	esriKilometers	1.0	BlockApportionment:US.BlockGroups;PointsLayer:...	2706.0	66.0	38.6	1727.0	...	218.0	381908.0	419728.0	1.46	1.74	1.08	7604.0	6222.0	1382.0	{"x": -122.90748349999996, "y": 47.04502490000...
1	1	RingBuffer	esriKilometers	esriKilometers	1.0	BlockApportionment:US.BlockGroups;PointsLayer:...	4672.0	75.0	41.8	2145.0	...	116.0	323665.0	360023.0	0.73	0.71	0.54	4623.0	2558.0	2065.0	{"x": -122.87495999999999, "y": 47.04640310000...

2 rows × 27 columns

Specify Proximity Value¶

If wanting to use a value different from the default of one kilometer (highly recommended), this can easily be specified using the proximity_value parameter.

[8]:

pt2_enrich_df = usa_local.enrich(
    geographies=geom_lst,
    enrich_variables=kv,
    proximity_value=5
)

pt2_enrich_df.info()
pt2_enrich_df.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 27 columns):
 #   Column                 Non-Null Count  Dtype
---  ------                 --------------  -----
 0   has_data               2 non-null      int64
 1   area_type              2 non-null      object
 2   buffer_units           2 non-null      object
 3   buffer_unit            2 non-null      object
 4   buffer_radii           2 non-null      float64
 5   aggregation_method     2 non-null      object
 6   keyusfacts_totpop_cy   2 non-null      float64
 7   keyusfacts_gqpop_cy    2 non-null      float64
 8   keyusfacts_divindx_cy  2 non-null      float64
 9   keyusfacts_tothh_cy    2 non-null      float64
 10  keyusfacts_avghhsz_cy  2 non-null      float64
 11  keyusfacts_medhinc_cy  2 non-null      float64
 12  keyusfacts_avghinc_cy  2 non-null      float64
 13  keyusfacts_pci_cy      2 non-null      float64
 14  keyusfacts_tothu_cy    2 non-null      float64
 15  keyusfacts_owner_cy    2 non-null      float64
 16  keyusfacts_renter_cy   2 non-null      float64
 17  keyusfacts_vacant_cy   2 non-null      float64
 18  keyusfacts_medval_cy   2 non-null      float64
 19  keyusfacts_avgval_cy   2 non-null      float64
 20  keyusfacts_popgrw10cy  2 non-null      float64
 21  keyusfacts_hhgrw10cy   2 non-null      float64
 22  keyusfacts_famgrw10cy  2 non-null      float64
 23  keyusfacts_dpop_cy     2 non-null      float64
 24  keyusfacts_dpopwrk_cy  2 non-null      float64
 25  keyusfacts_dpopres_cy  2 non-null      float64
 26  SHAPE                  2 non-null      geometry
dtypes: float64(21), geometry(1), int64(1), object(4)
memory usage: 560.0+ bytes

[8]:

	has_data	area_type	buffer_units	buffer_unit	buffer_radii	aggregation_method	keyusfacts_totpop_cy	keyusfacts_gqpop_cy	keyusfacts_divindx_cy	keyusfacts_tothh_cy	...	keyusfacts_vacant_cy	keyusfacts_medval_cy	keyusfacts_avgval_cy	keyusfacts_popgrw10cy	keyusfacts_hhgrw10cy	keyusfacts_famgrw10cy	keyusfacts_dpop_cy	keyusfacts_dpopwrk_cy	keyusfacts_dpopres_cy	SHAPE
0	1	RingBuffer	esriKilometers	esriKilometers	5.0	BlockApportionment:US.BlockGroups;PointsLayer:...	67415.0	1265.0	45.4	29537.0	...	1431.0	371293.0	415153.0	1.59	1.57	1.44	82942.0	50340.0	32602.0	{"x": -122.90748349999996, "y": 47.04502490000...
1	1	RingBuffer	esriKilometers	esriKilometers	5.0	BlockApportionment:US.BlockGroups;PointsLayer:...	70224.0	1350.0	48.5	30852.0	...	1636.0	369053.0	415710.0	0.99	1.01	0.87	89116.0	54806.0	34310.0	{"x": -122.87495999999999, "y": 47.04640310000...

2 rows × 27 columns

Specify Proximity Value and Metric¶

If desiring to use a different measure of distance, such as miles, this can be specified as well using the proximity_metric parameter.

[11]:

pt3_enrich_df = usa_local.enrich(
    geographies=geom_lst,
    enrich_variables=kv,
    proximity_value=5,
    proximity_metric='miles'
)

pt3_enrich_df.info()
pt3_enrich_df.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 27 columns):
 #   Column                 Non-Null Count  Dtype
---  ------                 --------------  -----
 0   has_data               2 non-null      int64
 1   area_type              2 non-null      object
 2   buffer_units           2 non-null      object
 3   buffer_unit            2 non-null      object
 4   buffer_radii           2 non-null      float64
 5   aggregation_method     2 non-null      object
 6   keyusfacts_totpop_cy   2 non-null      float64
 7   keyusfacts_gqpop_cy    2 non-null      float64
 8   keyusfacts_divindx_cy  2 non-null      float64
 9   keyusfacts_tothh_cy    2 non-null      float64
 10  keyusfacts_avghhsz_cy  2 non-null      float64
 11  keyusfacts_medhinc_cy  2 non-null      float64
 12  keyusfacts_avghinc_cy  2 non-null      float64
 13  keyusfacts_pci_cy      2 non-null      float64
 14  keyusfacts_tothu_cy    2 non-null      float64
 15  keyusfacts_owner_cy    2 non-null      float64
 16  keyusfacts_renter_cy   2 non-null      float64
 17  keyusfacts_vacant_cy   2 non-null      float64
 18  keyusfacts_medval_cy   2 non-null      float64
 19  keyusfacts_avgval_cy   2 non-null      float64
 20  keyusfacts_popgrw10cy  2 non-null      float64
 21  keyusfacts_hhgrw10cy   2 non-null      float64
 22  keyusfacts_famgrw10cy  2 non-null      float64
 23  keyusfacts_dpop_cy     2 non-null      float64
 24  keyusfacts_dpopwrk_cy  2 non-null      float64
 25  keyusfacts_dpopres_cy  2 non-null      float64
 26  SHAPE                  2 non-null      geometry
dtypes: float64(21), geometry(1), int64(1), object(4)
memory usage: 560.0+ bytes

[11]:

	has_data	area_type	buffer_units	buffer_unit	buffer_radii	aggregation_method	keyusfacts_totpop_cy	keyusfacts_gqpop_cy	keyusfacts_divindx_cy	keyusfacts_tothh_cy	...	keyusfacts_vacant_cy	keyusfacts_medval_cy	keyusfacts_avgval_cy	keyusfacts_popgrw10cy	keyusfacts_hhgrw10cy	keyusfacts_famgrw10cy	keyusfacts_dpop_cy	keyusfacts_dpopwrk_cy	keyusfacts_dpopres_cy	SHAPE
0	1	RingBuffer	esriMiles	esriMiles	5.0	BlockApportionment:US.BlockGroups;PointsLayer:...	121938.0	2969.0	47.4	51980.0	...	2798.0	370690.0	428025.0	1.31	1.29	1.14	140743.0	79614.0	61129.0	{"x": -122.90748349999996, "y": 47.04502490000...
1	1	RingBuffer	esriMiles	esriMiles	5.0	BlockApportionment:US.BlockGroups;PointsLayer:...	148032.0	2696.0	51.4	61965.0	...	3451.0	359612.0	412573.0	1.42	1.37	1.26	162638.0	87230.0	75408.0	{"x": -122.87495999999999, "y": 47.04640310000...

2 rows × 27 columns

Specify Proximity Type¶

The above examples all use the default proximity_type of straight_line. However, based on what transportation network you have available with the GIS source you are using, other methods are also available. These can be discovered using the travel_modes property of the Country. Any of the vaues in the names column are valid values for proximity_type in addition to the default straight_line.

[13]:

usa_local.travel_modes

[13]:

	name	alias	description	type	impedance	impedance_category	time_attribute_name	distance_attribute_name
0	driving_time	Driving Time	Models the movement of cars and other similar ...	AUTOMOBILE	TravelTime	temporal	TravelTime	Kilometers
1	driving_distance	Driving Distance	Models the movement of cars and other similar ...	AUTOMOBILE	Kilometers	distance	TravelTime	Kilometers
2	trucking_time	Trucking Time	Models basic truck travel by preferring design...	TRUCK	TruckTravelTime	temporal	TruckTravelTime	Kilometers
3	trucking_distance	Trucking Distance	Models basic truck travel by preferring design...	TRUCK	Kilometers	distance	TruckTravelTime	Kilometers
4	walking_time	Walking Time	Follows paths and roads that allow pedestrian ...	WALK	WalkTime	temporal	WalkTime	Kilometers
5	walking_distance	Walking Distance	Follows paths and roads that allow pedestrian ...	WALK	Kilometers	distance	WalkTime	Kilometers
6	rural_driving_time	Rural Driving Time	Models the movement of cars and other similar ...	AUTOMOBILE	TravelTime	temporal	TravelTime	Kilometers
7	rural_driving_distance	Rural Driving Distance	Models the movement of cars and other similar ...	AUTOMOBILE	Kilometers	distance	TravelTime	Kilometers

Hence, if we want to use both paved and gravel roads (because gravel roads are fun), we can use rural_driving_time. Before selecting, we can investigate the details of the method by looking at the description.

[18]:

usa_local.travel_modes[usa_local.travel_modes.name == 'rural_driving_distance'].iloc[0]['description']

[18]:

'Models the movement of cars and other similar small automobiles, such as pickup trucks, and finds solutions that optimize travel distance. Travel obeys one-way roads, avoids illegal turns, and follows other rules that are specific to cars, but does not discourage travel on unpaved roads.'

Most people aren’t going to be driving as fast on a gravel road as they are on an interstate. This enables us to take into consideration the differences in speed based on the road type. Using drive time as a method to define proximity around a location is a much better represenation of how people actually move around and interact with their surrouding environemnt…such as finding food at a grocery store.

[20]:

pt4_enrich_df = usa_local.enrich(
    geographies=geom_lst,
    enrich_variables=kv,
    proximity_type='rural_driving_time',
    proximity_value=12,
    proximity_metric='minutes'
)

pt4_enrich_df.info()
pt4_enrich_df.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 27 columns):
 #   Column                 Non-Null Count  Dtype
---  ------                 --------------  -----
 0   has_data               2 non-null      int64
 1   area_type              2 non-null      object
 2   buffer_units           2 non-null      object
 3   buffer_unit            2 non-null      object
 4   buffer_radii           2 non-null      float64
 5   aggregation_method     2 non-null      object
 6   keyusfacts_totpop_cy   2 non-null      float64
 7   keyusfacts_gqpop_cy    2 non-null      float64
 8   keyusfacts_divindx_cy  2 non-null      float64
 9   keyusfacts_tothh_cy    2 non-null      float64
 10  keyusfacts_avghhsz_cy  2 non-null      float64
 11  keyusfacts_medhinc_cy  2 non-null      float64
 12  keyusfacts_avghinc_cy  2 non-null      float64
 13  keyusfacts_pci_cy      2 non-null      float64
 14  keyusfacts_tothu_cy    2 non-null      float64
 15  keyusfacts_owner_cy    2 non-null      float64
 16  keyusfacts_renter_cy   2 non-null      float64
 17  keyusfacts_vacant_cy   2 non-null      float64
 18  keyusfacts_medval_cy   2 non-null      float64
 19  keyusfacts_avgval_cy   2 non-null      float64
 20  keyusfacts_popgrw10cy  2 non-null      float64
 21  keyusfacts_hhgrw10cy   2 non-null      float64
 22  keyusfacts_famgrw10cy  2 non-null      float64
 23  keyusfacts_dpop_cy     2 non-null      float64
 24  keyusfacts_dpopwrk_cy  2 non-null      float64
 25  keyusfacts_dpopres_cy  2 non-null      float64
 26  SHAPE                  2 non-null      geometry
dtypes: float64(21), geometry(1), int64(1), object(4)
memory usage: 560.0+ bytes

[20]:

	has_data	area_type	buffer_units	buffer_unit	buffer_radii	aggregation_method	keyusfacts_totpop_cy	keyusfacts_gqpop_cy	keyusfacts_divindx_cy	keyusfacts_tothh_cy	...	keyusfacts_vacant_cy	keyusfacts_medval_cy	keyusfacts_avgval_cy	keyusfacts_popgrw10cy	keyusfacts_hhgrw10cy	keyusfacts_famgrw10cy	keyusfacts_dpop_cy	keyusfacts_dpopwrk_cy	keyusfacts_dpopres_cy	SHAPE
0	1	Rural Driving Time	Minutes	Minutes	12.0	BlockApportionment:US.BlockGroups;PointsLayer:...	89167.0	2085.0	47.4	39012.0	...	2045.0	370989.0	423914.0	1.38	1.36	1.2	113049.0	68827.0	44222.0	{"x": -122.90748349999996, "y": 47.04502490000...
1	1	Rural Driving Time	Minutes	Minutes	12.0	BlockApportionment:US.BlockGroups;PointsLayer:...	125186.0	2604.0	52.1	53293.0	...	3074.0	359599.0	403034.0	1.43	1.41	1.3	144557.0	81408.0	63149.0	{"x": -122.87495999999999, "y": 47.04640310000...

2 rows × 27 columns

Use a Spatially Enabled Data Frame¶

Finally, although using a list of Geometry objects may be useful, likely a much more common paradigm is the Spatially Enabled Data Frame. Above, we created a Spatially Enabled Data Frame. We can use this as input into the geographies parameter.

Also, just to demonstrate the interchangability of GIS sources, we are going to create a Country instance using the connection to ArcGIS Online, and perform the same workflow.

[23]:

# create country
usa_gis = Country('usa', gis=gis_agol)

# select variables
ev = usa_gis.enrich_variables
kv = ev[
    (ev.data_collection.str.lower().str.contains('key'))
    & (ev.name.str.lower().str.endswith('cy'))
].reset_index(drop=True)

# perform enrichment
pt5_enrich_df = usa_gis.enrich(
    geographies=geom_lst,
    enrich_variables=kv,
    proximity_type='rural_driving_time',
    proximity_value=12,
    proximity_metric='minutes'
)

pt5_enrich_df.info()
pt5_enrich_df.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 27 columns):
 #   Column                 Non-Null Count  Dtype
---  ------                 --------------  -----
 0   has_data               2 non-null      int64
 1   area_type              2 non-null      object
 2   buffer_units           2 non-null      object
 3   buffer_unit            2 non-null      object
 4   buffer_radii           2 non-null      float64
 5   aggregation_method     2 non-null      object
 6   keyusfacts_totpop_cy   2 non-null      float64
 7   keyusfacts_gqpop_cy    2 non-null      float64
 8   keyusfacts_divindx_cy  2 non-null      float64
 9   keyusfacts_tothh_cy    2 non-null      float64
 10  keyusfacts_avghhsz_cy  2 non-null      float64
 11  keyusfacts_medhinc_cy  2 non-null      float64
 12  keyusfacts_avghinc_cy  2 non-null      float64
 13  keyusfacts_pci_cy      2 non-null      float64
 14  keyusfacts_tothu_cy    2 non-null      float64
 15  keyusfacts_owner_cy    2 non-null      float64
 16  keyusfacts_renter_cy   2 non-null      float64
 17  keyusfacts_vacant_cy   2 non-null      float64
 18  keyusfacts_medval_cy   2 non-null      float64
 19  keyusfacts_avgval_cy   2 non-null      float64
 20  keyusfacts_popgrw10cy  2 non-null      float64
 21  keyusfacts_hhgrw10cy   2 non-null      float64
 22  keyusfacts_famgrw10cy  2 non-null      float64
 23  keyusfacts_dpop_cy     2 non-null      float64
 24  keyusfacts_dpopwrk_cy  2 non-null      float64
 25  keyusfacts_dpopres_cy  2 non-null      float64
 26  SHAPE                  2 non-null      geometry
dtypes: float64(21), geometry(1), int64(1), object(4)
memory usage: 560.0+ bytes

[23]:

	has_data	area_type	buffer_units	buffer_unit	buffer_radii	aggregation_method	keyusfacts_totpop_cy	keyusfacts_gqpop_cy	keyusfacts_divindx_cy	keyusfacts_tothh_cy	...	keyusfacts_vacant_cy	keyusfacts_medval_cy	keyusfacts_avgval_cy	keyusfacts_popgrw10cy	keyusfacts_hhgrw10cy	keyusfacts_famgrw10cy	keyusfacts_dpop_cy	keyusfacts_dpopwrk_cy	keyusfacts_dpopres_cy	SHAPE
0	1	Rural Driving Time	Minutes	Minutes	12.0	BlockApportionment:US.BlockGroups;PointsLayer:...	89167.0	2085.0	47.4	39012.0	...	2045.0	370989.0	423914.0	1.38	1.36	1.2	113049.0	68827.0	44222.0	{"x": -122.90748349999996, "y": 47.04502490000...
1	1	Rural Driving Time	Minutes	Minutes	12.0	BlockApportionment:US.BlockGroups;PointsLayer:...	125186.0	2604.0	52.1	53293.0	...	3074.0	359599.0	403034.0	1.43	1.41	1.3	144557.0	81408.0	63149.0	{"x": -122.87495999999999, "y": 47.04640310000...

2 rows × 27 columns

BA-Python-API-Examples

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