Geoenrichment - Introduction to Enrich¶
Discovering what is available and distilling this down to something usable is the first step in analysis. Consequently, this is the first piece of functionality we added support for, introspection. This provides the ability to discover what countries are available, and within a country, what enrichment variables are available and what travel modes are available for dynamically creating trade areas.
[16]:
from arcgis.gis import GIS
from arcgis.geoenrichment import get_countries, Country
from demo_data import demo_data
GIS Source¶
The source GIS being used determines the countries available. Business Analyst can be accessed either locally (ArcGIS Pro with Business Analyst and Data) or through a connection to a Web GIS (ArcGIS Enterprise or ArcGIS Online). In this case, we are connecting to an instance of ArcGIS Online.
[2]:
gis_agol = GIS(profile='ba')
gis_agol
[2]:
Discovering Countries¶
Since the data is organized into countries, this is the first instrospection step, discovering countries.
Country Source - local¶
A local gis source can be used by passing in an instance of the GIS object created using the 'pro' keyword. As you can see, I have quite a few datasets installed on my machine.
[3]:
get_countries(GIS('pro'))
[3]:
| iso2 | iso3 | name | vintage | country_id | data_source_id | |
|---|---|---|---|---|---|---|
| 0 | CA | CAN | Canada | 2021 | CAN_ESRI_2021 | LOCAL;;CAN_ESRI_2021 |
| 1 | US | USA | United States | 2020 | USA_ESRI_2020 | LOCAL;;USA_ESRI_2020 |
| 2 | US | USA | United States | 2022 | USA_ESRI_2022 | LOCAL;;USA_ESRI_2022 |
Country Source - Web GIS¶
Similarly, we can access the countries available on the Web GIS through the arcpy.gis.GIS object. Obviously, if this is ArcGIS Online, this is a lot of countries.
[4]:
get_countries(gis_agol)
[4]:
| iso2 | iso3 | name | alt_name | datasets | default_dataset | continent | |
|---|---|---|---|---|---|---|---|
| 0 | AL | ALB | Albania | ALBANIA | [ALB_MBR_2021] | ALB_MBR_2021 | Europe |
| 1 | DZ | DZA | Algeria | ALGERIA | [DZA_MBR_2021] | DZA_MBR_2021 | Africa |
| 2 | AD | AND | Andorra | ANDORRA | [AND_MBR_2021] | AND_MBR_2021 | Europe |
| 3 | AO | AGO | Angola | ANGOLA | [AGO_MBR_2021] | AGO_MBR_2021 | Africa |
| 4 | AI | AIA | Anguilla | ANGUILLA | [AIA_MBR_2020] | AIA_MBR_2020 | North America |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 172 | VE | VEN | Venezuela | VENEZUELA, BOLIVARIAN REPUBLIC OF | [VEN_MBR_2020] | VEN_MBR_2020 | South America |
| 173 | VN | VNM | Vietnam | VIET NAM | [VNM_MBR_2020] | VNM_MBR_2020 | Asia |
| 174 | VI | VIR | Virgin Islands | UNITED STATES VIRGIN ISLANDS | [VIR_MBR_2020] | VIR_MBR_2020 | North America |
| 175 | ZM | ZMB | Zambia | ZAMBIA | [ZMB_MBR_2021] | ZMB_MBR_2021 | Africa |
| 176 | ZW | ZWE | Zimbabwe | ZIMBABWE | [ZWE_MBR_2021] | ZWE_MBR_2021 | Africa |
177 rows × 7 columns
Creating a Country¶
Before digging into enrichment variables, we need to create a Country object instance. A Country is created using the ISO3 code displayed in the data frame above along with the corresponding gis source.
[5]:
usa = Country('USA', gis=GIS('pro'))
usa
[5]:
<Country - United States 2022 ('local')>
If recalling from the introspection previously, three vintages of data are available on my machine for the USA; 2019, 2020, and 2021. If a model was developed against a specific country and data vintage, being able to specifically reference this data vintage is possible using the year parameter.
[7]:
usa2020 = Country('USA', gis=GIS('pro'), year=2020)
usa2020
[7]:
<Country - United States 2020 ('local')>
Enrichment Variables¶
Discovering enrichment variables available is possible through the Country object’s enrich_variables property.
[22]:
ev = usa.enrich_variables
ev.info()
ev.sample(5)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27975 entries, 0 to 27974
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 name 27975 non-null object
1 alias 27975 non-null object
2 data_collection 27975 non-null object
3 enrich_name 27975 non-null object
4 enrich_field_name 27975 non-null object
dtypes: object(5)
memory usage: 1.1+ MB
[22]:
| name | alias | data_collection | enrich_name | enrich_field_name | |
|---|---|---|---|---|---|
| 10444 | MP14143a_B_I | 2022 Used Insomnia Prescription Drug : Index | HealthPersonalCare | HealthPersonalCare.MP14143a_B_I | HealthPersonalCare_MP14143a_B_I |
| 25221 | RACE3BPO20 | 2020 Pop 3 Races: BL-PI-OTH | raceandhispanicorigin | raceandhispanicorigin.RACE3BPO20 | raceandhispanicorigin_RACE3BPO20 |
| 14056 | X8030_I | 2022 Index: Prescription Drugs | Health | Health.X8030_I | Health_X8030_I |
| 2885 | AIF60C10 | 2010 American Indian Females 60-64 | agebyracebysex | agebyracebysex.AIF60C10 | agebyracebysex_AIF60C10 |
| 18019 | X3042FY_X_A | 2027 Tools/Equipment-Paint/Paper (Renter) : Av... | HousingHousehold | HousingHousehold.X3042FY_X_A | HousingHousehold_X3042FY_X_A |
Filtering Variables¶
The usefulness of relevant metadata, especially categorical data, cannot be overstated. Using relevant criteria we can quickly identify variables to use for enrichment.
Get Current Income Metrics¶
Since a Pandas DataFrame, finding income indicies for use in analysis is relatively straightforward.
[10]:
inc_vars = ev[
(ev.alias.str.lower().str.contains('income'))
& (ev.name.str.endswith('CY'))
].drop_duplicates('name').reset_index(drop=True)
inc_vars
[10]:
| name | alias | data_collection | enrich_name | enrich_field_name | |
|---|---|---|---|---|---|
| 0 | AVGHINC_CY | 2022 Average Household Income | AtRisk | AtRisk.AVGHINC_CY | AtRisk_AVGHINC_CY |
| 1 | HINC0_CY | 2022 HH Income <$15000 | Policy | Policy.HINC0_CY | Policy_HINC0_CY |
| 2 | HINC15_CY | 2022 HH Income $15000-24999 | Policy | Policy.HINC15_CY | Policy_HINC15_CY |
| 3 | HINC25_CY | 2022 HH Income $25000-34999 | Policy | Policy.HINC25_CY | Policy_HINC25_CY |
| 4 | HINC35_CY | 2022 HH Income $35000-49999 | Policy | Policy.HINC35_CY | Policy_HINC35_CY |
| 5 | HINC50_CY | 2022 HH Income $50000-74999 | Policy | Policy.HINC50_CY | Policy_HINC50_CY |
| 6 | HINC75_CY | 2022 HH Income $75000-99999 | Policy | Policy.HINC75_CY | Policy_HINC75_CY |
| 7 | HINC100_CY | 2022 HH Income $100000-149999 | Policy | Policy.HINC100_CY | Policy_HINC100_CY |
| 8 | HINC150_CY | 2022 HH Income $150000-199999 | Policy | Policy.HINC150_CY | Policy_HINC150_CY |
| 9 | HINC200_CY | 2022 HH Income $200000+ | Policy | Policy.HINC200_CY | Policy_HINC200_CY |
| 10 | MEDHINC_CY | 2022 Median Household Income | Policy | Policy.MEDHINC_CY | Policy_MEDHINC_CY |
| 11 | PCI_CY | 2022 Per Capita Income | Policy | Policy.PCI_CY | Policy_PCI_CY |
| 12 | AGGINC_CY | 2022 Aggregate Income | Policy | Policy.AGGINC_CY | Policy_AGGINC_CY |
| 13 | AGGHINC_CY | 2022 Aggregate HH Income | Policy | Policy.AGGHINC_CY | Policy_AGGHINC_CY |
| 14 | HINCBASECY | 2022 Households by Income Base | Policy | Policy.HINCBASECY | Policy_HINCBASECY |
| 15 | MEDDI_CY | 2022 Median Disposable Income | disposableincome | disposableincome.MEDDI_CY | disposableincome_MEDDI_CY |
| 16 | AVGDI_CY | 2022 Average Disposable Income | disposableincome | disposableincome.AVGDI_CY | disposableincome_AVGDI_CY |
| 17 | AGGDI_CY | 2022 Aggregate Disposable Income | disposableincome | disposableincome.AGGDI_CY | disposableincome_AGGDI_CY |
| 18 | DIBASE_CY | 2022 Disposable Income Base | disposableincome | disposableincome.DIBASE_CY | disposableincome_DIBASE_CY |
| 19 | INCMORT_CY | 2022 Pct of Income for Mortgage | householdtotals | householdtotals.INCMORT_CY | householdtotals_INCMORT_CY |
| 20 | AVGIA15_CY | 2022 Avg HH Income: HHr 15-24 | incomebyage | incomebyage.AVGIA15_CY | incomebyage_AVGIA15_CY |
| 21 | IA15BASECY | 2022 HH Income Base: HHr 15-24 | incomebyage | incomebyage.IA15BASECY | incomebyage_IA15BASECY |
| 22 | AVGIA25_CY | 2022 Avg HH Income: HHr 25-34 | incomebyage | incomebyage.AVGIA25_CY | incomebyage_AVGIA25_CY |
| 23 | IA25BASECY | 2022 HH Income Base: HHr 25-34 | incomebyage | incomebyage.IA25BASECY | incomebyage_IA25BASECY |
| 24 | AVGIA35_CY | 2022 Avg HH Income: HHr 35-44 | incomebyage | incomebyage.AVGIA35_CY | incomebyage_AVGIA35_CY |
| 25 | IA35BASECY | 2022 HH Income Base: HHr 35-44 | incomebyage | incomebyage.IA35BASECY | incomebyage_IA35BASECY |
| 26 | AVGIA45_CY | 2022 Avg HH Income: HHr 45-54 | incomebyage | incomebyage.AVGIA45_CY | incomebyage_AVGIA45_CY |
| 27 | IA45BASECY | 2022 HH Income Base: HHr 45-54 | incomebyage | incomebyage.IA45BASECY | incomebyage_IA45BASECY |
| 28 | AVGIA55_CY | 2022 Avg HH Income: HHr 55-64 | incomebyage | incomebyage.AVGIA55_CY | incomebyage_AVGIA55_CY |
| 29 | IA55BASECY | 2022 HH Income Base: HHr 55-64 | incomebyage | incomebyage.IA55BASECY | incomebyage_IA55BASECY |
| 30 | AVGIA65_CY | 2022 Avg HH Income: HHr 65-74 | incomebyage | incomebyage.AVGIA65_CY | incomebyage_AVGIA65_CY |
| 31 | IA65BASECY | 2022 HH Income Base: HHr 65-74 | incomebyage | incomebyage.IA65BASECY | incomebyage_IA65BASECY |
| 32 | AVGIA75_CY | 2022 Avg HH Income: HHr 75+ | incomebyage | incomebyage.AVGIA75_CY | incomebyage_AVGIA75_CY |
| 33 | IA75BASECY | 2022 HH Income Base: HHr 75+ | incomebyage | incomebyage.IA75BASECY | incomebyage_IA75BASECY |
| 34 | IA55UBASCY | 2022 HH Income Base: HHr 55+ | incomebyage | incomebyage.IA55UBASCY | incomebyage_IA55UBASCY |
| 35 | AVGIA55UCY | 2022 Avg HH Income: HHr 55+ | incomebyage | incomebyage.AVGIA55UCY | incomebyage_AVGIA55UCY |
| 36 | IA65UBASCY | 2022 HH Income Base: HHr 65+ | incomebyage | incomebyage.IA65UBASCY | incomebyage_IA65UBASCY |
| 37 | AVGIA65UCY | 2022 Avg HH Income: HHr 65+ | incomebyage | incomebyage.AVGIA65UCY | incomebyage_AVGIA65UCY |
Get Current Key Metrics¶
One of the more common example datasets I use when exploring if an idea is vialbe are the current year key metrics.
[11]:
kv = ev[
(ev.name.str.contains('CY'))
& (ev.data_collection.str.lower().str.contains('key'))
].reset_index(drop=True)
kv
[11]:
| name | alias | data_collection | enrich_name | enrich_field_name | |
|---|---|---|---|---|---|
| 0 | TOTPOP_CY | 2022 Total Population | KeyUSFacts | KeyUSFacts.TOTPOP_CY | KeyUSFacts_TOTPOP_CY |
| 1 | GQPOP_CY | 2022 Group Quarters Population | KeyUSFacts | KeyUSFacts.GQPOP_CY | KeyUSFacts_GQPOP_CY |
| 2 | DIVINDX_CY | 2022 Diversity Index | KeyUSFacts | KeyUSFacts.DIVINDX_CY | KeyUSFacts_DIVINDX_CY |
| 3 | TOTHH_CY | 2022 Total Households | KeyUSFacts | KeyUSFacts.TOTHH_CY | KeyUSFacts_TOTHH_CY |
| 4 | AVGHHSZ_CY | 2022 Average Household Size | KeyUSFacts | KeyUSFacts.AVGHHSZ_CY | KeyUSFacts_AVGHHSZ_CY |
| 5 | MEDHINC_CY | 2022 Median Household Income | KeyUSFacts | KeyUSFacts.MEDHINC_CY | KeyUSFacts_MEDHINC_CY |
| 6 | AVGHINC_CY | 2022 Average Household Income | KeyUSFacts | KeyUSFacts.AVGHINC_CY | KeyUSFacts_AVGHINC_CY |
| 7 | PCI_CY | 2022 Per Capita Income | KeyUSFacts | KeyUSFacts.PCI_CY | KeyUSFacts_PCI_CY |
| 8 | TOTHU_CY | 2022 Total Housing Units | KeyUSFacts | KeyUSFacts.TOTHU_CY | KeyUSFacts_TOTHU_CY |
| 9 | OWNER_CY | 2022 Owner Occupied HUs | KeyUSFacts | KeyUSFacts.OWNER_CY | KeyUSFacts_OWNER_CY |
| 10 | RENTER_CY | 2022 Renter Occupied HUs | KeyUSFacts | KeyUSFacts.RENTER_CY | KeyUSFacts_RENTER_CY |
| 11 | VACANT_CY | 2022 Vacant Housing Units | KeyUSFacts | KeyUSFacts.VACANT_CY | KeyUSFacts_VACANT_CY |
| 12 | MEDVAL_CY | 2022 Median Home Value | KeyUSFacts | KeyUSFacts.MEDVAL_CY | KeyUSFacts_MEDVAL_CY |
| 13 | AVGVAL_CY | 2022 Average Home Value | KeyUSFacts | KeyUSFacts.AVGVAL_CY | KeyUSFacts_AVGVAL_CY |
| 14 | POPGRWCYFY | 2022-2027 Growth Rate: Population | KeyUSFacts | KeyUSFacts.POPGRWCYFY | KeyUSFacts_POPGRWCYFY |
| 15 | HHGRWCYFY | 2022-2027 Growth Rate: Households | KeyUSFacts | KeyUSFacts.HHGRWCYFY | KeyUSFacts_HHGRWCYFY |
| 16 | FAMGRWCYFY | 2022-2027 Growth Rate: Families | KeyUSFacts | KeyUSFacts.FAMGRWCYFY | KeyUSFacts_FAMGRWCYFY |
| 17 | MHIGRWCYFY | 2022-2027 Growth Rate: Median HH Inc | KeyUSFacts | KeyUSFacts.MHIGRWCYFY | KeyUSFacts_MHIGRWCYFY |
| 18 | PCIGRWCYFY | 2022-2027 Growth Rate: Per Capita Inc | KeyUSFacts | KeyUSFacts.PCIGRWCYFY | KeyUSFacts_PCIGRWCYFY |
| 19 | DPOP_CY | 2022 Total Daytime Population | KeyUSFacts | KeyUSFacts.DPOP_CY | KeyUSFacts_DPOP_CY |
| 20 | DPOPWRK_CY | 2022 Daytime Pop: Workers | KeyUSFacts | KeyUSFacts.DPOPWRK_CY | KeyUSFacts_DPOPWRK_CY |
| 21 | DPOPRES_CY | 2022 Daytime Pop: Residents | KeyUSFacts | KeyUSFacts.DPOPRES_CY | KeyUSFacts_DPOPRES_CY |
| 22 | GQPOP_CY_P | 2022 Group Quarters Population : Percent | KeyUSFacts | KeyUSFacts.GQPOP_CY_P | KeyUSFacts_GQPOP_CY_P |
| 23 | AVGHHSZ_CY_I | 2022 Average Household Size : Index | KeyUSFacts | KeyUSFacts.AVGHHSZ_CY_I | KeyUSFacts_AVGHHSZ_CY_I |
| 24 | MEDHINC_CY_I | 2022 Median Household Income : Index | KeyUSFacts | KeyUSFacts.MEDHINC_CY_I | KeyUSFacts_MEDHINC_CY_I |
| 25 | AVGHINC_CY_I | 2022 Average Household Income : Index | KeyUSFacts | KeyUSFacts.AVGHINC_CY_I | KeyUSFacts_AVGHINC_CY_I |
| 26 | PCI_CY_I | 2022 Per Capita Income : Index | KeyUSFacts | KeyUSFacts.PCI_CY_I | KeyUSFacts_PCI_CY_I |
| 27 | OWNER_CY_P | 2022 Owner Occupied HUs : Percent | KeyUSFacts | KeyUSFacts.OWNER_CY_P | KeyUSFacts_OWNER_CY_P |
| 28 | RENTER_CY_P | 2022 Renter Occupied HUs : Percent | KeyUSFacts | KeyUSFacts.RENTER_CY_P | KeyUSFacts_RENTER_CY_P |
| 29 | VACANT_CY_P | 2022 Vacant Housing Units : Percent | KeyUSFacts | KeyUSFacts.VACANT_CY_P | KeyUSFacts_VACANT_CY_P |
| 30 | MEDVAL_CY_I | 2022 Median Home Value : Index | KeyUSFacts | KeyUSFacts.MEDVAL_CY_I | KeyUSFacts_MEDVAL_CY_I |
| 31 | AVGVAL_CY_I | 2022 Average Home Value : Index | KeyUSFacts | KeyUSFacts.AVGVAL_CY_I | KeyUSFacts_AVGVAL_CY_I |
| 32 | POPGRWCYFY_I | 2022-2027 Growth Rate: Population : Index | KeyUSFacts | KeyUSFacts.POPGRWCYFY_I | KeyUSFacts_POPGRWCYFY_I |
| 33 | HHGRWCYFY_I | 2022-2027 Growth Rate: Households : Index | KeyUSFacts | KeyUSFacts.HHGRWCYFY_I | KeyUSFacts_HHGRWCYFY_I |
| 34 | FAMGRWCYFY_I | 2022-2027 Growth Rate: Families : Index | KeyUSFacts | KeyUSFacts.FAMGRWCYFY_I | KeyUSFacts_FAMGRWCYFY_I |
| 35 | MHIGRWCYFY_I | 2022-2027 Growth Rate: Median HH Inc : Index | KeyUSFacts | KeyUSFacts.MHIGRWCYFY_I | KeyUSFacts_MHIGRWCYFY_I |
| 36 | PCIGRWCYFY_I | 2022-2027 Growth Rate: Per Capita Inc : Index | KeyUSFacts | KeyUSFacts.PCIGRWCYFY_I | KeyUSFacts_PCIGRWCYFY_I |
| 37 | DPOPWRK_CY_P | 2022 Daytime Pop: Workers : Percent | KeyUSFacts | KeyUSFacts.DPOPWRK_CY_P | KeyUSFacts_DPOPWRK_CY_P |
| 38 | DPOPRES_CY_P | 2022 Daytime Pop: Residents : Percent | KeyUSFacts | KeyUSFacts.DPOPRES_CY_P | KeyUSFacts_DPOPRES_CY_P |
Onto Enrich¶
From here the next step is enriching data using the retrieved variables. As of the 2.0.1 release of the Python API for ArcGIS you can pass this data frame directly into the enrich function.
Enrich Study Area Polygons¶
[17]:
study_area_df = demo_data.pdx_coffee_study_areas_3min.df
study_area_df.info()
study_area_df.head()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 650 entries, 0 to 649
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 LOCNUM 650 non-null object
1 SHAPE 650 non-null geometry
dtypes: geometry(1), object(1)
memory usage: 10.3+ KB
[17]:
| LOCNUM | SHAPE | |
|---|---|---|
| 0 | 105550909 | {"rings": [[[-13659712.491841972, 5689050.6789... |
| 1 | 105633002 | {"rings": [[[-13683812.381583042, 5712092.0429... |
| 2 | 105759815 | {"rings": [[[-13655812.509662066, 5706201.2159... |
| 3 | 177692910 | {"rings": [[[-13651962.527299367, 5690511.2941... |
| 4 | 180308389 | {"rings": [[[-13640787.578395057, 5708949.7851... |
[20]:
study_area_enrich_df = usa.enrich(study_area_df, enrich_variables=kv)
study_area_enrich_df.info()
study_area_enrich_df.head()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 650 entries, 0 to 649
Data columns (total 43 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 locnum 650 non-null object
1 has_data 650 non-null int32
2 aggregation_method 650 non-null object
3 totpop_cy 650 non-null float64
4 gqpop_cy 650 non-null float64
5 divindx_cy 650 non-null float64
6 tothh_cy 650 non-null float64
7 avghhsz_cy 650 non-null float64
8 medhinc_cy 650 non-null float64
9 avghinc_cy 650 non-null float64
10 pci_cy 650 non-null float64
11 tothu_cy 650 non-null float64
12 owner_cy 650 non-null float64
13 renter_cy 650 non-null float64
14 vacant_cy 650 non-null float64
15 medval_cy 650 non-null float64
16 avgval_cy 650 non-null float64
17 popgrwcyfy 650 non-null float64
18 hhgrwcyfy 650 non-null float64
19 famgrwcyfy 650 non-null float64
20 mhigrwcyfy 650 non-null float64
21 pcigrwcyfy 650 non-null float64
22 dpop_cy 650 non-null float64
23 dpopwrk_cy 650 non-null float64
24 dpopres_cy 650 non-null float64
25 gqpop_cy_p 650 non-null float64
26 avghhsz_cy_i 650 non-null int32
27 medhinc_cy_i 650 non-null int32
28 avghinc_cy_i 650 non-null int32
29 pci_cy_i 650 non-null int32
30 owner_cy_p 650 non-null float64
31 renter_cy_p 650 non-null float64
32 vacant_cy_p 650 non-null float64
33 medval_cy_i 650 non-null int32
34 avgval_cy_i 650 non-null int32
35 popgrwcyfy_i 650 non-null int32
36 hhgrwcyfy_i 650 non-null int32
37 famgrwcyfy_i 650 non-null int32
38 mhigrwcyfy_i 650 non-null int32
39 pcigrwcyfy_i 650 non-null int32
40 dpopwrk_cy_p 650 non-null float64
41 dpopres_cy_p 650 non-null float64
42 SHAPE 650 non-null geometry
dtypes: float64(28), geometry(1), int32(12), object(2)
memory usage: 188.0+ KB
[20]:
| locnum | has_data | aggregation_method | totpop_cy | gqpop_cy | divindx_cy | tothh_cy | avghhsz_cy | medhinc_cy | avghinc_cy | ... | medval_cy_i | avgval_cy_i | popgrwcyfy_i | hhgrwcyfy_i | famgrwcyfy_i | mhigrwcyfy_i | pcigrwcyfy_i | dpopwrk_cy_p | dpopres_cy_p | SHAPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 105550909 | 1 | BlockApportionment:US.BlockGroups;PointsLayer:... | 6138.0 | 143.0 | 41.9 | 2575.0 | 2.33 | 119103.0 | 176581.0 | ... | 231 | 191 | 408 | 294 | 275 | 109 | 79 | 73.54 | 26.46 | {"rings": [[[-122.70728508062291, 45.427343047... |
| 1 | 105633002 | 1 | BlockApportionment:US.BlockGroups;PointsLayer:... | 39.0 | 0.0 | 61.0 | 13.0 | 3.00 | 111816.0 | 135177.0 | ... | 163 | 130 | -208 | 0 | 0 | 46 | 121 | 99.09 | 0.91 | {"rings": [[[-122.92377807415231, 45.572420302... |
| 2 | 105759815 | 1 | BlockApportionment:US.BlockGroups;PointsLayer:... | 11643.0 | 1335.0 | 55.8 | 7136.0 | 1.44 | 66266.0 | 107837.0 | ... | 222 | 181 | 304 | 248 | 289 | 302 | 134 | 85.79 | 14.21 | {"rings": [[[-122.6722509453407, 45.5353649659... |
| 3 | 177692910 | 1 | BlockApportionment:US.BlockGroups;PointsLayer:... | 5949.0 | 62.0 | 53.5 | 2612.0 | 2.25 | 55293.0 | 75609.0 | ... | 147 | 116 | 28 | 16 | -18 | 88 | 118 | 50.23 | 49.77 | {"rings": [[[-122.6376659650056, 45.4365507371... |
| 4 | 180308389 | 1 | BlockApportionment:US.BlockGroups;PointsLayer:... | 12853.0 | 677.0 | 68.0 | 4924.0 | 2.47 | 63848.0 | 81311.0 | ... | 132 | 108 | -100 | -119 | -146 | 136 | 120 | 43.26 | 56.74 | {"rings": [[[-122.53727969104438, 45.552657456... |
5 rows × 43 columns
Enrich Point Locations¶
[24]:
pt_df = demo_data.pdx_coffee_locations.df
pt_df.info()
pt_df.head()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 650 entries, 0 to 649
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 LOCNUM 650 non-null object
1 SHAPE 650 non-null geometry
dtypes: geometry(1), object(1)
memory usage: 10.3+ KB
[24]:
| LOCNUM | SHAPE | |
|---|---|---|
| 0 | 105550909 | {"x": -13661187.466078103, "y": 5686302.261006... |
| 1 | 105633002 | {"x": -13683283.32746541, "y": 5709438.5596519... |
| 2 | 105759815 | {"x": -13656422.54659419, "y": 5704599.4372188... |
| 3 | 177692910 | {"x": -13651479.293286022, "y": 5687492.553844... |
| 4 | 180308389 | {"x": -13640908.50575978, "y": 5706009.5641631... |
[25]:
usa.travel_modes
[25]:
| 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 |
[26]:
enrich_pt_df = usa.enrich(pt_df, kv, proximity_type='driving_time', proximity_metric='minutes', proximity_value=3)
enrich_pt_df.info()
enrich_pt_df.head()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 650 entries, 0 to 649
Data columns (total 47 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 locnum 650 non-null object
1 has_data 650 non-null int32
2 area_type 650 non-null object
3 buffer_units 650 non-null object
4 buffer_unit 650 non-null object
5 buffer_radii 650 non-null float64
6 aggregation_method 650 non-null object
7 totpop_cy 650 non-null float64
8 gqpop_cy 650 non-null float64
9 divindx_cy 650 non-null float64
10 tothh_cy 650 non-null float64
11 avghhsz_cy 650 non-null float64
12 medhinc_cy 650 non-null float64
13 avghinc_cy 650 non-null float64
14 pci_cy 650 non-null float64
15 tothu_cy 650 non-null float64
16 owner_cy 650 non-null float64
17 renter_cy 650 non-null float64
18 vacant_cy 650 non-null float64
19 medval_cy 650 non-null float64
20 avgval_cy 650 non-null float64
21 popgrwcyfy 650 non-null float64
22 hhgrwcyfy 650 non-null float64
23 famgrwcyfy 650 non-null float64
24 mhigrwcyfy 650 non-null float64
25 pcigrwcyfy 650 non-null float64
26 dpop_cy 650 non-null float64
27 dpopwrk_cy 650 non-null float64
28 dpopres_cy 650 non-null float64
29 gqpop_cy_p 650 non-null float64
30 avghhsz_cy_i 650 non-null int32
31 medhinc_cy_i 650 non-null int32
32 avghinc_cy_i 650 non-null int32
33 pci_cy_i 650 non-null int32
34 owner_cy_p 650 non-null float64
35 renter_cy_p 650 non-null float64
36 vacant_cy_p 650 non-null float64
37 medval_cy_i 650 non-null int32
38 avgval_cy_i 650 non-null int32
39 popgrwcyfy_i 650 non-null int32
40 hhgrwcyfy_i 650 non-null int32
41 famgrwcyfy_i 650 non-null int32
42 mhigrwcyfy_i 650 non-null int32
43 pcigrwcyfy_i 650 non-null int32
44 dpopwrk_cy_p 650 non-null float64
45 dpopres_cy_p 650 non-null float64
46 SHAPE 650 non-null geometry
dtypes: float64(29), geometry(1), int32(12), object(5)
memory usage: 208.3+ KB
[26]:
| locnum | has_data | area_type | buffer_units | buffer_unit | buffer_radii | aggregation_method | totpop_cy | gqpop_cy | divindx_cy | ... | medval_cy_i | avgval_cy_i | popgrwcyfy_i | hhgrwcyfy_i | famgrwcyfy_i | mhigrwcyfy_i | pcigrwcyfy_i | dpopwrk_cy_p | dpopres_cy_p | SHAPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 105550909 | 1 | Driving Time | Minutes | Minutes | 3.0 | BlockApportionment:US.BlockGroups;PointsLayer:... | 4132.0 | 40.0 | 40.5 | ... | 230 | 198 | 444 | 310 | 286 | 117 | 82 | 75.67 | 24.33 | {"x": -122.72053500019666, "y": 45.41001299996... |
| 1 | 105633002 | 1 | Driving Time | Minutes | Minutes | 3.0 | BlockApportionment:US.BlockGroups;PointsLayer:... | 223.0 | 0.0 | 78.6 | ... | 230 | 190 | -36 | -77 | -111 | 0 | 27 | 95.98 | 4.02 | {"x": -122.91902550031074, "y": 45.55573200030... |
| 2 | 105759815 | 1 | Driving Time | Minutes | Minutes | 3.0 | BlockApportionment:US.BlockGroups;PointsLayer:... | 13632.0 | 1818.0 | 56.0 | ... | 213 | 174 | 412 | 377 | 346 | 374 | 142 | 86.68 | 13.32 | {"x": -122.67773100005218, "y": 45.52528499988... |
| 3 | 177692910 | 1 | Driving Time | Minutes | Minutes | 3.0 | BlockApportionment:US.BlockGroups;PointsLayer:... | 11053.0 | 145.0 | 48.6 | ... | 151 | 127 | 120 | 100 | 57 | 159 | 114 | 44.02 | 55.98 | {"x": -122.63332500012554, "y": 45.41751899971... |
| 4 | 180308389 | 1 | Driving Time | Minutes | Minutes | 3.0 | BlockApportionment:US.BlockGroups;PointsLayer:... | 11636.0 | 669.0 | 67.6 | ... | 133 | 108 | -104 | -126 | -150 | 150 | 121 | 43.15 | 56.85 | {"x": -122.53836600036125, "y": 45.53415900023... |
5 rows × 47 columns
[ ]: