Part 4 - What to enrich with? (What are Data Collections and Analysis Variables?)
Data Collections and GeoEnrichment coverage
As described earlier, a data collection is a preassembled list of attributes that will be used to enrich the input features. Collection attributes can describe various types of information, such as demographic characteristics and geographic context of the locations or areas submitted as input features.
Some data collections (such as default) can be used in all supported countries. Other data collections may only be available in one or a collection of countries. Data Browser can be used to examine the entire global listing of variables, and associated datasets for each country.
List Countries with GeoEnrichment Data
The get_countries() method can be used to query the countries for which GeoEnrichment data is available, and it returns a list of Country objects with which you can further query for properties. This list can also be viewed here.
from arcgis.gis import GIS
from arcgis.geoenrichment import Country, enrich, get_countries# Create a GIS Connection
gis = GIS(profile='your_online_profile')countries = get_countries()
print("Number of countries for which GeoEnrichment data is available: " + str(len(countries)))
#print a few countries for a sample
countries[0:10]Number of countries for which GeoEnrichment data is available: 177
| iso2 | iso3 | name | alt_name | datasets | default_dataset | continent | hierarchy | |
|---|---|---|---|---|---|---|---|---|
| 0 | AL | ALB | Albania | ALBANIA | [ALB_MBR_2021] | ALB_MBR_2021 | Europe | [census] |
| 1 | DZ | DZA | Algeria | ALGERIA | [DZA_MBR_2021] | DZA_MBR_2021 | Africa | [census] |
| 2 | AD | AND | Andorra | ANDORRA | [AND_MBR_2021] | AND_MBR_2021 | Europe | [census] |
| 3 | AO | AGO | Angola | ANGOLA | [AGO_MBR_2021] | AGO_MBR_2021 | Africa | [census] |
| 4 | AI | AIA | Anguilla | ANGUILLA | [AIA_MBR_2020] | AIA_MBR_2020 | North America | [census] |
| 5 | AR | ARG | Argentina | ARGENTINA | [ARG_MBR_2020] | ARG_MBR_2020 | South America | [census] |
| 6 | AM | ARM | Armenia | ARMENIA | [ARM_MBR_2020] | ARM_MBR_2020 | Europe | [census] |
| 7 | AW | ABW | Aruba | ARUBA | [ABW_MBR_2020] | ABW_MBR_2020 | North America | [census] |
| 8 | AU | AUS | Australia | AUSTRALIA | [AUS_ABS_2016, AUS_MBR_2020] | AUS_ABS_2016 | Oceania | [AUS_ABS, census] |
| 9 | AT | AUT | Austria | AUSTRIA | [AUT_MBR_2021] | AUT_MBR_2021 | Europe | [census] |
Data Collections for U.S.
The data_collections property of a Country object lists its available data collections and analysis variables under each data collection as a Pandas dataframe.
In order to discover the data collections for a particular country, you may first access the reference variable to it using the country.get() method, and then fetch the data collections from country.data_collections property. Once we know the data collection we would like to use, we can look at analysisVariables available in that data collection.
# Get US as a country
usa = Country.get('US')
type(usa)arcgis.geoenrichment.enrichment.Country
usa_df = usa.data_collections
# print a few rows of the DataFrame
usa_df.head()| analysisVariable | alias | fieldCategory | vintage | |
|---|---|---|---|---|
| dataCollectionID | ||||
| 1yearincrements | 1yearincrements.AGE0_CY | 2022 Population Age <1 | 2022 Age: 1 Year Increments (Esri) | 2022 |
| 1yearincrements | 1yearincrements.AGE1_CY | 2022 Population Age 1 | 2022 Age: 1 Year Increments (Esri) | 2022 |
| 1yearincrements | 1yearincrements.AGE2_CY | 2022 Population Age 2 | 2022 Age: 1 Year Increments (Esri) | 2022 |
| 1yearincrements | 1yearincrements.AGE3_CY | 2022 Population Age 3 | 2022 Age: 1 Year Increments (Esri) | 2022 |
| 1yearincrements | 1yearincrements.AGE4_CY | 2022 Population Age 4 | 2022 Age: 1 Year Increments (Esri) | 2022 |
usa_df.shape(18946, 4)
Unique Data Collections for U.S.
Each data collection and analysis variable has a unique ID. When calling the enrich() method (explained earlier in this guide) these analysis variables can be passed in the data_collections and analysis_variables parameters.
As an example, here we see a subset of the data collections for US showing 2 different data collections and multiple analysis variables for each collection.
usa_df.iloc[500:600,:]| analysisVariable | alias | fieldCategory | vintage | |
|---|---|---|---|---|
| dataCollectionID | ||||
| 1yearincrements | 1yearincrements.FAGE75_FY | 2027 Females Age 75 | 2027 Age: 1 Year Increments (Esri) | 2027 |
| 1yearincrements | 1yearincrements.FAGE76_FY | 2027 Females Age 76 | 2027 Age: 1 Year Increments (Esri) | 2027 |
| 1yearincrements | 1yearincrements.FAGE77_FY | 2027 Females Age 77 | 2027 Age: 1 Year Increments (Esri) | 2027 |
| 1yearincrements | 1yearincrements.FAGE78_FY | 2027 Females Age 78 | 2027 Age: 1 Year Increments (Esri) | 2027 |
| 1yearincrements | 1yearincrements.FAGE79_FY | 2027 Females Age 79 | 2027 Age: 1 Year Increments (Esri) | 2027 |
| ... | ... | ... | ... | ... |
| 5yearincrements | 5yearincrements.MEDAGE_CY | 2022 Median Age | 2022 Age: 5 Year Increments (Esri) | 2022 |
| 5yearincrements | 5yearincrements.MALES_CY | 2022 Male Population | 2022 Age: 5 Year Increments (Esri) | 2022 |
| 5yearincrements | 5yearincrements.MALE0_CY | 2022 Males Age 0-4 | 2022 Age: 5 Year Increments (Esri) | 2022 |
| 5yearincrements | 5yearincrements.MALE5_CY | 2022 Males Age 5-9 | 2022 Age: 5 Year Increments (Esri) | 2022 |
| 5yearincrements | 5yearincrements.MALE10_CY | 2022 Males Age 10-14 | 2022 Age: 5 Year Increments (Esri) | 2022 |
100 rows × 4 columns
The table above shows 2 different data collections (1yearincrements and 5yearincrements). Since these are Age data collections, the analysisVariables for these collections are similar. vintage shows the year that the demographic data represents. For example, a vintage of 2020 means that the data represents the year 2020.
Let's get a list of unique data collections that are available for U.S.
usa_df.index.nunique()115
United States has 150 unique data collections. Here are the first 10 data collections.
list(usa_df.index.unique())[:10]['1yearincrements', '5yearincrements', 'Age', 'agebyracebysex', 'AgeDependency', 'AtRisk', 'AutomobilesAutomotiveProducts', 'BabyProductsToysGames', 'basicFactsForMobileApps', 'businesses']
Looking at fieldCategory is a great way to clearly understand what the data collection is about. fieldCategory combines vintage, datacollectionID columns along with the year and data collection. However, to query a data collection its unique ID (dataCollectionID) must be used.
Let's look at the fieldCategory column for a few data collections in US.
usa_df.fieldCategory.unique()[:10]array(['2022 Age: 1 Year Increments (Esri)',
'2027 Age: 1 Year Increments (Esri)',
'2010 Age: 1 Year Increments (U.S. Census)',
'2022 Age: 5 Year Increments (Esri)',
'2027 Age: 5 Year Increments (Esri)',
'2010 Age: 5 Year Increments (U.S. Census)',
'2016-2020 Age: 5 Year Increments (ACS)',
'2022 Age by Sex by Race (Esri)', '2027 Age by Sex by Race (Esri)',
'2010 Age by Sex by Race (U.S. Census)'], dtype=object)Data Collections by Socio-demographic Factors
You can filter the data_collections to get collections for a specific factor using Pandas expressions. Let's loook at data collections for different socio-demographic factors such as Age, Population, Income.
Data Collections for Age
Age_Collections = usa_df['fieldCategory'].str.contains('Age', na=False)
usa_df[Age_Collections].fieldCategory.unique()array(['2022 Age: 1 Year Increments (Esri)',
'2027 Age: 1 Year Increments (Esri)',
'2010 Age: 1 Year Increments (U.S. Census)',
'2022 Age: 5 Year Increments (Esri)',
'2027 Age: 5 Year Increments (Esri)',
'2010 Age: 5 Year Increments (U.S. Census)',
'2016-2020 Age: 5 Year Increments (ACS)',
'2022 Age by Sex by Race (Esri)', '2027 Age by Sex by Race (Esri)',
'2010 Age by Sex by Race (U.S. Census)',
'2022 Age Dependency (Esri)', '2027 Age Dependency (Esri)',
'2022 Disposable Income by Age (Esri)',
'2010 Households by Age of Householder (U.S. Census)',
'2016-2020 Households by Type and Size and Age (ACS)',
'2010 Housing by Age of Householder (U.S. Census)',
'2022 Income by Age (Esri)', '2027 Income by Age (Esri)',
'2016-2020 Income by Age (ACS)', 'Age: 5 Year Increments',
'2022 Net Worth by Age (Esri)',
'2016-2020 Females by Age of Children and Employment Status (ACS)'],
dtype=object)Data Collections for Population
Pop_Collections = usa_df['fieldCategory'].str.contains('Population', na=False)
usa_df[Pop_Collections].fieldCategory.unique()array(['2010 Population (U.S. Census)',
'2016-2020 Population by Language Spoken at Home (ACS)',
'2022 Daytime Population (Esri)',
'2022 Population by Generation (Esri)',
'2027 Population by Generation (Esri)',
'2020 Group Quarters Population (U.S. Census)',
'2020 Group Quarters Population by Type (U.S. Census)',
'2010 Group Quarters Population (U.S. Census)',
'2020 Hispanic Population by Race (U.S. Census)',
'2020 Hispanic Population of Two or More Races (U.S. Census)',
'2020 Hispanic Population <18 Years by Race (U.S. Census)',
'2020 Hispanic Population 18+ Years by Race (U.S. Census)',
'2020 Hispanic Population 18+ Years of Two or More Races (U.S. Census)',
'2022 Population Time Series (Esri)',
'2010 Population by Relationship and Household Type (U.S. Census)',
'2016-2020 Population by Relationship and Household Type (ACS)',
'2020 Non Hispanic Population by Race (U.S. Census)',
'2020 Non Hispanic Population of Two or More Races (U.S. Census)',
'2020 Non Hispanic Population <18 Years by Race (U.S. Census)',
'2020 Non Hispanic Population 18+ Years by Race (U.S. Census)',
'2020 Non Hispanic Population 18+ Years of Two or More Races (U.S. Census)',
'2022 Population (Esri)', '2020 Population (U.S. Census)',
'2020 Population by Race (U.S. Census)',
'2020 Population of Two or More Races (U.S. Census)',
'2020 Population <18 Years by Race (U.S. Census)',
'2020 Population 18+ Years by Race (U.S. Census)',
'2020 Population 18+ Years of Two or More Races (U.S. Census)'],
dtype=object)Data Collections for Income
Income_Collections = usa_df['fieldCategory'].str.contains('Income', na=False)
Income_Collections.index.unique()Index(['1yearincrements', '5yearincrements', 'Age', 'agebyracebysex',
'AgeDependency', 'AtRisk', 'AutomobilesAutomotiveProducts',
'BabyProductsToysGames', 'basicFactsForMobileApps', 'businesses',
...
'travelMPI', 'unitsinstructure', 'urbanizationgroupsNEW', 'vacant',
'vehiclesavailable', 'veterans', 'Wealth', 'women', 'yearbuilt',
'yearmovedin'],
dtype='object', name='dataCollectionID', length=115)As mentioned earlier, using a data_collection's unique ID (dataCollectionID) is the best way to further query a data collection. Let's look at the dataCollectionID for various Income data collections.
usa_df[Income_Collections].index.unique()Index(['AtRisk', 'basicFactsForMobileApps', 'disposableincome',
'foodstampsSNAP', 'Health', 'householdincome', 'households',
'incomebyage', 'KeyUSFacts', 'Policy', 'population', 'Wealth'],
dtype='object', name='dataCollectionID')Analysis variables for Data Collections
Once we know the data collection we would like to use, we can look at all the unique variables available in that data collection using its unique ID. Let's discover analysisVariables for some of the data collections.
Analysis variables for Age data collection
usa_df.loc['Age']['analysisVariable'].unique()array(['Age.MALE0', 'Age.MALE5', 'Age.MALE10', 'Age.MALE15', 'Age.MALE20',
'Age.MALE25', 'Age.MALE30', 'Age.MALE35', 'Age.MALE40',
'Age.MALE45', 'Age.MALE50', 'Age.MALE55', 'Age.MALE60',
'Age.MALE65', 'Age.MALE70', 'Age.MALE75', 'Age.MALE80',
'Age.MALE85', 'Age.FEM0', 'Age.FEM5', 'Age.FEM10', 'Age.FEM15',
'Age.FEM20', 'Age.FEM25', 'Age.FEM30', 'Age.FEM35', 'Age.FEM40',
'Age.FEM45', 'Age.FEM50', 'Age.FEM55', 'Age.FEM60', 'Age.FEM65',
'Age.FEM70', 'Age.FEM75', 'Age.FEM80', 'Age.FEM85'], dtype=object)Analysis variables are typically represented as dataCollectionID.<analysis variable name> as seen above.
Analysis variables for DaytimePopulation data collection
usa_df.loc['DaytimePopulation']['analysisVariable'].unique()array(['DaytimePopulation.DPOP_CY', 'DaytimePopulation.DPOPWRK_CY',
'DaytimePopulation.DPOPRES_CY', 'DaytimePopulation.DPOPDENSCY'],
dtype=object)Data Collections for Another Country
Let's look at data collections for New Zealand. Data Browser can be used to examine the entire global listing of variables, and associated datasets for New Zealand.
In order to discover the data collections for a particular country, you may first access the reference variable to it using the country.get() method, and then fetch the data collections from country.data_collections property. Once we know the data collection we would like to use, we can look at analysisVariables available in that data collection.
# Get US as a country
nz = Country.get('New Zealand')
type(nz)arcgis.geoenrichment.enrichment.Country
nz_df = nz.data_collections
# print a few rows of the DataFrame
nz_df.head()| analysisVariable | alias | fieldCategory | vintage | |
|---|---|---|---|---|
| dataCollectionID | ||||
| 15YearIncrements | 15YearIncrements.PAGE01_CY | 2020 Total Population Age 0-14 | 2020 Population Totals (MBR) | 2020 |
| 15YearIncrements | 15YearIncrements.PAGE02_CY | 2020 Total Population Age 15-29 | 2020 Population Totals (MBR) | 2020 |
| 15YearIncrements | 15YearIncrements.PAGE03_CY | 2020 Total Population Age 30-44 | 2020 Population Totals (MBR) | 2020 |
| 15YearIncrements | 15YearIncrements.PAGE04_CY | 2020 Total Population Age 45-59 | 2020 Population Totals (MBR) | 2020 |
| 15YearIncrements | 15YearIncrements.PAGE05_CY | 2020 Total Population Age 60+ | 2020 Population Totals (MBR) | 2020 |
nz_df.shape(193, 4)
Unique Data Collections for New Zealand
Let's get a list of unique data collections that are available for New Zealand.
nz_df.index.unique()Index(['15YearIncrements', 'EducationalAttainment', 'Gender',
'HouseholdsbyIncome', 'HouseholdsbyType', 'HouseholdTotals', 'KeyFacts',
'KeyGlobalFacts', 'MaritalStatus', 'PopulationTotals',
'PurchasingPower', 'Spending'],
dtype='object', name='dataCollectionID')New Zealand has 12 unique data collections.
We can look at the fieldCategory column to understand each category better.
nz_df.fieldCategory.unique()array(['2020 Population Totals (MBR)',
'2020 Male Population Totals (MBR)',
'2020 Female Population Totals (MBR)',
'2020 Educational Attainment (MBR)',
'2020 Households by Income (MBR)', '2020 Households by Type (MBR)',
'2020 Household Totals (MBR)', '2020 Marital Status (MBR)',
'2020 Purchasing Power (MBR)', 'Key Demographic Indicators',
'Age: 5 Year Increments',
'2020 Food & Beverage Expenditures (MBR)',
'2020 Alcoholic Beverage Expenditures (MBR)',
'2020 Tobacco Expenditures (MBR)',
'2020 Clothing Expenditures (MBR)',
'2020 Footwear Expenditures (MBR)',
'2020 Furniture & Furnishing Expenditures (MBR)',
'2020 Household Textiles Expenditures (MBR)',
'2020 Household Appliances Expenditures (MBR)',
'2020 Household Utensils Expenditures (MBR)',
'2020 House & Garden Expenditures (MBR)',
'2020 Household Maintenance Expenditures (MBR)',
'2020 Medical Products & Supplies Expenditures (MBR)',
'2020 Consumer Electronics Expenditures (MBR)',
'2020 Recreation & Culture Durable Expenditures (MBR)',
'2020 Entertainment Expenditures (MBR)',
'2020 Recreational & Cultural Service Expenditures (MBR)',
'2020 Books & Stationery Expenditures (MBR)',
'2020 Catering Services Expenditures (MBR)',
'2020 Personal Care Expenditures (MBR)',
'2020 Jewelry & Personal Effects Expenditures (MBR)'], dtype=object)Looking at fieldCategory is a great way to clearly understand what the data collection is about. However, to query a data collection its unique ID (dataCollectionID) must be used.
Data Collections for Socio-demographic Factors
New Zealand has fewer data_collections compared to U.S. Let's look at data collections for Key Facts, Education and Spending.
Data Collection for Key Facts
nz_df.loc['KeyGlobalFacts']| analysisVariable | alias | fieldCategory | vintage | |
|---|---|---|---|---|
| dataCollectionID | ||||
| KeyGlobalFacts | KeyGlobalFacts.TOTPOP | Total Population | Key Demographic Indicators | NaN |
| KeyGlobalFacts | KeyGlobalFacts.TOTHH | Total Households | Key Demographic Indicators | NaN |
| KeyGlobalFacts | KeyGlobalFacts.AVGHHSZ | Average Household Size | Key Demographic Indicators | NaN |
| KeyGlobalFacts | KeyGlobalFacts.TOTMALES | Male Population | Age: 5 Year Increments | NaN |
| KeyGlobalFacts | KeyGlobalFacts.TOTFEMALES | Female Population | Age: 5 Year Increments | NaN |
Data Collection for Education
nz_df.loc['EducationalAttainment']| analysisVariable | alias | fieldCategory | vintage | |
|---|---|---|---|---|
| dataCollectionID | ||||
| EducationalAttainment | EducationalAttainment.EDUC01A_CY | 2020 Pop 15+/Edu: No Qualification | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC02A_CY | 2020 Pop 15+/Edu: Level 1 | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC03A_CY | 2020 Pop 15+/Edu: Level 2 | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC04A_CY | 2020 Pop 15+/Edu: Level 3 | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC05A_CY | 2020 Pop 15+/Edu: Level 4 | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC06B_CY | 2020 Pop 15+/Edu: Level 5 Diploma | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC07A_CY | 2020 Pop 15+/Edu: Level 6 Diploma | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC08A_CY | 2020 Pop 15+/Edu: Bachelor Degree | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC09A_CY | 2020 Pop 15+/Edu: Post-graduate and Honours de... | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC10A_CY | 2020 Pop 15+/Edu: Master's Degree | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC11A_CY | 2020 Pop 15+/Edu: Doctorate | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC12A_CY | 2020 Pop 15+/Edu: Overseas Secondary School | 2020 Educational Attainment (MBR) | 2020 |
| EducationalAttainment | EducationalAttainment.EDUC13_CY | 2020 Pop 15+/Edu: Not Included Elsewhere | 2020 Educational Attainment (MBR) | 2020 |
Data Collection for Spending
nz_df.loc['Spending']| analysisVariable | alias | fieldCategory | vintage | |
|---|---|---|---|---|
| dataCollectionID | ||||
| Spending | Spending.CS01_CY | 2020 Food & Beverage: Total | 2020 Food & Beverage Expenditures (MBR) | 2020 |
| Spending | Spending.CS01PRM_CY | 2020 Food & Beverage: Per Mill | 2020 Food & Beverage Expenditures (MBR) | 2020 |
| Spending | Spending.CSPC01_CY | 2020 Food & Beverage: Per Capita | 2020 Food & Beverage Expenditures (MBR) | 2020 |
| Spending | Spending.CS01IDX_CY | 2020 Food & Beverage: Index | 2020 Food & Beverage Expenditures (MBR) | 2020 |
| Spending | Spending.CS02_CY | 2020 Alcoholic Beverage: Total | 2020 Alcoholic Beverage Expenditures (MBR) | 2020 |
| ... | ... | ... | ... | ... |
| Spending | Spending.CS19IDX_CY | 2020 Personal Care: Index | 2020 Personal Care Expenditures (MBR) | 2020 |
| Spending | Spending.CS20_CY | 2020 Personal Effects: Total | 2020 Jewelry & Personal Effects Expenditures (... | 2020 |
| Spending | Spending.CS20PRM_CY | 2020 Personal Effects: Per Mill | 2020 Jewelry & Personal Effects Expenditures (... | 2020 |
| Spending | Spending.CSPC20_CY | 2020 Personal Effects: Per Capita | 2020 Jewelry & Personal Effects Expenditures (... | 2020 |
| Spending | Spending.CS20IDX_CY | 2020 Personal Effects: Index | 2020 Jewelry & Personal Effects Expenditures (... | 2020 |
80 rows × 4 columns
Analysis variables for Data Collections
Once we know the data collection we would like to use, we can look at all the unique variables available in that data collection using its unique ID. Let's discover analysisVariables for some of the data collections we looked at earlier.
Analysis variables for KeyGlobalFacts data collection
nz_df.loc['KeyGlobalFacts']['analysisVariable'].unique()array(['KeyGlobalFacts.TOTPOP', 'KeyGlobalFacts.TOTHH',
'KeyGlobalFacts.AVGHHSZ', 'KeyGlobalFacts.TOTMALES',
'KeyGlobalFacts.TOTFEMALES'], dtype=object)Analysis variables for EducationalAttainment data collection
nz_df.loc['EducationalAttainment']['analysisVariable'].unique()array(['EducationalAttainment.EDUC01A_CY',
'EducationalAttainment.EDUC02A_CY',
'EducationalAttainment.EDUC03A_CY',
'EducationalAttainment.EDUC04A_CY',
'EducationalAttainment.EDUC05A_CY',
'EducationalAttainment.EDUC06B_CY',
'EducationalAttainment.EDUC07A_CY',
'EducationalAttainment.EDUC08A_CY',
'EducationalAttainment.EDUC09A_CY',
'EducationalAttainment.EDUC10A_CY',
'EducationalAttainment.EDUC11A_CY',
'EducationalAttainment.EDUC12A_CY',
'EducationalAttainment.EDUC13_CY'], dtype=object)Analysis variables for Spending data collection
nz_df.loc['Spending']['analysisVariable'].unique()array(['Spending.CS01_CY', 'Spending.CS01PRM_CY', 'Spending.CSPC01_CY',
'Spending.CS01IDX_CY', 'Spending.CS02_CY', 'Spending.CS02PRM_CY',
'Spending.CSPC02_CY', 'Spending.CS02IDX_CY', 'Spending.CS03_CY',
'Spending.CS03PRM_CY', 'Spending.CSPC03_CY', 'Spending.CS03IDX_CY',
'Spending.CS04_CY', 'Spending.CS04PRM_CY', 'Spending.CSPC04_CY',
'Spending.CS04IDX_CY', 'Spending.CS05_CY', 'Spending.CS05PRM_CY',
'Spending.CSPC05_CY', 'Spending.CS05IDX_CY', 'Spending.CS06_CY',
'Spending.CS06PRM_CY', 'Spending.CSPC06_CY', 'Spending.CS06IDX_CY',
'Spending.CS07_CY', 'Spending.CS07PRM_CY', 'Spending.CSPC07_CY',
'Spending.CS07IDX_CY', 'Spending.CS08_CY', 'Spending.CS08PRM_CY',
'Spending.CSPC08_CY', 'Spending.CS08IDX_CY', 'Spending.CS09_CY',
'Spending.CS09PRM_CY', 'Spending.CSPC09_CY', 'Spending.CS09IDX_CY',
'Spending.CS10_CY', 'Spending.CS10PRM_CY', 'Spending.CSPC10_CY',
'Spending.CS10IDX_CY', 'Spending.CS11_CY', 'Spending.CS11PRM_CY',
'Spending.CSPC11_CY', 'Spending.CS11IDX_CY', 'Spending.CS12_CY',
'Spending.CS12PRM_CY', 'Spending.CSPC12_CY', 'Spending.CS12IDX_CY',
'Spending.CS13_CY', 'Spending.CS13PRM_CY', 'Spending.CSPC13_CY',
'Spending.CS13IDX_CY', 'Spending.CS14_CY', 'Spending.CS14PRM_CY',
'Spending.CSPC14_CY', 'Spending.CS14IDX_CY', 'Spending.CS15_CY',
'Spending.CS15PRM_CY', 'Spending.CSPC15_CY', 'Spending.CS15IDX_CY',
'Spending.CS16_CY', 'Spending.CS16PRM_CY', 'Spending.CSPC16_CY',
'Spending.CS16IDX_CY', 'Spending.CS17_CY', 'Spending.CS17PRM_CY',
'Spending.CSPC17_CY', 'Spending.CS17IDX_CY', 'Spending.CS18_CY',
'Spending.CS18PRM_CY', 'Spending.CSPC18_CY', 'Spending.CS18IDX_CY',
'Spending.CS19_CY', 'Spending.CS19PRM_CY', 'Spending.CSPC19_CY',
'Spending.CS19IDX_CY', 'Spending.CS20_CY', 'Spending.CS20PRM_CY',
'Spending.CSPC20_CY', 'Spending.CS20IDX_CY'], dtype=object)Perform Enrichment using Data Collections and Analysis Variables
Data Collections can be used to enrich various study areas. data_collections and analysis_variables can be passed in the enrich() method. Details about enriching study areas can be found in Enriching Study Areas section.
Let's look at a few similar examples of GeoEnrichment here.
Enrich using Data Collections
Enrich with Age data collection
Here we see an address being enriched by data from Age data collection.
# Enriching single address as single line imput
age_coll = enrich(study_areas=["380 New York St Redlands CA 92373"],
data_collections=['Age'])age_coll| source_country | x | y | area_type | buffer_units | buffer_units_alias | buffer_radii | aggregation_method | population_to_polygon_size_rating | apportionment_confidence | ... | fem45 | fem50 | fem55 | fem60 | fem65 | fem70 | fem75 | fem80 | fem85 | SHAPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | USA | -117.19479 | 34.057265 | RingBuffer | esriMiles | Miles | 1.0 | BlockApportionment:US.BlockGroups;PointsLayer:... | 2.191 | 2.576 | ... | 366.0 | 392.0 | 365.0 | 345.0 | 322.0 | 277.0 | 168.0 | 103.0 | 132.0 | {"rings": [[[-117.19479001927878, 34.071773611... |
1 rows × 48 columns
age_coll.columnsIndex(['source_country', 'x', 'y', 'area_type', 'buffer_units',
'buffer_units_alias', 'buffer_radii', 'aggregation_method',
'population_to_polygon_size_rating', 'apportionment_confidence',
'has_data', 'male0', 'male5', 'male10', 'male15', 'male20', 'male25',
'male30', 'male35', 'male40', 'male45', 'male50', 'male55', 'male60',
'male65', 'male70', 'male75', 'male80', 'male85', 'fem0', 'fem5',
'fem10', 'fem15', 'fem20', 'fem25', 'fem30', 'fem35', 'fem40', 'fem45',
'fem50', 'fem55', 'fem60', 'fem65', 'fem70', 'fem75', 'fem80', 'fem85',
'SHAPE'],
dtype='object')When a data collection is specified without specific analysis variables, all variables under the data collection are used for enrichment as can be seen above.
Enrich with Health data collection
Here we see a zip code being enriched by data from Health data collection.
redlands = usa.subgeographies.states['California'].zip5['92373']redlands_df = enrich(study_areas=[redlands], data_collections=['Health'] )redlands_df| std_geography_level | std_geography_name | std_geography_id | source_country | aggregation_method | population_to_polygon_size_rating | apportionment_confidence | has_data | rel65_hi2_oc | acscivnins | ... | pop85_cy | pop18up_cy | pop21up_cy | medage_cy | hhu18_c10 | medhinc_cy | s27_bus | s27_sales | s27_emp | SHAPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | US.ZIP5 | Redlands | 92373 | USA | Query:US.ZIP5 | 2.191 | 2.576 | 1 | 2.0 | 31157.0 | ... | 1205.0 | 28208.0 | 27076.0 | 42.2 | 3851.0 | 91009.0 | 224.0 | 306371.0 | 4093.0 | {"rings": [[[-117.16767396036383, 33.976847519... |
1 rows × 431 columns
redlands_df.columnsIndex(['std_geography_level', 'std_geography_name', 'std_geography_id',
'source_country', 'aggregation_method',
'population_to_polygon_size_rating', 'apportionment_confidence',
'has_data', 'rel65_hi2_oc', 'acscivnins',
...
'pop85_cy', 'pop18up_cy', 'pop21up_cy', 'medage_cy', 'hhu18_c10',
'medhinc_cy', 's27_bus', 's27_sales', 's27_emp', 'SHAPE'],
dtype='object', length=431)Enrich using Analysis Variables
Data can be enriched by specifying specific analysis variables of a data collection with which we want to enrich our data. In this example, we will look at analysis_variables for Age data_collection and then use specific analysis variables to enrich() a study area.
# Unique analysis variables for Age data collection
usa = Country.get('US')
usa.data_collections.loc['Age']['analysisVariable'].unique()array(['Age.MALE0', 'Age.MALE5', 'Age.MALE10', 'Age.MALE15', 'Age.MALE20',
'Age.MALE25', 'Age.MALE30', 'Age.MALE35', 'Age.MALE40',
'Age.MALE45', 'Age.MALE50', 'Age.MALE55', 'Age.MALE60',
'Age.MALE65', 'Age.MALE70', 'Age.MALE75', 'Age.MALE80',
'Age.MALE85', 'Age.FEM0', 'Age.FEM5', 'Age.FEM10', 'Age.FEM15',
'Age.FEM20', 'Age.FEM25', 'Age.FEM30', 'Age.FEM35', 'Age.FEM40',
'Age.FEM45', 'Age.FEM50', 'Age.FEM55', 'Age.FEM60', 'Age.FEM65',
'Age.FEM70', 'Age.FEM75', 'Age.FEM80', 'Age.FEM85'], dtype=object)Now, we will enrich our study area with Age.FEM45, Age.FEM55, Age.FEM65 variables
enrich(study_areas=["380 New York St Redlands CA 92373"],
analysis_variables=["Age.FEM45","Age.FEM55","Age.FEM65"])| source_country | x | y | area_type | buffer_units | buffer_units_alias | buffer_radii | aggregation_method | population_to_polygon_size_rating | apportionment_confidence | has_data | fem45 | fem55 | fem65 | SHAPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | USA | -117.19479 | 34.057265 | RingBuffer | esriMiles | Miles | 1.0 | BlockApportionment:US.BlockGroups;PointsLayer:... | 2.191 | 2.576 | 1 | 366.0 | 365.0 | 322.0 | {"rings": [[[-117.19479001927878, 34.071773611... |
Enriching Spatially Enabled Dataframes
One of the most common use case for GeoEnrichment is enriching existing data in feature layers. As a user, you may need to analyze and enrich your data that already exists in feature layers. Spatially Enabled DataFrame (SeDF) helps us bring the data from layer into a dataframe which can then be GeoEnriched.
Let's look at an example using an existing layer of Covid-19 dataset. This feature layer includes latest Covid-19 Cases, Recovered and Deaths data for U.S. at the county level.
# Get the layer
gis = GIS(set_active=False)
covid_item = gis.content.get('628578697fb24d8ea4c32fa0c5ae1843')
print(covid_item)
covid_layer = covid_item.layers[0]
covid_layer<Item title:"COVID-19 Cases US" type:Feature Layer Collection owner:CSSE_covid19>
<FeatureLayer url:"https://services1.arcgis.com/0MSEUqKaxRlEPj5g/arcgis/rest/services/ncov_cases_US/FeatureServer/0">
We can query the layer as a dataframe and then use the dataframe for enrichment.
covid_df = covid_layer.query(as_df=True)
covid_df.shape(3272, 19)
covid_df.head()| OBJECTID | Province_State | Country_Region | Last_Update | Lat | Long_ | Confirmed | Recovered | Deaths | Active | Admin2 | FIPS | Combined_Key | Incident_Rate | People_Tested | People_Hospitalized | UID | ISO3 | SHAPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Alabama | US | 2022-10-27 17:22:34 | 32.539527 | -86.644082 | 18511 | <NA> | 228 | <NA> | Autauga | 01001 | Autauga, Alabama, US | 33132.864379 | <NA> | <NA> | 84001001 | USA | {"x": -86.64408226999996, "y": 32.539527450000... |
| 1 | 2 | Alabama | US | 2022-10-27 17:22:34 | 30.72775 | -87.722071 | 65973 | <NA> | 716 | <NA> | Baldwin | 01003 | Baldwin, Alabama, US | 29553.293853 | <NA> | <NA> | 84001003 | USA | {"x": -87.72207057999998, "y": 30.727749910000... |
| 2 | 3 | Alabama | US | 2022-10-27 17:22:34 | 31.868263 | -85.387129 | 6930 | <NA> | 103 | <NA> | Barbour | 01005 | Barbour, Alabama, US | 28072.591752 | <NA> | <NA> | 84001005 | USA | {"x": -85.38712859999998, "y": 31.868263000000... |
| 3 | 4 | Alabama | US | 2022-10-27 17:22:34 | 32.996421 | -87.125115 | 7575 | <NA> | 108 | <NA> | Bibb | 01007 | Bibb, Alabama, US | 33826.024828 | <NA> | <NA> | 84001007 | USA | {"x": -87.12511459999996, "y": 32.996420640000... |
| 4 | 5 | Alabama | US | 2022-10-27 17:22:34 | 33.982109 | -86.567906 | 17320 | <NA> | 258 | <NA> | Blount | 01009 | Blount, Alabama, US | 29951.92474 | <NA> | <NA> | 84001009 | USA | {"x": -86.56790592999994, "y": 33.982109180000... |
To showcase GeoEnrichment, we will create a subset of the original data and then enrich() the subset.
# Create subset
test_df = covid_df.iloc[:100].copy()
test_df.shape(100, 19)
# Check geometry
test_df.spatial.geometry_type['point', None]
A dataframe can be passed as a value to study_areas parameter of the enrich() method. Here we are enriching our dataframe with specific variables from Age data collection.
# Enrich dataframe
new_df = enrich(study_areas=test_df.spatial,
analysis_variables=["Age.FEM45","Age.FEM55","Age.FEM65"])new_df.head()| ID | OBJECTID_0 | sourceCountry | Long_ | Recovered | Country_Region | FIPS | Last_Update | Combined_Key | ISO3 | ... | bufferUnitsAlias | bufferRadii | aggregationMethod | populationToPolygonSizeRating | apportionmentConfidence | HasData | FEM45 | FEM55 | FEM65 | SHAPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1 | US | -82.461707 | 0 | US | 45001 | 1596857725000 | Abbeville, South Carolina, US | USA | ... | Miles | 1 | BlockApportionment:US.BlockGroups | 2.191 | 2.576 | 1 | 2 | 2 | 2 | {"rings": [[[-82.46170657999994, 34.2378420028... |
| 1 | 1 | 2 | US | -92.414197 | 0 | US | 22001 | 1596857725000 | Acadia, Louisiana, US | USA | ... | Miles | 1 | BlockApportionment:US.BlockGroups | 2.191 | 2.576 | 1 | 3 | 3 | 2 | {"rings": [[[-92.41419697999997, 30.3095821536... |
| 2 | 2 | 3 | US | -75.632346 | 0 | US | 51001 | 1596857725000 | Accomack, Virginia, US | USA | ... | Miles | 1 | BlockApportionment:US.BlockGroups | 2.191 | 2.576 | 1 | 14 | 16 | 14 | {"rings": [[[-75.63234615, 37.781571251121655]... |
| 3 | 3 | 4 | US | -116.241552 | 0 | US | 16001 | 1596857725000 | Ada, Idaho, US | USA | ... | Miles | 1 | BlockApportionment:US.BlockGroups | 2.191 | 2.576 | 1 | 0 | 0 | 0 | {"rings": [[[-116.24155159999998, 43.467142851... |
| 4 | 4 | 5 | US | -94.471059 | 0 | US | 19001 | 1596857725000 | Adair, Iowa, US | USA | ... | Miles | 1 | BlockApportionment:US.BlockGroups | 2.191 | 2.576 | 1 | 1 | 1 | 1 | {"rings": [[[-94.47105873999998, 41.3452468224... |
5 rows × 31 columns
new_df.drop(['OBJECTID_0', 'ID','Last_Update'], axis=1, inplace=True)# Check shape
new_df.shape(91, 28)
We can see that enrichment resulted in 91 records and 31 columns. There are some areas in our dataframe for which enrichment information is not available. Hence, we have 91 records instead of 100. Geoenrichment adds some additional columns along with the analysis variables we enriched for and so we see 31 columns however we are dropping duplicates and unnecessary columns to bring the count down to 28 columns.
Visualize on a Map
Let's visualize the enriched dataframe on a map. We will use FEM65 column to classify our data for plotting on the map.
covid_map = gis.map('USA', 4)
covid_map
# Plot on a map
covid_map.remove_layers()
new_df.spatial.plot(map_widget=covid_map,
renderer_type='c', # for class breaks renderer,
method='esriClassifyNaturalBreaks', # classification algorithm,
class_count=5, # choose the number of classes,
col='FEM65', # numeric column to classify,
cmap='viridis', # color map to pick colors from for each class,
alpha=0.7)True
Conclusion
In this part of the arcgis.geoenrichment module guide series, you saw how data_collections property of a Country object lists its available data_collections and analysis_variables. You explored different data collections, their analysis variables and then enriched study areas using the same. Towards the end, you experienced how spatially enabled dataframes can be enriched.
In the subsequent pages, you will learn about Generating Reports and Standard Geography Queries.