from epymorph.adrio import us_tiger
from epymorph.kit import *
county_scope = CountyScope.in_states(["AZ"], year=2020)US TIGER
Description
The TIGER (Topologically Integrated Geographic Encoding and Referencing) files are a digital geographic database, provided by the US Census Bureau, giving shape files for all US Census geographic delineations (State, countries, census tracts, etc), as well as some limited metadata about each. TIGER files are mainly utilized for mapping purposes and demographic research through linking their geographic entity codes (GEOIDs) to the Census Bureau’s demographic data sources.
Data Collection
The TIGER/Line shapefiles are extracted from selected geographic data from the U.S. Census Bureau’s MAF/TIGER Database, a national digital map database of geographic features, including statistical boundaries, census tracts, block groups, and other delineations.
TIGER cartographic boundary files are typically updated by the U.S. Census Bureau annually, with boundary reassessments every ten years to reflect significant population changes. For example, census tracts may split or combine due to major changes in population size, which trickles down to shifts in blocks and block groups. When using US TIGER shapefiles, be sure to select the appropriate year with the geographic data that aligns with your desired time frame.
Geographic Coverage
Within this ADRIO, US TIGER includes only the 50 states, District of Columbia, and Puerto Rico, aligning with the scope provided by ACS5. The ADRIO focuses on the Census delineations and boundaries from TIGER data.
Supported years for this ADRIO include 2000 and any year between 2009-2023.
Attributes
The TIGER/Line files incorporates attributes from the TIGER database. Some of the notable features and their attributes are included below:
| Category | Attributes |
|---|---|
| Census Boundaries | Type, GEOID, Name |
| Area/Point landmarks | Type (e.g., school, church, park, cemetery), Name |
Additional Resources
A good starting point to get to know US TIGER can be found on the “TIGER Data Products Guide”.
Another resource is the “TIGER/Line Shapefiles” to learn about the shapefiles themselves.
For more information concerning the features and attributes included in TIGER/Line files, consult the following link for the full table: “TIGER/Line Features and Attributes”
US TIGER ADRIO
- Import the US TIGER ADRIO and define scopes for use.
Geometric Centroid
The centroid provided by the GeometricCentroid call is the point located in the geographic center of a given polygon. For example, when given all of the counties of Arizona, calling GeometricCentroid will output an array of each county’s centroid.
us_tiger.GeometricCentroid().with_context(scope=county_scope).evaluate()array([(-109.48884962, 35.3955288 ), (-109.75126314, 31.87963709),
(-111.77052096, 35.83872483), (-110.81163686, 33.79970237),
(-109.88745163, 32.9326627 ), (-109.24035541, 33.21540167),
(-113.98157752, 33.72938684), (-112.49151144, 33.34903944),
(-113.75790301, 35.70406832), (-110.32141935, 35.39955034),
(-111.7898635 , 32.09739903), (-111.3447399 , 32.90436651),
(-110.84651691, 31.52596126), (-112.55373567, 34.59984444),
(-113.9056188 , 32.76961884)],
dtype=[('longitude', '<f8'), ('latitude', '<f8')])Internal Point
The internal point is another manner of retrieving a centroid of a location. In contrast to the GeometricCentroid, InternalPoint is made up of points selected by Census workers, guaranteeing the point to be within the given geographic polygons, while geometric centroids cannot guarantee the same.
us_tiger.InternalPoint().with_context(scope=county_scope).evaluate()array([(-109.4901722, 35.385084 ), (-109.7751627, 31.8401287),
(-111.7737277, 35.8296919), (-110.8118696, 33.7896177),
(-109.8783103, 32.9318277), (-109.2423231, 33.2388723),
(-114.038806 , 33.7276104), (-112.4989296, 33.3451756),
(-113.7496891, 35.717705 ), (-110.3210248, 35.3907852),
(-111.7836574, 32.128038 ), (-111.3663396, 32.9185209),
(-110.8452278, 31.525733 ), (-112.5772247, 34.631071 ),
(-113.910905 , 32.7739424)],
dtype=[('longitude', '<f8'), ('latitude', '<f8')])Name
The proper name of a state or county will be given when using the Name call. If the scope is anything other than county or state, such as tract, the GEOID will be returned instead.
us_tiger.Name().with_context(scope=county_scope).evaluate()array(['Apache', 'Cochise', 'Coconino', 'Gila', 'Graham', 'Greenlee',
'La Paz', 'Maricopa', 'Mohave', 'Navajo', 'Pima', 'Pinal',
'Santa Cruz', 'Yavapai', 'Yuma'], dtype='<U10')Land Area M2
This call will return the land area of the given geo node, represented in meters-squared.
us_tiger.LandAreaM2().with_context(scope=county_scope).evaluate()array([2.90034862e+10, 1.60831781e+10, 4.82161354e+10, 1.23214265e+10,
1.19706060e+10, 4.77112817e+09, 1.16460493e+10, 2.38325307e+10,
3.45300248e+10, 2.57690610e+10, 2.37985977e+10, 1.38979972e+10,
3.20185324e+09, 2.10381423e+10, 1.42807748e+10])Postal Code
For only state use, the PostalCode will return the postal code abbreviation for the given state, such as “AZ” for Arizona.
state_scope= StateScope.in_states(["04", "35", "08", "24"], year=2020)
us_tiger.PostalCode().with_context(scope=state_scope).evaluate()array(['AZ', 'CO', 'MD', 'NM'], dtype='<U2')