1. U.S. Census Data & TIGER/Line Files
Census Bureau:
Charged with the Constitutional responsibility of carrying out the decennial census
Census of Population and Housing
Very large mapping component involved in undertaking a national census!
Census demographic/socioeconomic data:
Demographic, economic, & social data about persons & households
Aggregated by census enumeration units: e.g. block, block group, tract, county, metropolitan area, etc…
TIGER/Line files:
The “geography” of the census
Topogically Integrated Geographic Encoding & Referencing
e.g., polygons for enumeration units, streets & landmarks

2. TIGER/Line files - background
New Haven Census Use Study
test digital data structures for storing census data by geographic areas
test processes for creating computerized Census maps
had topology!!
1970s - Census DIME files
expansion of New Haven study into production version
data coverage: U.S. urban areas
important component of 1980 decennial Census
1980s - development of TIGER/Line files
incorporated DIME files for urban areas (DIME updated in 1981 & 1985)
incorporated nationwide 1:100,000 USGS DLG data
additional information from local officials & Census fieldwork
1990s – TIGER in use
used for 1990 Census
TIGER updated nearly yearly after 1990 from variety of sources
: major update & prep for 2000 Census
latest Census
2nd use of TIGER for Census
data being released now

3. TIGER/Line Files Nominal scale: 1:100,000 Data "layers":
Enumeration units
blocks, block groups, tracts/block numbering areas, counties, cities/MA, etc.
multiple hierarchies
Voting districts
used for Congressional redistricting
Supporting geography
roads/streets/highways
basic hydrography
point & area landmarks
etc...
TIGER designed to:
support pre-census functions in preparation for Census of Population and Housing
support census-taking efforts
evaluate success of the Census
provide geographic framework for analysis

4. TIGER Area (polygon) & Landmark Data
Point and poly landmarks
Census geography (tracts, blocks, etc.) used for reporting Census data
ID linkage from polygons in TIGER/Line data to Census attribute data

5. TIGER Line and Address Data
Linear features...
Form polygon boundaries
Roads
attributes include basic road type, address ranges
also hydro features, etc.

6. Link to Census Data Census attribute data
- Summary Tape File (STF) data files
Link to Census geographic entities in TIGER/Line files using unique Census geography IDs 
Lets us merge a tremendously rich souce of detailed socioeconomic data (Census) with a comprehensive geography for the entire country…
Orange County, NC block groups w/ median income data (darker green = higher income)

7. Census Geographic Hierarchy
hierarchical tabulation systems, e.g.:
USA
Region
Division
State
County
Tract
Block Group
Block
2000 Census tallies for entire US:
65,443 tracts
208,790 block groups
8,205,582 blocks
for NC:
1,563 tracts
5,271 block groups
232,403 blocks

8. TIGER Address Data
address ranges: street address numbers at beginning and ending of arc/line in database
allows address geocoding
 match data with address to a spatial location using an interpolated estimate
data use implication:
explosion of analysis and data integration capabilities!
extremely large (and growing) amount of data tied to addresses
problem: incomplete address range data, esp. in rural areas
--why?
some areas simply have incomplete data (very large data collection task)
PO rural routes (though this is changing due to E-911 systems)
Census Bureau steadily improving rural address data
private street/address data providers enhance address range data

9. Relational Database Structure

10. Relational DBMS
Data stored as tuples (tup-el), conceptualized as tables
Table – data about a class of objects
Two-dimensional list (array)
Rows = objects
Columns = object states (properties, attributes)

12. Column = property
Table = Object Class
Row = object
Object Classes with
Geometry called Feature Classes

13. Relation Rules
Only one value in each cell (intersection of row and column)
All values in a column are about the same subject
Each row is unique
No significance in column sequence
No significance in row sequence

17. Joined Table

18. Relational Join Fundamental query operation Occurs because
Normalization
Data created/maintained by different users, but integration needed for queries
Table joins use common keys (column values)
Table (attribute) join concept has been extended to geographic case

19. Normalization
Process of converting tables to conform to relational rules
Split tables into new tables that can be joined at query time
The relational join
Several levels of normalization
Forms: 1NF, 2NF, 3NF, etc.
Normalization creates many expensive joins
De-normalization is OK for performance optimization

22. Spatial Relations Equals – same geometries
Disjoint – geometries share common point
Intersects – geometries intersect
Touches – geometries intersect at common boundary
Crosses – geometries overlap
Within– geometry within
Contains – geometry completely contains
Overlaps – geometries of same dimension overlap
Relate – intersection between interior, boundary or exterior

23. Two Possible Relations

31. Point Quadtree

32. Region Quadtree