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

TIGER/Line files - background 1967 - 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 1998-1999: major update & prep for 2000 Census 2000 - latest Census 2nd use of TIGER for Census data being released now

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

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

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

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)

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

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

Relational Database Structure

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)

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

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

Joined Table

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

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

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

Two Possible Relations

Point Quadtree

Region Quadtree