The Census Bureau's Geographic Support System Initiative – An Update

Council of Professional Associations on Federal StatisticsSeptember 21, 2012

Tim TrainorChief, Geography Division

U.S. Census Bureau

For the 2020 Census – The GSS Initiative

For the 2010 Census – Realigned the street network through the MAF/TIGER Enhancement Program

For the 2000 Census – Introduced the Master Address File

Census Geographic Support – Major Initiatives Over Time

For the 1990 Census – Introduced TIGER

A Change in MethodologyIn Taking a Census

Prior

to 1960

•Door to door enumeration

1960

•First mail-out census

•USPS delivered a questionnaire to every household on their routes

•Enumerators collected the completed forms

1970

•Census created an address register for densely populated USPS routes

•First mail-out/mail-back census

•Urban areas mailed back their forms; rural area forms were collected by enumerators

1980•~95% of the U.S. population is now included in the mail-out/mail-back census•Address list created from the ground-up

1990•First use of TIGER

•Address list created from the ground-up

2000

•Birth of the MAF

•MAF/TIGER Enhancement Project

•1990 Address list was used as a starting point

•Began receiving the DSF from the USPS

2010•Continuous update of the MAF to support the ACS•Address canvassing covered the entirety of the U.S. prior to Census day

2020•Introduction of Targeted Address Canvassing and the Geographic Support System Initiative (GSS-I)

Improving Data Quality

3: Monitor and Improve the quality

of the:

Existing MAF/TIGER

Data

IT processes for updating

the MAF/TIGER

System

Geographic products

output from the

MAF/TIGER System

1: Establish quantitative measures of

address and spatial data quality

2: Assign Quality Indicators to

MAF/TIGER data

New incoming

data

New Tools Partners

Enhanced Feedback

New and Enhanced Programs

TIGERweb

Community TIGER

Crowd Sourcing

Web-based Address Tools

Volunteered Geographic Information (VGI)

Enhanced collaboration

Expand ExistingPartnerships

Engage NewPartners

Utilize new toolsand programs to acquire address and spatial data in the most efficient and least intrusive ways

Address Feedback adhering to Title 13 confidentiality laws

Build on and Expand Feedback for Spatial

Features

Improved Partnerships

Research Activities• GSS-I Working Groups• Address Summit

– Address Pilots

• External Expert Reports• Research Project Examples

– iSimple– GSS Lab Data Viewer– Quality Indicators– Census 2010 Road Update Operations Evaluation– Targeted Address Canvassing Continuum

iSIMPLE

Problem Capture Tool

Features Source Evaluation

Metadata Improvements

MTAGBetter Meeting

MAF “Facility” Data User Needs

The CATT

Quality Indicators Improving Group Quarters Data

To date, 11 IPTs formed

Highway Median “Flag”

Improvements

Parcel Data and Centroid Use

FY2011 10 GSS-I Working Groups

Policy

Research andDevelopment PartnershipsAddress Coverage

and Sources

Project/ContractManagement

Feature Coverage and Sources

Quality Assessments

MAF/TIGER Integration/ Linkage

Geocoding

Global Positioning Systems (GPS)

Census Address Summit Goals• Educate our partners about the Geographic Support

System Initiative (GSS-I) and the benefits of targeted address canvassing

• Gain a common understanding regarding the definition of an address

• Learn how our partners are collecting, using, and maintaining address data

Address Summit Participants

Participants - All Levels of Government

Observations• Continuous partnerships are needed and welcome• Public safety is a driving factor for local governments• Urban and rural areas will pose different challenges• Address coverage varies and is sometimes not known or

quantifiable• Communication and engagement are key

Results of the Address Summit

• Five Pilot Projects• Address Authority Outreach and Support for Data

Sharing Efforts• FGDC Address Standard and Implementation• Federal/State/Tribal/Local Address Management

Coordination• Data Sharing – Local/State/USPS/Census • Hidden/Hard to Capture Addresses

2012 Address Pilot Schedule

Moving Forward

These pilots will provide:• The Census Bureau with a testing ground for future

geographic partnership programs• The Census Bureau with an opportunity to identify the best

methods for the continual update of the MAF/TIGER System

• www.census.gov/geo/www/gss/address_summit/

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Benefits of Establishing an Census Address Ontology

• Establishing an Ontology allows for– Effective communication – Common language– Ease the burden of data sharing – Explicit terminology, concepts, and relationships

Expert Research at Census• Five reports created by outside experts:

– The State and Anticipated Future of Addresses and Addressing– Identifying the Current State and Anticipated Future Direction of Potentially Useful Developing

Technologies– Measuring Data Quality– Use of Handheld Computers and the Display/Capture of Geospatial Data– Researching Address and Spatial Data Digital Exchange– http://www.census.gov/geo/www/gss/reports.html

• Summer at Census:– Steve Guptill; USGS Chief Scientist (Retired)

• Quantifying the Quality of the MAF/TIGER Database

– David Cowen; Distinguished Professor Emeritus• Use of Parcel Data to Update and Enhance Census Bureau Geospatial Data

– http://www.census.gov/geo/www/gss/qaewg.html

• 2 In-Progress Reports – Change Detection– Master Address File (MAF) Evaluation

Analysis of the MAF/TIGER System

• iSIMPLE– Evaluation of road features in TIGER– Is TIGER consistent with imagery?– 852,090 grid cells reviewed

• 94% had NO missing features• 5% had 4 or less missing features• 70% had NO misaligned features• 26% had 4 or less misaligned features

– First web service based review– Research will assist with targeting efforts

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iSIMPLE Missing Road Features

GSS Lab Data Viewer

• An on-line, interactive mapping tool to facilitate visualization of data and information

• Examples include:– 2010 Census Data

• Address Canvassing adds• Type A adds• Undeliverable as Addressed

– Delivery Sequence File Statistics– Natural Disaster Information

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21

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Quality Indicators

• Evaluating the current quality of the MTDB- Addresses- Features- Geographic areas- Geocodes

• And only evaluate MTDB• Unit of work is the (current) census tract

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Address Indicators

• Overall Address QIs- Address consistency- Mailability- Deliverability- Locatability- Geocode accuracy- Tests for ‘other’

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Feature Indicators

• Overall Feature QIs- Spatial accuracy- Feature naming- Address ranges- Feature classification

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Geographic Area Indicators

• For each Geographic Area, four major tests or sub-indicators- Local review/approval of areas- Regional review/approval of areas- Program review/approval of areas- Independent subject matter review/approval

of areas

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Geographic Area Indicators

• Additional tests for statistical criteria, attributes, type of submission, contiguity, etc…

• Also tests for geographic interaction (slivers), and block size and shape

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Geocode Indicators

• Combines specific sub-indicators from each other category- Locatability and geocode accuracy (Address)- Spatial accuracy & address ranges (Feature)- Block size & shape (Geography)

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Overall indicators & weighting

• Addresses, Features, Geographic Areas, and Geocodes QIs are then aggregated according to subject matter formulas

• Each census tract will receive a single overall score, and category scores where relevant

• History and tendency will be tracked

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External sources

• Quality Indicators are MTDB only• In the future, external sources may also

help determine MTDB quality, such as:- Population estimates- Building permits (new development)- Comparison to Imagery

• Additional tests to check for completeness of MTDB (omission/commission)

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Tract profiles

• Additional ability to adjust Quality Indicators based upon profile elements of the tract, such as:- Natural disaster- Unique address types- Rapidly changing development- Special land use areas

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Rapid Landscape Change: Picher, OK

• Census 2000: 708 housing units– 621 occupied– 87 vacant

• 2010 Census: 30 housing units – 10 occupied– 20 vacant

The Result

• All census tracts will be tested and ranked• Work and updates can then be targeted to

specific areas most in need of update- Prioritization of internal work- Prioritization of partner contact and file

ingestion- Improved resource allocation

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Project Scope:

The project evaluated the spatial accuracy of new road edges added to the MAF/TIGER database (MTDB) by 2010 Decennial Update Operations.

The Decennial Operations in the Study:• Address Canvassing• Update Leave• Update Enumerate• Enumeration at Transitory Locations• Group Quarters Enumeration• Group Quarters Validation

2010 Road Update Operations Evaluation

2010 Road Update Operations Evaluation

Hypothesis (1): By using imagery to systematically assess the spatial accuracy of road edges added by different operations, we can choose update methods that consistently produce higher quality linear features.

2010 Road Update Operations Evaluation

Hypothesis (2): Road updates made with GPS were more spatially accurate than paper-based road updates.

2010 Road Update Operations Evaluation

Project Phases:

1. SQL Metrics – Queried MTDB for counts of new road edges added during 2010 operations, by county.

2. Sample Design – Worked with DSSD, to design a sample of counties, as random as possible, that would include all Operations and all Regions.

3. Spatial Evaluation – Assessed selected edges, overlaid on imagery. Tested spatial accuracy of the imagery to a CE95 of 5 meters or less.

4. Data Analysis – With DSSD, obtained metrics from observations.

5. Conclusions

2010 Road Update Operations EvaluationWe looked at over 42,000 edges… in 72 counties…….

• An estimated 90% of road edges added with GPS were spatially accurate. • An estimated 67% of road edges digitized from paper-based operations were

spatially accurate.

Conclusions

• Road updates made with GPS were more spatially accurate than paper-based road updates.

• By using imagery to systematically assess the spatial accuracy of road edges added by different operations, we can choose update methods that consistently produce higher quality linear features.

Suggestions for Further Study

• Find other ways to glean what contributes to spatial quality using the data obtained in this review.

• Are edges with SMIDs (Spatial Metadata IDs) more likely to be spatially accurate than edges without SMIDs?

• Are roads that were named more likely to be spatially accurate than those not named?

• Why was the incidence of roads with no name information 38%? Were road names not collected?

• Is there a correlation between the use of NAIP imagery and the number of edges not visible in the imagery because NAIP is collected leaf-on?

• Is it possible to operationalize or automate this review so that it may be applied at a larger scale?

• What other operations that add linear features would benefit from the use of imagery for quality control?

Targeted Address Canvassing Continuum

Targeted Address Canvassing Continuum

Targeted Address Canvassing Continuum

Targeted Address Canvassing Continuum Scores, Census Tract 6069.04, Howard County, Maryland

2010 BaseOverall Score = 93.7

2010 Base: Category ScoreRatio of 2010 HU counts to 2010 MAF units 81.5

Percentage of area governments participating in LUCA (one local government) 100.0

Type A non-ID adds as percent of total housing units (Score = 100 – Percent Type A non-ID adds) 99.5Mail back rate 81.2No successful CQR cases (no cases = score of 100) 100.0

Undeliverable as Addressed (UAA) as a percentage of total housing units (Score = 100 – Percent UAA) 91.4

DSF Stability Index 97.3Ratio of Spring 2010 DSF to 2010 Census housing unit count 98.5

Total Points 749.4Overall Score 93.7

Targeted Address Canvassing Continuum Scores, Census Tract 6069.04, Howard County, Maryland

Current StateOverall Score = 96.9

Current State: Category Score

Quality Indicator Score

Percent City Style Addresses 100.0

Lack of/presence of hidden units 99.0

Lack of/presence of informal or unique housing situations 100.0

Lack of/presence of seasonal housing (Score = 100 = pct seasonal vacant HUs) 99.5

Conversion from single to multi-unit or multi-unit to single (Score = 100 – conversions as pct of all housing units) 100.0

Area not known to subdivide single housing units into multi-unit structures

Lack of/presence of hard to count populations 99.0

Percent MAF TIGER agreement on geocodes 98.0

Percent MAF address confirmation rate (matching rate) with admin records

Percentage of city-style address MAF units preferred MSPs 76.3

Area classified/not classified as "needs to be canvassed" in field survey staff feedback 100.0

DSF Stability Index 97.3

GEO Change detection processes indicate no changes have occurred

Overall Score 96.9

High Stability Census Tracts

Category Value

00-10 DSF Stability 1.0

Ratio Fall 09 DSF to 2010 Census HU Count

1.0

Ratio Spring 09 DSF to 2010 Census HU Count

1.0

Type A adds 3

UAA 7

2010 HU 988

DSF Spring 11 988

DSF Fall 10 988

Census 2000 HU 989

Ad Can True Adds 0

Ad Can Deletes 3

Category Value

00-10 DSF Stability 1.0

Ratio Fall 09 DSF to 2010 Census HU Count

1.0

Ratio S09 DSF to 2010 Census HU Count

1.0

Type A adds 5

UAA 14

2010 HU 910

DSF spring 11 910

DSF fall 10 910

Census 2000 HU 911

Ad Can True Adds 1

Ad Can Deletes 17

Tract 3406, Harris County, TX Tract 4302.03, Fairfax County, VA

• Questions?