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The Census Bureau's Geographic Support System Initiative – An Update Council of Professional Associations on Federal Statistics September 21, 2012 Tim.

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Presentation on theme: "The Census Bureau's Geographic Support System Initiative – An Update Council of Professional Associations on Federal Statistics September 21, 2012 Tim."— Presentation transcript:

1 The Census Bureau's Geographic Support System Initiative – An Update Council of Professional Associations on Federal Statistics September 21, 2012 Tim Trainor Chief, Geography Division U.S. Census Bureau

2 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

3 A Change in Methodology In 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)

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5 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 1: Establish quantitative measures of address and spatial data quality 2: Assign Quality Indicators to MAF/TIGER data 2: Assign Quality Indicators to MAF/TIGER data New incoming data

6 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 Existing Partnerships Engage New Partners Utilize new tools and 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

7 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

8 iSIMPLE Problem Capture Tool Features Source Evaluation Metadata Improvements MTAG Better 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 FY GSS-I Working Groups Policy Research and Development Research and Development Partnerships Address Coverage and Sources Project/Contract Management Project/Contract Management Feature Coverage and Sources Quality Assessments MAF/TIGER Integration/ Linkage Geocoding Global Positioning Systems (GPS)

9 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

10 Address Summit Participants

11 Participants - All Levels of Government

12 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

13 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

14 2012 Address Pilot Schedule

15 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 15

16 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

17 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.htmlhttp://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.htmlhttp://www.census.gov/geo/www/gss/qaewg.html 2 In-Progress Reports –Change Detection –Master Address File (MAF) Evaluation

18 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 18

19 iSIMPLE Missing Road Features

20 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 20

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23 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 23

24 Address Indicators Overall Address QIs -Address consistency -Mailability -Deliverability -Locatability -Geocode accuracy -Tests for ‘other’ 24

25 Feature Indicators Overall Feature QIs -Spatial accuracy -Feature naming -Address ranges -Feature classification 25

26 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 26

27 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 27

28 Geocode Indicators Combines specific sub-indicators from each other category -Locatability and geocode accuracy (Address) -Spatial accuracy & address ranges (Feature) -Block size & shape (Geography) 28

29 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 29

30 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) 30

31 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 31

32 Rapid Landscape Change: Picher, OK Census 2000: 708 housing units –621 occupied –87 vacant 2010 Census: 30 housing units –10 occupied –20 vacant

33 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 33

34 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

35 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.

36 2010 Road Update Operations Evaluation Hypothesis (2): Road updates made with GPS were more spatially accurate than paper-based road updates.

37 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

38 2010 Road Update Operations Evaluation We looked at over 42,000 edges… in 72 counties…….

39 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.

40 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?

41 Targeted Address Canvassing Continuum

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44 Targeted Address Canvassing Continuum Scores, Census Tract , Howard County, Maryland 2010 Base Overall Score = Base: CategoryScore Ratio of 2010 HU counts to 2010 MAF units81.5 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)99.5 Mail back rate81.2 No 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 Index97.3 Ratio of Spring 2010 DSF to 2010 Census housing unit count98.5 Total Points749.4 Overall Score93.7

45 Targeted Address Canvassing Continuum Scores, Census Tract , Howard County, Maryland Current State Overall Score = 96.9 Current State: CategoryScore Quality Indicator Score Percent City Style Addresses100.0 Lack of/presence of hidden units99.0 Lack of/presence of informal or unique housing situations100.0 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 populations99.0 Percent MAF TIGER agreement on geocodes98.0 Percent MAF address confirmation rate (matching rate) with admin records Percentage of city-style address MAF units preferred MSPs76.3 Area classified/not classified as "needs to be canvassed" in field survey staff feedback100.0 DSF Stability Index97.3 GEO Change detection processes indicate no changes have occurred Overall Score96.9

46 High Stability Census Tracts CategoryValue DSF Stability1.0 Ratio Fall 09 DSF to 2010 Census HU Count 1.0 Ratio Spring 09 DSF to 2010 Census HU Count 1.0 Type A adds3 UAA HU988 DSF Spring DSF Fall Census 2000 HU989 Ad Can True Adds0 Ad Can Deletes3 CategoryValue DSF Stability1.0 Ratio Fall 09 DSF to 2010 Census HU Count 1.0 Ratio S09 DSF to 2010 Census HU Count 1.0 Type A adds5 UAA HU910 DSF spring DSF fall Census 2000 HU911 Ad Can True Adds1 Ad Can Deletes17 Tract 3406, Harris County, TX Tract , Fairfax County, VA

47 Questions?


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