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Geospatial Data Quality for Next Generation 9-1-1

Geospatial Data Quality for Next Generation 9-1-1

Sean Moran, Brian Christman - Austin Community College

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  1. Geospatial Data Quality
    for Next Generation
    9-1-1
    by Sean Moran and Brian Christman
    Austin Community College
    for Texas GIS Forum
    on October 26, 2016

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  2. Introduction to Project >

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  3. ACC Incubator for professional skills>
    Entry-Level Professional Employment Application
    ☑ Academic Skills
    Minimum: General Equivalency Degree (GED) or High School Diploma
    Preferred: Associate's or Bachelor’s Degree
    ☑ Technical Skills
    Minimum: Demonstrated Experience
    Preferred: Certificate or License
    ☑ Professional Skills
    Minimum: Internship or Part-time Experience
    Preferred: 1 to 3-years Experience

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  4. ACC Incubator for professional skills>
    College sponsors incubator
    PI coordinates projects
    Org funds project
    Students staff project
    (e.g. NG911)
    ACC Inc

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  5. Goals of Presentation >
    Present baseline assessments of accuracy
    for NG911 geospatial data from six Texas
    counties.
    Outline the application of the National
    Standard for Spatial Data Accuracy
    (NSSDA) to NG911 geospatial data.

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  6. Next Generation 9-1-1 (NG911) >
    Legacy 911 NG911
    Began service in 1968 Initiated by NENA in 2001
    Based on telephone landlines IP Network Infrastructure
    Voice and Limited Text Voice, Text, Video, Sensor Data
    Tabular MSAG Geospatial MSAG
    Emergency calls handled locally National interconnectivity

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  7. Texas NG911 Entities >
    GIS data for NG911 are
    produced for Texas counties by
    either the parent Regional
    Planning Commission (RPC) or
    Emergency Communication
    District (ECD).
    The Commission on State
    Emergency Communications
    (CSEC) maintains the
    Enterprise Geospatial Database
    Management System
    (EGDMS).

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  8. Importance of GIS >
    Spatial Accuracy
    Will emergency personnel
    know where to go?
    We used the National Standard for
    Spatial Data Accuracy (NSSDA) to
    quantify the horizontal spatial
    accuracy of address points and road
    centerlines.

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  9. Importance of GIS >
    Topology
    Will emergency personnel
    be routed efficiently?
    We used ArcMap geodatabase
    topology, attribute queries and
    spatial queries to validate 18 rules
    for address points, road centerlines,
    Emergency Service Number (ESN)
    boundaries, and Public Safety
    Answering Point (PSAP)
    boundaries.

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  10. Importance of GIS >
    Geocoding Logic
    Will emergency personnel
    be able to find the correct
    house?
    We used MapSAG software to
    check address-point and road-
    centerline data against 26
    geocoding logic tests.

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  11. Why NSSDA for Spatial Assessment? >
    A National Standard for Spatial Data Accuracy
    Developed by the Federal Geographic Data Cmte,
    1998
    Procedure is independent of map scale
    Readily applied within a GIS
    Yields a single accuracy statistic at 95% confidence
    level

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  12. NSSDA Fundamental >
    The basis of the NSSDA
    is the comparison of the
    positions from a sample
    of data test points
    against the positions of
    corresponding points
    from an independent
    dataset of higher-order
    accuracy.

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  13. Higher-Order Dataset >
    Photo-identifiable
    structure, driveway and
    intersection features in
    the Texas Google
    Imagery Service served
    as the higher-order
    dataset to which address
    points and road
    intersection points were
    compared.

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  14. Application of NSSDA in a GIS>
    Calculate statistically-derived sample sizes for address
    points (~ 112) and road intersections (~ 87).
    Generate random samples of address points and road
    intersections as new feature classes of test points.
    Manually compare test points to corresponding, photo-
    identifiable features in Google Imagery Service.

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  15. Point Δx (Easting) Δy (Northing)
    ID (meters) (meters)
    2 8 -3 64 9
    35 -20 19 400 361
    39 26 -22 676 484
    41 -50 -11 2,500 121
    88 17 41 289 1,681
    Sum of Squares 3,929 2,656
    MSE x,y (m2) 785.800 531.200
    RMSE x,y (m) 28.032 23.048
    RMSE r (m) 36.290
    Accuracy (m) 63
    Using the National Standard for Spatial Data
    Accuracy, the data set tested 63 meters
    horizontal accuracy at 95% confidence level.
    NSSDA Statement of Accuracy
    Δx2 (meters2) Δy2 (meters2)
    minus for
    Test (Digitized) Coordinates RMSE Calculations
    Independent (Identified) Differences Squared
    1. Square the coordinate X and
    Y differences between test and
    independent point positions.
    2. Sum the squares of X and Y
    coordinate differences.
    3. Calculate the means of the X
    and Y squared differences
    (Mean Squared Error).
    4. Take square roots of mean
    squared X and Y differences
    (RMSE component x and y).
    Example Calculation of NSSDA >
    1
    2
    3
    4

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  16. Point Δx (Easting) Δy (Northing)
    ID (meters) (meters)
    2 8 -3 64 9
    35 -20 19 400 361
    39 26 -22 676 484
    41 -50 -11 2,500 121
    88 17 41 289 1,681
    Sum of Squares 3,929 2,656
    MSE x,y (m2) 785.800 531.200
    RMSE x,y (m) 28.032 23.048
    RMSE r (m) 36.290
    Accuracy (m) 63
    Using the National Standard for Spatial Data
    Accuracy, the data set tested 63 meters
    horizontal accuracy at 95% confidence level.
    NSSDA Statement of Accuracy
    Δx2 (meters2) Δy2 (meters2)
    minus for
    Test (Digitized) Coordinates RMSE Calculations
    Independent (Identified) Differences Squared
    5. Calculate Root Mean Square
    Error:
    RMSE r = sqrt (MSE x + MSE y).
    6. Calculate NSSDA Statistic for
    horizontal accuracy at 95%
    confidence level:
    a. If RMSE x = RMSE y, then
    Accuracy = (RMSE r) * 1.7308
    b. If RMSE x ≠ RMSE y, then
    Accuracy =
    (RMSE x + RMSE y) * 0.5 *
    2.4477
    Example Calculation of NSSDA >
    6
    5

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  17. Visualization of Example NSSDA >
    Based on this example, the
    true ground position for 95%
    of the address points in the
    dataset will be at or within a
    distance of 63 meters.

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  18. Upshur (± 13 m)
    Spatial Accuracy of Road Centerlines >
    Camp (± 24 m) Brewster (± 17 m)
    Burleson (± 10 m)
    Culberson (± 21 m)
    Madison (± 17 m)

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  19. Upshur (± 112 m)
    Spatial Accuracy of Address Points >
    Camp (± 102 m) Brewster (± 37 m)
    Burleson (± 39 m)
    Culberson (± 53 m)
    Madison (± 172 m)
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  20. Upshur
    Topology Assessment for Structures >
    Camp Burleson
    Madison
    Brewster
    Culberson

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  21. Geocoding Assessment for Structures
    >
    Camp Brewster
    Burleson
    Upshur Culberson
    Madison

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  22. Perspective from the Wilde Study >
    1 Minute Delay = 17 % Increase in Mortality
    Elizabeth Wilde (Columbia University, 2009) analyzed 73,706
    EMS calls that occurred in Salt Lake City, Utah during Year
    2001.
    The results of the analysis show that EMS response times affect
    patient mortality.
    On average, a one minute increase in EMS response time
    increases 90-day patient mortality by 17 percent.

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  23. Importance of GIS >
    Spatial Accuracy
    Will emergency personnel
    know where to go?
    We used the National Standard for
    Spatial Data Accuracy (NSSDA) to
    quantify the horizontal spatial
    accuracy of address points and
    road centerlines.

    View full-size slide

  24. Importance of GIS >
    Topology
    Will emergency personnel
    be routed efficiently?
    We used ArcMap geodatabase
    topology, attribute queries and
    spatial queries to validate 18 rules
    for address points, road centerlines,
    ESN boundaries, and PSAP
    boundaries.

    View full-size slide

  25. Importance of GIS >
    Geocoding Logic
    Will emergency personnel
    be able to find the correct
    house?
    We used MapSAG software to
    check address-point and road-
    centerline data against 26
    geocoding logic tests.

    View full-size slide

  26. Acknowledgements >
    Susan Seet with CSEC for envisioning and
    sponsoring the project.
    Vonda Payne with CSEC for managing the project.
    Jennifer Lindsey with ACC for her contributions in
    streamlining the workflow and completing Madison
    and Culberson County assessments.

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