Candice Kasprzak, GISP Project Manager & David Hart, CP President

PortlandMadisonIndianapolis

www.continentalmapping.comMapping from Earth, Sky, and Space

Candice Kasprzak, GISPProject Manager

& David Hart, CP

President

Continental Mapping Consultants, Inc.Madison, WI

ASPRS/MAPPS ConferenceNovember 18, 2009

San Antonio, TX

Is This Data Fit to Serve?Defining Acceptance Criteria

for Completeness of Digital Mapping Products



• Continental Mapping is a Production Photogrammetry firm

• We provide foundational geospatial data from satellite, airborne and terrestrial sensors (imagery and lidar)

• We process imagery, derive topographic and planimetric mapping data

• Photogrammetrists and image/geospatial analysts bulk of our workforce

• Develop mapping products and GIS datasets for fed/state/local government clients utility, manufacturing and other private clients

Who we are and why we developed this paper



• We have developed an understanding and PROCESS for Mapping Specifications Development

• See a need to educate users on:

– The process of developing mapping specifications that are Fit for a Specific Use

– Defining Appropriate Acceptance Evaluation Criteria

Background



Mapping Specifications Are Hard Because…

Developing a Digital Mapping Specification is Developing an AbstractionThis concept is misunderstood and often lost. Why?



• The strategy of simplification (abstraction) applies to any map

• Focus of this presentation limited to vector mapping products derived from imagery (planimetric data)

• Plan mapping specs more difficult that topographic or imagery specs because they require more interpretation (far more abstraction decisions)

A Map is an Abstraction



Developing acceptance criteria for digital mapping products is difficult to define because a map is a(n):

• Generalization & Simplification• Abstraction

– the process or result of generalization by reducing the information content of a concept or an observable phenomenon, typically to retain only information which is relevant for a particular purpose

It is hard to evaluate an abstraction objectively

http://en.wikipedia.org/wiki/Information

http://en.wikipedia.org/wiki/Phenomenon

http://en.wikipedia.org/wiki/Phenomenon



• The Past: hard copy mapping products were 2D cartographic representations

• The Present: the digital age of mapping introduced:– 3D– Topology– Multi-Scale display– Data Association (GIS)– Spatial relationships (such as contiguity and connectivity)

• The Future: interactive geo-enabled content, multi-scale, multi-temporal, interactive, real-time (currency, clarity, content improvements)

Mapping Specs in a Digital Age



Example Mapping Spec Guidance

• Interpretive• Subjective

• Evaluation foracceptance needsto focus on itsFitness for Use

Fair/Dry Weather, Loose/Unpaved Road (FDWLU) Cart TrackWidth : Look for a fairly EVEN width along the length Will be much more VARIABLE. of the thoroughfare. Edges : Look for a reasonably SHARP edge to the road bed. Will be much less distinct.Tone : Look for a white road surface, contrasting Track margin will be less distinct with surrounding tone. from the Curves : Regular, almost “sweeping” curves. Sharp, irregular curves and meanderings.Complexity : The route should NOT split into more than The route may appear to split into two or one road bed. more roadbeds Directness : Between its adjudged “start” and “end” points, Between its adjudged “start” and “end” points, the route will take a fairly direct course. the route will Improvements : Look for signs of thoroughfare improvements In general, there will be no sign of improvements. (e.g. , Bridges, culverts, cuts).Embankments : Difficult to determine, but in flat terrain, the roadbed Generally none, almost always at ground level. will be on a raised embankment.Importance : The purpose of the route should be more likely Less likely to connect towns or villages, to connect towns or villages, or lead to or lead to significant installations.

Figure --- Comparison between Fair/Dry Weather, Loose/Unpaved (FDWLU)

Rutted surface in soft ground. Irregular width.

Rough, carved edges. Multiple routes and deviations.

Meandering course. Fording points.

Smoother surface Even width Sharp edges Single road bed More direct route Culverts under road

FDWLU Road Cart Tracks



• Many spatial data standards related to Positional Accuracy

• Some existing methodologies related to evaluation of quality and completeness but…

• Many of these are automated topology and attribute checks

• These processes do not have mechanisms for evaluating abstraction objectively. This leads to conflict between data creators and reviewers and higher costs

Existing Standards and Evaluation Criteria are Inadequate or Incomplete



So what is the solution

• The Problem– Subjective review of abstract mapping data leads to conflict, higher cost,

and a lost focus on “what’s important on this map”

• The Solution– Develop a method that provides guidance on a spatial sampling methods

that are systematic, repeatable, and eliminate total inspection– Develop a method that provides a framework for evaluation of data

quality and completeness

– Provides a measure of a digital maps Fitness for Use



IGSM – Integrated Geospatial Sampling Model (Gillies)

• Combination of two federally accepted Quality Management standards– ISO 19114 – MIL-STD 1916 – Charles Gillies Paper – Geospatial Statistical Quality

Management: Integration of MIL-STD 1916 and ISO 19114:2003

• Uses statistical process control (SPC) methods– Systematic & repeatable– Eliminates total inspection



ISO 19114

• International Standard for geographic information quality evaluation procedures and data characteristics

• Provides guidance on random spatial sampling framework

• Outlines Data Quality Elements for geospatial data



ISO 19114 – Data Quality Elements

• 5 data quality elements and 16 sub-elements to measure various aspects of geospatial data.

• Methodology focuses on Four– Completeness – Omission– Completeness – Commission– Thematic Accuracy – Classification Correctness– Thematic Accuracy – Non-Quantitative Attribute

Correctness




• Completeness – Feature Based Element– Commission – Excess data in a dataset

• Over collection - either collected houses that weren't houses, or over collected a feature - collected all buildings within a school when only the boundary was needed

– Omission – Data absent in a dataset• Under collection - Missed feature or groups of features -

missed a house in the woods, or collected a BUA instead of each individual house within it



Completeness Example










• Thematic Accuracy – Attribute Based Element– Classification Correctness -Comparison of the classes

assigned to features or their attributes to a universe of discourse (ground truth or reference data set)• A secondary class stream (class B) is classified as a tertiary

stream (class C)– Non-Quantitative Classification Correctness -

Correctness of non-quantitative attributes• The Name attribute "Main St" on road layer is Main St on the

universal/reference map.



ISO 19114 – Sampling Plans



ISO 19114 – Evaluation Measures

• How do you measure whether a data element is acceptable– Nominal – use of a classification scheme - wet/dry,

yes/no, pass/fail– Ordinal – ranking scale – Grade (A,B,C,D) or

numerical rating (1-5)– Interval – assign exact number – elevation,

temperature– Ratio – Percentage of features not meeting a criteria



ISO 19114 – Sampling Strategy



MIL-STD 1916

• AKA: ISO 21247:2005• Department of Defense accepted

methodology for sampling data• Guide for creating sample population sizes



MIL-STD 1916 – Verification Levels & Code Letters

• Combination Defines the Population Lot Size and whether or not a sample or full inspection evaluation method is needed

• Switching Procedures for Tightened and Reduced lot sizes based on past performance



MIL-STD 1916 – Verification Levels (VL) & Code Letters (CL)

For Tightened Inspection Level move to the left one VL, for Reduced move to the Right

Can use VL as a guide for fitness of use for certain features. More important features will have a higher VL level then less important ones.



Integrated Geospatial Sampling Model - Methodology

• Step 1 - Identify features and data quality elements and sub elements

• Step 2 - Determine the appropriate evaluation measure for each feature type and DQ element (nominal, ratio, ordinal)

• Step 3 – Define Sampling Strategy– Population Definition– Sampling Procedure– Verification Level




• Step 4 – Determine Evaluation Method – sample or full inspection – based on VL/CL

• Step 5 – Inspect• Step 6 – Report

• These steps will populate table for reporting measures






Where to go next

• Develop Standards for collection (based on fitness for use categories) – Not reinventing the wheel each time

• Develop Beta Software for aiding in sampling population sizes and reporting findings

• Fully test and report on findings – Is this a viable methodology for QA/QC geospatial data



Conclusions

• The Problem– Subjective review of abstract mapping data leads to conflict, higher cost,

and a lost focus on “what’s important on this map”

• The Solution– The development of a method that provides guidance on a spatial

sampling methods that are systematic, repeatable, and eliminate total inspection

– That provides a framework for evaluation of data quality and completeness and

– Provides a measure of a digital maps Fitness for Use



• Our contact info:

• David Hart, [email protected]

• Candice Kasprzak, GISPProject [email protected]

Questions?

mailto:[email protected]

mailto:[email protected]

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Candice Kasprzak, GISP Project Manager & David Hart, CP President

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