Implementing ISO Data Quality Standards Using ESRI’s GIS ... · –The Conceptual Schema can be a...

Know the Earth…Show the Way

NATIONAL GEOSPATIAL-INTELLIGENCE AGENCY

Implementing ISO Data Quality Standards Using ESRI’s GIS Data ReViewer

Implementing ISO Data Quality Standards Using ESRI’s GIS Data ReViewer

Dave Wesloh/John SturleyAugust 2004ESRI User Conference

Dave Wesloh/John SturleyAugust 2004ESRI User Conference

2



Agenda

• NGA/ESRI Cooperative Research and Development Agreement– What’s a CRADA– History of this CRADA

• Goals and Objectives

• Geospatial Data– Data Overload– ISO Data Quality– GIS Data ReViewer

3



NGA/ESRI CRADA

• CRADA (Cooperative Research and Development Agreement)

• CRADA Agreement signed by NGA (formerly NIMA) and ESRI April 1999– Original Objectives

• Jointly research training methods for data production using ESRI’s commercial-off-the-shelf (COTS) software and tools

• Jointly research and enhance production processes that create NGA standard digital geospatial data and paper maps using ESRI's COTS software.

4



NGA/ESRI CRADA

• Research and enhance production processes.

• GIS Data ReViewer software (COTS)– QC Tool enhancement (ArcView, PLTS)

• Sampling, Edge Match, Point on Poly, etc.– Arc Info format– Shape file format– VPF format (VMap, DNC®, FFD)

5



NGA/ESRI CRADA

• ESRI and NGA determined need to extend CRADA based on several factors.– ESRI shifts from ArcInfo to ArcGIS– NGA shifts from VPF production to Country

Databases– NGA shifts from QC to QA

• QC - NGA checks the data• QA - The data producer (contractor) has implemented a

quality process which when followed results in a quality product. NGA verifies the process not the products.

6



NGA/ESRI CRADA

• 3 Year Extension signed April 2002• New CRADA Task: “Quality as defined within

the ISO Standards is a factor of data completeness, logical consistency, positional accuracy, temporal accuracy and thematic accuracy. NGA currently has few systems or procedures for determining quality based on these factors. ESRI and NGA will research the requirements and functionality needed to address each of these Data Quality elements.”

7



• How to tell good data from bad data?• Need to understand the Quality of the data

– What is it?– How do you measure it?– How can you understand the value provided by a

quality measurement?• Does the data meet your intended use?

Data Overload

8



• ISO 19113 – Quality Principles– Geographic datasets are increasingly being shared,

interchanged and used for purposes other than their producers’ intended ones. Information about the quality of available geographic datasets is vital to the process of selecting a dataset in that the value of data is directly related to its quality.

• ISO 19114 – Quality Evaluation Procedures– For the purpose of evaluating the quality of a dataset, clearly

defined procedures must be used in a consistent manner. This enables data producers to express how well their product meets the criteria set forth in its product specification and data users to establish the extent to which a dataset meets their requirements.

ISO Data Quality

9



• ISO 19113 Quality Principles– Positional Accuracy– Logical Consistency– Thematic Accuracy– Completeness– Temporal Accuracy

ISO Data Quality

Dataset

Themes

Features

Attributes

Scope versus Universe of Discourse (UOD)

10



Geospatial Data Componentsfor Quality Evaluation

• Dataset– Structure– Spatial

• Geometry• Geometric relationships (topology)

– Features• Attributes• Attribute values

– Thematic layers (feature groupings)

11



ISO Positional Accuracy

• Absolute– Horizontal and Vertical – closeness of reported coordinate values to

values accepted as being true• Relative

– Point to Point– closeness of the relative positions of

features in a dataset to their respective relative positions accepted as being true

12



GIS Data ReViewer

Positional Accuracy

Absolute Circular ErrorData to Image Comparison

Building corner

Road Intersection

Dam edge

Road/RiverIntersection

MODIS IMAGE

Image courtesy MODIS (Moderate Resolution Imaging Spectroradiometer) Web site

13



GIS Data ReViewer

Positional Accuracy

Relative AccuracyFeature to Feature

MODIS IMAGE

Image courtesy MODIS (Moderate Resolution Imaging Spectroradiometer) Web site

14



GIS Data ReViewer

• Positional Accuracy Assessment Tool– Works in conjunction with BAE Socet Set or

ERDAS Imagine– Calculates at 90% and 95% confidence level– Compares raster to raster, raster to vector, vector to

vector– Uses stereo images, ortho-rectified mono images or

geodetic control points as source– Populates dataset level metadata and/or feature

level positional accuracy attributes

15



ISO Logical Consistency

• Logical Consistency– Conceptual Consistency– Domain Consistency– Format Consistency– Topological Consistency

16



GIS Data ReViewer

• Conceptual Consistency:– Verify data content matches Conceptual Schema

desired.– The Conceptual Schema can be a product

specification or a set of User Defined Requirements.

– This will be a Pass/Fail test– For example if the Universe of Discourse (UOD)

is defined by a VPF specification, and the Scope (dataset) is an Arc Shapefile, this test would result in a Fail.

17



GIS Data ReViewer

• Format Consistency - Feature Coding and structure requirements compare to requested format

Tunnels - AQ130 (DGIWG FACC Coding)Feature Code: Text Field (5 Characters)Length: Short IntegerName: Text Field (variable length)TUC: Short Integer

• Reporting will be done on a Percent of Violating Items

18



GIS Data ReViewer

• Domain Consistency - Attribute values that fall outside the domain as established by the UOD

Feature Code: AQ130Length: 0 (unknown)

>= 75 metersName: Character String

“UNK” no name presentTUC: 0 (unknown)

1 Both Road and Railroad3 Railroad4 Road38 Canal


19



GIS Data ReViewer• Topological Consistency

– Implements three types of topological evaluations, captures information from ArcGIS and GIS Data ReViewer, report on Percent of Violating Items:

• Conformance to standard definitions and conventions for point (i.e. single vertex), line (i.e. minimum of 2 vertices, contiguous), and area features (i.e. minimum of 4 vertices, contiguous)

– ArcGIS, Validate Simple Geometry (duplicate vertices, minimum area

• Relational conformance (i.e. nodes at intersections, overshoots, undershoots, slivers, etc.)

– ArcGIS, Duplicate Point, Sliver Check, Validate Simple Geometry (find overlaps, crosses-self), dangle renderer

• Feature to Feature conformance (i.e. bridge to road connectivity, buildings inside lakes, etc)

– ArcGIS, Point On Poly, , Validate Simple Geometry (crosses-others), Batch Validate (condition tables)

20



ISO Thematic Accuracy

• Classification Correctness• Non Quantitative Attribute Correctness• Quantitative Attribute Correctness

21



GIS Data ReViewer• Classification Correctness - Compare data against

UOD to verify the correct classification of features and their relationships

Thematic Layer: TransportationFeature Code: AQ130 (tunnel)

Attributes: LEN, NAM, TUCFeature Code: AP010 (cart track)

……..Feature Code: AP030 (road)

……..Feature Code: AN010 (railroad)

……..


22



GIS Data ReViewer

• Non Quantitative Attribute Correctness– Reporting on % of Non quantitative attributes found in error– Based on sampling or Compare tool if specification driven

Thematic Layer: TransportationFeature Code: AQ130

Length: 0 (unknown)>= 75 meters

Name: Character String“UNK” no name present

TUC: 0 (unknown)1 Both Road and Railroad3 Railroad4 Road38 Canal

23



GIS Data ReViewer

• Quantitative Attribute Correctness– Report on % of quantitative attributes found in error– Based on sampling

Thematic Layer: TransportationFeature Code: AQ130

Length: 0 (unknown)>= 75 meters

Name: Character String“UNK” no name present

TUC: 0 (unknown)1 Both Road and Railroad3 Railroad4 Road38 Canal

24



ISO Completeness

• Completeness: presence and absence of features, their attributes and relationships;Completeness COMMISSION: – Feature present that doesn’t belong to the UOD– Attribute present that doesn’t belong to the UOD

Completeness OMISSION:– Feature in UOD not found in Scope (not necessarily an error)– Feature in UOD and Scope but Attribute doesn’t match

• Sum by F_CODE, Frequency, Data Loader• Report will be on Percentage of extra or missing items

25



GIS Data ReViewer• Tool will run in an automated method when

UOD is a specification• A user defined UOD is under consideration

GIFD

Feature Database

MSD LV1

x-ref x-ref

x-ref

User defined x-ref

MSD LV2MSD LV3

26



GIS Data ReViewer

• Visual evaluation of data density

= heavy

= medium

= light

27



Reporting Quality

• Provide a “Data Quality Report”, automatically populate metadata fields, and create a data quality coverage .

• Error Shapefile (points)• Error Table

28



Summary

• Substantial amount of work still to be done.• A lot has been accomplished.

– Positional Accuracy Assessment Tool– Some work on remaining DQ elements

• NGA requires Data Quality reporting. capabilities.

• NGA will use ISO for DQ guidelines.• GIS Data ReViewer is our best option for

success.

29





Implementing ISO Data Quality Standards Using ESRI’s GIS ... · –The Conceptual Schema can be a...

Documents

Transcript of Implementing ISO Data Quality Standards Using ESRI’s GIS ... · –The Conceptual Schema can be a...