GIS Data Quality Evaluator Version 4.0 DataLOGIC, Inc. DataLOGIC Corporation 72 Dartmouth Avenue Avondale Estates, GA 30002 404-289-4050 www.datalogic-systems.com.

Slides:



Advertisements
Similar presentations
KEYS TO SUCCESS DATA PREPARATION AND ORGANIZATION
Advertisements

School of Geography FACULTY OF ENVIRONMENT Working with Tables 1.
CC SQL Utilities.
JTX Overview Overview of Job Tracking for ArcGIS (JTX)
June 11, 2008West Virginia GIS Conference 2008 Integration of Mobile GIS Technologies in the West Virginia Department of Environmental Protection Bond.
Introducing ArcGIS Desktop
Exploring Microsoft Excel 2002 Chapter 7 Chapter 7 List and Data Management: Converting Data to Information By Robert T. Grauer Maryann Barber Exploring.
Introduction to the Architecture of Arc GIS
ArcGIS Geodatabase Miles Logsdon Spatial Information Technologies, UW Garry Trudeau - Doonesbury.
Technical Support: (989) GIS and Mapping Procedures in ArcMap 9.x Creating an ArcMap Project Editing an ArcMap Project Printing an ArcMap Project.
19 th Advanced Summer School in Regional Science An introduction to GIS using ArcGIS.
Overview of Software Requirements
Introduction to Structured Query Language (SQL)
ArcEditor ArcInfo ArcView Display map, query & analyze spatial relationships, features & attributes Same functions as ArcView, plus abilty to create, &
Access Lecture 1 Database Overview and Creating Tables Create an Employee Table.
Tutorial 11: Connecting to External Data
ESRM 250 & CFR 520: Introduction to GIS © Phil Hurvitz, KEEP THIS TEXT BOX this slide includes some ESRI fonts. when you save this presentation,
Intro. To GIS Lecture 4 Data: data storage, creation & editing
© 2002 ComputerPREP, Inc. All rights reserved. Word 2000: Forms, Merges, and Macros.
Introduction to the Architecture of ArcGIS
Lecture 4 Data. Why GIS? Ask questions Solve a problem Support a decision Make Maps Involve others, share data, procedures, ideas.
5.1 © 2007 by Prentice Hall 5 Chapter Foundations of Business Intelligence: Databases and Information Management.
Mid-Semester Lecture Exam Vocabulary Obvious steps within GIS –Selection and query processes –Working with tables –Classification concepts Projections.
© 2008 The McGraw-Hill Companies, Inc. All rights reserved. ACCESS 2007 M I C R O S O F T ® THE PROFESSIONAL APPROACH S E R I E S Lesson 4 – Creating New.
GEODATABASE Lower Adirondack GIS Users Group Meeting March 2, 2005 Lower Adirondack GIS Users Group Meeting March 2, 2005.
10-1 aslkjdhfalskhjfgalsdkfhalskdhjfglaskdhjflaskdhjfglaksjdhflakshflaksdhjfglaksjhflaksjhf.
DAY 14: ACCESS CHAPTER 1 Tazin Afrin October 03,
OCAN College Access Program Data Submissions Vonetta Woods HEI Analyst, Ohio Board of Regents
Introduction to ArcGIS. Goals Become familiar with ArcGIS ▫Locating and running the program ▫Introduction to the 3 ArcGIS interfaces ▫Experience with.
Threats Database V4 Model Geodatabase Relation Class Creation and Data Population June 25, 2007 Marlene McKinnon, GIS Specialist.
Eurotrace Hands-On The Eurotrace File System. 2 The Eurotrace file system Under MS ACCESS EUROTRACE generates several different files when you create.
Analyzing Data For Effective Decision Making Chapter 3.
Computer Science 101 Database Concepts. Database Collection of related data Models real world “universe” Reflects changes Specific purposes and audience.
Lesson 2.  To help ensure accurate data, rules that check entries against specified values can be applied to a field. A validation rule is applied to.
GIS Tutorial 1 Lecture 4 Geodatabases. Outline  Data types  Geodatabases  Data table joins  Spatial joins  Field calculator  Calculate geometry.
Esri UC 2014 | Technical Workshop | Fundamentals of working with geographic data Miriam Schmidts.
Introduction to the Geodatabase. What is a Geodatabase? What are feature classes and feature datasets? What are domains Design a personal Geodatabase.
Discovering Computers Fundamentals Fifth Edition Chapter 9 Database Management.
Esri UC 2014 | Technical Workshop | Esri Roads and Highways: Integrating and Developing LRS Business Systems Tom Hill.
7 1 Chapter 7 Introduction to Structured Query Language (SQL) Database Systems: Design, Implementation, and Management, Seventh Edition, Rob and Coronel.
Database Design and Management CPTG /23/2015Chapter 12 of 38 Functions of a Database Store data Store data School: student records, class schedules,
GIS 1 GIS Lecture 4 Geodatabases Copyright – Kristen S. Kurland, Carnegie Mellon University.
Introduction to GeoDatabase Lecture
Data Queries Selecting features in ArcMap Data queries  Important part of a GIS project Can be a part of your data preparation or final analysis  Data.
Prepared By Prepared By : VINAY ALEXANDER ( विनय अलेक्सजेंड़र ) PGT(CS),KV JHAGRAKHAND.
Arch: 383 Introduction to GIS Week 2 Introduction to GEOGRAPHIC INFORMATION SYSTEMS Can Kara Faculty of Architecture 2011 ARC 383.
XP Chapter 2 Succeeding in Business with Microsoft Office Access 2003: A Problem-Solving Approach 1 Building The Database Chapter 2 “It is only the farmer.
Intro to GIS | Summer 2012 Attribute Tables – Part 1.
AS Level ICT Data entry: Creating validation checks.
0 / Database Management. 1 / Identify file maintenance techniques Discuss the terms character, field, record, and table Describe characteristics.
NSF DUE ; Wen M. Andrews J. Sargeant Reynolds Community College Richmond, Virginia.
Selecting features in ArcMap
ESRI Education User Conference – July 6-8, 2001 ESRI Education User Conference – July 6-8, 2001 Introducing ArcCatalog: Tools for Metadata and Data Management.
Copyright © 2006 by Maribeth H. Price 13-1 Chapter 13 Working with Geodatabases.
Description and exemplification use of a Data Dictionary. A data dictionary is a catalogue of all data items in a system. The data dictionary stores details.
@2007 Austin Troy Lecture 2: Introduction to the Architecture of ArcGIS By Weiqi Zhou University of Vermont Thanks are due to Prof. Troy, upon whose lecture.
HEI/OCAN College Access Program Data Submissions.
William Perry U.S. Geological Survey Western Ecological Research Center Geography 375 Final Project May 22, 2013.
@2007 Austin Troy Lecture 2: Introduction to the Architecture of ArcGIS By Weiqi Zhou University of Vermont Thanks are due to Prof. Troy, upon whose lecture.
Coastal Applications Using ArcGIS eCoastal Database Model Data Management Introduction to eCoastal Part II Exercise A Finding Coastal Data Exercise B Creating.
COMPREHENSIVE Excel Tutorial 12 Expanding Excel with Visual Basic for Applications.
2 Copyright © 2008, Oracle. All rights reserved. Building the Physical Layer of a Repository.
Key Terms Attribute join Target table Join table Spatial join.
Microsoft Office Access 2010 Lab 1
Integration of Mobile GIS Technologies in the West Virginia Department of Environmental Protection Bond Forfeiture Program Sarah Clapham and Yueming.
ArcGIS Topology Shapefiles, Coverages, Geodatabases
Template library tool and Kestrel training
Designs for Data Integrity, validations, security and controls
ESRM 250/CFR 520 Autumn 2009 Phil Hurvitz
ArcCatalog and Geodatabases
Presentation transcript:

GIS Data Quality Evaluator Version 4.0 DataLOGIC, Inc. DataLOGIC Corporation 72 Dartmouth Avenue Avondale Estates, GA

DataQE – Data Quality Evaluator 1 Overview Concepts Types of QA Checks Performed Evaluation Process Future Development

DataQE – Data Quality Evaluator 2 What is DataQE? GIS Data Quality Evaluation Tool Evaluations based on user-specified rules Focuses on evaluations of attribute fields and values Includes: –Layer Checks –Data Structure Checks –Field Checks –Query Checks

DataQE – Data Quality Evaluator 3 What Can You Evaluate with DataQE? Compliance Is data compliant with established standards, such as a Data Dictionary? Completeness Has required data been appropriately populated? Suitability Is a data source appropriate for a given purpose? Does data make sense from a real-world perspective?

DataQE – Data Quality Evaluator 4 Who Can Use DataQE? Anyone who needs to evaluate the quality or suitability of a GIS data layer or tabular data –Data Entry Staff –Data Migration Specialists –GIS Managers –Quality Assurance Staff –Analysts

DataQE – Data Quality Evaluator 5 Requirements ESRI ArcMap 9.3 or higher Data Layers or Tables in the following formats are currently supported: –Geodatabases (SDE, File, Personal) –Shapefiles –Coverages –Database Tables (SQL Server, Oracle, MS Access, MS Excel, DBF, CSV)

DataQE – Data Quality Evaluator 6 The Data Evaluation Process Create Rule Sets containing the rules your data should follow Assign a Rule Set to the data source being evaluated Launch an Evaluation of the data source using the selected Rule Set View Evaluation Results and identify data errors Correct any errors as necessary using ArcMap or other editing tools Re-evaluate as necessary

DataQE – Data Quality Evaluator 7 Layer Checks Data Source/File Name Check –Verifies that the name of the evaluated data source name matches a list of valid names Layer Type Check –Verifies that the layer type of the evaluated data source matches specified requirements (GDB vs. Shapefile vs. Coverage, etc.) Projection Check –Verifies that a layer’s current projection matches established projection requirements Verify commonly used properties within a spatial data source.

DataQE – Data Quality Evaluator 8 Data Structure Checks Field Exists Check –Verifies that required fields exist in the data source being evaluated Field Type Check –Verifies that each field is the correct type (Text, Date, etc.) Field Width Check –Verifies that each field is the correct width Verify that a data source’s field structure matches appropriate requirements.

DataQE – Data Quality Evaluator 9 Field Checks Required Value Check –Verifies that each record in a field contains a value Unique Value Check –Verifies that each record in a field contains a unique value List of Values (LOV) Check –Verifies that each record in a field contains a value that matches a specified list of values (domain) Valid Range Check –Verifies that each record in a numeric field contains a value that falls within a specified range Verify that attribute values within a field match established standards.

DataQE – Data Quality Evaluator 10 Query Checks Used to perform in-depth evaluations of a data source Query Rules can be created using the DataQE Query Builder, or copied from existing ArcMap or database queries Allows queries to be stored permanently in Rule Sets Can be used to expand basic compliance evaluations to determine whether data meets established Business Rules Can be used by analysts to evaluate whether data is appropriate for a particular purpose Can be used to validate whether data “makes sense”, as opposed to simply meeting data dictionary standards User-defined rules based on customizable queries.

DataQE – Data Quality Evaluator 11 Data Quality Evaluator Window View Rule Set Assignments and properties Review Evaluation Results Used to perform evaluations of selected data sources

Data Quality Evaluator Results Window Data Sources Window Rule Set Explorer

DataQE – Data Quality Evaluator 13 Rule Set Manager Create Rule Groups and Rule Sets Add/Edit rules within a Rule Set Rule Sets can be shared among users Used for managing and editing Rule Sets

Rule Set Manager – Properties Window Rule Set Explorer Properties Window

Rule Set Manager – Layer Rule Properties Rule Set Explorer Layer Rules

Rule Set Manager – Field Rule Properties Rule Set Explorer Field Rule Properties

Rule Set Manager – Field Rules Grid Rule Set Explorer Field Rules Grid

DataQE – Data Quality Evaluator 18 Query Checks Allow users to build custom Query Rules. Query Builder window helps users build a query statement that can be used to evaluate a field or combination of fields.

Query Check Rule Set Explorer Query Rule Properties

DataQE – Data Quality Evaluator 20 LOV Manager Create/Import New LOV’s Add/Edit Values within an LOV Assign LOV’s to Rule Sets Used for managing and editing Lists of Values (LOV’s) for Domain Checks

LOV Manager – Valid Value Lists Value Lists Valid Values

LOV Manager – Rule Set Assignments Value Lists Rule Set Assignments

LOV Manager – Create LOV’s from Existing Data Value Lists Create LOV’s From Existing Data

Evaluation Manager Allows creation of Evaluations with pre-assigned properties Easy selection of Data Sources and Rule Sets Evaluation Definitions can be stored & shared Allows definitions to be saved and performed at any time, individually or in batches DataQE – Data Quality Evaluator 24 Used to manage and edit predefined Evaluation Definitions to support batch processing

Evaluation Definitions Window Properties Window Evaluation Manager

DataQE – Data Quality Evaluator 26 Future Development Coming in future versions of DataQE: Support for Citrix implementations Password Protected Public Rule Sets Associated Tables/Orphan Records Check Metadata Check User Definable Field Rules –Business Logic Check –Spatial Overlay Check