[PDF] [PDF] Chapter 1

[PDF] Attribute Data Input and Management

Attribute data describe the characteristics of the map AGS 722 Linking Attribute Data and Spatial Data ▫Defined by data types allowed in GIS package

[PDF] Chapter 8 ATTRIBUTE DATA INPUT AND - Fresno State

8 1 Attribute Data in GIS 8 1 1 Type of Attribute Table 8 1 2 Database Management 8 1 3 Type of Attribute Data Box 8 1 Categorical and Numeric Data 8 2 The 

[PDF] Présentation PowerPoint - University of New Brunswick

Visualization Methods and GIS Functionality Data and Querying • The database attribute structure of these datasets allows for a user to query spatial

[PDF] Chapter 4 Spatial data & spatial DB sys 1 Introduction spatial DB

1) spatial data : data of spatial attributes that denote a location / near the surface OGC geometry object model (Open GIS Simple Feature Specification for SQL) spatially extended SQL consists of 2 parts : query language + presentation 

[PDF] ECUS Presentation - Esri

18 nov 2015 · Model Real-Time Enterprise Data Imagery Web Maps Web Scenes GIS Maps Data Portal Attributes Construction Multifeature

[PDF] Chapter 7: Methods for GIS Data Manipulation, Analysis, and

importance For organizational and presentation purposes, this chapter describes the methods of each For this driver, the team analyzed GIS data that Selected all wetland areas from the attribute table and assigned them a score of 10 All

[PDF] GIS DATA MODELS

types of data in GIS; spatial data and non-spatial data (Attribute data) Non- spatial Data Model In order to represent the spatial information and their attributes, a data model unrealistic presentation unless the resolution is sufficiently high

[PDF] Principles of GIS

(Satellite images, aerial photos, etc ) Representation of Geographic Data Page 17 PRESENTATION GRAPHICS ○ Thematic mapping is a 

[PDF] Chapter 1

In GIS uncertainty and quality issues are much more broad In addition to positional accuracy there is: attribute accuracy, completeness of the data, sources and

[PDF] attribute data in gis slideshare

[PDF] attribute data management in gis ppt

[PDF] attribute in statistics

[PDF] attribute measures

[PDF] attribute of database

[PDF] attribute types

[PDF] attributes and methods in java

[PDF] attributes dataset h5py

[PDF] attributes dataset js

[PDF] attributes dataset python

[PDF] attributes of data mining

[PDF] attributes of data warehouse

[PDF] attributes of dataframe

[PDF] attributes of dataframe in python

[PDF] attributes of dataframe pandas

1

Data Quality

and

Error Analysis in GIS

Joshua Greenfeld, PhD, LS Professor emeritus, NJIT Professor, Israel Institute of Technology Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 1

ABSTRACT

One of the major challenges of GIS is dealing with the uncertainty and the assessment of the quality of spatial information.

The challenge is to assess the quality of spatial

information not just the quality of spatial data.

Many professionals are involved in providing GIS

services. Surveying is only one of them. For surveying to make a mark on the GIS industry and become a prominent stake holder of GIS, it has to offer some expertise that most other professionals cannot. Unfortunately, the ability to collect spatial data is becoming a common skill and the surveyors positioning expertise is not as unique as it used to be. Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 2

ABSTRACT

There is one area that surveyors have an advantage over other GIS professionals is their propensity and ability to understand and quantify spatial errors and accuracies. In surveying, the uncertainty and quality assessment is mostly confined to positioning or positional accuracies. The quality of surveying results is typically assessed on the basis of measurement accuracy and the propagation of these accuracies into other computed quantities. In GIS uncertainty and quality issues are much more broad. In addition to positional accuracy there is: attribute accuracy, completeness of the data, sources and lineage of the data, logical consistency, fuzziness of the spatial phenomenon, currency of the data and other uncertainty issues. Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 3

Objective

The objective of this seminar is to enable surveyors to understand the broader issues of accuracy assessment beyond positional accuracies. It will outline the extended definition of uncertainty and quality as it applies to GIS. It will include an overview on the errors and uncertainties that could impact the quality of spatial data. This will be followed by discussing the impact of errors in spatial data on spatial information. The ISO geospatial standards will be reviewed as well. Finally, some practical tools and examples of numerical and statistical assessment of uncertainty and quality of spatial information will be discussed and demonstrated. Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 4 2

Importance of Quality

Gain confidence in geodata

Minimize consecutive costs caused by decisions

or actions based on erroneous data Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 5

No unified definition of data quality

1. Data Quality refers to the degree of excellence

exhibited by the data in relation to the portrayal of the actual phenomena. GIS Glossary

2. The state of completeness, validity, consistency,

timeliness and accuracy that makes data appropriate for a specific use. Government of British Columbia

3. The totality of features and characteristics of data

that bears on their ability to satisfy a given purpose; the sum of the degrees of excellence for factors related to data. Glossary of Quality Assurance Terms Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 6

No unified definition of data quality

4. Information Quality : the fitness for use of

information; information that meets the requirements of its authors, users, and administrators. (Martin Eppler)

5. Data quality: The processes and technologies

involved in ensuring the conformance of data values to business requirements and acceptance criteria

6.ISO/PAS 26183:2006 defines product data quality as

a measure of the accuracy and appropriateness of product data, combined with the timeliness with which those data are provided to all the people who need them. Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 7

Error and Uncertainty in GIS

One of the major problems currently existing within GIS is the aura of accuracy surrounding digital geographic data Often hardcopy map sources include a map reliability rating or confidence rating in the map legend This rating helps the user in determining the fitness for use for the map However, rarely is this information encoded in the digital conversion process Often because GIS data is in digital form and can be represented with a high precision it is considered to be totally accurate Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 8 3

Error and Uncertainty in GIS

In reality, a buffer exists around each feature which represents the actual positional location of the feature

For example, data captured at the 1:20,000 scale

commonly has a positional accuracy of ± 20 metres This means the actual location of features may vary 20 metres in either direction from the identified position of the feature on the map Considering that the use of GIS commonly involves the integration of several data sets, usually at different scales and quality, one can easily see how errors can be propagated during processing Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 9

Error and Uncertainty in GIS

The ease with which geographic data in a GIS can be used at any scale highlights the importance of detailed data quality information.

Although a data set may not have a specific scale

once it is loaded into the GIS database, it was produced with levels of accuracy and resolution that make it appropriate for use only at certain scales, and in combination with data of similar scales. Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 10

Error and Uncertainty in GIS

Error - Two sources of error:

Inherent and Operational

‡Inherent error is the error present in source

documents and data ‡ Operational error is the amount of error produced through the data capture and manipulation functions of a GIS Both contribute to the reduction in quality of the products that are generated by GIS. Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 11

Error and Uncertainty in GIS

‡Possible sources of operational errors include :

Mislabelling of areas on thematic maps

Misplacement of horizontal (positional)

boundaries

Human error in digitizing classification error

GIS algorithm inaccuracies

human bias ‡ While error will always exist in any scientific process, the aim within GIS processing should be to identify existing error in data sources and minimize the amount of error added during processing Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 12 4

Errors in Database Creation

Errors are introduced at almost every step of database creation Concerns the degree to which the data exhausts the universe of possible items

Are all possible objects included within the

database? Affected by rules of selection, generalization and scale Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 13

Error and Uncertainty in GIS

Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 14 Error induced by data cleaning, Longley et al., chapter 6, pages

132-133

Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 15

Merging. Longley et al., chapter 6, pages 132-133

Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 16 5 Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 17 classification error -- difference in pixel class between the map and a reference 1939
1956
1971
1995
Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 19

Error and Uncertainty in GIS

Because of cost constraints it is often more appropriate to manage error than attempt to eliminate it! There is a trade-off between reducing the level of error in a data base and the cost to create and maintain the database An awareness of the error status of different data sets will allow user to make a subjective statement on the quality and reliability of a product derived from GIS processing The validity of any decisions based on a GIS product is directly related to the quality and reliability rating of the product Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 20 6

Error and Uncertainty in GIS

Depending upon the level of error inherent in the source data, and the error operationally produced through data capture and manipulation, GIS products may possess significant amounts of error Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 21

Error and Uncertainty in GIS

Tools to get a handle on uncertainty

Models of uncertainty: methods for assessing and

describing error

Error propagation (during analysis)

Fuzzy approaches (membership of classes)

Sensitivity analysis (effect of errors)

Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 22

Error and Uncertainty in GIS

Error assessment, reporting, interpretation - more difficult

Quality of data: standards and metadata

But: No professional GIS currently in use can present the user with information about the confidence limits that should be associated with the results of an analysis. Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 23

Classification of Errors in GIS

Resulting in

Forms of Error

Source of Error Data Collection and Compilation Data Processing Data Usage

Positional Error Logical Error

Attribute Error Completeness

(Primary) (Secondary)

Final Product Errors

Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 24
7

Uncertainty

Uncertainties in geographic information originate from different sources: Uncertainty due to the inherent nature of geography: different interpretations can be equally valid; Cartographic uncertainty resulting in positional and attribute errors; Conceptual uncertainty as a result of differences in being mapped Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 25

Uncertainty (Definition of a Forest)

0 2 4 6 8 10 12 14 16

0102030405060708090

Tree Height (m)

Canopy Coverage (%)

Portugal

Mexico

U.S. Israel

Belgium Malaysia UN

Turkey

Estonia

Switzerland

Somalia New Zealand UNESCO Australia Japan

Denmark

Morocco

Kenya

Zimbabwe

Sudan

Tanzania

Ethiopia

South Africa

Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 26

Internal and External Data Quality

internal quality - Corresponds to the level of similarity that external quality - Corresponds to the similarity between the data produced and user needs

Data that should have been produced

Data produced

User needs 1

User needs 2

User needs n

Internal

Quality

External

Quality 2

Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 27

Characteristics to define the

internal quality

Completeness: presence and absence of features,

their attributes and relationships. Logical consistency: degree of adherence to logical rules of data structure, attribution, and relationships (data structure can be conceptual, logical or physical).

Positional accuracy: accuracy of the position of

features.

Temporal accuracy: accuracy of the temporal

attributes and temporal relationships of features. Thematic accuracy: accuracy of quantitative attributes and the correctness of non-quantitative attributes and of the classifications of features and their relationships. Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 28
8 six characteristics to define the external quality (Beard and Vallière) Definition: to evaluate whether the exact nature of a corresponds to user needs (semantic, spatial and temporal definitions). Coverage: to evaluate whether the territory and the

Lineage: to find out where data come from, their

acquisition objectives, the methods used to obtain them, data meet user needs. Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 29
six characteristics to define the external quality (Beard and Vallière) Precision: to evaluate what data is worth and whether it is acceptable for an expressed need (semantic, temporal, and spatial precision of the object and its attributes). Legitimacy: to evaluate the official recognition and the legal scope of data and whether they meet the needs of de facto standards, respect recognized standards, have legal or administrative recognition by an official body, or legal guarantee by a supplier, etc.; Accessibility: to evaluate the ease with which the user can obtain the data analyzed (cost, time frame, format, confidentiality, respect of recognized standards, copyright, etc.). Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 30

Conceptual model of

uncertainty in spatial data

Uncertainty

Poorly Defined Objects

Well Defined Objects

Error

Vagueness

Probability

Fuzzy Set Theory

Ambiguity

Discord

Non-Specifity

Expert Opinion Dempster Schafer

Endorsement Theory, Fuzzy Set Theory

Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 31

Definitions of geographic objects

An examples of well-defined geographical objects is land ownership. The boundary between land parcels is commonly marked on the ground, and shows an abrupt and total change in ownership Examples of poorly defined geographical objects are the rule in natural resource mapping. The conceptualization of mappable phenomena and the spaces they occupy is rarely clear-cut There are rarely sharp transitions from one vegetation type to another In a region there could be several types of vegetation Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 32
9

Five dimensions of objects A and B

Relation

Scale Space Time

Attribute

B A Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 33
Error Ideally, if an object is conceptualized as being definable in both attribute and spatial dimensions, then it has a Boolean occurrence; any location is either part of the object, or it is not. Within GIS, for a number of reasons, a location or the assignment of an object to a location or to the a class may be expressed as a probability. Data Quality and Error Analysis in GIS (c) Dr. J. Greenfeld 34

Common reasons for a

database being in error

Type of Error Cause of error

Measurement Measurement of a property is erroneous. Assignment The object is assigned to the wrong classquotesdbs_dbs17.pdfusesText_23