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Geographic Information Systems. Data Quality . 1. Necessity. Practical issues Legal issues Theoretical issues. Necessity . Accuracy - A measure of how closely data match the true value or descriptions Precision - A measure of how exactly data are measured and stored. Necessity .

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1 necessity

1. Necessity

Practical issues

Legal issues

Theoretical issues


Necessity ..


- A measure of how closely data match the true value or descriptions


- A measure of how exactly data are measured and stored


Necessity ..


- The deviation between the measured value and the true value of a feature


- A lack of confidence in the use of data due to incomplete knowledge

2 sources of error

2. Sources of Error

Inherent errors

Operational errors

2 1 inherent errors

2. (1) Inherent Errors

Errors contained in source data, and they cannot be eliminated

Map creation

- Projection, generalization, etc.

Map availability

- Age of map, area coverage, map scale, map

format, map accuracy, and map accessibility, etc.



age of map

Age of Map

Northallerton circa 1999

Northallerton circa 1867

area coverage

Area Coverage



Many data sets do not have a uniform coverage of information

Arthur Lembo, Jr. Cornell University

map scale



Map Scale



City of Sapporo, Japan


Map Accuracy Standards

1:1,200 ± 3.33 feet

1:2,400 ± 6.67 feet

1:4,800 ± 13.33 feet

1:10,000 ± 27.78 feet

1:12,000 ± 33.33 feet

1:24,000 ± 40.00 feet

1:63,360 ± 105.60 feet

1:100,000 ± 166.67 feet

2 2 operational errors

2. (2) Operational Errors

Errors introduced during data entry and manipulation

Inherent errors may be enhanced by operational errors

Data entry

Data storage

Data manipulation

operational errors

Operational Errors ..

Data entry

Data storage

- Numeric precision

- Locational precision

Data manipulation

- Sampling/interpolation

- Conversion

- Overlay

Output accuracy can only be as accurate as the least accurate individual layer – “GIGO”


Data Entry Errors

conversion raster to vector
Conversion - Raster to Vector

Vector data converted to raster with 10’ grid cells

Raster data converted back to vector, using 10’ grid cells

Vector Data

of Buildings

Arthur Lembo, Jr. Cornell University

overlay sliver
Overlay - Sliver
  • In the following example, there are two polygons. When we overlay the two of them, the resulting polygon has not only the logical intersection between the two polygons, but also many small polygons that are probably due more to the fact that the representation of the polygon boundaries are slightly different. These smaller, or sliver polygons, represent spatial errors in the data.

Arthur Lembo, Jr. Cornell University

3 data quality assessment

3. Data Quality Assessment

Data quality: "Fitness for use”

Data quality assessment: "Truth in labeling“

- It is data producer's responsibility to provide

detailed information about the data

- It is users' responsibility to make their judgment

of "fitness for use"



Data about data

Federal Geographic Data Committee (FGDC)

- Content Standard for Digital Geospatial Metadata

- U.S. National Committee for Digital Cartographic

Data Standards (NCDCDS)

components of data quality

Components of Data Quality

Positional accuracy

Attribute accuracy

Logical consistence



positional accuracy

Positional Accuracy

Closeness of coordinates to the true position

attribute accuracy

Attribute Accuracy

Closeness of attribute values to their true value

Locational accuracy and attribute accuracy are closely related to each other

- correct attributes but wrong locations

- correct locations but wrong attributes

logical accuracy

Logical Accuracy

How well the relationships between data elements are maintained

- geometry-attribute link consistency

- topological consistency



The proportion of data available for the area of interest

The level of classification for different categories of attributes

attribute completeness
Attribute Completeness

- High density

- Residential - Low density

Urban or build-up - Commer/indust

Agricultural - Pasture

- Row crop

- Small grains


Forest land - Deciduous forest

- Evergreen forest

- mixed forest

Water - Open water

lineage accuracy

Lineage Accuracy

Data sources and the process steps used to produce the data

- Data sources, methods of compilation, dates of data, map projection used, audit trails, etc



Chapter 4