Vector functionality

Because vector data consist of three different data primitives (points, lines, polygons) instead of one as for grid (the grid cell) and because all the components (location, topology, attributes) need to be maintained, vector operations are more complex than raster operations in general. Both the spatial data and the attribute data must be handled. Further the link between spatial and attribute data must be maintained.

Vector operations include:

Display and query

of spatial and attribute data, both individually and in combination

Data generalisation and abstraction

building desired points, lines, areas and relationships from input data

Data manipulation

manipulate data coordinates to provide georeferencing, remove distortion, etc.

Measurement

measure distance, shape, volume, etc.

Topological overlay

overlay points, lines and polygons

Buffering

buffer points, lines or polygons to produce new polygons

Data generalisation and abstraction

Generalisation and abstraction may involve:

	matching data across map sheet edges
	thinning out coordinates (vertices) to simplify lines and polygon boundaries
	calculation of centroids and label points in polygons
	automatic contouring
	proximal mapping - finding areas of proximity
	vector/raster conversion

Data generalisation and abstraction also may involve:

	reclassification of points, lines and areas
	dissolving polygons and dropping lines between them - note that the attributes of such polygons must be merged

The spatial overlay of two coverages results in a new coverage which is subjected to planar enforcement. In an overlay operation, both the spatial and the attribute data must be updated to reflect the new geometry/topology/attributes.

A number of different types of spatial overlay exist:

	point-in-polygon
	line-on-polygon
	polygon overlay (polygon-on-polygon

Buffering

Buffering must cater for:

	point, line and polygon-based features
	different buffer shapes
	variable size buffers
	interior/exterior buffers (for polygons)

The result of all buffer operations is a polygon coverage which must have appropriate attributes assigned. Each polygon must have an attribute that identifies whether or not it is a polygon inside the buffer or outside the buffer.

Describing a location on the Earth's surface

	Projection (assumes a spherical earth). ONLY a mathematical method for drawing a 3D object (the earth) on a 2D surface (map, computer screen).
	Datum - a modification on a sphere - a spheroid. In other words, a mathematical representation of the earth's shape
	Geoid - even more detail than a spheroid. It's an irregular surface.
	Coordinates - locations. Most common include lat/long (geographic), UTM, and State Plane.

More on Datums...... First in common use in the US was Clarke1866. Following in chronological order are NAD27, GRS80, NAD83, and WGS84. Everything from 1980 on are based upon the center of the earth. Those earlier are based on a point on the surface of the earth. To give a little info on the significance of a datum, there is up to a 300 meter difference in x,y coordinates between the NAD27 and NAD83 datums.

More on the Geoid - the definition of the shape of the earth is the field of Geodesy. In theory, the geoid runs through sea level and is a representation of the gravity field of the earth. Far more accurate than mere datum measurements.

Displaying Map Projections - A map projection is a mathamatical method for projection the surface of a globe onto a sheet of paper (3d - 2d)

Only a few (maybe about 20) are in actual use, although there are hundreds out there.

distortion:

	there is always distortion (period)
	there are different types of distortion
	there are different degrees of distortion dependent on where you are on the map or which type of projection that you select. Map Classification

Describing the different projections (2 methods)

1) by geometric construction

	conic: projected onto a cone
	cylindrical -- projected onto a cylinder
	azimuthal - projected onto a single surface

modified by aspect

	equatorial
	polar
	oblique

modified by case

	tangent cone touches earth along one line -- no distortion along this line
	secant two lines -- have two lines along which there is no distortion
	Polyconic Do a number of cones and sort of stack them up

2) by Preserved properties:

	Area: correct relative size (equal area or equivalent projections) cyl. equal area, sinusoidal, mollweide, eckert IV
	angle: correct shapes. Note that area and angle are mutually exclusive. (conformal Projections). Mercator, lambert conformal conic
	Distance: distances between points are correct (equidistant). Stereographic, gnomonic
	Azimuth: Great circles are straight lines. Often viewed with the pole at the center.
	Compromise Nothing is preserved, but nothing is super distorted. Robinson

Note that both distance and azimuth preserved projections only apply to or from the center of the map projection. Also, they usually only show 1 hemisphere or half the earth. gnomonic

These can be modified by interruptions

We discussed the URISA Ethics in GIS page. It can be found at http://www.urisa.org/ethics/code_of_ethics.htm

History of GIS

	Historic use of Multi-theme maps
	Setting the stage for computerised GIS
	Canadian GIS
	Harvard Labs

	US Census Bureau
	ESRI
	Tie in with previous trends in GIS lecture

Week 5 Lectures

Data Quality Measurement and Assessment

(Data Quality: this section modified from the NCGIA core curriculum in GIScience:)

Written by Howard Veregin, Department of Geography,University of Minnesota, Room 414267 19th Avenue South, Minneapolis, MN 55455, USA veregin@atlas.socsci.umn.edu

This section was edited by Gary Hunter, Department of Geomatics, University of Melbourne, Australia.

This unit is part of the NCGIA Core Curriculum in Geographic Information Science. These materials may be used for study, research, and education, but please credit the authors Howard Veregin, and the project, NCGIA Core Curriculum in GIScience. All commercial rights reserved. Copyright 1998 by Howard Veregin.

1. Data Quality

What is quality?

	Quality is commonly used to indicate the superiority of a manufactured good or to indicate a high degree of craftsmanship or artistry. We might define it as the degree of excellence in a product, service or performance.
	In manufacturing, quality is a desirable goal achieved through management and control of the production process (statistical quality control). (Redman, 1992)
	Many of the same issues apply to the quality of databases, since a database is the result of a production process, and the reliability of the process imparts value and utility to the database.

Why is there a concern for DQ?

	Increased data production by the private sector, where there are no required quality standards. In contrast, production of data by national mapping agencies (e.g., US Geological Survey, British Ordnance Survey) has long been required to conform to national accuracy standards (i.e., mandated quality control).
	Increased use of GIS for decision support, such that the implications of using low-quality data are becoming more widespread (including the possibility of litigation if minimum standards of quality are not attained).
	Increased reliance on secondary data sources, due to the growth of the Internet, data translators and data transfer standards. Thus, poor-quality data is ever easier to get.

Who assesses DQ?

Model 1. Minimum Quality Standards.

	This is a form of quality control where DQ assessment is the responsibility of the data producer. It is based on compliance testing strategies to identify databases that meet quality thresholds defined a priori.
	An example is NMAS, the National Map Accuracy Standards adopted by the US Geological Survey in 1946.
	This approach lacks flexibility; in some cases a particular test may be too lax while in others it may be too restrictive.

Model 2. Metadata Standards.

	This model views error as inevitable and does not impose a minimum quality standard a priori. Instead, it is the consumer who is responsible for assessing fitness-for-use; the producer’s responsibility is documentation, i.e., “truth-in-labeling.”
	An example is SDTS, the Spatial Data Transfer Standard.
	This approach is flexible, but there is still no feedback from the consumer, i.e., there is a one-way information flow that inhibits the producer’s ability to correct mistakes.

Model 3. Market Standards.

	This model uses a two-way information flow to obtain feedback from users on data quality problems. Consumer feedback is processed and analyzed to identify significant problems and prioritize repairs.
	An example is Microsoft’s Feedback Wizard, a software utility that lets users email reports of map errors.
	This model is useful in a market context in order to ensure that databases match users’ needs and expectations.

A definition of geographical data includes the three dimensions of space, time and theme (where-when-what). These three dimensions are the basis for all geographical observation. (Berry, 1964; Sinton, 1978)

Geographical data quality is likewise defined by space-time-theme. Data quality also contains several components such as accuracy, precision, consistency and completeness.

2. Accuracy

Accuracy is the inverse of error. Many people equate accuracy with quality but in fact accuracy is just one component of quality.

Definition of accuracy is based on the entity-attribute-value model.

	Entities = real-world phenomena
	Attribute = relevant property
	Values = Quantitative/qualitative measurements

An error is a discrepancy between the encoded and actual value of a particular attribute for a given entity. “Actual value” implies the existence of an objective, observable reality. However, reality may be:

	Unobservable (e.g., historical data)
	Impractical to observe (e.g., too costly)
	Perceived rather than real (e.g., subjective entities such as “neighborhoods")

In fact, it is not necessary to posit an objective reality in order to assess accuracy, since all geographical data are collected with the aid of a model that specifies -- implicitly or explicitly -- the required level of abstraction and generalization.

	This is the database “specification” and is closely related to the “terrain nominal” concept of perceived reality (Salgé, 1995).
	The specification serves as the standard against which accuracy is assessed. Thus the “actual” value is the value we would expect based on the specification (Brassel et al., 1995).
	Accuracy is always a relative measure, since it is always measured relative to the specification.
	To judge fitness-for-use, one must judge the data relative to the specification, and also consider the limitations of the specification itself (CEN, 1995).

2.1. Spatial Accuracy

Spatial accuracy is the accuracy of the spatial component of the database. The metrics used depend on the dimensionality of the entities under consideration.

For points, accuracy is defined in terms of the distance between the encoded location and “actual” location.

	Error can be defined in various dimensions: x, y, z, horizontal, vertical, total.
	Metrics of error are extensions of classical statistical measures (mean error, RMSE or root mean squared error, inference tests, confidence limits, etc.) (American Society of Civil Engineers 1983; American Society of Photogrammetry 1985; Goodchild 1991a).

For lines and areas, the situation is more complex. This is because error is a mixture of positional error (error in locating well-defined points along the line) and generalization error (error in the points selected to represent the line) (Goodchild 1991b).

	The epsilon band is usually used to define a zone of uncertainty around the encoded line, within which “actual” line exists with some probability.
	However, there is little agreement (and little empirical work) on the shape of the band, both planimetrically and in cross-section (Chrisman, 1982; Blakemore, 1983; Honeycutt, 1986; Caspary and Scheuring, 1993).

2.2. Temporal accuracy

Temporal accuracy is the agreement between the encoded and “actual” temporal coordinates for an entity.

Temporal coordinates are often only implicit in geographical data, e.g., a time stamp indicating that the entity was valid at some time. Often this is applied to the entire database (e.g., a map dated “1995”).

More realistically, temporal coordinates are the temporal limits within which the entity is valid (e.g., Pothole Q54D-35-021 existed between 2/12/96 and 8/9/96).

Temporal accuracy is not the same as “database time”, which is the time the information was entered into the database.

Temporal accuracy is not the same as “currentness” (or up-to-dateness) which is actually an assessment of how well the database specification meets the needs of a particular application. A database can be temporal accurate but still out of date; historical applications depend on such data.

2.3. Thematic Accuracy

Thematic accuracy is the accuracy of the attribute values encoded in a database.

The metrics used here depend on the measurement scale of the data:

	Quantitative data (e.g., precipitation) can be treated like a z-coordinate (elevation) and assessed using metrics normally used for vertical error (such as the RMSE). See section 2.1.
	Qualitative data (e.g., land use/land cover) is normally assessed using a cross-tabulation of encoded and “actual” classes at sample of locations. This produces a classification error matrix (confusion matrix).

	Element in row i, column j of the matrix is the number of sample locations assigned to class I but actually belonging to class j.
	The sum of the main diagonal divided by the number of samples is a simple measure of overall accuracy.
	An error of omission means a sample that has been omitted from its actual class. An error of commission means an error that is included in the wrong class. Ever error of omission is also an error of commission.
	There is a large body of research on this topic (e.g., van Genderen and Lock, 1977; Congalton et al., 1983; Aronoff, 1985; Rosenfield and Fitzpatrick-Lins, 1986

3. Resolution (precision)

Resolution (or precision) refers to the amount of detail that can be discerned in space, time or theme. Resolution is always finite because no measurement system is infinitely precise, and because databases are intentionally generalized to reduce detail (Veregin and Hargitai, 1995).

Resolution is an aspect of the database specification that determines how useful a given database may be for a particular application. High resolution is not always better; low resolution may be desirable when one wishes to formulate general models.

Resolution is linked with accuracy, since the level of resolution affects the database specification against which accuracy is assessed. Two databases with the same overall accuracy levels but different levels of resolution do not have the same quality; the database with the lower resolution has less demanding accuracy requirements. (For example, thematic accuracy will tend to be higher for general land use/land cover classes like “urban” than for specific classes like “residential”.)

3.1. Spatial Resolution

Spatial resolution is well-defined in the context of raster data were it refers to the linear dimension of a cell.

For vector data resolution might be defined as the minimum mapping unit size. Sometimes mean polygon size is used instead, but this is erroneous since smaller polygons may be observable but just not present on the map.

3.2. Temporal Resolution

Temporal resolution is length (temporal duration) of the sampling interval.

	For example, the shorter the shutter speed of a camera, the higher the temporal resolution (other factors being equal).
	Temporal resolution affects the minimum duration of an event that is discernible. If the duration is less than the resolution, the event is invisible or at best leaves a smudge (like carriages on nineteenth-century daguerreotypes).

Temporal resolution is distinct from temporal sampling rate.

	Resolution is the length of the sampling interval, while sampling rate is the frequency of sampling over time (e.g., once a day, once a week, etc.).
	For example, a motion picture camera might have a temporal resolution of 1/1000 second (i.e., the shutter speed to capture a single frame ), and sampling rate of 24 frames per second.

3.3. Thematic Resolution

Thematic resolution refers to the precision of the measurements or categories for a particular theme.

	For categorical data, resolution is the fineness of category definitions (e.g., “urban” vs. “residential” and “commercial”).
	For quantitative data, thematic resolution is analogous to spatial resolution in the z-dimension (i.e., the degree to which small differences in the quantitative attribute can be discerned). [FIGURE 6]

4. Consistency

Consistency refers to the absence of apparent contradictions in a database. Consistency is a measure of the internal validity of a database, and is assessed using information that is contained within the database.

Consistency can be defined with reference to the three dimensions of geographical data.

	Spatial consistency includes topological consistency, or conformance to topological rules, e.g., all one-dimensional objects must intersect at a zero-dimensional object (Kainz, 1995).
	Temporal consistency is related to temporal topology, e.g., the constraint that only one event can occur at a given location at a given time (Langran, 1992).
	Thematic consistency refers to a lack of contradictions in redundant thematic attributes. For example, attribute values for population, area, and population density must agree for all entities.

5. Completeness

Completeness refers to a lack of errors of omission in a database. It is assessed relative to the database specification, which defines the desired degree of generalization and abstraction (selective omission).

There are two kinds of completeness (Brassel et al., 1995)

	“Data completeness” is a measurable error of omission observed between the database and the specification. Even highly generalized databases can be “data complete” if they contain all of the objects described in the specification.
	“Model completeness” refers to the agreement between the database specification and the “abstract universe” that is required for a particular database application. A database is “model complete” if its specification is appropriate for a given application.

Incompleteness can be measured in space, time or theme . Consider a database of buildings in Minnesota that have been placed on the National Register of Historic Places as of the end of 1995.

	Spatial incompleteness: The list contains only buildings in Hennepin County (one county in Minnesota, rather than all of Minnesota).
	Temporal incompleteness: The list contains only buildings placed on the Register by June 30, 1995.
	Thematic incompleteness: The list contains only residential buildings.

Errors of commission can also be assessed. These errors can lead to “over-completeness”.

Errors of commission in space, time and theme for the previous example: The list also contains buildings in Wisconsin; the list contains buildings added to the list in 1996; the list contains historic districts as well as buildings.

6. Summary of Important Points

Data quality is the degree of excellence in a database. Quality is assessed relative to the database specification, which defines the desired level of generalization and abstraction. The quality of this specification, and its appropriateness for particular applications, can also be assessed.

Quality assessment and reporting is based on minimum quality standards (compliance testing or quality control), metadata standards (truth-in-labeling and fitness-for-use), or market standards (feedback from users).

Data quality is contains several components, including accuracy, precision, consistency and completeness. Each component can be assessed in space, time and theme (the three basic dimensions of geographical data).

Various assessment methods can be used for each component/dimension combination. Some methods are well-developed and others are not.

7. References and Bibliography

American Society of Civil Engineers (Committee on Cartographic Surveying, Surveying and Mapping Division) 1983 Map uses, scales and accuracies for engineering and associated purposes. New York: American Society of Civil Engineers.

American Society of Photogrammetry (Committee for Specifications and Standards, Professional Practice Division) 1985 Accuracy specification for large-scale line maps. Photogrammetric Engineering and Remote Sensing 51: 195-199.

Aronoff S 1985 The minimum accuracy value as an index of classification accuracy. Photogrammetric Engineering and Remote Sensing 51: 99-111.

Beard M K 1989 Use error: The neglected error component. Proceedings, Auto Carto 9; 808-817.

Berry B 1964 Approaches to regional analysis: A synthesis. Annals, Association of American Geographers 54: 2-11.

Blakemore M 1983 Generalisation and error in spatial data bases. Cartographica 21: 131-139.

Brassel K, Bucher F, Stephan E-M and Vckovski A 1995 Completeness. In Guptill S C and Morrison J L (eds) Elements of spatial data quality. Oxford, Elsevier: 81-108.

Burrough P A 1986 Principles of geographical information systems for land resources assessment. Oxford, Clarendon.

Campbell W G and Mortenson D C 1989 Ensuring the quality of geographic information system data. Photogrammetric Engineering and Remote Sensing 55: 1613-1618.

Caspary W and Scheuring R 1993 Positional accuracy in spatial databases. Computers, Environment and Urban Systems 17: 103-110.

Chrisman N R 1982 A theory of cartographic error and its measurement in digital data bases. Proceedings, Auto Carto 5: 159-168.

Chrisman N R 1991 The error component in spatial data. In Maguire D J, Goodchild M F and Rhind D W (eds) Geographical information systems. New York, Wiley: 165-174.

Comité Européen de Normalisation (CEN) 1995 Geographic Information - Data Description - Quality (Draft). Brussels: CEN Central Secretariat.

Congalton R G, Oderwald R G and Mead R A 1983 Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing 49: 1671-1678.

Duecker G T and Platt J T 1990 The role of automated data checks in the quality assurance of GIS data bases. GIS/LIS '90: 264-271.

Federal Geographic Data Committee (FGDC) 1994 Content Standards for Digital Geospatial Metadata (June 8). Washington DC: Federal Geographic Data Committee.

Fegeas R G, Cascio J L and Lazar R A 1992 An overview of FIPS 173, The Spatial Data Transfer Standard. Cartography and Geographic Information Systems 19: 278-93.

Goodchild M F 1988a Stepping over the line: Technological constraints and the new cartography. The American Cartographer 15: 311-319.

Goodchild M F 1988b The issue of accuracy in global databases. In Mounsey H (ed) Building Databases for Global Science. London, Taylor and Francis: 31-48.

Goodchild M F 1991a Issues of quality and uncertainty In Muller J C (ed) Advances in cartography. London, Elsevier: 113-139.

Goodchild M F 1991b Keynote address. Proceedings, Symposium on Spatial Database Accuracy: 1-16.

Goodchild M F 1995 Sharing imperfect data. In Onsrud H J and Rushton G (eds) Sharing geographic information. New Brunswick NJ, Center for Urban Policy Research: 413-425.

Guptill S C 1993 Describing spatial data quality. Proceedings, 16th International Cartographic Conference: 552-560.

Honeycutt D M 1986 Epsilon, generalization and probability in spatial data bases. Unpublished manuscript.

Kainz W 1995 Logical consistency. In Guptill S C and Morrison J L (eds) Elements of spatial data quality. Oxford, Elsevier: 109-137.

Langran G 1992 Time in geographic information systems. London: Taylor and Francis.

Lanter D 1991 Design of a lineage-based meta-database for GIS. Cartography and Geographic Information Systems 18(4): 255-261.

Lanter D and Veregin H 1992 A research paradigm for propagating error in layer-based GIS. Photogrammetric Engineering and Remote Sensing 58: 526-533.

Moellering H (ed) 1991 Spatial database transfer standards: Current international status. London: Elsevier.

Parkes D N and Thrift N J 1980 Times, spaces, and places: A chronogeographic perspective. New York: Wiley.

Redman T C 1992 Data quality. New York: Bantam.

Rosenfield G H and Fitzpatrick-Lins K 1986 A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing 52: 223-227.

Salgé F 1995 Semantic accuracy. In Guptill S C and Morrison J L (eds) Elements of spatial data quality. Oxford, Elsevier: 139-151.

SDTS 1992 The Spatial Data Transfer Standard (FIPS-173).

Sinton D 1978 The inherent structure of information as a constraint in analysis. In Dutton G (ed) Harvard papers on geographic information systems. Reading MA, Addison-Wesley.

Stearns F 1968 A method for estimating the quantitative reliability of isoline maps. Annals, Association of American Geographers 58: 590-600.

Thapa K and Bossler J 1992 Accuracy of spatial data used in geographic information systems. Photogrammetric Engineering and Remote Sensing 58(6): 835-841.

Tychon G G and Johnson M R 1990 GIS data exchange: Standards and formats. In Heit M and Shortreid A (eds) GIS applications in natural resources. Boulder CO, GIS World Inc: 155-161.

van Genderen J L and Lock B F 1977 Testing land-use map accuracy. Photogrammetric Engineering and Remote Sensing 43: 1135-1137.

Veregin H and Hargitai P 1995 An evaluation matrix for geographical data quality. In Guptill S C and Morrison J L (eds) Elements of spatial data quality. Oxford: Elsevier 167-188.

Week 6 Lectures

Data Types and the NSDI

Data Input

the number 1 bottleneck in GIS applications. Often 80%+ of the project cost

Need to automate data input process, but that causes problems. Common problems/considerations include:

	map projections, scale, coordinate system
	raster/vector conversions
	paper distortions
	error corrections
	control points
	discrepancies across map sheets
	user fatigue and boredom

Modes of data input include the keyboard, scanners, direct conversion from other digital data, and voice input

Socio-Economic data

Generally include: (may be aggregate or disaggregate)

	demographics
	housing
	migration
	transportation
	economics
	retail

Sources of Socio-economic data

	field surveys
	government statistics
	government administrative records
	other stuff, including marketing info, mailing lists, etc.

Issues in using socio-economic data

	cost
	documentation
	data quality
	data conversion
	aggregation
	accuracy of location

Environmental and Natural Resource Data

Purposes of resource-based GIS are primarily

	Inventory tool
	better manage the marketing of the resource
	protect the resource from improper development
	model the complex interactions between phenomenae so that predictions can be made

Contents of Environmental databases include not only natural information, but info regarding any human impacts on the area.

General notes on resource data

	Comparatively static
	generally low spatial resolution
	typically raster
	Often uses satellite imagery.

We then went over an example of all the data that might be required for siting a waste incinerator.

National Spatial Data Infrastructure (NSDI)

Concept not new

more and more people requiring spatial data

to maximise benefits, dataset must be accessable

benefits: decisions only as good as the information upon which they are based.

	provide government with reliable, consistent, timely data
	Provide community access to data
	minimise waste & duplication of datasets
	common standards allow maximum integration of datasets

Current shortcomings and obstacles

	knowledge of data availability
	metadata
	access impeded or not possible
	inconsistent policies regarding data use
	fundamental datasets incomplete/out of date
	lack of data consistency across nation
	pricing
	duplication among agencies/agency competition
	national security considerations
	enforcing standards
	low levels of customer focus
	poor/inconsistent funding

Process of data capture

The process of data capture must involve both spatial and attribute data. If they are considered separately, then more effort usually is required to integrate both components within a GIS database. It is better to consider both components in planning the capture process to ensure that appropriate IDs and tags are assigned to enable attributes to be correctly attached to the appropriate spatial units.
Essentially three general operations are required for the capture of geographic data:

Entering the spatial data

Entering the non-spatial (attribute) data

Linking the spatial data to the non-spatial data

Manual data capture

Manual data capture includes the entry of data from field notes and maps, data entry sheets, data recordings sheets and graphs, etc. The process of manual entry can be sped up through by-passing the intermediate paper records and directly recording data through laptop computers in the field, electronic surveying instruments, automated GPS position recording, etc.

Most attribute data is entered via a keyboard into a database. Often, much attribute data exists prior to the GIS being built. In terms of volume, usually the vast majority of a GIS database consists of attribute data. The entry of attribute data and its pitfalls is well known and hence we will concentrate primarily on the spatial component of geographic data.

The process of manually entering spatial data is dependent on whether a grid-based or a vector-based database is to be generated.

Vector

	Type in the coordinates of points, lines, areas
	Coordinates can be two dimensional (X and Y coordinates) or three dimensional (X, Y and Z)
	Usually an integer ID must be attached to each coordinate (used to attach attributes)

Grid

	Determine the grid cell size
	Overlay the grid on the data
	Determine the grid cell values
	Type in the values

Global positioning system

The Global Positioning System, more commonly referred to as GPS, is used to compute positions in 2 or 3 dimensional space from signals obtained from a series of NAVSTAR satellites. It is owned by the U.S. Department of Defence.

Satellite details:

	21 satellites (and 3 spares) provide continuous coverage of the earth
	6 orbits at 20,200 km
	one rotation every 12 hours
	6 satellites are in view at all times at a given location on the earth

The Russian equivalent is GLONASS - Global Navigation Satellite System.

GPS Accuracies

	"standard" uncorrected, x and y to 15 meters. Z is a bit worse
	Differential GPS - correct using base station data. x and y to 1-5 meters
	RTK - Real time kinematic - accuracies to sub-centimeter.

Digitising

A digitiser consists of a tablet with an electronic mesh and a cursor (also known as puck or mouse). A map sheet is mounted on the digitiser and the cursor is used to enter required points or trace the desired lines on the map. Lines are digitised as a series of points which can then be processed by the software (eg. editing, conversion to raster format, etc.).

Digitising is largely a manual process involving the concentration and skillfulness of a digitising operator. It is both a time-consuming and boring task.

Accuracy of digitising

The accuracy of digitising is limited by a number of factors related to the data source (map), equipment being used and human factors.

Source map - may be:

- inaccurate in the first place because of missing or misplaced data
- poor in resolution (ie. how do you digitise on a "thick" road line?)
- stretched (paper stretch) due to aging and storage conditions

Digitiser resolution:

- must exceed the required resolution
- is usually not a problem for most digitisers

Digitising process - dependent on factors such as:

- positioning the cursor and generating points on the line (human factors)
- representing lines in digital form
- operator skillfulness and fatigue

During the digitising process, a number of errors may occur. Some of these errors can be prevented or alleviated during the digitising process, while others can be correctly/edited in a following step (either automated or manually). Such (potential - not all are necessarily incorrect) errors are discussed in the following sections and include:

	Dangle nodes
	Pseudo nodes
	Multiple or missing labels
	Sliver polygons
	etc.

Data input errors (Dangles)

Dangles, also referred to as Dangling Nodes, are identified as nodes with only one line (arc) attached. They include both overshoots (extended too far past another line) and undershoots (not quite reaching another line). In such a case, the line was intended to connect up with another line and hence overshoots and undershoots are errors.

However, dangles are also identified with lines that are not connected to another line at one end, such as cul-de-sac's or dead-end streets in a road network. Obviously, such dangles are NOT errors.

Correcting dangles (that are errors!) can be partially automated by setting a dangle length tolerance value which specifies the maximum distance within which nodes will automatically be "snapped" to a line. Any dangles falling outside this tolerance level will have to be corrected maually in the editing process.

Data input errors (Pseudo nodes)

Pseudo nodes are identified by nodes that have only two arcs attached to it. This often occurs two lines are digitised and are connected together at one end, but the connection point does not occur at a junction with other lines (ie. node breaks up a long and complex line such as a contour line). If the lines are intended to represent the same entity with the same attributes, then the pseudo node is an error and should be removed. In other cases, pseudo nodes do not cause any problems, but can be removed to simplify the storage and provide a "cleaner" representation.

In certain cases, pseudo nodes are REQUIRED and therefore certainly are not errors. The most obvious example is an island polygon where the bounding line begins and ends at the SAME node.

Removing a pseudo node involves joining the two (different!) arcs on either side of it into one. This usually means ensuring that the attributes of each arc are the same so that there is no problem in merging them. It may also be possible to specify a "snapping distance" tolerance value which will cause all nodes within a specified distance to "snap" together, thereby eliminating some pseudo nodes.

Data input errors (Too many label points)

Label points (often referred to as a "centroid") are associated with polygons and are used to attach the attributes for the polygon. The labels have to be placed in a polygon (either manually or automatically) with the correct attribute (or identification number) attached. Errors may occur when polygons have more than one label or too many labels.

The correction of label errors requires either the removal, editing or addition of labels. It should be noted that some label errors may be due to existing dangle errors where two adjacent polygons appear as ONE polygon simply because of an undershoot (on the common arc of the two polygon boundaries). In such a case, the undershoot should be corrected.

Data input errors (Sliver polygons)

Sliver polygons

, also referred to as

Spurious polygons

, are polygons that result when two lines are digitised twice along the same boundary and don't exactly coincide (because of digitising inaccuracies - it is impossible to digitise two lines that coincide exactly!). This can occur as a result of digitising the same line twice or overlaying two layers (containing the same line which has been captured separately on two different occasions).

The solution is to either:

	be more careful in digitising (enter line once!),
	try to use existing (already digitised!) lines for boudaries that are common to two or more features (ie. soil types and river boundaries, vegetation and lake boundaries, etc.), or
	eliminate all polygons with an area less than a specified tolerance value

Data input errors (Other)

Other data input errors are also possible. Some can be identified and corrected (either automatically or manually) and others are very difficult, if not impossible, to detect.

Such errors may include:

Missing arcs

Arcs are missing. They may be detected by studying the original map (if they exist on it), otherwise they may be impossible to identify. Any missing arcs detected must be captured and integrated into the appropriate dataset.

Too many vertices

Having too many vertices within an arc may increase storage space and cause processing problems. Vertices that are not required (ie. multiple vertices on a straight line) can be eliminated and vertices that are too close together can be thinned out by specifying a "weed" tolerance value.

Weird polygons

	Inventory available forest and mineral resources.
	Obtain flora and fauna requirements.
	Determine water availability and quality.
	Examine extent of disease (ie. dieback).
	Which resources are protected or in short supply (ie. national heritage listing)?
	Evaluate how resources are currently being exploited.
	Predict how availability and quality of these resources will change in the next 10, 20 or even 100 years.
	Assess conflicts with environment, quality of life, populated areas, visual impact, etc.
	Comply with local, regional and national regulations and legislation.

	decision-oriented reporting
	effective processing of data
	effective management of data
	adequate flexibility
	a satisfying user environment

	Integrates spatial and other (aspatial) data across a diverse range of applications
	Identifies connections between activities based on geographic proximity
	Manipulate and display geographic knowledge
	Provides access to administrative records
	A tool for enhancing decision making
	Increases ability to model science and management problems
	A catalyst to further development

	zoning - urban and regional
	subdivision planning and review
	environmental impact assessment
	water quality management
	maintenance of land ownership
	land valuation and taxation
	town planning schemes

	transport route planning
	street address matching
	location analysis, site selection
	disaster planning and evacuation usage and planning of roads, sewer and water reticulation, drainage, telephone lines, gas and electricity, etc..

	population distribution and forecasting
	demographic marketing and analysis
	monitoring of patient health
	epidemiology
	police crime statistics and monitoring
	census information public services and access

	GIS World - http://www.geoplace.com
	Geo Info Systems - http://www.geoinfosystems.com

	The traditional analog map has been in use for centuries!.
	Divided into physical map sheets
	Based on the communication paradigm - emphasis is on visual communication

	Maps are stored in digital form on computers to create a cartographic database
	Still based on the "analog" map concept
	Has greatly enhanced the map-making process and the production of various types of maps.

	A geographic database involves much more than a cartographic database (ie. much more than simple a map or maps)
	The emphasis is on the structure and management of data and their relationships
	Based on the analytical paradigm - focus is on analysis
	The concepts of GIS extend far beyond the map!

	stores pixels (picture elements) in an image and cells in a grid
	most closely represents continuous data

	stores points, lines and polygons to represent the features
	most closely represents object-based data

	values are divisible and multiplicative (an absolute scale defined around zero (0))
	eg. rainfall of Region 1 is twice that of Region 2

	values are additive and subtractive (on a relative scale)
	eg. Region 1 is 10 degress warmer than Region 2

	values establish order (ranking) only
	eg. Region A is most suitable (eg. value of 1) and Region B least suitable (eg. value of 5)

	technology (hardware and software)
	people
	data

	numbers establish identity only
	eg. lot numbers, postal code zones, etc.

	A grid GIS is based on the raster data model. The foundational unit of storage is the *grid cell*. Square grid cells are most commonly used to store grid data.
	Each cell specifies the type or value of an attribute. Only one value is stored per grid cell. Note that if no data is recorded for that grid cell, then a value must still be stored - usually a zero (0) or a special "no data" symbol. A group of contiguous cells having an identical value is referred to as a *region*.
	Data is arranged in a matrix and located by coordinates which relate to the row and column numbers. Generally speaking, grid cells (matrices) are easy to store, manipulate and display.

	Center of the cell?
	Top-right-hand corner?
	Bottom left-hand corner?

	Dominant feature of cell?
	Most important feature of cell?
	Mean value of features within cell?

	corporate (includes organisational and personnel issues)
	application
	technical (includes input, management, analysis, and output components)
	technology

Geographic Information Systems

Rough lecture notes

Week 1

What do I do with a GIS?

What is a geographic information system?

GIS as an information system

Advantages of GIS

Areas of application of GIS technology

GIS-related disciplines

Rough lecture notes

Week 2

General GIS references: taken from:

Basic and practical introductions to GIS

GIS magazines

Solving real world problems

How do we represent the real world?

Steps of the generalisation process

Data representation with GIS

Essential GIS components

Structure of geographic data

GIS data models

Rough lecture notes

Week 3

Scale of measurement

Grid GIS

Grid database and layering

Grid resolution

Cell values

Week 4 Lectures

Vector functionality

Data generalisation and abstraction

Buffering

Describing a location on the Earth's surface

Data Quality Measurement and Assessment

1. Data Quality

2. Accuracy

2.1. Spatial Accuracy

2.2. Temporal accuracy

2.3. Thematic Accuracy

3.1. Spatial Resolution

3.2. Temporal Resolution

3.3. Thematic Resolution

4. Consistency

5. Completeness

6. Summary of Important Points

7. References and Bibliography

Week 6 Lectures

Data Types and the NSDI

Process of data capture

Manual data capture

Global positioning system

GPS Accuracies

Accuracy of digitising

Data input errors (Dangles)

Data input errors (Pseudo nodes)

Data input errors (Too many label points)

Data input errors (Sliver polygons)

Data input errors (Other)

Tolerance values

Data Capture - Scanning

Data Capture - Aerial Photography

Data Capture - Satellite Imagery

Week 7 Lectures

Network analysis

Elements of a network

Example of attributes used in network analysis

Main network functions

Applications for networking

Framework for GIS selection, implementation and management

Corporate aspects: integration, commitment

Corporate aspects: people issues, payback

Application aspects: methodologies, data availability

Technical aspects

Technical aspects: data input

Technical aspects: data management

Technical aspects: data analysis

Technical aspects: data output

Other issues (Technology)

Costs of GIS

Benefits of GIS