LECTURE NOTES

INTRODUCTION TO GEOGRAPHICAL NFORMATION SYSTEMS

...................................................................................................................................................................2

WHO IS ROOT ANYWAY?..................................................................................................................4

ARC/INFO SYSTEMS..........................................................................................................................10

ARC/INFO - TOPOLOGY AND OTHER STUFF...........................................................................12

DATA INPUT.........................................................................................................................................16

GETTING ATTRIBUTE AND OTHER DATA INTO ARC INFO.................................................36

DIGITAL ELEVATION MODELS (DEM)........................................................................................43

SPATIAL DATABASE MODELS.......................................................................................................47

MAP DESIGN AND COMPOSITION................................................................................................56

DATA MODELS AND AXIOMS.........................................................................................................63

GEOGRAPHICAL DATA IN THE COMPUTER.............................................................................66

CURRENT TRENDS AND ISSUES IN GIS.......................................................................................70

RECENT DEVELOPMENTS IN GIS IN SA.....................................................................................73

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Introduction to Geographic Information Systems (GIS)

This lecture seeks to provide a brief introduction to the world of GIS and GPS.

It considers:

What is GIS?

What are the uses of these technologies?

Briefly considers applications in this area.

What is GIS?

GIS has been variously described and defined by authors.

Most commentators would agree that it is a system for the electronic input, storage, retrieval and analysis of spatially referenced data.

Shares many common features with other types of databases – e.g. the sort of database this University uses to record information about students – but with some important differences.

Data held in a GIS includes spatial and attribute data. That is it records information about what a thing is (its size, age, density etc.) as well as information on where it is.

- This permits analysis of spatial relationships in the distribution of phenomena.

How the data is held depends, in very large part, on the type of GIS being used. Two primary models – raster GIS and vector GIS.

A raster model tells what occurs everywhere - at each place in the study area, The vector model tells where everything occurs.

Concepually raster are the simplest available models.

Raster GIS are not necessarily inferior or superior to Vector GIS.

Each is best suited for particular purposes.

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Vector is best for most applications requiring great precision, e.g.

infrastructure

transport planning

land use planning

archaeological sites

terrain mapping

Raster is Best for:

remotely sensed imagery (aerial photos, satellite images etc)

3D modeling.

Advantages

It’s a universal technology. Once mastered – GIS can be applied to many areas of academic and business life – population studies/demography urban planning architecture economic development archaeology biology oceanography etc.

Can add significantly to productivity –

Continual production and updating of maps

Allows high level analysis – 3 Dimensional modeling - buffering - spatial selection

Allows the combination (or separation) of different layers of information.

Allows you to tie into other data sources, and query those data on spatial criteria.

Disadvantages

an expensive technology (400 K in the GIS lab)

expensive software

needs highly trained operatives (although this is generating considerable demand for skilled labour within the Spatial Information Industry)

data sets can be expensive to acquire and maintain

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Who is Root Anyway?

The Network

We run our ARC INFO software on a UNIX network consisting of 10 IBM RS 6000 workstations.

In many ways, UNIX is just another operating system.

first developed in late 1970s as a common system for a large number of brands of computer (one system)

it is an open system, it is more powerful (quicker etc) than DOS and equivalent

The network is set up to work as an integrated unit. There is one file server – called McCaskill and nine other workstations. (Workstations are just grown up PCs. However, you are not working on a PC and you should never, for example, switch the workstation off).

The other nine workstations go by names such as: Zelinsky Taylor Reclusé de la Blache (Can you name the common element here?)

The fileserver is the centrepoint of the network. All the software licences sit on the fileserver and it also undertakes all the file storage.

So, when you open ARC INFO (or any other application) your workstation contacts McCaskill and downloads a version of the software. If you download ARC INFO @ 200mb a portion will stay in RAM (and the workstations have 96 mb of RAM) and the rest will be written to the disk in temporary storage.

So, it can take some time to initiate an application session. Exercise patience.

If there is heavy demand on the system – eg in the middle of the tute, the fileserver might appear slow. This is because all 9 users are pulling information off the fileserver, writing files to the fileserver and otherwise clogging up the system.

When you undertake a function – eg building a coverage or joining an item – that work is undertaken on the workstation on which you sit.

However, when you go to save a file it is saved on the fileserver.

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Human Ware The network is maintained by a systems administrator (in this case a really nice bloke called David Schroeder).The systems administrator is in charge of creating new accounts, deleting old accounts and keeping the hardware and software running.

In any network, someone has to do the “housekeeping” backing up files removing old software installing new software ensuring there is diskspace available

On a PC you do it yourself.

On a network it is far more complex and there are multiple users, all with different permissions. There are also questions of confidentiality and security. The systems administrator is the one to deal with all these issues.

In most networks, the top level of the directory tree is called the root directory – often USR01. Those with access to the root directory have the ability to do all sorts of things including: delete accounts, look in other peoples accounts etc.

In our system we have a setup such that Andrew Beer isUSR01/staff/andrew

and you would be:USR01/student/tiffinglis (and all your directories would appear under this eg USR01/student/tiffinglis/firstlesson and USR01/student/tiffinglis/project).

So the root level sits at the top of the directory tree and is usually only held by the systems administrator (which is why hackers always prize root access).

Your Account Each of you will be given an account - with a password and an account number.

Your account gives you access to the system and also gives you a certain amount of disk space on the fileserver. If you do a lot of processing, you can end up using up all your allocated space. Hence the desirability of knowing how to delete files.

On the ‘Net David is in the process of completing a Webpage for the GIS lab. He has also produced a new desktop for the workstations (the screen you see when you first start a session).

From that desktop, Netscape is available and it should automatically place you in the GIS Lab’s Webpage. Look around it, there is some useful stuff.

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Licences and Closing Off Applications

You may have worked out by now that ARC INFO is a licensed piece of software.

We have 11 licences

So every time you log on and start an ARC INFO session, you initiate and use up one of those licences. The same goes for ARC VIEW. In any session you can run more than one ARC INFO licence at a time and you can have more than one application going at a time – eg ARC INFO, ARC VIEW, SPSS etc.

However, if all 11 licences are gone, someone coming to start a session cannot do so.

It is therefore most important that you log out properly and exit from whatever software you are using.

Twelve Helpful Unix Commands

Command: ls

Usage: The ls command lists the files and subdirectories of the current directory you are in. You can also add some arguments to the ls command to make it more meaningful.

If you type "ls -F" it will append a forward slash to the subdirectory names so you can easily distinguish them from file names.

If you type "ls -a" it will show all hidden files such as .profile files.

If you type "ls -l" it will show detailed information about each file and directory, including permissions, owners, size, and when the file was last modified.

Example: Type "ls -al" at a telnet command prompt and it will return your file names including hidden files, and a forward slash will be inserted in front of subdirectories.

DOS Equivalent: dir

2.Command: mkdir

Usage: The mkdir command makes a new directory. Simply type "mkdir directory" at a telnet command prompt and replace directory with the name of the directory you want to create.

Example: Type "mkdir temp" at a telnet command prompt to create a new directory called temp.

DOS Equivalent: md, mkdir

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3.Command: cd

Usage: The cd command changes the current working directory you are in on the Server.

Example: Simply type "cd temp" at a telnet command prompt where temp is the name of a directory below the current working directory.

DOS Equivalent: cd

4.Command: rmdir

Usage: The rmdir command deletes (removes) a directory. Simply type "rmdir directory" at a telnet command prompt and replace directory with the name of the directory you want to delete.

Example: Type "rmdir temp" at a telnet command prompt to remove a directory called temp.

DOS Equivalent: rd, rmdir

5.Command: cp

Usage: The cp command copies a file to a new location or filename. Simply type "cp filename copyname" at a telnet command prompt and replace filename with the name of the file you want to copy, and copyname with the name of the new copy.

You can also add a directory structure if you want to copy the file to a completely new location. Simply type "cp filename directory/copyname" and replace directory with the name of the directory in which you want the new copy placed.

Example: Type "cp home.html index.html" at a telnet command prompt to copy a file called home.html to a file called index.html

DOS Equivalent: copy

6.Command: mv

Usage: The mv command renames a file or moves it to a new location. Simply type "mv oldfile newfile" at a telnet command prompt and replace oldfile with the name of the file you want to rename or move, and newfile with the new name of the new file.

You can also add a directory structure if you want to move the file to a completely new location. Simply type "mv oldfile directory/newfile" and replace directory with the name of the directory in which you want the file moved.

Example: Type "mv test.conf test.old.conf" at a telnet command prompt to move a file called test.conf to a file called test.old.conf

DOS Equivalent: move, rename

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7.Command: rm

Usage: The rm command deletes (removes) a file. Simply type "rm filename" at a telnet command prompt and replace filename with the name of the file you want to delete.

Example: Type "rm test.html" at a telnet command prompt to remove a file called test.html.

DOS Equivalent: del

8.Command: grep

Usage: The grep command finds lines in files that match specified text patterns. Simply type "grep "text" filenames" at a telnet command prompt and replace "text" with the word or phrase you want to search for, and replace filenames with the files you want to search in. To search all files in the current directory, simply replace filenames with * .

Example: Type "grep "for sale" *" at a command prompt to find any files in the current directory that contain the text "for sale" in them.

9. Command: tar

Usage: The tar command copies a file or files to or from an archive. To put all the files in a directory into one tar format file, simply type "tar cvf tarfile directory" at a telnet command prompt and replace tarfile with the name you want to call your archived file, and replace directory with the name of the directory that contains the files you want to tar.

To extract the files from a tar format archive, simply type "tar xvf tarfile at a telnet command prompt and replace tarfile with the name of the archived file you are extracting.

Example: Type "tar cvf html.tar public_html" at a telnet command prompt to archive the files in the public_html directory to a tar format file called html.tar.

You could also type "tar xvf html.tar" at a telnet command prompt to extract into the current directory the files in the archive html.tar.

10.Command: passwd

Usage: The passwd command is used to change your password on the server. Simply type "passwd" at a telnet command prompt and you will be prompted to enter a new password. You will then be asked to retype the password to ensure you made no typos.

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11.Command: chmod

Usage: The chmod command is used to change the permissions on files or directories.

12.Features:

The Login "shell" you use on the server has a couple of handy features enabled by default.

Tab Completion

The tab completion feature allows the tab key to complete a command or filename. Eg: typing br at the prompt followed by a tab will try to find a command that starts with br. If there is more than one command starting with br, then another tab will list all commands starting with br, so you can add the next letter of the command you seek then press tab again. Tab completion also works on filename, where a command can be issued then tab will attempt to complete the command based on the files in the current directory - eg: rm pu tab will attempt to find a completion for pu. As with commands, if there is more than one option starting with pu the second tab will display all possible candidates.

History

The up-arrow/down-arrow keys will toggle through your command-line history. This means you need not type the same command over and over again, which can be handy if you are typing long commands.

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ARC/INFO Systems

ARC/INFO is made up of several subsystems - each handles a different group of functions.

MAJOR MODULES

ARC - the overall GIS manager. It is not a graphic module. Use it to:

Generate and manage coverages in the database. Convert and import data from other formats. Manipulate spatial and other data in one or more formats. Create new data relationships between coverages.

ARC MACRO LANGUAGE (AML)

The programming language of ARC/INFO. You can use AML directives in any subsystem - either interactively or in

programs. Use it to build programs that incorporate arc/info commands, as well as creating

on-screen menus.

ARCTOOLS

Collection of AML-based tools - that operate through a graphical interface. Use for common functions and operations.

TABLES

Tabular management system that uses INFO files to create store, edit and query tabular data.

ARC/INFO LIBRARIAN

Data management system to create and manage map libraries.

ARC PLOT

A graphic module for map display and query, used to:

Display spatial and attribute data for one or more coverages. Query spatial data and attribute characteristics. Create on-screen displays and maps, generate plot files.

ARCEDIT

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Interactive graphics editor used to digitise and edit one coverage at a time. Correct errors in spatial and attribute data.

Other bits and pieces of relevance here

ARC/INFO tin - for surface modelling. ARC/INFO grid - for applications requiring raster-based grid-cell analysis. ARCSCAN - for scan digitising. ARCSTORM - for feature oriented geo-relational database managements. ARCVIEW - a user-friendly front end to ARC/INFO. Not capable of spatial

analysis, but capable of queries etc. Help - part of ARC itself and a very useful guide and introduction to the software.

SOME BASIC CONCEPTS FOR ARC/INFO

The coverage is the basic unit of storage in ARC/INFO. Digital version of a single map sheet and generally contains one type of map

feature. Includes both locational data and the attribute data for features of a geographic

area. A coverage is stored as a directory which contains related files describing the

location and attributes of features. Each coverage represents a separate class or theme of data.

LINKAGES

Each individual feature in a coverage is given a unique numerical identifier. It has a unique location (spatial data) and attributes (descriptive data).

The coverage data model links spatial and aspatial data. The identifier is stored in both the co-ordinate file and the attribute file. This

results in a strict one-to-one correspondence between spatial and attribute data.

GEOGRAPHIC DATABASES

Is the core of a GIS (hence jobs). Its completeness and accuracy affects every aspect of the systems functions. The database is a collection for features in the same part of the globe and organised

in such a way as to serve one or more purposes. To be efficient different types of data are stored in different ways (and linked by the identifier discussed above). Attribute data are stored in tables and managed by the RDBMS. Spatial data are stored in files and managed by the GIS software.

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ARC/INFO - Topology and other stuff

Last week we looked at the basic structure of ARC/INFO with its various sub-systems and how the software links features through individual identifiers, coverages etc. Today we extend the coverage of this material.

ARC/INFO does not attempt to reproduce reality; instead it creates an abstract model based on a limited number of features - points, lines, areas and text.

BASIC FEATURES

Feature What they Represent ExamplesTypes

Points PointsSingle x,y locations Buildings, Trees etc

Arcs Linear Features Roads, rivers,String of x,y locations railways

Nodes single x, y locations traffic lights etcoccurring at intersection of arcs

Polygons areal features land, lakes homogenous areas enclosed by a boundary

EXTENDED FEATURES

Routes Linear features composed transport routesfrom ARCS, with a linear (eg buses)measurement systems

Regions Areal features composed of wildlife habitats etcone or more polygons

Annotation Descriptive Text street names etcAssociated with other geographic features

These are the way features are “modelled” in ARC/INFO

But the data is in fact stored as a series of tables - as a group referred to as feature attribute tables.

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When ARC/INFO constructs topology for a coverage, topological and geometric properties are defined and stored in a file called a feature attribute table. The contents of feature attribute tables differ, but they have some basic characteristics in common:

the feature attribute tables are INFO data files; each feature in your coverage occupies one record of data in the table; you can have more than one feature attribute table for a set of spatial data. Eg you

could have polygon and attribute data in a single coverage (and therefore two types of table).

In reality you find:

Point Attribute Tables - PAT Arc Attribute Tables - AAT Node Attribute Tables - NAT Polygon Attribute Tables - PAT

The Feature Attribute TableEach record in the feature attribute table contains: the record number (RECNO) a unique identifier for the feature called Cover# (assigned by ARC) and another feature identification number called Cover-ID (assigned by the user)

(see figure)

“Cover” always matches the name of the coverage it is associated with - eg parcels in the associated figure.

RECNO is the internal record number assigned to each row of data in any INFO data file, including feature attribute tablesIt is a unique row identifier - can be used for database query RECNO and Cover# always match in a feature attribute table

Cover# is a unique number assigned by ARC/INFO (and you can begin to see why they talk about building coverages - eg Build command) values are sequential - start with one and go up by one values change each time data are added or deleted from the coverage always match the RECNO also called the internal number

Cover-ID user defines the cover ID needs to be a unique number - usually based on some coding system can be any positive integer values do not change each time data are added or deleted from coverage values do not have to be sequential, but must be unique also called USER-ID and ID

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POINTS

Geographic features occupying a single location can be thought of as points.

ARC/INFO records and stores coordinate information about points as they are entered into the system. Co-ordinate files maintained in ARC EDIT - cannot be displayed or edited using table management commands. single pair of x,y coordinates no length or area (obviously)

The Point Attribute Table (PAT) is created when topology is constructed for a point coverage it contains four standard columns of attribute data (called items) area (always zero), perimeter (always zero), cover#, and cover-id. point and polygon data have the same table format so - point and polygon data

cannot be stored in the same coverage. Can differentiate a PAT for points from a PAT for polygons, as points have no area or perimeter.

Also note - ARC/INFO attributes are always to the left of the cover ID and user-defined attributes are always to the right of the cover-ID.

ARCS

Linear features.ARC/INFO records and stores information about arcs as they are entered into the system. vertices - a string of x,y pairs define the ARC and its shape an arc begins and ends with a node intersecting arcs connect at a node the length of the arc is defined by the coordinate system can have only 500 vertices per ARC (automatically converted to a node)

The Arc Attribute Table is created when topology is constructed it contains seven standard columns of attribute data called ITEMS users can attach additional descriptive data

Standard attributes are:FNODE# - internal number of node where ARC beginsTNODE# - internal number of node where ARC endsLPOLY# - internal number of polygon to the left RPOLY# - internal number of polygon to the rightlength - length of each arc in coverage coordinatescover# - internal feature numbercover-id - user defined feature number

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NODES

Nodes are distinct features as well as occurring at the end points of arcs - eg stoplightsCoordinate data for nodes is arbitrarily assigned by ARC/INFO to one of the associated arcsAttribute data stored in a Node Attribute Table

NATs have three standard items - ARC# - the internal number of one of the arcs that intersects at the node COVER# - internal feature identifier COVER-ID - user defined feature identifier

POLYGONS

ARC/INFO records and stores data about polygons as ARCS (ARC file) and label points (LAB file). A polygon identifier, entered as a label point, is linked to the identifiers of the ARCs that enclose the polygon, which are in turn tied to coordinate data. one or more arcs define the polygon boundary a single label point is located in the polygon

Attribute data stored in the Polygon Attribute Table Four standard items - see discussion above and the figure

Universe Polygon The first record in the PAT represents a special polygon - the area beyond the

coverage.- its area - reports the negative of the sum of all polygon areas in the coverage- its perimeter - perimeter of the bounding polygons of the coverage- cover# - always the internal feature numbers 1, and (rec no 1) - cover-id - always zero 0

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Data Input As with any computing, GIS depends on data - but GIS has special data requirements that distinguish it from, say, a spreadsheet.

Data input is a major bottleneck in GIS technology:

often 80% or more of project costs.

it is labour intensive, tedious and error prone.

danger data collection might become an end in itself.

all GIS applications need to find ways to reduce costs and increase accuracy.

Need to automate input as much as possible but:

automated input can create editing problems later.

maps and other documents often have to be redrafted to meet quality standards of automatic input.

A lot of research has gone into devising better input methods, but few reductions in cost have been realised.

Sharing of digital data one way around bottleneck.

Data input in GIS involves both locational and attribute data

Locational data is encoded as coordinates on a particular Cartesian coordinate system

Source maps may have different projections, scales.

Several stages of data transformation may be necessary to bring all data to common coordinate system.

Modes of data input

Keyboard entry.

Manual locating devices

- user manipulates a device whose location is recognised by a computer eg. a digitiser.

- automated devices

- automatically extract spatial data from maps and photography eg. Scanning.

- voice input - has been tried - needs recalibration.

- conversion - from other digital data sources.

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Digitisers

Most common device for information from maps and photos.

Hardware

Position of an indicator over the surface of the digitising tablet is detected by the computer and interpreted as pairs of x4 coordinates.

- could be a stylus or cursor.- range from 25 cm x 25 cm to 2 m x 1.5 m (Cost $500 - $5000.)- the tablets use a grid of wires embedded in the tablet to generate a magnetic

field, which is detected by the cursor.- accuracy better than 0.1mm (better than human hand).

To digitise

Map is affixed to digitising tablet.

3 or more control points are digitised for each map sheet- eg. intersections - major points, peaks, points on coast.

Their coordinates in the coordinate system (planar, lat/long etc) to be used in the GIS are known and the control points and used to calculate the coordinates to the final system.

- more control points the better.

Map contents are captured by operator pointing and clicking.

Problems with digitising maps

Problems arise, as most maps are not drafted for the purpose of digitising:

paper maps unstable, each time the map is moved from the digitising table the reference points must be re-entered.

if map is stretched or shrunk in interim - you are stuffed.

map errors are entered into the GIS.

the level of error in GIS is directly related to level of error in source maps.

Maps are meant to display info and may not accurately record information.

- eg. railway, highway and power lines through a mountain pass.

User error causes overshoots, undershoots (gaps) and spikes

- fatigue, boredom.

Editing errors from digitising

Some errors can be corrected automatically - small gaps, spikes etc.

Error rates depend on the complexity of the map.

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Scanners

Video-scanners

Essentially television scanners with appropriate electronics to create a computer readable data set.

can be black and white or colour.

extremely fast (under 1 second).

relatively cheap ($500 - $10000).

produce a raster array of brightness values that are then processed like any other raster array.

often have poor geometric and radiometric characteristics - eg. spatial distortion and brightness.

difficult to use for map interpretation because of these problems.

Electo-mechanical scanners

more expensive and slower - $10,000 to $100,000.

often attach map to drum and have a single light source.

- creates a single column of pixels, then on to next.

- results in less distortion.

- can scan down to 3 x 10-3 mm.

Requirements for scanning

documents must be clean.

lines should be at least 0.1 mm wide.

complex lines increases chance of error.

text could be scanned as features.

contour lines cannot be broken with text.

automatic figure recognition not easy (contour, road).

special symbols (eg. marshes) must be recognised and dealt with.

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Criteria for closing modes of input

The type of data source:

- images (eg. air photo) favour scanning.

- maps can be scanned or digitised.

Database model of the GIS:

- scanning easier for raster, digitising for vector.

Density of data:

- dense lines make for difficult digitising.

Expected applications of the GIS.

Integrating different sources.

A Format - many different format standards exist for geographical data (ie. way stored and represented).

Some have been established by public agencies, some by vendors of software

- a good GIS should take data in a variety of formats but there are inevitably problems.

B Projections - different types of projections.

- Mercator, Conicete

A good GIS should be able to convert from one projection to another.

A GIS with data from different sources often contains data in more than one projection.

C Scale

Data may be input at a variety of scales.

Although a GIS will most likely not store scale as an attribute of the data set, scale is an important indicator of accuracy.

Maps of the same area at different scales will often show the same features.

- eg. features are generalised at smaller scales, enhanced in detail at larger scales.

Variation in scales can be a major problem in integrating data

- eg. scale for most input maps for GIS is 1:250,000 but Geological features are 1:7,000,000.

- if the 2 maps are combined might think the geological layer equally accurate. In fact it is so generalised it is useless.

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Data sources.

Primary collections (getting own data)

We commonly use a sample to capture the variation present in a larger universe.

- all variation in time and place.

- this can affect how we collect data - eg.

- random sample.

- systematic sample (according to a rule) - rule creates no bias.

- stratified sample (researcher knows universe contains significantly different sub-populations and samples within each sub population to get a random sample).

When using secondary data sources, useful to obtain info on how data collected.

Errors in data collection

Due to:

source errors (because of errors in source).

processing errors (added during data capture and storage).

occur when data taken from computer.

arise when various layers of data are combined in an analytical exercise.

Errors in sources

Very common in non-mapped source data - eg. lot descriptions.

Can be caused by misinterpreting aerial photography.

Occur because base maps relied on too heavily.

The data standard used in US allows error of .5 mm on 1:24,000 = 12 m on ground.

Boundaries create errors

- lakes fluctuate widely in area, yet have permanently recorded shores.

- boundaries of soil type are actually zones of transition.

Classification errors

Common when tabular data entered on a map.

Simple typing errors may be invisible until presented graphically;

- flood plain soils on hill tops, etc.

More complex classification errors could be due to poor sampling originally.

Manual data input can introduce another set of errors.

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Map Projections and GIS

The ability to transfer data from maps to GIS and back is important as: -

maps are a common source of input data for GIS.

and often maps are in different projections requiring transformation of one or all maps to make coordinates compatible.

this will mean that the GIS will need the mathematical expression of one or all maps to make coordinates compatible.

often GIS is used for projects on a global or regional scale, so co-consideration of earth’s surface is necessary.

monitor screens are like a flat sheet of paper;

- thus need to provide transformations from the curved surface.

Map projections allow us to display locations on the curved surface on a flat sheet of surface.

Mathematically it is a process of transforming global location (Q, ) to a planar position (X, Y) or (r, O)

For example the transformation for a Mercator projection are:X = Y = loge tan (TT/4 + O/2)

These projects are inherently flawed

angles, areas, directions, shapes and distances become distorted when transformed from a curved surface to a plane.

all of these properties cannot be kept undistorted in a single projection.

- usually the distortion in one property will be kept to a minimum while other properties become very distorted.

the Mercator projection- is a type of conformal (Orthomorphic) projection- over small areas preserves the shape of regions- however some areas are enlarged and others shrunk.

Equal Area - or EquivalentAll representations of areas are equally preserved so that all areas on earth

equally preserved.- cannot be conformal, so shapes distorted- Third World

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Equidistant - Conic

Cannot make a single projection over which all distances are maintained

Maintain distances relative to one or 2 points only- eg Macquarie map of Australia.

Those 3 types of projection - Mercator, Conic and Equal Area are ways of representing of the earth’s shape in 2 Dimensions. The trouble is, of course, that the earth isn’t 2D which is why all sorts of corrections are needed.

Mathematically speaking - GIS and geographers can deal with this problem in 3 ways:

1. The ostrich method:- assume the earth is a plane (use no projection)- used only for general maps or maps of small areas- at scales larger than 1:10,000 planar representation has little effect on accuracy- usually assumed when working with air photos. This, however, introduces an error into the representation

2. The School Room method:- earth is a perfect sphere- close but not close enough

3. The Exact method -- Earth as spheroid - models the fact that earth is 0.3% bigger around the tummy than between the poles.- geologists and aeronautical engineers have developed complex models of the earth’s surface but at the scale we operate on the difference between the sphere and spheroid is small.

Importantly, satellite systems such as GPS, can determine latitude and longitude at any point on the earth’s surface to a fraction of a second.

- we can now observe otherwise unknown errors due to the fact that map projections were based on a faulty shape of the earth.All of the discussion is concerned with the accuracy of data representation.

The different types of projection all introduce distortion when applied to a GIS

Hopefully we should all recognise that we can’t just take a map or aerial photograph from a wide area and input the data into a GIS as if the relativities evident in the photo corresponded with geographic location.

- the different types of projection can be more or less appropriate for particular locations - given their position on their earth.

For eg American states have developed State Plane Coordinates which provide Plane references that are simpler than the standard national Transverse Mercator for the US, and may better represent that State (area, shape etc.). Interestingly, states use 2 or more planar zones depending on their size, which

are referred to either a transverse Mercator projection or a Lambert Conic projection (TM suits N-S states/LC suits E-W states).

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Summary

We need to understand maps if we are to use them as a data source for GIS.

Because the earth is in 3 dimension and rather unusual in shape, there is scope for error in determining location

The larger the region examined (the smaller the scale) the more important the differences between 2 dimensional and 3 dimensional representations

The different types of map projections all contain different distortions - cannot be resolved

For any projection it is possible to convert the global position into a planar position (look up for each one)

Which allows us to take the data into the GIS.

As complex as the Earth’s shape is, GPS will allow us to determine our location on the globe with great accuracy.

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The Relationship between Maps and GIS - GPS

Systems and Applications(or why we should love the US Military)

GPS - Global Positioning Systems

GPS is based on a series of satellites (Navstars)

they transmit a signal, the user on the ground receives it via receiver

there are a number of satellites (about 29 of which 26 or so are operational)

the receiver on the ground picks up the signal from a number of satellites, calculates the time taken to receive the signal from each and from that calculates location

GPS was built to guide US military, the signal of the satellites was encoded (so opposition couldn’t use it) called P-Code and has an accuracy of approx 10m

pressure from civilians led to the release of a degraded signal called CA code (coarse acquisition). 95 % of the time the receiver will be within 100m of the true position.

100 m accuracy is not too good. Civilians have therefore developed Differential GPS or D-GPS.

there are a number of different sorts, but work by comparing the signal received at two separate ground stations from which it is possible to establish the distance between them.

this occurs because any error received at one is exactly the same as the error at the other. Doesn’t matter what the error source is - eg clock error, selective availability.

this establishes distance between the two but not the position. Find position by setting up a reference station on a known point - eg trig station. By comparing the position determined by the GPS and the position of the known point it is possible to derive an error signal. This can then be subtracted from the signal received by any mobile unit that is capable of receiving the same GPS signal from the same set of satellites.

can use either CA or phase differential GPS (where they compare the phases of one of the GPS signals). In theory can be accurate to within 2mm!!

often a radio-link is established between the roving and static receivers so the differential information is transmitted in real time. The reference receiver has to decode the GPS signal, calculate the error and retransmit to the rover. As error signal is constantly changing, it is important that the differencing is done on signals transmitted at same time. To overcome this rover needs to store signals for a few seconds for comparison.

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or can go for post-processing mode (stores both signals)

increasingly using satellites for the link as the VHF transmitter can lose signal.

there are practical problems in running a D-GPS. Eg someone nicking the static station!!

operators are now setting up D-GPS systems and selling the service. Operator sets up and runs the base station, the service is then sold via bulletin boards or via decoding encrypted signals.

eg Land Information Centre in NSW - uses GPS corrections sent by landline to Perth then uplinked to an Optus Satellite where broadcast over Australaia (cost $200 per day)

AUSLIG provides a similar service called AUSNAV

Issues.

cost of units - differential and non-differential

legal issues - can GPS give a legal measurement?

need to recognise limitations - eg numbers of satellites

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The Geoid, Datums and Changes to our Datum

1. What is the Geoid and Spheroid?

2. Regional Spheroid vs Global/Geocentric Spheroid

3. What is a Datum?

4. Australian Spheroid and Datum.

5. How do we use the UTM Projection in Australia?

6. Changes to our Datum in Australia and its Benefits.

7. Coordinate effects of the new Geocentric Datum of Australia (GDA).

1a. What is the Geoid?

The Geiod is the equipotential surface of the Earth’s gravity field, which corresponds most closely with mean sea level and extends continuously through the continents.

Geoid

Surface

Spheroid

The relation between the geoid, spheroid and the earth's surface

The Geoid is a geometrically and mathematically complicated surface, which is impractical to use for mapping purposes.

- gravity field effected by land mass and topography.

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1b. What is a Spheroid?

A simplified figure of the earth is used as the fundamental reference surface for horizontal coordinates - this figure or reference surface is called the spheroid or ellipsoid.

a

b

a

An oblate spheroid flattened towards the poles, with semi-major axis a and semi-minor axis b.

Defined by its revolution about its semi-minor axis (b), the size of the spheroid is defined by its semi-major axis (a) and a function of flattening (f=1-b/a).

These ‘properties’ of the spheroid are chosen or calculated to produce a figure of best fit to the geoid.

2. Classes of Spheroids

Regional Spheroid: corresponds to a best fit to the geoid in a particular region or country. Enables geodetic data, collected on the physical surface of the earth to be reduced to the surface of the regional spheroid without the introduction of significant horizontal scale error.

The Geoid is an undulating surface, so the best fitting spheroid in one region is not necessarily the best fit in another.

eg. Australian National Spheroid (ANS)

Global Spheroid: corresponds to a best fit to the geoid over the whole earth - known also as a Geocentric spheroid.

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Surface

Global SpheroidRegional Spheroid

Global/Geocentric and Regional/Local Spheroids

The first internationally recognised global spheroid was the 1924 International Ellipsoid.

The next internationally recognised global geometric spheroid was derived from the inclusion of satellite observation - World Geodetic System 1984 (WGS84).

3. What is the Datum?

Known geographical coordinates, (latitude and longitude), comprise the datum and are referenced to the surface of the spheroid.

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Geographical latitude ( is the angle measured in the meridional plane, between the spheroid surface and the equatorial surface and the equatorial plane of the spheroid.

The geographical longitude () is the angle measured in the equatorial plane, from the Greenwich meridian to the meridian through the point of interest.

4. Australian Datums

Before 1966 coordinate discrepancies on maps - due to different datum’s used.The first continental wide Australian Geodetic datum (AGD66) - introduced in 1966.The AGD66 datum is based upon the regional Australian National Spheroid (ANS).

A re-adjustment was performed using Satellite data to produce the Australian Geodetic Datum 1984 (AGD84), which is also based on the ANS.AGD84 Geographical coordinates differ from the AGD66 coordinates by up to six metres.

5. Universal Transverse Mercator Projection (UTM) in Australia

Eastings and Northings on maps (projected) are derived from projected AGD66 and AGD84 geographical coordinates on the ANS, using a Transverse Mercator Projection.

UTM projection in Australia is Modified for our own use.Example projection information:

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Projection: UTM, AGD1984Grid: AMG84, Zone 54, CM 1410

The UTM projection is a cylindrical projection.

N

S

N

S

datum/surface features

projected surface features (map)Point of contact -

no distortion

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To minimise this distortion the cylinder is moved around the globe so as to move the line of contact from one position to another - by 60.

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Projected Map:

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Adelaide

Zone 54 Zone 55

CM 1410

CM 1470

03

60

030303

Australian Map Grid

The combination of the UTM projection, local spheroid (ANS), local datum (AGD84), and a few mathematical modifications to the projection and coordinate origin, to suite our local area (Australia) - is known as the Australian Map Grid (AMG).

6. The Geocentric Datum of Australia (GDA)

Australia has intended to move from using a local or regional datum to a global geocentric datum.

The Inter-governmental Committee on Surveying and Mapping (ICSM) initiated this conversion in 1994, for implementation by the 1st of January 2000.

The new datum is called the Geocentric Datum of Australia (GDA), and is intended to supersede the AGD66 and AGD84.

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Summary of changes include:

The Spheroid:

The Geocentric GRS80 Spheroid will replace the Australian National Spheroid (ANS).

The Datum:

The Geocentric Datum of Australia (GDA) will replace the Australian Geodetic Datum (AGD).

The Projection:

The Map Grid of Australia (MGA) will be used to display GDA geographical coordinates and will eventually supersede the AMG84 and AMG66.

Disadvantages:

Global datum may not provide a best fit to the geoid of the local area.

Difficulty of conversion.

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Advantages:

The GDA will ensure a geometric homogeneous, GPS-compatible datum.

The GDA will not suffer from edge or boundary related discontinuity’s.

For SIS users it will enable the exchange of compatible data at local, State, National and International levels.

GDA will be compatible with GPS and we will be able to determine positions directly on Australian maps, without the need for a series of coordinate transformations.

The International Maritime Organisation and Royal Australian Navy use geocentric datum’s for hydrographic charting - compatibility with topographic maps.

7. The Coordinate Effects of the Spheroid/Datum Change

One major implication of the Spheroid and Datum change - ground coordinates will differ from their predecessors by approximately 200 metres in a North-Easterly direction.

Spatial analysis overlay problems with incompatible coordinate data - GIS.

Map solution for Identification - is to incorporate GDA/MGA tick marks along the map boundaries of existing maps.

Scheduled revisions of all Australian map series - Stamped MGA

SIS solution - digital data must be transformed.

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The effect of a 200 metre coordinate change on Australian maps

map scale shift on map (mm)1:250,000

1:100,000

1:50,000

1:25,000

1:10,000

1:5,000

1:2,500

1:1,000

0.8

2.0

4.0

8.0

20

40

80

200

Summary

Earth Surface - Too mathematically complicated to use as a reference surface.

Geoid - is a surface of best fit to the Earth’s figure based on the Earth’s gravity field - coincides most closely to mean sea level (through continents) - still too mathematically complex to use as a reference surface.

Spheroid - is a simplified mathematical surface or figure of best fit to the geoid and is used as a reference surface.

Datum - known geographical coordinates (latitude and longitude), are referenced to the surface of the spheroid - Local vs Global datum’s.

How we use the UTM projection in Australia - ‘composite properties’ (AMG) will be replaced by the Map Grid of Australia (MGA)

The adoption of the Geocentric Datum of Australia (GDA) will introduce a 200m coordinate shift on maps.

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Getting Attribute and Other Data into ARC INFO

This lecture is concerned with practical ways of inputting data into a GIS. As noted previously, data input remains one of the most significant barriers to any GIS project.

The type of data input strategy suggested in the text book suffers from two key problems:

you need to digitize all the spatial data

it suggests you input all attribute information through INFO. However, INFO is not user friendly, or quick.

The question worth asking is what are the practical ways of creating coverages? What data acquisition strategies are available? What are the relevant costs?

The Purchase of Data Sets

On way of acquiring information is to simply buy it! Certain commonly used data sets are available from commercial providers. For example, Mapinfo sells road centre lines as a “commercial product”.

It is worth noting here that many data sets are sold to universities at reduced prices

But in the USA, data does not belong to their equivalent of the Crown, it belongs to the people and is therefore provided free!

Other products commonly used include things such as the:

Retail Data Base – produced by the then Dept of Housing and Urban Development;

the Digital Cadastral Data Base (DCDB);

the Industrial Land Data Base;

the Census of Population and Housing (ABS);

the orthophotometric series of CD Roms produced for the Murray-Darling Basin Commission.

It should be noted these last sources are all from public sector agencies and sold (more or less) as commercial products. Eg. DCDB data is sold to real estate agents, valuers, etc. The Retail Data Base is sold to planners, local governments, etc. The Census data is sold to virtually everyone.

All these data sets include spatial data and attribute data

in most cases, data is made available in Arc Info compatible formats (either ARC INFO, Map Info or ARC VIEW) although in some instance the data has to be translated to these formats.

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Many of these data sets bring with them additional (non-paid for) treasures. Eg Census Collector boundaries, roads or whatever, and it is possible (but not legal) to obtain these other objects from the purchased data set.

It cost $1500 for the ABS postcodes

It is also possible to purchase the coverages or other data layers produced by public sector agencies – usually for the cost of transfer (which may still be considerable)

AUSLIG (Australian and Survey Land Information Group) will make the 1:250,000 digital map series available free to students for 1 year.

Purchase the Australian River Basins Data from AUSLIG for $70.

Obtain a coverage of biosphere types in Australia off the Web. It is therefore possible to get some information for free!

Environment Australia is currently involved in putting together a database that will act as an audit of Australia’s land environments – potentially, it too will be available in the future.

Public Sector Agencies are Large Holders of GIS Information

It is often (?) possible to gain access to the data they hold.

On some occasions it will be possible to gain access to privately produced coverages, such as those produced by planning consultants - eg Hassells produced a coverage of the mouth of the Onkaparinga that was used by one of our Honours students.

Local governments also hold substantial databases, as do the public utilities.

How Do You Know What Data is Available?

In South Australia, the South Australian Government publishes the Spatial Information Directory.

This is essentially a list of coverages generated by the major GIS agencies. It includes: What is held; When it was generated/updated; What area it covers; Who do you contact to gain access to it; How much it costs?

Some entries tell you the data is held, but that it is confidential. However, it provides an important starting point.

The Spatial Information Directory was last produced in 1995, and a new version, is about to be released. It is envisaged this new version will be maintained on the Web.

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Remotely Sensed Data

Remotely sensed data is an important, relatively cheap source of information. Cheap in the sense that the cost per unit area remains low, while the purchase price for the imagery remains high.

Aerial Photographs

Can be scanned into GIS systems and then corrected for parallax and other errors. Through the process of “rubber sheeting” the scanned images can be corrected for distortion and used as a source of information on vegetation cover, roads, housing etc.

Relatively cheap, roughly $50 per photo (was recently $7) and available at a variety of scales, often historical data sets available.

Satellite Imagery

Covers a much larger area. Relatively expensive – eg cost $1400 for a quarter scene of KI for Landsat Thematic Mapper.

However, much cheaper than any form of manual data input, particularly over the area being considered. Satellite imagery brings with it the ability to classify and reclassify the digital data. As well as window in.

Attribute Data

In most instances it is better to input attribute data through some other data base system (ie not Info). Info is too clumsy and slow.

Could use standard products like Microsoft’s Access or even Oracle.

Often far easier to add attribute data to an existing coverage through ARC VIEW, as far less demanding on the human ware.

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Raster versus Vector GIS

Data Models

Geographical variation is infinitely complex .

It would take an infinitely large data base to capture all of that complexity.

Data must be reduced to a manageable quantity - geographical variation must be represented in terms of discrete elements or objects

Rules used to convert geographical variation into discrete objects are referred to as the data model.

“a set of guidelines for the representation of the logical organisation of the data in a database.... (consisting) of named logical units of data and the relationship between them” (Tsicherizis and Lochovosky, 1977).

Two main models - raster and vector each suited for different purposes

Raster models divide the entire study area into a regular grid of cells in specific sequence:

usually from top left each cell contains a single value is space filling as every location in the study area correpsonds to a cell

in the raster one set of cells and associated values is a LAYER

Vector models used discrete line segments or points to identify locations roads or cities or rivers formed by joining line segments vector objects do not necessarily fill space as not all locations need to

be used

TO HIGHLIGHT THIS DIFFERENCE

A raster model tells what occurs everywhere - at each place in the study area.A vector model tells where everything occurs.

Conceptually raster are the simplest available models.

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Generating Raster Data Models

Consider overlaying a map - note example from figure

A raster database is created by coding each cell with values that represent the ground cover - lake, river, road etc. Each cell had a coded value.

In most cases the values assigned to each cell are written into a file, often coded in ASCII (file can be created in a spreadsheet, word processor, database or other digital information mode)

Then it is normally imported into the GIS so that the system can reformat it for specific processing needs

Can enter data on a cell by cell basis (what a job!) direct entry may be done within the GIS or into an ASCII file for importing each program may have specific requirements (eg IDRISI) tedious and time consuming - each Landsat image has 7.4 * 106 pixels

Can enter data from digital sources much raster data is already in digital form as images etc. However manipulation may

be needed to ensure the pixels coincide in each layer because remote sensing generates images, it is easier to use a raster GIS for this

purpose than other types elevation data is commonly found through responsible agencies -eg AUSLIG (the borrowing habit)

Cell Values

The types of values contained in cells in a raster depend on the reality encoded and the GIS

Different systems allow different classes of values including whole numbers, real values alphabetic characters

some systems only allow integers, others allow different types but restrict each separate raster layer to a single type - eg all integer, all real etc

integer values often act as code numbers which point to an associated table or legend eg 1 = cor

each pixel or cell is assumed to have only one value, this is often inaccurate, it is assumed that the predominant of two or three or more types is then encoded, or the value at the middle point of the cell

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The Vector Data Model

fundamental building block is the point objects are created by connecting points with straight lines, some systems allow you

to use arcs of circles areas are defined by sets of lines, the term polygon is synonymous with area in vector

GIS because of the use of straight line connections between points vector tends to dominate in transport, utility and marketing, both vector and raster

used in resource applications ARCs (boundary lines) are fundamental in vector GIS because -

- arcs have attributes with identify the polygon on either side

All of the boundaries that arise out of the encoded points are "enforced" into one layer of space, and areas cannot overlap, each boundary line therefore is common boundary between two areas.(just consider the scope for error!)

Areas can be stored in two ways - each polygon is stored as a series of coordinates - although most boundaries are shared between adjacent areas, all are input twice, once for each adjacent polygon- the two different versions of each internal boundary may not coincide - not commonly used- Arc storage is mostly used

every arc is stored as a sequence of coordinates areas are built by linking arcsonly one version of each internal boundary is input and stored

Database Creation

Spatial data is entered via digitised points and lines, scanned and vectored lines of directly from other digital sources

Database creation involves several stages- input of the spatial data (see above) - input of the attribute data (eg a data base)- linking the spatial and attribute data

Once the spatial data base is created much work is still needed - the typology must be "built - this involves calculating and encoding relationships between points, lines and areas - this area may be automatically encoded into tables of information in the database - data entry problems also appear in this stage (see forthcoming lecture on data entry etc)

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Digital Elevation Models (DEM)

Surfaces such as the earth’s surface are continuous phenomena rather than discrete objects.

To fully model the surface we need an infinite number of points. There are various ways of representing continuous surfaces in digital form using a finite amount of storage.

Digital Elevation Models (DEMs) are one way of representing surfaces.

What is a DEM?

Frequently used to refer to any digital representation of the earth’s surface.

usually used to refer to raster representations or regular grid spot heights.

Digital terrain model (DTM) is a more generic term for any digital representation of the earth’s surface.

The DEM is the simplest form of representation and most commonly used.

The resolution or the distance between adjacent grid points is the critical factor

10m resolution for Australia through NatMap

coverages of the entire globe available, including the ocean floor, at various scales.

Creation of DEM

Several different methods have been used:

Conversion of printed contour lines.

- existing plates used for maps are scanned.

- contours are tagged with elevations.

- finally, an algorithm is used to interpolate elevations at every grid point from the contour data.

Through photogrammetry.

an instrument calculates the parallax error of a large number of points.

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Uses of DEMS

Determining attributes of terrain, such as elevation, slope, aspect.

Finding features of the terrain such as drainage basins, watersheds, drainage networks.

Modelling - fires and floods.

Estimating Elevation

To estimate the elevation in a raster we need to know if the point of interest is exactly at a point in the raster or in between.

in the former - take the elevation from the database.

if the latter - need to interpolate elevation from surrounding data.

Can use the elevation from the nearest point but this leads to sharp changes in elevation halfway between points.

The usual approach is to fit a plane to the nearby raster points and use it to estimate elevation at any point. Since the plane will not fit through the all points a plane which minimises the sum of the squared elevation differences between the plane and the data of the nearby points is often used.

The equation of the plane is determined by:

Use the four nearest grid points (the neighbourhood or the 2X2 window). Define an origin in the middle of the window and assign neighbouring points the coordinates -1,-1; -1,1; 1,-1 and 1,1.

The elevation of the point is then determined by an averaging equation. (Precise maths available on request).

Estimating Slope and Aspect

Slope and aspect can be calculated from the fitted plane (see above).

Normally a slope or aspect map will display values generalised over areas instead of points (eg 10-15%). To generate these maps slope or aspect is usually determined at each raster point and then aggregated into polygons based on pre-defined ranges.

Determining Drainage Networks

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A raster DEM has sufficient info to determine general patterns of a water shed.

Essentially you can model waterflow based on volume of water, slope and other data.

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TINS

The triangulated irregular network is a significant alternative to the regular raster DEM.

The TIN model was developed in the 1970s as a simple way of building a surface from irregular spaced points.

First began to appear in commercial packages in the 1980s.

The TIN model

Irregularly spaced sample points can be adapted to the terrain, with more points in areas of rough terrain and fewer in smooth terrain.

An irregularly spaced surface is therefore more efficient at representing the surface.

In a TIN model the sample points are connected by a straight line to form triangles, within each triangle the surface is usually represented by a plane.

By using triangles we ensure that each piece of the mosaic will fit with its neighbours, that it will be a continuous surface and each triangles surface will be defined by the elevations of the three corner points.

It might be worthwhile using more complex polygons in some cases but they can always be broken down into triangles.

For vector GIS TINS can be seen as polygons having slope, aspect and area, with three nodes having elevation attributes and three edges with slope and direction attributes.

The TIN model is attractive because of its simplicity and economy.

In addition certain types of terrain are very effectively divided into triangles with plane facets - eg fluvially eroded landscapes.

However glaciated landscapes are not well represented - as triangles work best in areas with sharp breaks in slope eg along ridges.

Creating TINS

Despite its simplicity creating a TIN involves many choices.

How to pick sample points: in many cases these must be selected from some existing dense DEM or contours. normally a tin of 100 points will do as well as a DEM of hundreds.

At least three methods: finding surface specific points which are important in the land area eg peaks and pits. window on local areas and select (mathematically) the important points. optimise the fit between the DEM and the TIN.

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Spatial Database Models

The real world is too complex for our immediate and direct understanding and far too complex for capture within a GIS

We create models of reality that are intended to have some similarity with selected aspects of the real world.

Databases are created from these models as a fundamental step in coming to know reality.

Definitiona spatial database is a collection of spatially referenced data that acts as a

model of reality.

1. A database is a model of reality in the sense that the database represents a selected set or approximation of phenomena

2. These selected phenomena are deemed important enough to be represented in digital form

3. The digital representation might be for some past, present or future time period

Organisation FactorsOrganisations have mandates to preform certain tasks - these are the reasons they exist as organisations.

Organisations have different needs for data depending on their mandates or missions and the activities needed to carry out these activities. Mandates often help to identify and define entities of interest, requiring a certain

view of the world. What might seem at first glance to be the same data requirement in two different

organisations can actually be quite different when we look at a more detailed level?

eg foresters and the National Parks and Wildlife Service all need vegetation data, but the detail needed differs

Mandates affect designIn each organisation only certain phenomena are important enough to collect and represent on a database

Identifying the phenomena and then choosing an appropriate data representation for them is part of a process called database design.

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Fundamental Database Elements Elements of reality modelled in a GIS database have two identities:1. the element in reality - entity2. the element as represented in the database - object

A third identity that is important in cartographic applications is the symbol used to depict the object/entity on the database

Entity An entity is a phenomena of interest in reality that is not further subdivided into

phenomena of the same kind.- eg a city could be considered an entity and subdivided into component parts, but these parts would not be called cities but neighbourhoods etc- a forest could be divided into smaller forests

Object An object is the digital representation of all or part of an entity The method of digital representation of a phenomenon varies according to scale,

purpose and other factors.- eg a city could be represented geographically as a point if the area under consideration was the entire continent. The same city would be an area if we were looking at a database for the state.

Entity Types Similar phenomena stored in a database are identified as entity types. An entity type is any grouping of similar phenomena that should eventually get

represented and stored in a uniform way, eg roads, rivers, elevations, vegetation (eg all roads get stored and described in one way, all rivers etc).

Provides a convenient conceptual framework for describing phenomena at a general level.

Precise definitions should be generated for each entity type - helps identify overlaps between categories (eg veg and grasslands), helps clarify the content of a database.

The first step in the development of a database is to choose the entity types to be included, this is guided by the organisations mandate, can be as important as the actual data that is included.

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Spatial Object TypeThe second step of database design in to choose an appropriate method of spatial representation of each of the entity types.

The digital representation of entity types in a spatial database requires the selection of appropriate spatial object types:0-D - an object having a position in space but no length

a point1-D - an object having a length

composed of two or more 0-D objectsa line

2-D - an object having a length and a widthbounded by at least three 1-D segment objectsan area

3-D an object having a length, width, height, depth bounded by at least four 2-D objectsa volume

Attributes An attribute is a characteristic of an entity selected for representation.Usually non-spatial.

Attribute value The actual value of the attribute that has been measured and stored in the database.An entity type is almost always labelled and known by attributes

eg a road usually has a name and is known by its class - secondary road, tollway etc

Attribute values are often conceptually organised in attribute tables, which list the individual entities in the rows and columns of the attribute table.

Database model Is the conceptual description of a database defining the entity type and the

associated attributes (ie a description of how the database was put together, how the entities and the attributes were related etc).

After the database is constructed, the database model is a view of the database that can be presented to the user. Of use to the user because it tells the user about the databases abilities, limitations etc. - Eg are areas available as polygons or do they need to be constructed out of line

segments?

Layers Spatial objects can be grouped into layers, also called OVERLAYS, coverages and themes.One layer may represent a single entity type or group of conceptually related entity types, eg a layer may have only streams, or streams, lakes and swamps.

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Error and Error Propagation

Concept of Error and Accuracy

Error - deviation (or distance) between a measurement and the ‘true value’. - measures fitness for use.

Accuracy - the closeness of an observation to a true value (or one accepted as true).

Do disciplines that adopt the ‘error’ concept find it an embarrassing inconvenience, compared with disciplines that hold a concept of ‘accuracy’ rather then ‘error’, thus retaining a somewhat more optimistic attitude towards strengthening the final product by identifying its weaknesses?

Fundamental Sources of GIS Data

Global Positioning Systems (GPS)Remote SensingField SamplingField SurveyAerial PhotographyMost dominant source - Maps

Currency and Age of Data

Definition of a map - Keates (1989), refers to a map as a ‘two-dimensional graphic image which shows the location of things in space, that is, in relationship to the Earth’s surface’- no mention of time!

Maps - are a static representation of the Earth.The Earth - is not static but highly dynamic.

Map Scale and Resolution

Map Scale - dictates what features and how features will be drawn on a map.Map features will be excluded, included, exaggerated, generalised, simplified and categorised based on the scale of the map.

General rule for resolution of a map - features with dimensions smaller than 0.5 of a millimetre on a paper map will not be distinguishable

Eg 1: 50,000 map scale - 1 mm = 50 mOnly map features having a resolution of 25m or more can be distinguishable.

Does a road line feature on a 1:250,000 map with a line thickness of 1mm suggest that the road is 250 m across? Cartographic license (Exaggerated).

Users must be wary of scale - indication of how features are represented on a map in order to assess fitness for use.

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Map Coverage

Incompatible or partial levels of information over a study area may result in combining incompatible map scales.

Considered poor practice to combine different map scales (different levels of error) and extremely poor practice to overlay a smaller map scale (1:250,000) with a larger map scale (1:50,000).

Positional and Attribute Errors

Two basic categories:- Positional and Attribute Error

- Positional error refers to the position of features such as points, lines and areas.- Attribute error is associated with error in the values of the thematic attributes.

Further broken down into both Measurement error and Conceptual error.

Measurement Error- positional- attribute

Conceptual error- positional- attribute

Positional Measurement Error

Vertical stereo photography is the basis of most map products and most positional data is derived from these aerial photographs.

Aerial photography is subject to both photographic distortions and aircraft movement.

Orthophotography - photography rectified of image displacement caused by camera tilt and terrain relief.

Measurement error - also the result of cartographic symbolisation, production, the printing process, paper map distortions, poor field work, incorrect calibrated measurement instruments, incorrect reference to geodetic datum or inadequate ground control - also incorrect GPS position fix.

Positional Measurement error - also associated with the Precision.- Precision to which features are stored in a GIS is higher than the resolution of the map.

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If we can only distinguish features greater than 0.5 of a millimetre on a 1:50,000 map (>25 m), then the last 4 digits of the following coordinates would be spurious.

221542.478 m East 6345533.782 m North

Problems arise when two versions of the same line consistent to the accuracy of the data and map scale, are overlayed with a much higher precision - generation of spurious polygons.

Attribute Measurement Error

Attribute measurement error is expressed as either ‘numerical’ or ‘categorical’ in nature.

Numerical error - elevation is recorded to some specified level of accuracy. Eg. + or - 2 m of what is considered to be the true value.

A spot elevation may have a coordinate positional accuracy quite separate from its thematic attribute accuracy of elevation.

Categorical attribute errors - do not have a metric of deviation - they are simply right or wrong.- eg incorrect landuse; incorrect zoning; incorrect vegetation.

Categorical errors - broad classification (small scale maps) versus detailed classification (larger scale) - produces different boundaries

Positional Conceptual Error

- Positional uncertainties of line and area phenomena.

eg the location of a coast line placed at the high, low or medium water level?

- Should keep a standard across all data sets

When two versions of the one coastline are overlayed sliver polygons are created.

Attribute Conceptual Error

Conceptual fuzziness - fuzzy boundaryUncertainty of attribute data due to a transitional zone - eg vegetation or soil typeLocating boundaries within transitional zones may well be determined by the ‘experience’ that the decision-maker has about the respective phenomena.

Eg for vegetation - botanist versus photogrammetrist

Make sure you know who or what organisation determined the boundary information - fitness for use.

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Socio-economic Data

Age of data - change of marital status, change of job, etc.

Incorrect counts - from census dataeg Aboriginal social groups - multiple residence.

Under-counting - resulting from illiteracy, homelessness, or a simple unwillingness to complete census returns.

Difficulty acquiring data in remote regions.

Ecological Fallacy - assumes a normal distribution

Map to digital transformation

Two primary methods of analogue to digital transformation- manual digitising- automated scanning

Digitising

Digitising generates a series of straight line segments used to represent the ‘true’ line which is often a smooth curved line.

Digitised lines are unlikely to match that of the original line and are dependant upon:- the skill of the technician,- the digitising mode used - stream or point mode,- and the thickness of line on the source map.

Digitised points in error from the original line can be expressed as an ‘epsilon’ distance (band) or model about a cartographic line.

The band is determined by the extent of positional uncertainty based on a probability density function from the source line and the digitised line.

Generally digitised documents are constructed to within the resolution of the map.

Positional uncertainty of digitised lines can also be linked to map registration and can often be slightly higher than the digitising error its self.

Error width of epsilon generates concerns regarding point-in-polygon applications.If a point feature representing a place of residence falls within a width of epsilon, then there is a degree of uncertainty to which side of the line, or to what polygon, the point attribute really belongs.

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Positional uncertainty in digitised lines also gives rise to measurement error since an area estimate is subject to a large degree of uncertainty of its boundary position.

Uncertainty = the total length of all digitised lines on the map sheet multiplied by the width of the epsilon band.

Logical consistency - topological problems

Completeness - are all possible objects included within the database?

Automated Scanning

Raster scanners are equipped with a high-resolution sensor called a charge-couple devise (CCD). This moves in a systematic manner back and forth across the source map resulting in an array of pixels registered as either ‘black’ or ‘white’.

Raster to vector conversion - to produce digital data features: ie. roads, rivers, etc.

During the conversion process position and measurement errors are predominantly introduced.

threaded line of ‘best fit’ through cell boundaries - resulting stepped boundaries between polygons.

Resolution of cells determines how close the new boundary is to the source boundary.

Error Propagation From Map Overlays and Precision

Map overlay includes the integration of two or more data layers combined to produce one composite map showing the intersections and spatial relationships between the individual mapping units of the original input maps.

Before the development of digital maps, manual overlay analysis was carried out by overlaying two or more hard copy transparencies to identify boundary coincidences.

The digital environment promised to identify and locate more accurately boundary relationships - however new problems were generated - due to the precision of coordinates:

- Generation of Sliver or spurious polygons

Sliver polygons generated when overlay mismatches of coincidental boundaries between diverse or incompatible data sets are brought together.

The number of spurious polygons on an overlayed composite map will rise exponentially as the number of polygons on the individual data layers increase.

- Generates area uncertainty

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Conclusion

Fundamental Sources of Error in Maps- Currency and age of data- Map scale and resolution- Map coverage- Positional and Attribute Errors- Socio-economic data

Map to Digital Transformation- manual Digitising- automated Scanning

Error Propagation From Map Overlays and Precision- Generation of sliver polygons- Area measurement uncertainty

Need an awareness of error and how they are introduced and propagated through the system to minimise its effect.

No representation of the real world in digital form can be assessed as a perfect replica - error will always be inherent in a GIS.

Error plays an important role in determining the ‘accuracy’ of spatial data - its fitness for use.

Documentation - include:Source data information such as its age, how it was digitised, from what scale, what agency provided the data and what steps were used to produce the final product.

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Map design and composition

1. PURPOSE AND TYPES OF MAPS

The objective of map design is to invoke in the minds of the user the desired image of the spatial environment appropriate to the intended purpose of the map.

The communication objectives of cartography vary along a continuum from general maps to thematic maps.

General maps - no relative importance between features

Thematic maps - theme of importance

Different design objectives exist depending on the type of map

2. SCALE AND CONTENT

Need to determine adequate content - to meet objectives of the map.

Content can determine scale and be restricted by scale also.

Maps must be at a scale that is practical for the user or audience over the area it portrays.

small scale maps - larger area of extent; features are generally smaller with generalised detail eg Map of Australia

large scale maps - smaller area of extent; features are generally larger with more detail

eg Map showing the district council area of Adelaide

Representative fraction is a ratio between a distance on the map and its corresponding distance on the ground - scale 1:50,000 ie. 1 millimetre on the map is equal to 50 metres on the ground.

Resolution of a map - smallest recognisable feature (feature keeps its own character and shape)

Resolution is generally no smaller than 0.5 of a millimetre on a paper map

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3. MAP GENERALISATION

A generalisation symbol is used when we can no longer represent features with their own characteristic.

Elements of Generalisation

1. Omission / exclusion - exclusion of unwanted features

2. Simplification - eg simplify drainage network at a reduced scale

3. Combination - eg combine areas

4. Exaggeration - eg retain important features below the map resolution

5. Displacement - as a feature is exaggerated it may displace other symbols

6. Classification - Qualitative - eg re-class major and minor roads to ‘transport network’

- Quantitative - eg re-class statistical distribution to broader classes

7. Balance - keep same level of generalisation

Content on maps is governed by rules of scale, generalisation and its relative importance.

4. CLASSIFICATION OF SYMBOLS

Figure below - shows some examples of a variety of point, line and area symbols used to portray some kinds of nominal, ordinal and interval / ratio data.

Nominal scales - are employed when we distinguish among a set of features only on the basis of their character. No quantitative relationship.

Ordinal scales - differentiate within a class of data on the basis of rank according to some quantitative measure.

Interval and Ratio scales - adds the information of distance or magnitude to the description of ordinal rank.

Interval and ratio scales are usually described separately.

Ratio scales

Become meaningful by employing an interval scale which begins at a zero point.Ratio scale measurements represent position on a linear scale eg distance, age,

frequency - therefore division makes sense for ratio measures.

Ie 100 m is twice as long as 50m; 40 years is twice as long as 20 years;

However 200C is not twice as hot as 100C (Interval Scale)

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Interval scale

Values are relative to each other and so qualitative differences make sense but proportions or ratios do not.

Interval scale eg temperature and dates

Ratio scale eg Elevation above a datum; barometric pressure, depth of precipitation; populations of cities; tons of freight.

For representative purposes, there is no difference between Interval and Ratio data, however the map user must be careful when interpreting such maps, since the two scales are different.

5. PRIMARY GRAPHIC ELEMENTS

In order to represent different data in a meaningful fashion we must vary the appearance of the symbols, by adjusting their graphic quality.

Ie their Hue, Value, Size, Shape, Spacing, Orientation and Location.

The Employment of Hue on Maps

Colour is used generally to show similarity and differences between and within a number of class phenomena.

Two basic considerations when using colour:

1. Is not to create bothersome combinations and contrasts that are garish or draw unwanted attention.

2. Designing a map involves making choices among conflicting principles and objectives - problems usually lead to compromises by assigning priorities (may be based on relative importance).

People have common reactions to colour on maps and this has resulted in a variety of colour conventions - based on our perception of colour.

Perception of Colour

1. Sensitivity to colour

The ability to recollect colours and to retain an impression (eg from a legend to a map) can be severely restricted.

General rule - when we use colour to distinguish one thing from another - if possible use no more than 8 or 10 colours. Even less if employing the same hue with different intensities of value.

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When coloured symbols such as lines or dots are very thin or small, its difficult to distinguish hues.

Keep symbols at a size where they are distinguishable, or introduce other additional contrasts of graphic elements such as shape.

2. Visual Acuteness

The more monochromatic the background, the easier it is for the eye to resolve detail.

3. Individuality of hues

Our sensitivity to hue varies, our eye is attracted more to some hues than others.

Most sensitive: red, green, yellow, blue and purple

Colours also look unique and distinctive (colours that appear not to be made up of two or more colours).

Perceptual Primary Colours: blue, green, yellow, red, brown, black and white

Green is made up of a mixture of both blue and yellow pigments, but it doesn’t look like a mixture.

The phenomenon of individual hues is important in cartography in two ways: -

1. Individual hues should be used to symbolise distinctly different phenomena.

2. Apparent mixers of colours should be used to portray items that share some of the attributes that are symbolised separately by the individual hues.

eg Ethnic group A might be blueEthnic group B might be redTherefore, a mixture of both ethnic groups might be coloured purple

4. Symbolic Connotations

Colours are widely associated with sensibilities and moods. So we keep this logic as a convention in our mapping - gains confidence from the viewer.

For Example:Green - is cool, vegetation, lowlands, forestsRed - is warm, important itemsYellow tans -Dryness, sparseness of vegetationBlue - is wet, also coolBrown - Landforms (mountains, hills)

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6. CHOOSING COLOUR ON MAPS

Colour and Qualitative Difference Mapping

Quantitative difference mapping looks at mapping phenomena that are divided into a series of classes by type.

If the data is qualitative in nature, then no colour should be employed to suggest that one type of class or feature is more important than another.

Employ colours that are distinct - Primary perceptual colours (look like non mixes)

Keep colours as separate as possible.

Colour balance associated with qualitative mapping and varying complexities of shape and size.

Smaller areas adjacent to larger area phenomena, run the risk of being lost or over-powered by larger areas.

The larger area will generally dominate the scene just on the basis of size.

Larger areas are therefore generally assigned lighter or less saturated hues, and smaller areas darker and more saturated hues - to provide balance.

Colour and Quantitative Difference Mapping

Quantitative difference mapping looks at mapping phenomena that varies in quantity.

If the data is quantitative in nature, then in most cases we are portraying relative magnitude within a class and therefore do not require distinctly different hues unless we are mapping two or more classes.

For one class we can employ graduated levels of the one colour.

Two basic methods of employing graded magnitudes:

1. Single Hue Method - function of relative lightness

2. Two Class Method - function of contrast in hue

Choice of colours for both quantitative and qualitative phenomena

Function of Natural phenomena and associations

Many colours we associate with natural phenomena

eg Bathometric layers - blues and purple colour schemes.

eg Maps of temperature - cold colours associated with pale green through blue and purple. Warmer colours associated with hotter climates - yellows through orange and red.

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The choice of colour can be independent of the nature of the phenomena when there are no colour associations. However, larger quantities are often associated with reds and smaller quantities with light green and yellow.

Note: The Graphic Element of colour has many parallels with other graphic elements

7. FIGURE-GROUND PHENOMENA

The eye and the mind work together to react spontaneously to any visual array, to immediately organise a display into two basically contrasting perceptual impressions.

1. A figure on which the eye settles and sees clearly.

2. The formless ground around it.

The figure is perceived as a coherent shape or form, with clear outlines, which appear in front of or above its surroundings.

The Figure-Ground phenomenon is a natural characteristic of perception and is therefore a primary component in graphic map design.

The user should be able to focus immediately on the cartographer’s objective without visually fumbling to find what he or she is supposed to be looking at.

The Figure below illustrates four aspects of the figure-ground relationship.

To promote differentiation between the figure-ground relationship

1. Colour, Brightness (Value) - The darker area tends to identify the figure.

2. Labelling/descriptive information - form a natural association

3. Closed forms - keep entire lakes, peninsulas, islands etc.

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8. MAP LAYOUT - TITLES, LEGENDS, NORTH ARROWS, SCALES, AND LOCALITY INSETS

All the above elements serve two functions in cartography.1. a. Title - used to identify the place and subject matter.

b. Legend - used to identify symbology.c. North Arrow - shows orientation.d. Scale - shows measurement.e. Locality Map - shows the location and extent of the study area.Where appropriate, these descriptive elements should be included on maps.

2. These elements also serve as visual masses that can be positioned on maps to provide visual balance.

Physical shape of the study area and the symbols used on the map may appear to be irregular and so the map may lack an overall balance. The map must have good layout balance so that nothing detracts from the communication objectives of the map.

Layout Conventions:

Desirable for the title to be at the top of the layout

The largest empty space should be reserved for the legend.

Don’t go over-board with credits and production information.

The scale bar or representative fraction, the north arrow and source information should not detract from the map.

The graphic map element should be as large as possible.

9. AUDIENCE

Both General and Thematic maps are made for a variety of audiences - all varying in their perceptual capacity.

For the geographically unsophisticated, the important basic shapes, names and colour should be emphasised in the graphic design.

Maps or signs read from a distance require a different treatment of graphic character.

A map on the TV may only be shown for a few seconds - limit text on maps.

People with sight impairments or older people have difficulty seeing small text on maps.

Topographic and geological maps can be complex - aimed at literate map readers.

Think about who your map’s audience is!

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Data Models and Axioms

This lecture considers the stages of developing a ‘model’ of geographical data that may be used by others in a graphical or digital form.

Note that creating models of an environment is something we do all the time – e.g. simple description

There are a number of approaches and at the extremes, either perceive space as occupied by a series of entities with are described by their properties and mapped on a co-ordinate system, or as a continuous field, of variation without distinct boundaries

Formalised geographical data models are used to characterise these ideas so they may be broken down into units that may be mapped.

Principle approaches use either a series of points, lines or polygons or a series of tessellated units to describe the phenomenon

The adoption of a model influences the type of data that may be used, and the type of analysis that might be undertaken.

Entities or Fields

Entities the most common view is that space is peopled with objects (entities).

Defining and recognising the object is the first step, listing the attributes is the second.

Fields

In the continuous field approach the simplest conceptual model represents Cartesian space in terms of continuous co-ordinates in two or three dimensions.

The attribute is usually assumed to vary smoothly and continuously over space – e.g. air pressure.

It is often difficult to choose between the approaches – e.g. would you use elevation in Switzerland and a smooth continuous factor, or something with considerable extremes.

As a simplification – the choice of entity or field approach also depends on the discipline. Disciplines that focus on the natural sciences are more likely to use the field approach.

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Geographical Data Models and Primitives

Geographical data models are the formalised equivalent of the conceptual model mentioned at the beginning.

They formalise how space is dissected into parts for analysis and assume the phenomena can be uniquely identified

Most anthropogenic phenomena can be handled best by the entity approach. The simplest and most frequently used data model of reality is the basic spatial entity which is further specified by attributes and location.

These are then sub-divided into the three geographical data primitives – points, lines and polygons.

With continuous field data, variation of things such as air pressure generally considered too complex to be captured by a simple mathematical formula. In some instances regression equations used to represent large-scale variations, but generally necessary to divide the surface into discrete spatial units (of various types). The resulting tessellation is taken as a reasonable approximation of reality.

Both the entity and field models assume that the phenomena can be specified with respect to both their attributes and spatial position. In most instances this assumption stands, but elsewhere it may not.

Vector Data Models

Simple points, lines and polygons are essentially static representations of phenomena in terms of X and Y co-ordinates

They are supposed to be unchanging and do not contain information about temporal or spatial variability

a point implies that a phenomena has an extent that can be adequately defined by one pair of X and Y co-ordinates

a line implies that the geographical extent of an object can be defined by a simple pair of X and Y co-ordinates, with no width, unless attached as a separate attribute

the simplest definition of a polygon implies a homogenous representation in 2D space – but they may contain holes etc

More complex definitions of points, lines and polygons can be developed.

Topological links can be developed to indicate how lines are linked into polygons or linked to networks (e.g. streams).

In recent years more highly structured ways of encapturing entity data have been possible through the object-oriented approach. This is the technical name for database and programming tools that provide nested hierarchies of functional relationships between related groups of entities that together form a single unit at a higher level of the hierarchy (discussed more fully in the next lecture).

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Tessellations of Continuous Fields

Continuous surfaces characterised into sets of basic units such as square, triangular or hexagonal cells which are tessellated to form geographical representations. (Tessellate – to construct, inlay or pave with a mosaic of small tiles).

Commonly use either TINS or a regular grid of pixels.

Each grid cell or pixel may be thought of as a separate entity that differs from vector polygons only in its regular form.

Both the triangles of tins and pixels allow for the use of mathematical models. For example, TINS used in modeling water flows provide a structure called finite elements which are the differences in attribute values between adjacent triangular cells, and these can be used through differential calculus to model the flow of water or dissolved elements

With pixels, when the grid cells are used to represent a continuously varying attribute in each cell, each cell with have a different attribute value and these are assumed to be mathematically continuous. Differential calculus can then be used to calculate local averages, rates of change, etc.

Pixels also allow for hydrological and related modeling, as the flow of materials may be computed using the finite difference.

Three-dimensional equivalents of pixels are termed voxels. They are the basic units of spatial variation in 3D space.

Data Types

The attributes of entities may be expressed as Boolean, nominal, ordinal, integer or real data types.

Real data types include decimals; integer and real are collectively known as scalar data.

Geographical coordinates are sometimes expressed as integers but mostly as real data types, topological linkages use integers.

Differentiable continuous surfaces require real data types, though integers are sometimes used as an approximation.

Non- differentiable continuous surfaces and their discrete forms – e.g. pixels – can take the same data types as entities.

Logical operations can be carried out with all data types but arithmetical operations are limited to real and integer data types. Consequently the type of analysis is limited by the data used in the model.

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Geographical Data in the Computer To take advantage of the possibilities a computerised spatial modeling tool may provide it is important to understand how the geographical models are coded in the computer.

Computers code data and carry out instructions using a series of switches that exist in an on or off state - coded by the numbers 0 and 1 respectively. The base 2 primary system is therefore the fundamental coding basis for all computing.

Therefore geographical data need to be coded into discrete records in the computer using these switches to represent the location, presence, type, etc. of phenomenon.

The main data structures are raster and vector, but recently object-oriented data structures have been used which use the same basic structures as raster and vector but structures the entities into hierarchial forms.

When entered into the computer the data need to be organised to allow efficient data accessing, retrieval and manipulation. Organisational systems range from simple lists through to highly structured hierachial network, relational and object oriented schemes.

These structures determine how the data are organised into data records and therefore control the way they are held in the computer.

Data

People represent numbers and do arithmetic in base 10. Computers use a different system and store numbers as 0 and 1, and therefore work with binary arithmetic (base 2).

As with ordinary numbers, the data in a binary system are represented using a series of columns and in a computer each column is used to represent a switch. The switches are really small magnetised areas on a computer disk or memory, usually grouped in packets of 8, known as a byte.

Several bytes are linked together to make a computer word, words are grouped together to make a data record, records are grouped to make a file, etc.

With a byte of 8 bits we can represent 256 numbers from 0 to 255 (28 –1) i.e. from 0 –255.

If we combine two bytes in a 16 bit word it is possible to code numbers from 0 to 65 535. However, we usually leave the 16th bit to determine sign (positive or negative), so we can code numbers from –32 767 to 32 767.

A number lacking a fraction is an integer so numbers in the range –32 767 to 32 767 are usually referred to as 16 bit integers.

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It is frequently necessary to code numbers outside this range or have fractional numbers so computer words with more bits are needed. Real numbers with positive and negative values and decimals require 32 bit or 64 bit words (called double precision) in which some of the bits are reserved for the decimal part of the number.

This method of coding numbers means numbers are not coded exactly which may lead to rounding errors when the user works with numbers larger than those allowed.

Data Models into the Computer

Data structures are the schemes used to represent the data within data models.

Vector and raster data have different storage requirements within computers. (See table, note implications for computing power required).

File and Data Access

The essential feature of any data storage system is that it should allow data to be accessed and cross-referenced quickly.

There are several ways of achieving this, some of which are more efficient than others.

There is however no single best fit.

Simple Lists

The simplest form of a data base is a simple list, each new item added to the data base is simply added to the end of the file which gets longer and longer,

Easy to add data to such a system, but retrieval is inefficient

Ordered Sequential Files

E.g. words in a dictionary are ordered sequentially.

Allows for faster searching, as uses a two stage iterative approach that divides list in half.

Indexed Files

Indexed files allow access to the database to be speeded up in two ways:

If the data items in the files themselves provide the main order of the file then the filers are known as direct files.

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The location of items in the main file may also be specified according to topic, which is given in a second file, known as an inverted file.

In the direct file the record for each item contains sufficient information for the search to jump over the unnecessary items.

Indexed files are fast, but have problems with files in which records are continually being updated – e.g. with interactive mapping systems.

Database Structures

The basic unit of a file is the data record or tuple, which contains all the relevant information for each entity

A computer program that is designed to store and manage large amounts of data is called a database management system (DBMS)

Modern DBMS use many methods to efficiently store and retrieve data, but all based on three approaches – hierarchial, network and relational

Hierarchial

Often used in the natural sciences.

Used when the data have a parent, child or one to many relation.

Data access for key variables is quick, but difficult for associated attributes – very quick in systems where all possible queries are known before hand.

Network

In hierarchial structures, travel within the database is limited to travel up and down the system.

In many situations more rapid linkage is required.

Can use compact network structures to avoid redundancy and linkage problems.

Relational

Simplest format has no pointers and no hierarchy.

Instead the data are stored in simple records known as tuples, which are sets of fields, each containing an attribute; tuples are grouped together in two-dimensional tables known as relations, somewhat like a spreadsheet program.

Data are extracted from the relational database by the user defining the relation that is appropriate for that query.

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This relation is not necessarily already present in files, so the controlling program uses the methods of relational algebra to construct new tables.

These are often encoded in Structured Query Language (SQL).

Relational databases are flexible and may meet the demands of Boolean logic and mathematical operations.

Querying across different relational tables is made by joining them through common fields, good for situations where all records have the same number attributes and there is no natural hierarchy.

Object oriented

In GIS their use stimulated by the need to handle complex spatial entities more intelligently than as simple point, line, polygon primitives.

Object oriented database structures combine the speed of hierarchial and network structures with the flexibility of relational approaches by organising the data around the actual entities, rather than the functions being processed.

In the relational structure, each entity is defined according to the data records and the logical relations that can be elucidated between the attributes and their values.

Data are defined in terms of a series of unique objects, which are organised into groups of similar phenomena (object classes), and relationships between different objects and different classes established through specific links.

The characteristics of an object may be described in the database in terms of its attributes (called its state) as well as a set of procedures that describe its behaviour (called operations or methods).

These data are encapsulated in an object that is defined by a unique identifier within the database and this remains the same regardless of the changes in values that describe their characteristics.

The relational model of a bicycle is merely a list of its parts. The object oriented model links the parts so that their function in relation to each other and the behaviour of the object is clearly expressed.

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Current Trends and Issues in GIS

This lecture examines some of the cutting edge issues in GIS. It is about where GIS has been and where it will go in the foreseeable future. In large part the history of GIS determines how it continues to evolve.

Some of the key issues include who uses GIS, what their skills sets are, where they are employed. It also includes new developments in technology, including the increasing power of PCs and the use of the Internet.

On GIS and SIS and all that Jazz

Traditionally GIS has been an area for experts. A demanding technology that required big computers, well trained operatives and relatively few application areas.

Over the last decade there has been an explosion in the use of GIS. GPS systems are now built into Land Rovers and some of the German makes of car have their own GIS systems to guide drivers through the streets.

In many other areas there has been the rapid expansion of the technology, in business, in public administration, in land administration etc.

The popularisation of GIS has seen some academics call for the technology to be relabeled Spatial Information Science (SIS) - with the emphasis on the science part.

SIS would contain:

Geographic Information Systems (GIS)

Land Information Systems (LIS)

Remote Sensing (RS) and

Global Positioning Systems (GPS)

Those who claim a need for SIS, wish to maintain intellectual rigor by separating out the more demanding and theoretical elements of GIS from the populist applications (e.g. letter drops)

(But note here a long standing debate/argument in the urban studies literature that GIS is a really cute technology with lots of applied applications, but no theoretical value).

One of the interesting points here is that while GIS can do remarkable 3 of 4 D modeling and other whizz-bang things, much of the commercial demand is for the basic product – relatively simple maps.

GIS allows the continued updating of maps

In many ways a new era in mapping with more flexibility and responsiveness, plus more attributes being mapped as a matter of course.

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Personnel

Traditionally GIS personnel have been drawn from a narrow range of specialists.

Surveyors/cartographers (e.g. Uni of SA) really dominated the field until quite recently. They were the first to develop GIS applications and write the software that is GIS.

eg Jack Dangermond – former cartographer

eg the writers of the IDRISI software

At the other end, remote sensing specialists tended to come from geography departments (e.g. UNSW) and natural resource management depts.

Both types of personnel developed and worked with GIS in a narrow range.

They were not well equipped to deal with the broader role GIS took on in society (i.e. new applications); and within the Australian university scene are not necessarily powerful academic units.

Within mainstream GIS, much more emphasis is given to computing science graduates, and now the generation of larger numbers of geography graduates with training in GIS. In some ways taking over from the traditional sources of spatial information specialists.

Hardware and Software

As with virtually all computing applications, the hardware and software has progressed.

While there has been a general increase in the capacity of large-scale programs (e.g. Arc Info), the most significant improvements have occurred at the bottom end.

That is, increases in the hard disk space, processing speeds and RAM of PCs now make software such as ARC VIEW and ARC INFO NT, much more powerful.

Two years ago, ARC INFO PC was a poor relation of ARC INFO for UNIX, now the NT Version offers the same power, but with portability and often greater speed.

Makes it possible to input data in the field, plot out maps, etc .

ARC View is far more user-friendly, but allows you to perform a lot of the data input and other tasks you would otherwise do in ARC INFO (so why didn’t we use ARC View in this topic?)

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Application Areas

As mentioned previously, there has been an explosion in the areas of application for GIS.

archaeology

business

regional development

population/demography

infrastructure provision

asset management

It has brought with it a change in the skills sets of users, and a change in the types of organisations using GIS.

GIS is no longer limited to large scale organisations running big computers

Many smaller organisations – e.g. Trees for Life and Murray Goulburn Catchment Management Board, now have GIS

Creates a demand for users expert in both areas (the GIS and the application area) and also creates the need for GIS services – technical experts, training providers etc

This is a fundamental shift and one that shouldn’t be underestimated.

The Internet

One way GIS is changing is through the use of the Internet.

GIS over the Internet has two attributes and a number of challenges.

One of the most important challenges is the size of files, that is, the Internet often cannot take very large files (5mb gateway here at Flinders) which is a problem.

However, if the Internet or the Web can work, you can be working in Patagonia and edit files on your computer in Adelaide (add values etc.)

The Internet also allows scope for outsourcing. You can contact GIS providers, who work over the Internet, email the data and they produce the GIS output – a coverage, a map or whatever.

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Recent Developments in GIS in SA

Purpose

Professional socialisation.

To understand the policy environment shaping the development of GIS into the next century.

To give you some idea of buttons to push in potential job applications

The Spatial Information Industry Program

One of five areas within information technology that the State Government has identified for industry growth

Not a major unit within the State Government – eg doesn’t employ that many people or have a large budget

Emphasis is on facilitating the industry, encouraging export growth and assisting the Spatial Information Cluster

Project One

Honest – this is what it was called.

The State Government sought a major industry partner to assist in the development of the spatial information industry here in South Australia

The partner (and there was a short list of two – Telstra and Fujitsu) was to help the industry in South Australia achieve world standing

- assist small firms in gaining access to world markets

- make a contribution to the GIS education industry

- sub-contracting some work to smaller firms

- undertake major investment in South Australia and use it to develop GIS solutions to be used elsewhere

In turn, the industry partner was to get the ‘right of first look’ on all major GIS projects. That is, the right to tender for projects on a first in basis.

- Fujitsu selected.

- no binding commitment on outcomes. That is, the agreement was a statement of intent on the part of both parties, without enforceable outcomes.

Fujitsu’s decision to become party to this exercise was taken at the Tokyo Boardroom. A major initiative: given that Fujitsu’s IT arm is the second largest in the world and the largest in the Asia-Pacific with turnover of approx $9bn.

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Fujitsu looks to use South Australia as an example of what its technology can achieve, ie take delegations from the Ukraine to South Australia to demonstrate land titling solutions.

Established the Spatial Education Foundation with a goal of making South Australia one of the top three locations for GIS in the world.

The Biennial Summit

As part of its industry promotion strategies the South Australian Government (through the auspices of AURISA) is planning a Summit to consider the application of GIS to social problems, particularly

- environmental management

- economic development (mobilisation of capital)

- assets management

Idea is to attract high level persons and then let them become aware of South Australia’s capacity to solve these problems.

Note here the major role of SAGRIC in acting as an international consultant in land titling, environmental monitoring and other systems

Due to take place, April 1999

Spatial Industry Clusters

Industry clusters are flavour of the month in regional economic development.

The idea of a cluster is to build competitive advantage (following on in large part from Michael Porter’s work on the competitive advantage of nations). That is, locations can develop synergies that can generate on-going world competitiveness as a result of firms innovating and competing together (plus a dose of strategic government input).

- Emilia Romagna the classic example

The state government established a Spatial Information Cluster. Largely comprised of small consulting firms. The goal is to generate innovation and attain a critical mass so the combined firms can bid for large projects –

- eg the defence project

The cluster includes about 45 small firms, plus related businesses such as the labour firms – eg Drake International, Western Star Services

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Co-ordination of State Government and Local Government.

The State Government has a number of mechanisms for coordinating GIS initiatives

The Spatial Information Committee (SICOM) is an officer level committee that considers issues of coordination and development at the inter-agency level.

- also includes the universities and local governments

- primary forum for airing and discussing major initiatives such as the change to the datum, for responding to NASA, for identifying other new developments

- all items to be discussed at SASIC are supposed to go through SICOM first

SASIC

The South Australian Spatial Information Committee

Comprised of the CEOs of the major government departments involved in GIS – Department of Environment, Heritage and Aboriginal Affairs; Department of Transport and Urban Planning; Department of Information Technology and Assets, plus Steve Haines from Salisbury Council etc

Supposed to be the primary decision making body.

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