practical 2 - the power of gis key learning outcomes · 3. performing spatial queries however, the...

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Practical 2 - The power of GIS

Key learning outcomes:

Displaying variables

Performing attribute queries

Performing spatial queries

Exporting results

Creating buffers

Calculating a viewshed

(OPTIONAL: Least cost paths)

(OPTIONAL: creating a DEM via interpolation)

1. Displaying variables

Now that we have collated our data and constructed our map, it is time to begin our investigation into the potential damage to

any archaeological features caused by the development. Make sure that all of the layers of the map are switched on. First of

all we are going to see what we can learn from the results of the field surveys, so make sure that they (and the SMR layer) are

all visible above any potentially obscuring layers:


If you linked the data tables properly to these layers during the last practical, we can now

display the variation in those variables using colour. Right click on the layer

“SilverpinePaddock” in the layer list

and select “Properties”. This brings

up the “Layer Properties” form that

we encountered in the last practical.

Make sure that you are on the

“Symbology” page.

The list on the left of the form shows the different ways in which we can display this layer. The different options provide

different display methods, as follows:

Features This option is for displaying a layer using a single symbol for all objects in the layer, as currently.

Categories This option is for displaying attributes that represent categories. For example, a period category

might contain entries such as prehistoric, Roman, post-Roman, medieval, modern. This would be the

option to use to display this type of attribute. If you click on “Categories”, you will see a series of

options underneath. The main one of these is “Unique values”, which would display a unique symbol

for each selected instance of a category. As such, you could display the different periods listed

above using different colours or different symbols.

Quantities This option is for displaying attributes that represent quantities. For example, a Roman pottery

category might contain a whole series of sherd counts. This would be the option to use to display this

type of attribute. Again, if you click on “Quantities”, you will see a series of options underneath:

Graduated colors: This is used to display variation in a quantity using colour.

Graduated symbols: This is used to display variation in a quantity using symbol size.

Proportional symbols: This option again displays variation using symbol size, but the size of the

symbol will be proportional to the quantity in question. For example, imagine a layer recording

different counts of Roman pottery sherds. Most of the items in the layer have between one and five

sherds, but a few have over thirty. Using graduated symbols, the variation in symbol size would be

constant, with the 30+ group having a symbol just slightly larger than the lesser groups. With

proportional symbols, items with thirty sherds would have a symbol thirty times the size of items with

one sherd.


Dot density: Dot density maps display quantities using the density of randomly placed dots within

each transect. They form quite an elegant way of displaying multiple quantities at once. For

example, you could use green dots to display Roman sherd counts and red dots to display Iron Age

sherd counts. Dot density maps are somewhat underused, and unfortunately could easily be

confused with find spots. However, they certainly have their merits if used intelligently and described

thoroughly.

Charts This option is for displaying multiple attributes using graphs. You could, for example, display counts

of early, middle and late Roman pottery as pie charts on each location. Charts probably work best

when used with a point dataset, or when zoomed in to one of the survey fields contained in the

practical dataset.

Multiple attributes This option is used to display multiple attributes of a dataset at once. Essentially, it is used to display

quantities and categories of a data layer at the same time using different colours and symbols. For

example, it would be possible to display the period (a category) of a site using symbol colour, and the

area of a site (a quantity) using symbol size. This can be a tricky method to get correct, however.

We shall set the “SilverpinePaddock” field to display medieval sherd

counts using a colour ramp. Click on

“Quantities” and then make sure “Graduated

colors” is selected in the list. Now, look to the

right to where it says “Value:” and use the drop

down box to select the “Medieval” field. The

“Normalisation:” field would be used to normalise this by another field,

such as transect area, but we will not be using that now. Look further to

the right and see where it says “Classes:”. This is where we select the

number of different classes we wish to order our data into. Select “5” using the drop down box. This will, as standard, use

„natural‟ breaks in the data: clicking on the “Classify” button would allow you to use different methods for

grouping the data into classes, such as equal intervals, etc. You may experiment with this if you wish.

Now, select a pretty “Color ramp:” using the drop down box, then click on “Apply”. You should see that the

Silverpine paddock is now coloured according to medieval sherd counts. Click “OK” or “Cancel” to close

the properties form.


We shall now set the SMR layer created in the last practical to display

the different categories of site type using different coloured symbols.

Open up its “Layer Properties” form as before

and again find the “Symbology” page. This

time, click on “Categories” and then make sure

“Unique values” is selected in the list. Under

“Value Field”, select “Object” from the drop

down list. Then click on the “Add All Values”

button. The list of symbols should then become populated with the

various types of site included in the SMR layer. Next, right click on the one of the symbols and select “Properties for All

Symbols” from the menu. Select a symbol that you like from the form that pops up,

then press “OK”. Now, make sure that none of the symbols are selected by clicking

on the “<Heading>” entry in the list, then change the “Color Ramp” as previously to a

colour scheme that you find pleasant. Alternatively, double click on each symbol to

set its parameters manually. Then click on “OK” and watch the SMR symbols change

on the map.

Try experimenting with displaying different attributes of the other survey fields, and the SMR layer. In particular, try to discover

any interesting patterns in the two Barrens fields, as they are close to the development site and were commissioned for your

investigation. Are there any peaks in particular periods and in any particular positions within the two fields? What does the

overall pattern look like across time for the survey area as a whole? Then, see if you can make the best map possible of

medieval presence across the region, using both the field surveys and the SMR records. When you are done, be sure to save

your progress.

2. Performing attribute queries

Hopefully, you will now understand how visualisation is one powerful way of exploring your

data using GIS. Another is through querying. The simplest form of querying is attribute

based, and is very similar to constructing queries in any non-spatial databases you might

have used. On the “Selection” menu, choose “Select By Attributes”. The following form

should appear:


To begin, we shall try to select Roman records within the SMR layer. To do so, set the

“Layer:” to the correct layer and ensure that the “Method:” is “Create a new selection”.

The list below shows you the different attribute fields associated with that layer. Scroll

down the list until you find “Period”, then double click on it. It should appear in the box

at the bottom. This box is where we shall construct our query. Next click on the

equals sign on the calculator. Then click on the “Get Unique Values” button. This will

populate a list above the button with all of the possible periods recorded in the layer.

Find “Roman” in the list and then double click on it. If you then click the “Verify”

button, ArcGIS will tell you if your query should work. Click on “Apply” and look at the

map. You should see that all of the Roman records in the SMR layer have been

highlighted with a turquoise blob.

We shall now add another level of complexity to our query. Click on “And” in the calculator. Then find “Source” in the field list

and double click on that. Then click on the equals sign again, then click on the “Get Unique Values” button. Double click on

the “Excavation” value. The query should read as follows:

"Period" = 'Roman' AND "Source" = 'Excavation'

Click on “Apply”. Now, you should see the selection change. It should show just the one record, next door to the school

building. Thus, in just a few simple steps, we have discovered all of the Roman excavations in our region. Click on “OK” to

close the form. Now, open the attribute table of the SMR layer (by right clicking on the layer in the layer list and selecting the

appropriate command). If you scroll down the table, you will see that the selection has also been highlighted in there too.

If you go to the “Selection” menu and choose “Clear Selected Features”, the selection will be cleared. Experiment with the

“Select by Attributes” tool and see if you can discover any interesting patterns in the data. For example, which SMR records

relate to survey sites entered into the SMR after 1999? Pressing the “Clear” button will clear any queries currently constructed

in the tool.

3. Performing spatial queries

However, the real strength of querying using a GIS lies in extending the tools available to

users to include spatial information. Clear the current selection, then choose “Select By

Location” from the “Selection” menu . This tool allows us to perform spatial queries:


Ensure that “I want to:” says “select features from”, then tick the SMR layer under

“the following layer(s):”. Under “that:” select “are completely within”, then select

“Development” under “the features in this layer:”. Read the tool from the top and

you should come up with the following sentence:

“I want to select features from the following layer(s): SMR, that are completely

within the features in this layer: Development”

If you click on “Apply”, you will see that the SMR records that fall within the

development site are selected on the map. However, one difficulty with SMR

records is that their location is often quite poorly known.1 As such, we cannot be

certain that records are not at risk of falling within the development site simply

because they are not enclosed by it. Therefore, where it says “Apply a buffer to

the features in Development”, first tick the box, then type in “250” next to “of:” and

make sure that the unit is “Meters”. Click “Apply”. You should see that the selection now contains all of the SMR records

within 250 metres of the development site. We shall look at another way of achieving the same result later.

We shall now try a more complex query. Close the “Select By Location” form and clear the current selection. Firstly, use the

“Select By Attribute” tool to select the church in the Buildings layer.2 Notice that it is now highlighted in turquoise. Then open

the “Select By Location” tool. Make sure the layer is set to the SMR. Under “that:” choose

“are within a distance of” and under “the features in this layer:” choose “Buildings”. Make

sure that the “Use selected features” box is ticked, and enter 250 metres into the buffer box

as before. Then click on “Apply”. If you close the form, you should see that we have now

selected all SMR records that fall within 250 metres of the church. As should be apparent, the two querying tools built into

ArcMap are very powerful and can help you to discover a great deal about your data. Save your progress.

4. Exporting results

You might wish to export the results of your query for further

usage, so we shall look at that next. Open up the attribute

table for the SMR layer, making sure not to clear the results

of the previous query. You should see that three of the

records are selected here too. Look for the “Options” button

at the base of the table and click on it. Find the “Export…”

option and select it. In the form that opens, make sure that

“Selected records” is present next to “Export:”, then click on the file opening icon and choose a

sensible place to save your exported table. Give it a sensible name, and then click “Save”, then “OK”. When it asks you if you

wish to add the table to the map, select “No”. This simple procedure has exported a .dbf table that you can now open up in

1 e.g. a four figure OS grid reference such as “SP1212” is only accurate to the nearest kilometre. 2 Clue: "Usage" = 'Church'


external software (such as Excel) for further analysis. Clear the selection (by selecting “Options” then “Clear Selection”) and

close the attribute table.

Next construct an attribute query that selects Neolithic records in the SMR

layer.3 We are going to need the results of this query later, so we want to export

it as a new shapefile. Right click on the

name of the SMR layer in the list, hover

over “Data” and click on “Export Data…”.

A similar form will pop up, but this time we

are going to export a shapefile rather than

simply a table. Make sure that “Selected

features” is highlighted next to “Export:”

and “this layer‟s source data” is selected under “Use the same coordinate system as:”. Click on the file opening icon , and

again choose a sensible location to save the new shapefile. Name it “longbarrow.shp” and click “Save”, then “OK”. When it

asks you if you want to add this layer to the map, say “Yes”. You should then see this new layer appear on the map,

containing the site of the Neolithic long barrow (you may need to clear selected features and change the symbol to see this

easily).

Finally, also try using the “Select Features” tool on the main toolbar and the “Identify” tool . What do they do differently?

Clear any selected features and save your progress.

5. Creating buffers

For our next task, we are going to create some buffers around the development site.

We know that we can already select data in a buffer around the site using the querying

tools, but what if we wished to show that buffer on the map? Well, we can easily use

one of the tools built into ArcGIS to complete this task. Click on the red ArcToolbox

icon on the main toolbar . You should see a new list appear, with lots of red toolbox

icons. This is where we find most of the tools built into ArcGIS. These tools are placed

in virtual toolboxes with other closely related tools. All of these tools perform different,

varied functions for the creation and analysis of our data. If you download any

extensions to ArcGIS, then the tools associated with them will appear somewhere in

this ArcToolbox.

3 Clue: "Period" = 'Neolithic'


At the bottom of the list, go down and click on the “Search” tab. Type “buffer” into the text box at the top

then press “Search”. A list of all tools containing the word buffer in their description should appear. Click

on the “Multiple Ring Buffer” tool and press the “Locate” button at the bottom of the ArcToolbox list. The

list will then return to its initial state, but with the tool we wish to use selected.

Double click on the highlighted “Multiple Ring Buffer” tool. A new form should open.4 Under “Input

Features” select “Development”. Click on the file opening icon next to “Output Feature Class” and

choose a sensible location / name for the results of our buffering

procedure. We want to create buffers at 200, 400, and 600 metres

from the development site, so enter each of those (one at a time) into

the text box under “Distances” then click on the plus button to add

each one. Then, click on “OK”. A progress window should appear.

Wait until it finishes, then close it (it may disappear by itself if a

previous user has ticked the option to close on complete). You should

then see our new buffer polygons appear on the map. Drag them to

below the development layer in the layer list, and (if you can remember how) set their transparency to 50%. See if you can

also change the symbol for the buffers so that the inner is red, the middle orange, and the outer yellow:

4 If ArcMap should crash at this time, you need to make sure that you have installed all of the Service Packs and that your computer‟s version of Internet Explorer is up to date. Please consult with your university‟s IT services if you are having difficulty.


In this way, we can construct a simple visual message about the danger of disturbance to archaeological features within a

particular distance of the development site. When you are done, switch off the buffer layer (by unticking it in the layer list) and

save your progress.

6. Calculating a viewshed

We are now going to try to construct a viewshed based upon the topography of the study area. Firstly, switch off the buffer

layer, the development layer, and the field survey layers, so we can get a better look at the rest of the map. If you zoom in on

the aerial photograph and geophysics layers, you will see what could be a Neolithic cursus monument hidden under the

ground. This is particularly worrying in light of the forthcoming development.

Add the layer to the map called “Observer.shp”. This represents a

person standing on the cursus terminus. We now want to find out what

a person standing on that point might have seen of the surrounding

landscape. To do so, we shall construct a viewshed. Search in

ArcToolbox for “viewshed” as you did to find the buffer tool above. Two

results will come up, both of which lead to the same tool. Double click

on one of them to launch the viewshed tool. Under “Input raster” select

“dem_sp”, and select “Observer” under “Input point or polyline observer

features”. Click on the file opening icon next to “Output raster” and choose a sensible location and name to save our

results to. Ignore the other parameters and click on “OK”. A progress box will appear: close it when it is finished if it does not

disappear automatically.

Our viewshed should then appear on the map, showing areas that are visible and invisible from the observer at the cursus

terminus (in the image below, green areas are visible from the pink spot, grey are invisible):

This is a very simple example of creating a viewshed and, thus, there are a few problems with this result, in that it does not

take into account past vegetation, nor the height of the observer. However, if we were to accept that the model is robust, we

might find it interesting that there is clearly an area quite close to the cursus terminus which would have been of restricted


visibility. This raises an intriguing possibility in that when people approached the cursus from the west along the river valley

they were hidden from view until the last minute. This orchestration of experience would give the moment a particular

dramatic edge, and we might suggest that it was a factor in the locating of the cursus and in any ceremonies that took place

there. Save your progress. This short example of viewshed creation is just intended as a taster, and further detail will be

provided in a later module: in particular, learning how to add offsets to take account of the height of an observer and how to

restrict the radius of the viewshed produced (see Wheatley & Gillings 2002: Chapter 10 for more discussion on issues in

viewshed analysis).

7. (OPTIONAL) Least cost paths

We can take this analysis further. You may recall that we extracted the SMR record relating to the Neolithic long barrow

earlier as a new layer of the map. We shall now try to work out the most energy efficient route from the long barrow to the

cursus. This task is going to be quite complex, so feel free to leave it out if you do not feel that this is something that interests

you (or if you are feeling overwhelmed). This type of analysis is known as least cost path. To construct such a path, we need

to first construct a raster grid that informs us about the energy cost of moving across the landscape.

The simplest such cost grid is based upon the topographic slope. We

can derive this from the DEM. To do so, search for “slope” in

ArcToolbox, and launch the “Slope” tool. Under “Input raster” select

“dem_sp”, and then click on the file opening icon next to “Output

raster” and choose a sensible location and name to save our results to.

Make sure that “Output measurement” is set to “DEGREE”, then click

“OK”. A new raster grid should appear on the map giving the slope of

each map cell.

Next, search in ArcToolbox for “cost path”, select the result, then click on “Locate”. The view should return

to the main ArcToolbox list, and we can see the tools that we shall use to construct our least cost path. We

need to create a cost distance layer next, which will

record the accumulated cost of travelling from the

origin point to each cell on the map. Find the “Cost

Distance” tool and launch it. Under “Input raster or

feature source data” select the “Observer” layer. Under “Input cost

raster” select the slope layer. Then click on the file opening icon

next to “Output distance raster” and choose a sensible location and

name to save your results to. We also need to construct a raster that

calculates which neighbouring cell forms the cheapest (in terms of


energy) route out of each cell, the so-called backlink raster. Click on the file opening icon next to “Output backlink raster”

and choose a sensible location and name to save your results to. Ignore the other parameters and click on “OK”.

We are now ready to create our first least cost path. Find the “Cost

Path” tool and launch it. Under “Input raster or feature destination data”

select the long barrow layer. Under “Input cost distance raster” choose

the cost distance layer that we just created. Under “Input cost backlink

raster” select the backlink raster that we just created. Then click on the

file opening icon next to “Output raster” and choose a sensible

location and name to save your results to. Ignore the other parameters

and click on “OK”.

The tool will then calculate what it believes to be the most energy efficient path between the cursus terminus and the long

barrow. This is a very simple example of creating a least cost path and, as such, there are a few problems with the results.

For a start, as it simply relies on the slope, it does not take account of the difference between travelling uphill and downhill. It

also takes no account of variation in land cover, nor the effect of having to cross the river. However, it we were to accept this

result as a genuine one, it is interesting to compare it against the viewshed that we constructed earlier. In particular, it is

intriguing how the path seems to almost try to keep out of sight of the cursus terminus until the last moment, when it turns

abruptly and approaches up the slope from the river valley. With a more robust viewshed and a more robust cost surface, we

might produce some very interesting, valid results. Save your progress. Again, this exercise is just intended as a taster and

further detail will be given in a later module.

If you want to try creating a more complex cost allocation, then feel free

to work through the following instructions. If this does not interest you,

then move on to the next exercise. We shall be taking forward our

hypothesis that Neolithic people may have wanted to avoid being seen

from the

cursus until

the last

possible

moment. As such, in this instance, we want to apply three

considerations to our cost allocation: difficulty of traversing the slope,

difficulty of traversing the river floodplain, and also we want to

encourage our approaching Neolithic people to remain out of sight of

the cursus as much as possible. We shall start with the slope.

Search in ArcToolbox for “Reclassify” and start the tool with that


name. Under “Input raster” select the slope layer. Under “Reclass field” select “Value”. Then click on the “Classify” button. In

the form that appears, select “Defined Interval” next to “Method:” and type 3 in the textbox next to “Interval Size:”, then click on

“OK”. This will populate the list with a series of values: we now want to set the associated

cost of each of those values. Click in each box in the list under “New Values” and set the

cost as follows: 0.004520 - 3 = 0; 3-6 = 1; 6-9 = 3; 9-12 = 6; 12-15 = 10; “NoData” = 0.

Scroll down and click on the file opening icon next to “Output raster” and choose a

sensible location and name to save your results to. Ignore the other parameters and click on “OK”. The layer created shows

the energy cost of travelling up or down the slope of each cell: a slope of less than 3º has no cost, a slope of 3-6º costs 1 unit

of energy, 6-9º costs 3 units, 9-12º six units, and 12-15º ten units of energy.

Next we want to create a cost field associated with the river floodplain. Find the “Euclidean Distance” tool in ArcToolbox and

launch it. Under “Input raster or feature source data” select the “Rivers” layer. Click on the file opening icon next to

“Output distance raster” and choose a sensible location and name to

save your results to. Set the “Maximum distance:” to 50 and the

“Output cell size” to 5. Ignore the other parameters and click on “OK”.

This will create a buffer raster around the rivers. Use the “Reclassify”

tool as above on this layer and create

a new layer that sets the cost of

travelling across or along the floodplain as 10 energy units for travelling

within 25 metres of the rivers, and 5 energy units for travelling within

25-50 metres (and 0 units for “NoData”; in ArcGIS 9.1, you will also

need a 50-650 category with a new value of 0).5 Next, we want to create a cost field associated with the viewshed created

earlier. Use the “Reclassify” tool as above on this layer and create a new layer that sets the

cost of travelling through the non-visible areas (cells with a value of 0) of the viewshed to 0

and the cost of travelling through the visible areas (cells with a value of 1) to 20 (and 0 units for “NoData”).

5 This is made easier by clicking on the “Classify” button and selecting two equal intervals.


To create our new cost allocation map, we now want to add these three layers together.

We do this using the raster calculator. From the “View” menu, hover over “Toolbars”

and then select the “Spatial Analyst”

toolbar. In the toolbar that appears, click

on “Spatial Analyst” and then “Raster

Calculator”. A form should appear that

looks much like a calculator. We shall use

this calculator to create a new raster layer that sums the three cost layers that we

just created. To do so, double click on the names of each of the three layers in

the list of raster layers and then put “+” signs between each of the layer names in

the expression text box. The expression should be something like “[cost_river] + [cost_slope] + [cost_view]”, where the three

items in square brackets are the three relevant layers. Then click on “Evaluate”.6 Find the new raster layer that appears in the

layer list (it should be called “Calculation”), right click on it and select “Data” then

“Make Permanent…” from the context menu that appears (simply directly “Make

Permanent…” in ArcGIS 9.1). Select a suitable location and name, and save the

layer. Remove the “Calculation” layer from the map and add the permanent version

you just saved. The resulting map is our complex cost allocation for the area of

interest, that takes into account slope (although still not uphill / downhill travel),

visibility of the cursus, and difficulty of travelling along the river floodplain.

Next, find the “Cost Distance” tool again and launch it. Under “Input raster or feature source data” select the “Observer” layer.

Under “Input cost raster” this time select the new cost allocation layer than you just created. Then click on the file opening

icon next to “Output distance raster” and choose a sensible location and name to save your results to. We also to

construct a raster that calculates which neighbouring cell forms the cheapest (in terms of energy) route out of each cell. Click

on the file opening icon next to “Output backlink raster” and choose a sensible location and name to save your results to.

Ignore the other parameters and click on “OK”.

We are now ready to create our second least cost path. Find the “Cost Path” tool and launch it. Under “Input raster or feature

destination data” select the long barrow layer. Under “Input cost distance raster” choose the cost distance layer that we just

created. Under “Input cost backlink raster” select the backlink raster that we just created. Then click on the file opening icon

next to “Output raster” and choose a sensible location and name to save your results to. Ignore the other parameters and

click on “OK”. This process is rather complex, so do not be alarmed if it does not turn out perfectly. Further detail will be

provided in a later module.

6 If you get a projection warning, just click on “Yes”.


Compare this new cost path against the previous and notice how different it looks. If you compare the new cost path against

the viewshed and the river layer, you should be able to see how it tries to avoid the river and tries to stay hidden from the

cursus, where possible. Hopefully, it will be obvious now how you can get very different results from the same tool depending

on how much information you put in and depending upon the quality of the input data. Also, take notice of how in using

visibility to model the second path, we extended our analysis from a pure measurement of energy to include an element of

conscious choice. This reflection of the psychological as well as the purely physical illustrates one way in which the degree of

environmental determinism inherent in this type of analysis can be reduced (see Wheatley & Gillings 2002: pp 151-159 for

more discussion on issues in cost surface analysis).

8. (OPTIONAL) Creating a DEM via interpolation

Finally, we are going to look briefly at the interpolation of a DEM (digital elevation model) from other sources of elevation data.

Again, this will be a somewhat complex task, so do not be afraid to leave this exercise to one side if you wish. Add the

following layers to the map: “Spotheights.shp” “Contour1m.shp”. These are spot heights for and a contour map of our region.

We are going to create a DEM from these two sources of data. In ArcToolbox, search for and launch the “Topo to Raster”

tool. This tool allows us to combine several sources of data to create a new DEM.

Under “Input feature data”, select the following layers to add

them to the list below: “Spotheights” “Contour1m” “Lakes”. To

the right, you will see two further attributes which we need to

set. First set the “Type” of the spot height layer to

“PointElevation” by clicking in the

field and choosing from the drop

down list, the type of the contour

layer to “Contour” and the type of

the lake layer to “Lake”. Then set

the “Field” of the spot height layer to “Elevation” and of the contour layer to “CONTOUR”. Then click on the file opening icon

next to “Output surface raster” and choose a sensible location and name to save your results to. Finally, set the “Output

cell size” to 1, then click on “OK”. The tool will then combine these three sources of data to construct a new DEM. It might

take a little bit of time to run. We could also have included the river layer, but this tends to produce rather jarring results.

When it is finished, compare the new DEM against the one already on your map. You may need to adjust the order of the

layers and switch some of them off to do this; you will also need to make sure the symbology of both layers is the similar. Can

you notice any differences?7 Decide whichever one you prefer, then remove the other. Save your progress.

7 The main one is that the lakes are now flat (hide the lakes layer and zoom in to see this), rather than running down the slope.


Appendix: further reading

Guides to GIS in archaeology:

Conolly, J. & Lake, M. 2006. Geographical information systems in archaeology. Cambridge: Cambridge University Press.

Wheatley, D. & Gillings, M. 2002. Spatial technology and archaeology: a guide to the archaeological applications of GIS.

London: Taylor & Francis.

General introductions to GIS:

Burrough, P. & McDonnell, R. 1998. Principles of geographic information systems. Oxford: Oxford University Press.

Chrisman, N. 2001. Exploring geographic information systems. Chichester: Wiley.

Jones, C. 1997. Geographical information systems and computer cartography. Harlow: Pearson.

Computer cartography:

Kraak, M. & Ormeling, F. 2002. Cartography: visualization of spatial data. Harlow: Pearson (new edition due November

2009).

Monmonier, M. 1996. How to lie with maps. Chicago: University of Chicago Press.

More technically detailed GIS textbooks:

Laurini, R. & Thompson, D. 1992. Fundamentals of spatial information systems. London: Academic Press.

Worboys, M. & Duckham, M. 2004. GIS: a computing perspective. London: CRC Press.

A selection of archaeological GIS case studies:

Chapman, H. 2003. “Rudston „Cursus A‟ - engaging with a Neolithic monument in its landscape setting using GIS.”

Oxford Journal of Archaeology 22(4), pp.345-356.

Gillings, M. 1995. “Flood dynamics and settlement in the Tisza valley of north-east Hungary: GIS and the Upper Tisza

Project.” In Lock, G. & Stančič, Z. (eds.) Archaeology and geographic information systems: a European perspective.

London: Taylor & Francis, pp.67-84.

Llobera, M. 2003. “Extending GIS-based visual analysis: the concept of „visualscapes‟.” International Journal of

Geographical Information Science 17, pp.25-48.

Wheatley, D. 1996. “Between the lines: the role of GIS-based predictive modelling in the interpretation of extensive

survey data.” Analecta Praehistorica Leidensia, 28(II), pp.275-292.

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