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:
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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.
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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.
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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:
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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:
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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'
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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'
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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.
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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
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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
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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
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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.
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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”.
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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.
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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.