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1 Lecture 2 - Data exploration This lecture will cover: Attribute queries Spatial queries Basic spatial analyses: Buffering Voronoi tessellation Cost paths / surfaces Viewsheds Hydrological modelling Autocorrelation and interpolation methods Raster manipulation and predictive modelling Theoretical considerations Querying by attribute Today we will be talking about how to get more from your data once you have it in your GIS. The simplest method of further analysis is through querying. Although straightforward, it can however form a powerful tool in the extraction of pattern and meaning from our data. Within a GIS, you can query data in two ways: firstly, you can perform standard attribute queries as you might in any other database software; secondly, you can also perform spatial queries (more on which later). Attribute queries effectively take the form of a question. The easiest way to conceptualise this is that you are constructing a sentence using a special syntax that will select a subset of your data as a result. In ArcGIS, this type of query is constructed using the Select by Attributes tool. The sentence will usually begin: SELECT * FROM layername WHERE: with the layername being the currently selected layer. This will produce a new selected set of objects. There are also options for adding to a previous selection, removing from one, or selecting a subset from one. It is then our job to finish this sentence. For example, if we wished to select Roman records from our data layer, we could use the phrase: "Period" = 'Roman' where Period is an attribute field in that layer and Roman is one of the possible recorded field entries. Pressing Apply would then perform this selection and display the results on the map. We can, of course, construct much more complex sentences, resulting in more specific results. These use what is called Boolean logic to extend their syntax (i.e. AND / OR / NOT). For example, if we wished to find excavations of Roman sites, we could construct the following sentence: SELECT * FROM layername WHERE: "Period" = 'Roman' AND "Source" = 'Excavation' Page 1 of 7 (document version 1)

2 This would produce a more restricted set of results than the previous query, but perhaps a more useful set for our particular purposes. As you will be able to imagine, as the size of your database increases, this tool becomes more and more useful. The results of a query can be exported, either as a new layer or as a data table: this allows us to query our data using the GIS and then export the results for further analysis in external software, perhaps to perform statistical tests. However, usually we could perform this type of query in our external software in any event: the real strength of GIS lies in constructing spatial queries. Spatial querying In ArcGIS, spatial queries are performed using the Select by Location tool. Again, with this tool we are effectively constructing a sentence, by selecting the sentence terms using drop down boxes. The variety of different spatial queries that can be performed using this tool is quite staggering. By way of example, we might wish to discover which SMR records fell within a 500 metre buffer around a potential development site. Performing this query just takes a few clicks. We can also combine the two querying tools. For example, we might wish to know which SMR records fell within a distance of 150 metres of the church on our map. We would first select the church using the Select by Attributes tool, and then we could select the SMR records using the Select by Location tool, taking care that the relevant box is ticked. Again, although a simple procedure, the potential uses and results are legion. There are many different types of spatial query built into this tool, reflecting the many different kinds of topological relationship that any two geographic objects might possess. Beyond distance from, this includes intersection and crossing, partial and complete containment, shared line segments, equivalence, boundary touching, etc. The use of this tool is an excellent way of exploring your data. Finally, it is also possible to make selections manually in ArcGIS using the appropriate tool, and a further tool provides the ability to recover object records by clicking on them. Spatial analysis Of course, beyond simple querying there are also a whole host of more involved methods of spatial analysis made possible using GIS. In ArcGIS, the standard versions of these tools are accessed through the red ArcToolbox icon, or from various toolbars. There are also many scripts and tools for ArcGIS written by other users that can be downloaded from ESRI s website. The provision of user generated content is also excellent if you are using GRASS, especially when seeking tools for raster analysis. From an archaeological perspective, many of these tools have been used successfully by members of our discipline. We shall now take a look at some of the most popular, although it should be borne in mind that the use of some tools is more appropriate to archaeology than others: the theoretical implications of conducting spatial analyses will be discussed later. Buffering Buffering is a very simple but useful form of analysis. It creates a buffer or series of buffers around the geographic objects in a layer at a set distance or distances. Buffers around points will be circular, they will be shaped like sausages around lines, and will fan out around polygons. They can be used in combination with queries to determine which objects in another layer might fall within the buffers (much like with the Select by Location tool). A series of buffers could thus be used to assess the degree of risk of known Page 2 of 7 (document version 1)

3 sites being affected by building development in an area, perhaps along a proposed new road. This could be important if you were not certain about either the location or extent of either the development or the sites. Voronoi tessellation The Voronoi tessellation, known in geographical contexts as Thiessen polygons, has been quite widely used in archaeology following its adoption by Clarke in his New Archaeology manifesto. Essentially, it divides up the space surrounding a series of points into a series of territories dependent upon which point is closest. The resulting maps are thus supposed to be taken as the hinterlands or territories of a series of sites, in situations where such territories are not known. The main problem with Thiessen polygons is that they are directly determined based upon distance, and thus they give no account to differences in power or influence between different sites in the past. The resulting maps also take no account of natural breaks in terrain, such as hills, mountains or rivers. As such, the technique is best thought of as producing only a very approximate model of the past spatial relationship between a set of geographic objects. Cost surfaces The construction of cost surfaces and least cost paths is another commonly used form of GIS analysis in archaeology. A cost surface usually models the energy cost of a person travelling across each cell of a raster grid. They are generally calculated from a combination of slope (derived from a DEM), known obstacles (such as rivers or even objects like defensive walls), possibly the nature of the ground surface, and either the estimated or calculated energy cost of a person crossing cells of each type. Obstacles can either cause a much higher cost or even make passage impossible. It is also possible to factor in known routeways by giving them a lower associated cost. Cost surfaces can be divided into two main types: isotropic surfaces ignore direction of travel; anisotropic surfaces, by contrast, take into account direction of travel. The latter are thus more realistic, as it is generally easier to travel downhill (until the slope becomes too steep) than uphill. Cost surfaces also depend on the mode of travel, which is generally taken to be walking in archaeology: however, it should be apparent that there is a big variation in terms of the cost of crossing a river dependent upon whether people had access to a boat, for example. Once a cost surface has been produced, it is possible to define the areas around a site or series of sites which a person could travel to within a given time. It is also possible to then construct least cost paths: these show the most energetically efficient path between two points on a map. Cost surfaces can be used to test how much effort people in the past might have been willing to expend on fetching different types of resources (e.g. they may have been more willing to travel to obtain certain raw materials). This forms another method of site catchment analysis, but a more realistic one than Thiessen polygons. Least cost paths can be used to try to work out where past routeways might have lain, or may be compared against known past routeways to discover whether they were energy efficient (and if they were not, to then think about why not). The quality of a cost surface is heavily reliant upon the quality of the data put into its construction. It requires a good DEM, and also produces the best results when local calculations are made as to the energy efficiency of different cell types. All told, the more and the better data you put into the calculation of cost surfaces, the better and more reliable results you will get out. Page 3 of 7 (document version 1)

4 There are two main problems with cost modelling. Firstly, the analysis is open to accusations of environmental determinism. This is in common with several other types of spatial analysis. Secondly, least cost paths tend to produce a single result, which neglects to reflect any degree of uncertainty in the route produced. There must inevitably be some uncertainty in any model produced, as human beings are not machines designed to calculate the least cost path to a location, so this is problematic. Future work into more fuzzily defined least cost corridors may be a step towards solving this difficulty. Viewsheds The construction of viewsheds is also very popular amongst GIS-using archaeologists. They can be used to calculate either the areas on a map which can be seen from a point, or the areas on a map which can see a point. Construction of a viewshed again requires a DEM, preferably of high resolution and otherwise good quality. Despite its popularity, there are several thorny issues that surround viewshed analysis: Viewsheds conventionally work with the raw elevation data and, as such, are not able to take into account past obstructions in the landscape. This is particularly important where an area may have been covered in vegetation that was disruptive to view distances. This can be countered where past vegetation cover was known by adding an offset equivalent to tree height etc. to the base DEM, but such a system relies upon good quality environmental modelling and still cannot take into account any partial visual obstruction by vegetation. As a result, the application of viewsheds works best in areas where past vegetation cover was known to be minimal. A viewshed calculated by a GIS will extend by default to the limits of the DEM used. However, the human eye can only see so far. The distance at which it can differentiate objects is of course dependent on the size of those objects and the atmospheric conditions. Therefore, it is best to construct several viewsheds for a point, limited by the distance at which different sizes of object could be seen easily. Buffers could be used to produce this result. The area viewable from a point will vary to a surprisingly large amount depending upon the height of the viewer. This is particularly the case where the viewer may have been placed in a watchtower or similar structure. This can be approximated, but is heavily dependent upon the quality of our knowledge. Viewsheds are prone to so-called edge effects and also to defects caused by poor quality DEMs. It is, thus, best only to create viewsheds when you are confident in the quality of your DEM and also able to obtain a DEM that provides a good overlap beyond your area of interest. Finally, theoretical issues with viewsheds include the equating of visibility with perception, and the dominance of sight over the other unmodelled senses in such a model. The construction of better models of perception is likely to be one area of future software development in regard to viewsheds. In any event, so long as only used where appropriate, and so long as conclusions reached are not overemphasised, these issues should not prove any great obstacle to the use of viewsheds. Adaptations to the standard viewshed model allow the calculation of multiple viewsheds of the area visible from at least one of a set of sites, the addition of cumulative viewsheds to record how many of a set of sites each cell is visible from, and the construction of total viewsheds to assess the general degree of visibility of cells on a DEM. Viewshed analysis can fit nicely into several archaeological themes. Particular areas where it has been applied in the past include discussions over dominance of monuments in Page 4 of 7 (document version 1)

5 the landscape, surveillance, and the choreographing of approaches to monuments. Viewsheds are also useful for assessing the likely visual impact of proposed building developments on the view from important archaeological sites and other historic monuments. Hydrological modelling Most GIS packages include quite extensive tools for modelling the hydrology of a region. Use of these tools relies again upon the availability of a good quality DEM. Using these tools, it is possible to map water flows across a region, delineate watersheds, measure flow length and accumulation, and even to derive stream patterns. This can be the most convenient way of determining the stream and river patterns for a region, particularly if you are not able to obtain the information by other means or if you are working in an arid region which was once wetter. Archaeologically, knowledge of hydrology is important if you wish to trace riverine trade routes, or if you believe there may be a relationship between site location and water sources, or if you are engaged in palaeoenvironmental reconstruction. Spatial autocorrelation and interpolation Interpolation is the process by which the unknown state of a variable between known points may be estimated. There are many different types of interpolation technique built into most GIS software, some of which are more complex than others. An essential test before undertaking any interpolation is to determine if the data has positive spatial autocorrelation. This is a tool that also provides a useful test of whether any visual patterns in your data are likely to be true. Essentially, spatial autocorrelation is a measure of similarity between objects in close proximity to one another. If close objects are dissimilar, then the data has negative spatial autocorrelation; where there is no pattern, there is no autocorrelation; where close objects tend towards similarity (in any particular attribute tested), then the data possesses positive spatial autocorrelation. The result will be a figure which will tell the user to what extent any patterns in their data might be down to random chance. If an attribute attached to data possesses some degree of positive spatial autocorrelation, then we can attempt to interpolate the state of that particular variable between our known points. Interpolation methodologies vary in their complexity, but all essentially attempt to find the best possible solution by which to fit a continuous surface to the data. The output will be a raster grid, giving the estimated value for the attribute in question at every point across the modelled region. Commonly used interpolators include inverse distance weighting, splines and kriging. One area of particular usefulness to archaeology is in the construction of trend surfaces. A trend surface is an interpolator that, rather than trying to fit the best surface to a series of points, tries to fit an average surface. If used to model pottery survey data, it can give a good indication of likely supply routes. The main application, however, is in then using the trend surface to calculate which known data points have an above or below average profile. This can help to discover sites of exceptional wealth or particular poverty, for which an explanation must then be sought. Interpolation can also be used as part of a predictive model (more on which later). However, probably the most common usage of interpolation amongst GIS-literate archaeologists is for the generation of DEMs from contour or other survey data. Page 5 of 7 (document version 1)

6 Raster manipulation and predictive modelling The real beauty of raster datasets lies in the simplicity with which you can use them to do mathematical calculations. For example, tools are provided in most GIS software to calculate useful data about slope and aspect from DEMs. These derived surfaces, and any other raster data layers, can then be combined in a tool known as a raster calculator to produce new raster layers. The calculator is in effect a way of building algebraic equations in which each term is a map layer. It depends upon the various rasters used being at a compatible resolution with one another, but so long as that is the case the possibilities are manifold. For example, you might wish to discover which areas of your map would be suitable for use as vineyards. In this case, you would derive aspect and slope maps from your DEM, and then combine them in the raster calculator to obtain a new raster layer containing all areas with an aspect facing south and a good steep slope. If you possessed data about soil types and microclimate as raster layers, these could also be combined to produce a final model of good wine growing regions. In archaeology, raster manipulation has been used in several important ways. The construction of costs surfaces as described earlier is one of these. Another important area is in predictive modelling of potential areas of archaeological sensitivity. This particular strand is less popular in the UK, but widely practiced in the US and Europe. Essentially, what are usually government agencies combine raster grids of a variety of factors believed to influence the probability of the presence of archaeological sites, such as soil type, proximity to water, aspect, slope, etc. The resulting raster map shows which areas of a region are more likely to be at risk of causing archaeological disturbance if building development were to take place, so that planners can then make their plans accordingly. The difficulties with predictive models are that they are hard to test without excavation, and that they can tend to become self-fulfilling: areas of predicted high archaeological sensitivity remain untouched, whereas areas of low sensitivity may be built upon without any further consultation with archaeologists if anything were to be discovered. Theoretical considerations Many of the above techniques raise certain theoretical considerations of which users ought to be aware. Firstly, some of these techniques are very closely associated with the New Archaeology. Further, many are likewise subject to some allegation of environmental determinism, as all models are inevitably deterministic in some fashion. Some users may be comfortable with this, others less so. In essence, users should maintain a question-based approach, only use techniques where appropriate, and remember to never treat any models as finalised replications of reality in the past. In this way and if any reservations held by users are expressed explicitly, any criticisms due to the above can be minimised. It should also be borne in mind that very few of the tools included in GIS packages were designed for application to archaeological data. Most have been borrowed from other disciplines, and some may not be appropriate for archaeological usage. Just because a tool is there, does not mean it should be used without careful thought and experimentation. Finally, it can be quite easy to disguise dubious or uncertain results using GIS, as computers tend to lend an air of authority to their output. If you are not certain about the quality of your data, make sure you record your reservations in your metadata, and if necessary relate them alongside any figures included in published material. It is also often possible to convey some degree of Page 6 of 7 (document version 1)

7 fuzziness or uncertainty using appropriate symbology. Furthermore in regard to the final production of maps, it is always best to try to keep things simple: it is very easy to cloud the message that you want to send by including too much detail on a map, or by displaying it in an overly complex fashion (more on this in the next lecture). Page 7 of 7 (document version 1)