The advanced training and its teaching materials were organised by Prof. Anna Leone (Durham University), Dr. Marco Nebbia (Durham University), Dr. Corisande Fenwick (UCL), Dr. Gai Jorayev (UCL), Dr. William Wotton (KCL) and Dr. Hiba Alkhalaf (KCL). The consultants were Prof. Alaa El-Habashi (Monafia University), John Stewart (English Heritage), Niki Savvides (Independent), and Gai Hopkinson (Archaeology South-East). The training was organised with the support of the Tunisian co-director Dr. Ammar Othman (INP) and with the help and assistance of Dr. Ahmed Saad (University of Benghazi) and the collaboration of Dr. Muftah Alhaddad (University of Tarhuna).
The first day of the advanced training was dedicated to the processing of field survey data and their integration, in GIS, with the pottery catalogue produced during the September training.
After the field survey carried out in July 2017 and September 2018, part of the team was trained on the identification and classification of the ceramic material picked up and bagged at each collection/sample spot in the field. During the cataloguing of the pottery the database of surface material was converted into an Excel spreadsheet with the different classes and types of ceramics associated with the spot where they were collected. Having already imported the GPS points, of all the collection spots into GIS, we discussed the best way of linking the Excel table to the survey points in QGIS. The information that we wanted to display across the surveyed area regarded the different types and sub-types of material and their chronologies (where determined). Since for each collection spot (represented by a GPS point) there are more than one potsherd, the best way to link the GPS points and the table of pottery types is through a relation one-to-many (parent-child) so that every potsherd catalogued per collection spot is represented and searchable (Figure 1). In this way, the two tables are linked and each type or class of pottery is searchable and displayable on the map across the surveyed area.
Figure 1. Attribute table showing an example of one-to-many relation between the GPS point and the table containing the pottery classification.
With the data stored in this way it is now possible to search for a specific category, class or type of material (from the pottery table) and then display the location and quantities of the specific queried class of objects across the surveyed area (Figure 2). These methods can be used by the heritage specialists such as staff members of DoA and INP to produce maps showing the location and quantities of a specific class of objects, collected during the systematic field survey, across the site.
In Figure 2 the colour gradient represents the quantity of all sherds per each collection point, thus showing the areas across the site with more or less surface material that can suggest the presence of sub-surface archaeological remains.
Figure 2. Distribution of surface material densities across the surveyed area in Iunca.
Figure 3. Distribution of surface sherds and definition of site limit (red polygon) and buffer zone (green) of Iunca.
Based on the distribution of showed in Figure 1 we then discussed with all participants what could we define as “limit” of the archaeological area of Iunca. After a long debate, we agreed that the red polygon shown in Figure 3 represents what we can define the edge of the main archaeological area, and the green polygon what we can call the buffer zone of Iunca.
One remarkable point that came out from the discussion in class, is that whilst in an academic research environment the definitions archaeological sites, landscapes, record, etc… have been debated but never agreed on for a long time, in a heritage conservation institution those have to be defined in order to produce a legislation that can protect these elements of the cultural heritage.
Afterwards, we focussed on building queries within the attribute table (by attributes), and between attribute tables (by location), so that participants can now select specific features based on their table values or their location.
In Day 2, we moved on to the integration of GIS with photogrammetry and condition assessment, the other two strands of the advanced training.
Therefore, we introduced the concept of georeferencing in GIS, which represent a fundamental tool for importing and utilise data sets of different sources into the same spatial framework.
To practice, we georeferenced the plans of two buildings at the site of Thyna, that were later used in the exercise of condition assessment in the last three days of the advanced training (Figure 4).
Figure 4. Screenshot of QGIS showing two of the buildings plans of Thyna georeferenced.
Having the plans georeferenced allows to digitise (as shapefiles) the elements analysed during the condition assessment and put the information in the attribute table of the shapefiles.
Therefore, we then reproduced the condition assessment table developed by King’s College team into an attribute table in GIS. Fields have been maintained the same, but the structure of the table was adapted for GIS display and analysis. For instance, wherever possible fields that represented magnitudes (e.g. conditions or priorities) have been created as numerical values so that quantities could be represented and displayed, with different gradients of the same colour, in QGIS.
This exercise represents the starting platform for the participants to import the data from the field into GIS during the condition assessment performed on site and provides a tool for managing the amount of data coming from recursive field visits. Using GIS, it is possible to have a visual overview of the state of preservation of either a specific building or a whole site, as well as displaying changes occurred between field assessments carried out within a specific time lapse.
Finally, we refocused our attention on the site of Iunca and we went through a number of basic tools that can be useful for managing and visualising archaeological data in QGIS.
Tools like creating Buffers around features allows the visualization of spatial patterns in the data. For example, during the field survey in September 2018 we found 4 looting pits in the northern part of the site of Iunca and we took their position with the hand-handle GPS, so that we could plot them in GIS. After having traced/digitised the main and secondary viability within the site, we created a buffer around the lines representing the viability at Iunca at equal intervals of 50 metres (Figure 5). Comparing then the location of the looting pits with the buffers it was clear that the areas affected by looting where remarkably close to the secondary viability, which in this case favoured the access to that part of the site. The efficacy of this tool was appreciated by the participants as it can have a number of different applications for the management of cultural heritage, especially large sites such as Iunca.
Figure 5. Map showing the buffering around Iunca viability and location of main archaeological remains.
In Day 3, we focussed on the import and visualization of Digital Elevation Models (DEM) into QGIS. Basic visualization techniques such as Hillshading and Slope have been shown. These also allowed to discuss about the different resolution in DEMs and appreciate the value of large scale low-res (30m) dems like SRTM (Shuttle Radar Topographic Mission) which is worldwide freely available VS the high-res (2cm) dems derived from the drone photo collection of a small area of the site. Even if the was not enough time to go into too much detail, the major advantages of using DEMs in archaeology and cultural heritage protection have been discussed and showed with presentations of other projects the trainers where involved in.