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Kokalj, Ziga

Nuninger, Laure

Oštir, Krištof

Pennors, Francoise

Saligny, Laure

Tolle, Florian

Zakšek, Klemen

Name not available

Confidence maps: a tool to evaluate archaeological data's relevance in spatial analysis

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Layers of Perception – CAA 2007272Krištof Oštir – Žiga Kokalj – Laure Saligny – Florian Tolle – Laure NuningerWith the collaboration of Françoise Pennors and Klemen ZakšekConfidence Maps: a Tool to Evaluate Archaeological Data’s  Relevance in Spatial AnalysisAbstract: Inventory data used in archaeology is often incomplete and heterogeneous. In the framework of the ArchaeDyn program, a method has been proposed to evaluate heterogeneity in archaeological invento-ries. The purpose of this work is to create a validation tool to interpret the results. This tool is called a “con-fidence map” and is produced by combining representation and reliability maps. The first step consists of generating representation maps to describe the clustering of archaeological items. The second step is based on reliability maps. Data providers are asked to define and outline the level of reliability of their data. Then the representation and reliability layers are combined using map algebra. The resulting maps allow for the comparison and analysis of data confidence.IntroductionInventory data used in archaeology is often incom-plete and heterogeneous, making its interpretation, dating and localization a difficult task. In fact it represents a sample of a more complex reality. The analysis of archaeological data using spatial analy-sis tools requires great caution in the interpretation that is drawn from them. The issue is to avoid the identification of spatial trends that are just a con-sequence of the degree of archaeological investiga-tion. In the framework of the ArchaeDyn program, a method has been proposed to evaluate and give spatial insight on the heterogeneity in archaeologi-cal inventories. ArchaeDyn combines the efforts of several archaeologists working on various topics, ranging from the diffusion of manufactured ob-jects in pre- and protohistorical times, to the use of land through the study of settlements, parcels and manuring during the antiquity (nuninGeR / touRneaux / favoRy 2008). A great diversity in analy-sis scales and studied objects led to different inven-tory protocols such as systematic field walking, bib-liographical studies, museum researches, etc. The variety of available data raises questions on the va-lidity of spatial results based on archaeological ma-terials of a different nature, temporality and spatial extent. The purpose of this preliminary study is to create a control tool that will be used for the inter-pretation of results while trying to extract the most valuable information to the archaeological interpre-tation. This tool is expressed spatially through what are called “confidence maps” which is a data layer produced by combining reliability and representa-tion of the data.Representation MapsEvidence for data dispersion/location over sepa-rate study areas is symbolized with representation maps. They were designed with the aim of being standardised in respect to the theoretical mean of the individual study area (i.e. variations from the average). Therefore they allow the quantification and visualization of spatial heterogeneity in the sampling and the inventory of the different data-sets. The number of archaeological items in each pre-defined grid cell is computed and this value is compared to the expected (usually mean) value in the study area, which gives an idea of the over- or under-representation of data.To begin the analysis grid size has to be defined for each individual study area. The proposed opti-mal cell size calculation is based on the assumption that archaeological data is approximately evenly distributed, which means that each data object is as-signed the same area, defined by the cell. The cell size is therefore “unique” for each study area be-cause it is directly related to the area of investiga-tion and the number of observations and in effect it is an average distance among observations (sánChez 2006). In our case we have computed the optimal cell size as cell_size = sqrt(total_area/Nobservations). This empirical method is based on the assumption 273Identifying Settlement Patterns and Territoriesthat if the objects are normally distributed, then a similar area should approximately belong to every object. Therefore, the average area of an object can be computed by dividing the whole area of interest by the number of objects. This average area is square shaped when working with a regular grid, and means that the cell size of the grid can be computed by square rooting the average area. This number is then rounded and represents the optimal reso-lution. A similar approach is mentioned by Shary (shaRy / shaRaya / mitusov 2002). However, data is rarely evenly distributed. In order to improve the statistical significance we have chosen the first larg-er grid size, fitting the “standard” resolution sys-tem used in ArchaeDyn, i.e. 1, 2.5, 5, 10, 25, 50, 100, 250 km. This produces grids that are both optimal and well populated: that is containing a significant number of points. In order to simplify the process of data transformations and comparison of differ-ent datasets further, the common point of origin has been defined for all the grids which means the cell boundaries of different resolutions and study areas overlap at the same coordinates. This means that even different scale phenomena can be processed as imagery in order to combine their information over the same or different areas when it is relevant.Representation classes were defined to stand for no data, normal, over and extreme representation (see Fig. 1). It was found that these types of classes cor-respond to the nature of archaeological data, whose frequency is typically exponentially distributed and hardly ever normal. If it were the case then classes would be under, normal, and over represented. The approach is different from the previous work done by the group (nuninGeR / touRneaux / favoRy 2008). Some unresolved issues that remain are the auto-matic or semiautomatic selection of thresholds for classes and the no-data phenomenon.Even though the process was designed with the aim of being non subjective and based purely on statistics, a uniform automatic statistical division of classes based on average proved to be unreasonable. This was due to the extreme data heterogeneity that included different distributions, differences in ab-solute values, no data phenomenon, and the use of integer values. According to our tests, the classifica-tion process has to be done (semi)manually and in-dividually for every dataset with the help of statisti-cal and mathematical tools. The usual procedure is based on histogram analysis and its modification us-ing a logarithmic function, and defining the natural breaks in the data. The latter are especially difficult to define if absolute frequencies (representations) are low. This implies the importance of selecting the optimal grid size.The problem of handling no data values has not been solved satisfactorily, but rather bypassed. The statistics can be significantly altered with the inclu-sion of cells with no data values in the calculation. The argument for including such values is the fact that the space is continuous and areas cannot be left out, however in cases where data is highly concen-trated this can lead to dramatic decrease of the aver-age and as a result even the areas with only one ob-ject can be classified as over represented. Increasing the cell size by one “standard” step and manual de-limitation of classes avoided this problem because with the latter, the interpreter can manually classify such areas as normally represented and then the ini-tial number of no data cells is effectively decreased anyhow. A problem which arises is the further con-centration of extreme values and the resulting re-duction of “contrast”, but if this is not the primary concern it is well supplemented by improved over-all legibility and accuracy of the final map.Reliability MapsReliability maps express the settings (and limita-tions) of inventory exploration (i.e. how the ar-chaeological sources were explored) in terms of common indicators such as survey level – sampling, visibility level, the quality of references etc., about a specific dataset. A reliability map gives information on the intensity of research and exploration (reli-ability of the inventory), and is not primarily con-cerned with the quality of the data’s location. This Fig. 1. A representation map of dated archaeological bronze objects in France (map: Z. Kokalj, data: F. Pennors).Layers of Perception – CAA 2007274means it also can be interpreted as a correlation between intensity of research and actually identi-fied sites or archaeological evidence. In our case a reliability map covers the entire study area and distinguishes three reliability levels: reliable, fair-ly reliable and not reliable. It has been defined by the providers of individual datasets and has been mostly drawn by hand according to a predefined set of rules. The rules were defined by each work-group and by each archaeological team. Indeed, these rules are depending on the kind of investi-gation. Nonetheless, each set of rules is written in accordance to the three predefined degrees which then allow comparisons to be made. The definition of reliability levels is adjusted according to the na-ture of data. For example, instead of field walking, data availability in museums or publications can be considered (Tab. 1). The identification of indi-vidual levels is based on an empirical method as its foundation is the knowledge of the data quality, and is therefore inherently biased. It is also highly Fig. 2. A reliability map of dated archaeological bronze ob-jects in France (map: Z. Kokalj, reliability zones and data: F. Pennors).Level 1 (reliable) Level 2 (fairly reliable) Level 3 (not reliable)WG2 (Gandini et al. 2008) and WG1-manuring  (PoiRieR / tolle 2008)1) areas where systematic field walking with spacing of 10 m maximum has been completed, and 2) where there are optimal visibility conditions (ploughing or vineyard or lavender).1) areas where systematic field walking with spacing of more than 10 m has been completed, or 2) where systematic field walking has been carried out but there is only partial visibility of the ground (wildland, fallow, meadow, woods)1) areas where only partial or no field walking has been performed and/or 2) there is very poor visibi-lity due to land use and/or areas where significant taphonomic problems are assumed (sedimentary covering or erosion).WG1-field systems  (GeoRGes-leRoy / tolle / nouvel 2008)1) areas where systema-tic field walking (under forest condition) has been completed and 2) where there are optimal visibility conditions, 3) with a good precision in recording fea-tures < or = 10 meters1) areas where punctual field walking has been com-pleted or 2) where there is poor visibility (high density of vegetation...) and/or 3) imprecise records of fea-tures (error > 10 meters)1) areas where very punctu-al or ancient field walking has been completedWG3-Bronze objects  (Fig. 2. and GauthieR 2008).1) areas where the author of the database paid a special attention. 2) where field walking and excavation have been completed with a relatively high density of research/field walking (due to preventive archaeology, dredging) on the study area. 3) where data is easily accessible (straight access to raw data, no access limitation to the stored data – archaeological services, museum, private collection) and with many publica-tions.1) areas where the author of the database paid a special attention and/or 2) where field walking and excava-tion have been completed with a relatively medium to high density of research/field walking on the study areas but with less suf-ficiency and/or 3) where data are easily accessible (straight access to raw data, no access limitation to the stored data – archaeological services, museum, private collection) but with few publications only.1) areas where the author of the database paid a good to fairly good attention and/or 2) where only partial or no field walking/excava-tions have been performed with almost no archaeolo-gists working on the study area or without sufficiency and/or 3) where data are less accessible (no or partial access to raw data, limited access to the stored data data – archaeological services, museum, private collection) and with few publications only.Tab. 1. Reliability rules (examples) defined by the workgroups of the ArchaeDyn's project (nuninGeR / touRneaux / favoRy 2008).275Identifying Settlement Patterns and Territoriesdependent on the phase of studies and this directly connected to the state of the studied database. The ArchaeDyn’s databases are, from now on, fixed at the present state of the investigation in order to provide analysis. New discoveries or new develop-ment of the database will be used by the end of the project during validation and for final interpreta-tions.Confidence MapsConfidence maps act as a tool to evaluate the rel-evance of archaeological data in spatial analysis. They give an impression about the confidence and faith that a user can have about the final re-sults based on the input data. The representation and reliability layers are combined using map al-gebra to produce confidence maps. The logic be-hind this lies in joining two spaces: location-based density (representation) and intensity of inventory (reliability). Results allow for the comparison and analysis of data confidence and thereby the evalu-ation of the interpretation and spatial modelling with respect to trustworthiness. They also give in-formation about the correlation between data rep-resentation and reliability. The map can be used to eliminate “spurious” zones for space-time analysis over the long-term according to the comparison of each study area along with its chronology and the interpretation key of the representation map.The proposed process is essentially based on simple algebraic operations and “binary” logic. The confidence was coded into two digit numbers, with one digit reserved for representation and the other for reliability. To technically enable the addi-tion, the representation map has to have “denary” classes, 10, 20, 30, and 40, being either an extreme representation, over representation, normal rep-resentation or no data, respectively, the reliability map was given values of 1, 2, and 3, ranging from high to moderate to low reliability. Another techni-cal issue is an accurate rasterization of the reliabil-ity map. Normal rasterization omits border areas with less than half cell occupancy. Consequently a 3/4 cell size buffered layer with preserved at-tributes has to be created and rasterized. Its outer buffer is then added to the rasterized reliability and the result combined with the representation map. An ArcGIS tool was designed to speed up and en-able batch processing.The ensuing confidence map is in effect an over-lay of both maps (see Fig. 3). By inspecting the map one can immediately find areas of different repre-sentation but also areas with low data reliability. The strongly coloured areas are more reliable than the light coloured areas. Both can and should be included in the analyses with a degree of caution. The proposed process can also be applied to ana-lyse and compare other spatial phenomena, and tests are underway to evaluate the process for ef-fectiveness in representing temporal changes.Some difficult to manage issues still remain Fig. 3. Confidence map processing model.Fig. 4. A confidence map of dated archaeological bronze objects in France (map: Z. Kokalj, data: F. Pennors).Layers of Perception – CAA 2007276in this approach. Questions, such as how to dis-cretize representation maps and how to interpret areas with no data will need to be addressed in the future.ConclusionsTo represent the level of trust of the spatial analy-sis and modelling results we have defined a tool called confidence maps. Confidence maps provide the user a spatial impression about the representa-tion and the reliability of the input data at the same time, which gives us the opportunity to then detect “artefacts” in the data. The same methodology has been defined for different scales and for different observed phenomena. Despite the fact that the data used can be very dissimilar the interpretation of confidence maps is the same. This is a welcome in-novation especially when considering the extent of the ArchaeDyn project.There are still some problems that remain to be solved. Confidence maps are not suitable for all databases. They better suit databases containing “noise” and perform better with large amount of sta-tistically well represented data. We have also found a rather strong scale dependence of the results. Dif-ferent tests have shown that the tool does perform better with small scale (big area), a large quantity of points (often it will be studies of objects and not sites or settlements), and a low positional accuracy (studies about the diffusion of material, circulation of artefacts).The confidence maps methodology is still in de-velopment and in the future we intend to improve the individual processing steps and overcome the mentioned limitations.AcknowledgmentsArchaeological data used in the study was obtained in the frame of ArchaeDyn project. Part of the work has been performed within the ModeLTER (Euro-pean Laboratory for Modelling of Landscapes and Territories over the Long Term), institute founded by ZRC SAZU and CNRS.ReferencesGandini et al. 2008C. Gandini / f. BeRtonCello / e. GauthieR / l. nuninGeR / f. tRéMent, Hierarchical Typology and Settlement Pat-terns Modeling at Interregional Scale. In: Present vol-ume, 278.GauthieR 2008e. GauthieR, Consumption and Circulation of Prehistor-ic Products in Europe: Characterization of Spatial Evo-lutions Using Map Algebra. In: Present volume, 375.GeoRGes-leRoy / tolle / nouvel 2008M. GeoRGeS-leRoy / f. tolle / P. nouVel, Analysis of the Intensity of Agrarian Exploitation by Spatial Analysis of Ancient Field Systems Preserved by the Forest Cover. In: Present volume, 281.nuninGeR / touRneux / favoRy 2008l. nuninGeR / f. P. touRneux / f. faVoRy, From Archae-omedes to ArchaeDyn. In: Present volume, 278.PoiRieR / tolle 2008n. PoiRieR / f. tolle, Measurements of Diachronic Sta-bility of Agrarian Exploitation. In: Present volume, 281.sánChez 2006J. SánChez, The Accuracy of Grid Digital Elevation Mod-els Linearly Constructed from Scattered Sample Data. International Journal of Geographical Information Sci-ence 20:2, 2006, 169–192.shaRy / shaRaya / mitusov 2002P. ShaRy / l. ShaRaya / a. MituSoV, Fundamental quanti-tative methods of land surface analysis. Geoderma 107, 2002, 1–32.277Identifying Settlement Patterns and TerritoriesKrištof OštirScientific Research CentreZRC SAZUModeLTERGosposka ul. 13 1000 Ljubljana, Sloveniakristof@zrc-sazu.siŽiga KokaljScientific Research CentreZRC SAZUModeLTERGosposka ul. 13 1000 Ljubljana, Sloveniazkokalj@zrc-sazu.siLaure SalignyPôle Géomatique et CartographieMaison des Sciences de l’Homme de Dijon UMS 2739, Dijon, FranceFlorian TolleUniversity of Franche-Comté,Laboratory of Chrono-Ecology UMR 656516 Route de Gray 25030 Besançon, Franceflorian.tolle@univ-fcomte.frLaure NuningerCNRS, Laboratory of Chrono-Ecology UMR 656516 Route de Gray 25030 Besançon, Francelaure.nuninger@univ-fcomte.frFrançoise PennorsProtohistoire égéenne-UMR 7041 (ArScAn)Maison R. Ginouvès, 21 allée de l’Université, 92023 Nanterre, FranceKlemen ZakšekScientific Research CentreZRC SAZUModeLTERGosposka ul. 13 1000 Ljubljana, Sloveniaklemen.zaksek@zrc-sazu.si

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Confidence maps: a tool to evaluate archaeological data's relevance in spatial analysis

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