So why are my predictive models not as powerful as other predictive models?

‘Gain’ is the accepted way of determining (and stating) the effectiveness of an archaeological predictive model, the higher the gain the better the model. In America it is considered that the maximum gain for a predictive model is probably restricted to around 70% due to inherent modelling problems and typically gains range between 50 – 70% (see Ebert. J, ‘The state of art in inductive modelling: seven big mistakes’, in Wescott. K & Brandon. R (ed.), Practical Applications of GIS for Archaeologists: a predictive modelling toolkit, 2000, Taylor & Francis, London). Typically the gains of my models are around 15 – 25% for predicting the model’s own input data and independent test datasets. So the question is why – what am I doing wrong?

 

As I live in Norfolk (UK), I’m currently developing my predictive modelling skills by modelling Norfolk only, with a view to expanding to the whole of East Anglia (UK) when I am proficient in the art. Oscar Wilde once said that Norfolk is very, very flat and I think that this is a clue – terrain! If one works within a landscape which funnels people to live in specific areas or the environment bars them for living in other areas, then modelling becomes easier. A classic example would be Egypt where the only place people can easily live (in the past or in the present) is next to the Nile (or an oasis) because of the water supply, fertile soil, wild life, etc. Any settlement away from the Nile (or an oasis) would require significant support from elsewhere.

 

Another factor is ground slope. A classic example is a predictive model in West Virginia (America) that produced high gains in a study area where 90% of the terrain is over 18° and 41% is over 31° (see Lock. G & Harris. T, ‘Enhancing Predictive Archaeological Modelling: Integrating Location, Landscape and Culture’ in Mehrer. M & Westcott. K (ed.), GIS and archaeological site location modelling, 2006, Taylor & Francis, London). Whilst foraging and hunting for food is possible in such a terrain, farming and building permanent settlements on flat ground is severely limited. Consequently, such terrain is ideal for hunter-gatherers who by their migratory life-style leave little evidence of their existence.

 

Compare these environments with Norfolk (UK); 90% of the county has a ground slope of less than 4° and as a consequence nowhere is very far from a source of open water! For comparison; in 1989 an archaeological predictive model comprising of 120 Km² of the Netherlands (the Regge Valley) was produced. Afterwards, the model was tested by intensely field walking a 2 Km² area within the study area and the settlements discovered were compared with the predictions of the model. The model achieved a high gain for predicting its own input data but a relatively low gain for predicting the field walking discovered settlements (see Brandt. R, Groenewoudt. B & Kvamme. K, ‘An experiment in archaeological site location: modelling in the Netherlands using GIS techniques’, World Archaeology 24: 2, 1992, Routledge, London). The terrain of the Regge Valley is similar to Norfolk and when tested, the Regge Valley model produced similar gains to Norfolk!

 

Concentrating on just one environmental factor – ground slope, I have examined sixteen published archaeological predictive models and have plotted their stated gain against the average ground slope of each study area (in some cases having to estimate it using various means such as Google Earth). Whilst it is not possible to consider that this plot shows a direct correlation between gain and ground slope (as there are numerous other factors involved) I have noted that the higher the average ground slope of the study area, the higher the gain is likely to be.

 

So, do I move to America in order to achieve models with higher gains to get my PhD or is it acceptable to carryout archaeological predictive modelling in any terrain? If you have any opinions on this subject I would be pleased to hear from you.