Locating Potential Archaeology Sites in Aomori, Japan Using Predicative and Maximum Entropy Modeling

William Goldby LA 221 Tessa Beach Amna Alruheili Final White Paper 13 May 2014

Locating Potential Archaeology Sites in Aomori, Japan Using Predicative and Maximum Entropy Modeling

Problem Statement Locating new archaeology sites can be both time consuming and financially challenging. To determine new site location, archaeologists rely on many variables, and sometimes sheer luck. By extrapolating geographical and topographical data from known sites where indigenous populations existed, one can use that information in a predicative model to locate similar areas in a region that might contain artifacts that are archaeologically significant. Professor Junko Habu’s research focuses on the indigenous population of Japan during the Middle- to Late-Jamon period, between about 5,900 - 4,300 years ago. By working with Professor Habu, and by using a spreadsheet with x,y coordinates of pre-established archaeology sites and the artifacts found at those sites, I will extrapolate the topographical, environmental, and geographical data from the most successful sites to use in a predicative and maximum entropy model. The analysis is concerned only with the Aomori prefecture region of Japan. The Aomori prefecture is located in northern Japan. The north side of the prefecture sits along the Pacific Ocean, while the south side is land locked and faces inland. In summary, my research question is the following: Given the topographical, environmental, and geographical criteria correlated with previous successful site locations, where would be the best area to locate a new archaeology site? History/Literature Review Except for carbon dating, nothing has been more important than GIS for archaeologists.1 GIS has been and, continues to be, extensively employed to determine new and existing site locations and characteristics. A predictive model allows one to project “known patterns and relationships into unknown times and places.”2 Predicative models are useful because of all the potential sites, only a very small fraction of them have been discovered and developed, while each year many sites are lost and destroyed due to urban and rural development.3 One of the best ways, then, to locate potential sites is to create formal models capable of predicating where historical dwelling and migratory locations existed. Predictive models work for archaeologists because populations tend to make settlements in favorable environmental conditions, environmental conditions that recur in numerous areas in large regions. “An underlying key to the success of these models,” Warren and Asch write, “is the fact that archaeological sites tend to recur in environmental settings favorable to human settlement. Predicative models take advantage of such redundancies; they exploit contrasts

1 Joe Brandon and Konnie WestCott, Practical Applications of GIS for Archaeologists: A Predictive Modeling Kit (Philadelphia: Taylor & Francis, 2000), 1. 2 Joe Brandon and Konnie WestCott, Practical Applications of GIS for Archaeologists: A Predictive Modeling Kit (Philadelphia: Taylor & Francis, 2000), 6. 3 Ibid

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between the environmental characteristic of places where sites do and do not occur.”4 By making two key assumptions, archaeologists can use small sample sizes to make predictions for very large areas. The two assumptions are the following: 1.) The settlement choices made by ancient peoples were strongly influenced or conditioned by characteristics of the natural environment; 2.) The environmental factors that directly influenced those choices are portrayed in modern maps of environmental variation across an area of interest.5 A predictive model’s success can be judged in the same way elevation interpolation is. By withholding known sites from the model, one can use them as criteria for determining the model’s accuracy. With these two assumptions in place, along with the criteria for determining the model’s accuracy, one can begin gathering data to construct a GIS in order to generate a predicative model, allowing one to apply the model to extensive unsurveyed tracts of land where the actual locations of sites and nonsites are not known.6 Based on a previous archaeological survey that generated 59 prehistoric sites and 89 distinct artifact concentrations, Warren and Asch made a predictive GIS model using the following primary variables: elevation, ranked streams, soil series, vegetation types, and archeological sites. Each of these primary variables was then used to create secondary variables: topographic relief, distance to streams, and soil drainage. The basic unit of analysis was the grid cell. “The survey sample contained 5,473 cells, including 265 site cells and 5,208 nonsite cells.”7 Each cell was a composite of numerical codes determined by the independent variables comprising each cell. Using logistic regression analysis, which predicates site locations based on independent variables, Warren and Asch produced an S-shaped probability curve showing the probability for a site location given the environmental conditions for an area. Using this method, they found that the differences between sites and nonsites were statistically significant.8 Their analysis allowed them to determine where sites should—and should not—occur on a landscape, finding that site location is best on “sloping, well-drained valley landforms on or near the floodplains of headwater streams, and on the crests of well-drained upland knolls scattered across the otherwise flat till plain.”9 With the conceptual underpinnings now in place that demonstrate how archaeologist use a GIS to generate a predictive model for site locations, I will review Maximum Entropy Modeling, a model normally used for exemplifying species distribution. 4 Ibid 5 Ibid 6 Joe Brandon and Konnie WestCott, Practical Applications of GIS for Archaeologists: A Predictive Modeling Kit (Philadelphia: Taylor & Francis, 2000), 8. 7 Joe Brandon and Konnie WestCott, Practical Applications of GIS for Archaeologists: A Predictive Modeling Kit (Philadelphia: Taylor & Francis, 2000), 14. 8 Joe Brandon and Konnie WestCott, Practical Applications of GIS for Archaeologists: A Predictive Modeling Kit (Philadelphia: Taylor & Francis, 2000), 16. 9 Joe Brandon and Konnie WestCott, Practical Applications of GIS for Archaeologists: A Predictive Modeling Kit (Philadelphia: Taylor & Francis, 2000), 26.

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Overview of MaxEnt MaxEnt is a software program using the concept of maximum entropy based on information science. Maximum entropy states that a probability distribution exists between known points of occurrence and unknown points of occurrence based on the variables influencing the known points of occurrence.10 The input variables are determined by knowing where a species exists and then discovering the environmental and geographical variables influencing the species to choose those locations over others. For instance, elevation, slope, aspect, vegetation, and distance to water would all be determining variables a species would consider when choosing habitat location. These variables, with their exact numerical value, would then be entered into the MaxEnt model. The model produces a visual probability map showing the probability of locating a species within a particular area.11 Claira Lietar writes that “[s]ince the program [MaxEnt] is designed to work with insufficient samples, it is very suitable for archaeological predictive modeling as well. MaxEnt provides probability map estimates by establishing the covariance between sites and environmental parameters.”12 By using MaxEnt, I will locate potential archaeology sites in Aomori, Japan by using the ideas and assumptions generated by archaeologists for constructing a GIS predictive model, but will use the MaxEnt program to visually represent the probability of dwelling locations, and thus potential archaeology site location of the prehistoric indigenous people of Aomori, Japan. Solution Based on the two assumptions used in the archaeology predictive modeling process, I will obtain data layers to use in ArcMap and MaxEnt in order to answer my research question. Those data layers will be the following:

Elevation Rivers Aspect Slope Vegetation Land cover

I am assuming that the prehistoric indigenous population considered the following variables before spending extensive time and energy in building their dwelling sites. They considered the elevation of the area, its location near water sources, the direction their dwellings faced, the steepness of the location, the flora and fauna near the location, and the soil drainage of the area. 10 Beach, Tessa. “MaxEnt.” Class lecture. UC Berkeley, March, 2014. 11 Theresa Adjaye, “MaxEnt Modeling of the Apennine Brown Bear” (2011): http://www.itc.nl/library/papers_2011/msc/nrm/adjaye.pdf 12 Claira Lietar, “Modelling Neolithic site location with MaxEnt,” (2011): https://www.ocs.soton.ac.uk/index.php/CAA/2012/paper/view/480

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Conceptual model

Data Location

1. A kind of clearing house of Japanese GIS data from Harvard, located at: http://www.fas.harvard.edu/~chgis/japan/datasets.html

2. GeoCommunity, at geocomm.com, has landuse/landcover for Japan in E00 format

3. The Geospatial Information Authority of Japan is a clearing house of GIS data

specifically for Japan. http://www.gsi.go.jp/kankyochiri/globalmap_e.html and http://www.gsi.go.jp/kankyochiri/gm_japan_e.html

4. The United Nations Environmental Programme has data on freshwater, population,

forests, emissions, climate, disasters, health and GDP for the world. http://geodata.grid.unep.ch

Data Processing My processing extent will be the entire Aomori prefecture. I will determine the raster cell size after I have a better indication of processing time and difficulty. Data Projection UTM_Zone54

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Preprocessing Insight I believe I will have difficulty finding all the layers I need. However, I plan on starting early to overcome any obstacles in order to focus on processing the data in MaxEnt. Milestones/Timeline

Results MaxEnt Inputs Using the following layers to process in MaxEnt – elevation, land cover, vegetation, aspect, slope, and known points of occurrence – four processing runs were made, varying cell size and input layers to obtain four distribution probability maps. An image of each input is provided in Figure 1.

Aspect Elevation Land cover Known points of occurrence

Slope Vegetation

Figure 1: Inputs into MaxEnt.

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MaxEnt Outputs MaxEnt provides three main outputs: a probability distribution map, an area under curve (AUC) graph, and a percent contribution table. The probability distribution map shows the probability of occurrence over the selected region, with warmer colors (red, orange, and yellow) showing areas with better predicated conditions. The AUC number tells the predictive power of the model. The diagonal line means it is no better than random, while numbers approaching one mean the model has a stronger predictive power. The AUC number allows one to compare the predictive power between models. I. Run One: A. Environmental inputs: Slope, Aspect, Elevation, Land cover, and Vegetation. Cell size: 153 meters x 153 meters. B. Outputs: 1. AUC number: .852 2. Percent contribution table:

3. Probability distribution graph:

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C. Run One Conclusions: Based on the high AUC number and the numerous rivers and large lake in the region, this site, specifically Mt. Kushinga-mine and Mt. O-dake, appears to be a reasonable location that a pre-historic population would have been located. II. Run Two: A. Environmental inputs: Slope, Aspect, Elevation, Land cover, and Vegetation. Cell size: 76 meters x 76 meters. B. Outputs: 1. AUC number: .852 2. Percent contribution table:

3. Probability distribution graph:

C. Run Two Conclusions: By running the model with the cell size twice as small, a difference was observed in the percent contribution for elevation. It increased from 35.7% to 50%, suggesting that elevation played a significant role for habitat location. The probability distribution map shows the same area, this time with an increase in red at the Mt. Kushinga-mine and Mt. O-dake region as shown in the maps above.

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III. Run Three A. Environmental inputs: Aspect, Elevation, Vegetation, and Land cover. Cell size: 76 meters x 76 meters. B. Outputs: 1. AUC number: .851 2. Percent contribution table:

3. Probability distribution graph:

C. Run Three Conclusions: Slope was removed from run three to determine if a covariance existed between it and elevation, as slope and elevation are typically correlated. However, running the model without slope did not significantly alter the results. The difference was mostly seen in increasing elevation’s contribution. The same areas as in the previous runs had the warmer colors.

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IV. Run Four A. Environmental inputs: Slope, Elevation, Aspect, and Land cover. Cell size: 76 meters x 76 meters B. Outputs: 1. AUC number: .83 2. Percent contribution table:

3. Probability distribution graph:

C. Run Four Conclusions: Run four was run without vegetation because issues were discovered with the metadata. In order to determine what factor vegetation played in the model, it was removed from the run. No significant difference was seen. The same areas are displayed in warm colors. An updated metadata file was discovered, so removing it from the model was no longer necessary.

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Conclusion Based on the predictive power of the model (AUC number), it appears that a preliminary investigation in the region around Mt. Kushinga-mine and Mt. O-dake would be worthwhile. The congruence of rivers, the lake, and the view of the ocean make it seem that this area might have been a significant place for either ritual purposes or food gathering for the prehistoric people of the Jamon period. Comparing all the models, run two was the most important run because it used the smallest cell size along with all the environmental variables. It would be wise to use this run over the other runs in making any geospatial decisions and drawing any conclusions.

However, before investing in a full dig, I would recommend obtaining more environmental variables that might contribute to a better model. For example, when the model was run without slope, the AUC was almost the same. Therefore, it appears that slope can be substituted for another variable that might be more of a significant contributor. Also, the land cover and vegetation data used might, and probably is, entirely different than the land cover and vegetation that existed 7,000 years ago, so obtaining data that might correlate prehistoric land cover and vegetation with current land cover and vegetation would most likely increase the predictive power of the model.

From the literature review, I found that archaeologist are using GIS to generate predictive models. Most of the models use a form of suitability analysis using map algebra and regression analysis. It might be worthwhile to use their data to run MaxEnt and compare results. Furthermore, it might also be worthwhile to locate other evidence through an extensive literature review on the Jamon period that might confirm or call into question my results. Archaeologist focusing on the Jamon period in Japan will now be equipped with a model for determining the likely dwelling sites for the prehistoric indigenous population in the Aomori prefecture. The model can be used in generating hypothesis regarding the migratory and land use practices of the indigenous population, along with testing and critically analyzing already made claims regarding the indigenous population This research project has taught me the importance of starting the research process as early as possible in order to overcome any obstacles that might set the project back. I also learned the importance of starting with a clear and precise research question, which helps narrow the search for relevant literature on the subject and for locating data sources. Overall, this project was a significant learning experience for me, and I really enjoyed it.

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Bibliography

Joe Brandon and Konnie WestCott, Practical Applications of GIS for Archaeologists: A Predictive Modeling Kit (Philadelphia: Taylor & Francis, 2000). Beach, Tessa. “MaxEnt.” Class lecture. UC Berkeley, March, 2014 Theresa Adjaye, “MaxEnt Modeling of the Apennine Brown Bear” (2011): http://www.itc.nl/library/papers_2011/msc/nrm/adjaye.pdf Claira Lietar, “Modelling Neolithic site location with MaxEnt,” (2011): https://www.ocs.soton.ac.uk/index.php/CAA/2012/paper/view/480