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Final Report Phases 1-3 (2002)

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Chapter 1

Management Summary

Statewide Survey Impelmentation Model Map


By Allyson Brooks, G. Joseph Hudak, Guy E. Gibbon, and Elizabeth Hobbs



Chapter 1 Table of Contents

1.1 Introduction
1.2 Purpose and Scope of Project
      1.2.1 Purpose
      1.2.2 Scope
1.3 Concepts
1.4 Methodology
1.5 Products
1.6 Results



The Minnesota Department of Transportation (MnDOT) budgets over one million dollars annually for the identification and evaluation of historic and archaeological resources (historic properties) that are threatened by transportation related undertakings. Without a means of determining the most probable locations for archaeological resources, archaeological surveys must be conducted to meet the requirements set forth in Section 106 of the National Historic Preservation Act (NHPA) of 1966, as amended. When archaeological resources are encountered, costs arise not only in determining their eligibility for nomination to the National Register of Historic Places, but also in potential project delays, mitigation measures, and the loss of the cultural resources.


In the early 1990s, MnDOT conducted a review of their cultural resource process that sought new approaches towards fulfilling the requirements set forth by Section 106 of the National Historic Preservation Act of 1966. Section 106 requires that federal agencies make a reasonable and good faith effort to identify historic properties within the project area and conduct an assessment of the potential impacts to cultural resources before any federally funded or federally overseen project can proceed. To meet this mandate, MnDOT previously relied on the largely intuitive precontact site location models of State Historic Preservation Office (SHPO) staff and other archaeological professionals. This method depended on the depth of experience of the professionals involved and could easily be altered with changes in personnel.


As a result of their review, MnDOT initiated the development of a statewide Geographic Information Systems (GIS) based predictive model for prehistoric archaeological site locations. The use of GIS allows for the efficient, cost-effective, and repeatable generation of scientifically testable conclusions as to the most probable locations for pre-1837 archaeological properties. The statewide predictive model, developed over a three-year period, is called Mn/Model. It was funded by MnDOT using money made available through the Federal Highway Administration’s Intermodal Surface Transportation Efficiency Act (ISTEA). The results of Mn/Model will be incorporated into the earliest phases of project concept planning, making transportation planners aware of the possible locations of precontact archaeological sites. Although survey efforts to actually identify sites will still be necessary, Mn/Model allows cultural resource managers to objectively focus their efforts and planners to prepare alternative avoidance design scenarios. It will also be instrumental in preparing budget and schedule estimates allotted for both individual projects and longer range management activities.


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1.2.1 Purpose

The primary objective of the Mn/Model project was to create a series of accurate digital maps capable of alerting planners to the presence of potential precontact archaeological properties in accordance with the identification requirements set forth in Section 106. By using Geographic Information Systems, digital maps were created that delineate areas of high, medium, and low archaeological site potential based on statistical correlations between environmental attributes and known archaeological site locations. Linking this information with areas of high, medium, and low survey coverage directs where archaeological survey efforts should be concentrated. It also assists planners in avoiding areas that potentially contain cultural resources requiring costly mitigation or in weighing the cost of their disturbance against other project effects, such as wetland disturbance or socioeconomic impacts. This change in approach permits planners to conduct advance planning and base decisions on sound scientific findings.


1.2.2 Scope

Mn/Model is a Geographic Information Systems-based predictive model encompassing the entire State of Minnesota. However, Minnesota’s wide range of environmental zones dictated the development of a series of submodels, rather than one single statewide model. Mn/Model became a series of regional models based on the Ecological Classification System subsections defined for the state by the Minnesota Department of Natural Resources and U. S. Forest Service (Hanson and Hargrave 1996). Both these regional models and Mn/Model as a whole are dynamic and will increase in accuracy as Mn/Model improves with new data.


Mn/Model's temporal scope is pre-1837. It is designed to predict the locations of both precontact (pre-A.D. 1650) and contact (1650-1837) period archaeological sites. Since the locations of historic (post-1837) sites can be determined more accurately through archival research, post-1837 sites were eliminated from consideration. There are a total of 45 sites included in the models dating to the contact period that lack precontact components. This compares to a total of 5,769 sites employed in the models.


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Over the past two decades, there has been a resurgence of interest among cultural resource managers in constructing predictive models for prehistoric site locations. This renewed interest is the result of both rapid developments in GIS technology that make the modeling process more efficient (Kvamme 1986; Kohler and Parker 1986; Judge and Sebastian 1988; Allen et al. 1990; Dalla Bona 1994; Marschner 1996) and public pressure to conduct cultural resource activities in a cost-effective manner.


Mn/Model, following the example of previous modeling efforts, is based on the assumption that the most important factors controlling precontact hunter-gatherer settlement and activity location decisions were physical and biotic attributes of the landscape (Dalla Bona 1994:17; Kohler and Parker 1986:400). This is in contrast to more technologically complex societies, where social, ideological, and political forces can take precedence over environmental factors in influencing settlement location. As a result of this assumption, Mn/Model and many other contemporary modeling efforts rely on a series of biophysical variables (e.g. slope, elevation, soils, proximity to various water sources, vegetation) to construct models. The reliance on these types of variables has led to criticism that the resulting models are environmentally deterministic (Kohler 1998:9). Although archeologists who construct predictive models recognize the importance of cultural factors in the location of settlements, they contend that the temporal control needed to establish contemporaneity between sites is usually lacking. Consequently it is difficult, if not impossible, in most situations to include these variables in the modeling process (Brandt et al. 1992:269: Kvamme 1997: 1-2).


This is not to negate the possibility that there were site locations chosen for reasons other than baseline economics. However, the foundational premise for archaeological predictive model building in regions like Minnesota is that, for the most part, activity location before the historic period was primarily determined by the distribution of local and regional environmental resources. Other basic assumptions of the project are that (1) environmental attributes of the precontact period are still identifiable, at least in two dimensions, in current data sources; and (2) correlations between precontact archaeological site locations and environmental variables reflect spatial organization across the landscape during the precontact period (Dalla Bona 1994:16-17). Mn/Model, through its geomorphological paleo-landscape component, incorporates the third and fourth dimensions of time and buried Holocene landscapes/surfaces for areas with higher probabilities for buried cultural resources. This was done for seven river valleys (Minnesota River, Mississippi River north of St. Cloud, Rainy River, Red River, Rock River, Root River, and St. Croix River), an ancient glacial lake bed (Red Lake Bog), and a limited number of small upland areas. This is a unique aspect of Mn/Model that has not been previously attempted by other modeling efforts on this scale.


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Mn/Model is designed to be an empiric correlative (inductive) rather than a deductive predictive model (Kohler and Parker 1986:399; Dalla Bona 1994:5). This is the most popular approach in predictive modeling (Thomas 1988:Table A.1). In empiric correlative models, no presuppositions are made other than:


The results of empiric correlative models are developed inductively by exploring associations between specific environmental variables and archaeological site locations. In these models, the dependent variables are archaeological events (i.e. sites), and the independent variables are biophysical characteristics of locations, such as slope, soil type, elevation, plant community, and distance to water. Statistical analysis is used to identify relationships between the environmental and archaeological attributes, generating combinations of environmental variables that correlate with the presence or absence of sites.


The other approach to predictive modeling relies more heavily on deductive reasoning. Sometimes referred to as explanatory or systemic modeling (Kohler 1988:35-38; Sebastian and Judge 1988:4), deductive models rely on hypothesized relationships between various biophysical and/or cultural factors to predict site locations (Thomas 1998:Table A.1). The ultimate goal is to explain the distribution of settlements across the landscape in terms of various social, political, ideological, and physical factors that are assumed to be significant. The location of sites and their contents are placed within a larger organizational system where past and anticipated uses of the land intertwine with how intensively resources are exploited and how they are distributed through time and space (Ebert and Kohler 1988: 107-109, 134-139). The models range from informal intuitive ones developed by most archaeologists through experience, to somewhat more formal ones (Cassell et al. 1997: Dalla Bona and Larcombe 1996). This approach is sometimes taken when there is insufficient data to build inductive-correlative models like Mn/Model.


To accommodate the technical requirements of the project, Mn/Model used Geographic Information Systems as the generative framework. GIS incorporates the essential elements of computer cartography and relational database management into one system. It efficiently handles very large databases, maintains links between maps and tabular data, and allows for the analysis of spatial relations. This includes analytical and modeling functions that are not practical or possible with other methods. Minnesota is 80,000 square miles in extent and has thousands of recorded archaeological sites. Each environmental database had to contain more than 586 million data values to cover the entire state at the required resolution. Without GIS, the storage and manipulation of the required high resolution geographic databases would have been unmanageable.


Archaeological site information was obtained from the SHPO database and from Chippewa and Superior National Forest and National Park Service databases. The SHPO database contains information from cultural resource surveys, Phase III (excavation) mitigation, the Minnesota Statewide Archaeological Survey (MnSAS), and other sources, such as early mound surveys in the state. These data were carefully filtered for quality control. Since archaeological site information from the Chippewa and Superior National Forest and National Park Service databases were not included in the SHPO database, and, since they represented the majority of surveys over large regions in northern Minnesota, their inclusion greatly facilitated site location modeling in the northeastern and north-central parts of the state. The Minnesota Statewide Archaeological Survey, a probabilistic survey conducted between 1977 and 1981, contributed data gathered through a variety of survey procedures, including stratified random sampling, sampling by landscape type, and professional intuition. These surveys were carried out to varying degrees in 26 counties, resulting in large samples of site and non-site locations.


The first archaeological field season of Mn/Model was designed to replicate the results of MnSAS in order to assess their conclusions. The second field season generated data based on a simple random sampling procedure. Along with site location data, negative survey data were collected from all parts of the state for comparison with site locations. Single artifacts (find locations), which are not considered significant archaeological properties, were excluded from the analysis when constructing the final models.


The following procedures were developed for Mn/Model:


The three-year project was divided into three phases. Phase 1 involved basic data accumulation and model development. Environmental and archaeological attributes for 26 counties, with site location data gathered through the MnSAS surveys, were incorporated into the GIS in conjunction with new information gained from the Mn/Model Phase 1 field inventories. Prototype GIS models were derived, using logistic regression for the Phase 1 counties.


Phase 2 consisted of incorporating the remaining 58 counties, which had site location data collected by non-random procedures, into the GIS. Phase II field surveys in seven counties were initiated to generate additional archaeological data, this time from truly probabilistic surveys. Modeling procedures were refined. Preliminary models were generated for the entire state, using Anfinson’s (1990:135-166) archaeological resource regions as the regionalization scheme. For some areas, additional models were built using data which are not yet available statewide to test the utility of these data sets. Finally, paleo-environmental data were developed and evaluated for their ability to contribute to the project.


Phase 3 of the project involved refining the modeling procedures further. This included adopting the Ecological Classification System subsections (Hanson and Hargrave 1996) as the regionalization scheme and incorporating more sites into the database used to build the models. Modeling was extended to using information about locations surveyed to build models of survey bias. Additional procedures for evaluating model performance were adopted. As a final product of this phase, site probability and survey probability models were combined into composite survey implementation models. These will be important components of the implementation and operational plan, which was also developed during Phase 3.


Concurrent with Phase 1 and Phase 2 was the creation of geomorphological maps, for seven river valleys and one ancient glacial lake bed, delineating both paleo-landscapes and their integrity. This mapping was extended to 16 (including portions of) upland quadrangles in Phase 3. Also in Phase 3, these geomorphic maps were used to develop a suitability model to determine the potential "suitable locations" of buried sites in the areas mapped.


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The primary product of Mn/Model is a series of high resolution, predictive models for pre-1837 archaeological site and survey potential across Minnesota. A number of secondary products were generated, most importantly a single repository of statewide environmental data in GIS format organized by county. MnDOT will make the models and the data available on CD-ROM. The final project report is also available in digital format on CD-ROM. Finally, an implementation plan was developed for use by MnDOT and coordinating agencies.


One other essential product of Mn/Model is the generation of a series of geomorphological maps of seven river valleys, one ancient glacial lake, and 16 (including portions of) upland quadrangles. These maps define the location, age, and integrity of paleo-landscapes when practical. The geomorphology maps do not predict site locations, but rather predict for suitable landscapes to contain in situ archaeological deposits.


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Site probability models are presented as low, medium, and high probability areas for site presence (see Section Survey probability models are represented as low, medium, and high potential that a landscape is similar to the places with particular environmental characteristics have been surveyed. Portions of the landscape where prehistoric sites are expected not to be located (under water, in mines, on steep slopes) were excluded from these probability classes. Survey implementation models also identify areas where surveys have been inadequate and, consequently, the probability of finding sites is unknown.


Models for individual regions of the state predict from 55.5 to 95 percent of known sites. Statewide, 85.5 percent of all known sites are predicted. Regional models classify from 6.4 to 48.7 percent of the landscape into high and medium probability classes. Only 20.6 percent of the entire state is assigned to the high and medium probability classes.


Besides providing a scientific, cost effective planning tool, Mn/Model can be used to generate hypotheses of the causal relationships between environmental variables and prehistoric site locations. These relationships can be used to generate theories about spatial settlement patterns across the landscape.


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Allen, M. S., S.W. Green, and E. B. W. Zubrow (editors)
  1990 Interpreting Space: GIS and Archaeology. Taylor and Francis, London.


Anfinson, S.F.
   1990 Archaeological Regions in Minnesota and the Woodland Period. In The Woodland
       Tradition in the Western Great Lakes: Papers Presented to Elden Johnson
, edited by G. E
       Gibbon, pp. 135-166. University of Minnesota Publications in Anthropology No. 4. Department
       of Anthropology. University of Minnesota, Minneapolis.


Brandt, R., B.J. Groenewoudt and K.L. Kvamme
   1992 An Experiment in Archaeological Site Location: Modeling in the Netherlands using GIS
      Techniques. World Archaeology 24:268-282.


Cassell, M.S., H.D. Mooers, C.A. Dobbs, T. Madigan, M. Covill, J. Berry, and D.A. Birk
   1997 An Archaeological Sensitivity Model of Prehistoric and Contact Period Settlement at
       Camp Ripley, Morrison County, Minnesota
. Reports of Investigation No. 397. Institute for
       Minnesota Archaeology, Minneapolis.


Dalla Bona, L.
   1994 Methodological Considerations. Cultural Heritage Resource Predictive Modelling Project
       Vol. 4. Centre for Archaeological Resource Prediction, Lakehead University, Thunder Bay,


Dalla Bona, L. and L. Larcombe
   1996 Modeling Prehistoric Land Use in Northern Ontario. In New Methods, Old Problems:
       Geographic Information Systems in Modern Archaeological Research,
edited by H.D.G.
       Maschner, pp. 252-271. Center for Archaeological Investigations Occasional Paper No. 23,
       Southern Illinois University, Carbondale.


Ebert, J.I. and T.A. Kohler
   1988 The Theoretical Basis of Archaeological Predictive Modeling and a Consideration of
      Appropriate Data-Collection Methods. In Quantifying the Present and Predicting the Past:
      Theory, Method,and Application of Archaeological Predictive Modeling,
edited by W.J.
      Judge and L, Sebastian, pp. 97-171. U.S. Government Printing Office, Washington, D.C.


Hanson, D.H. and B.C. Hargrave
   1996 Development of a Multilevel Ecological Classification System for the State of Minnesota.
        Environmental Monitoring and Assessment 39:75-84. Kluwer Academic Publishers. Netherlands.


Judge, J. W., and L. Sebastian (editors)
   1988 Quantifying the Present and Predicting the Past: Theory, Method, and Application
       Archaeological Predictive Modeling
. U.S. Department of the Interior, Bureau of Land
       Management, Denver. U.S. Government Printing Office, Washington, D.C.


Kohler, T.A.
   1988 Predictive Locational Modeling: History and Current Practice. In Quantifying the Present
        and Predicting the Past: Theory, Method, and Application of Archaeological Predictive
edited by W. J. Judge and L, Sebastian, pp. 19-59. U.S. Government Printing Office,
       Washington, D.C.


Kohler, T.A., and S.C. Parker
   1986 Predictive Models for Archaeological Resource Location. In Advances in Archaeological
       Method and Theory,
vol. 9, edited by M. B. Schiffer, pp. 397-452. Academic Press, New


Kvamme, K.L.
   1986 The Use of Geographic Information Systems for Modeling Archaeological Site Distributions. In
       Geographic Information Systems in Government, vol. 1, edited by B.K. Opitz, pp. 345-362.
       A. Deepak Publishing, Hampton.
   1992 A Predictive Site Location Model on the High Plains: An Example with an Independent Test.
       Plains Anthropologist 37:19-40.
   1997 Ranters Corner: Bringing the Camps Together: GIS and ED. Archaeological Computing


Maschner, H. D. G. (editor)
  1996 New Methods, Old Problems: Geographic Information Systems in Modern
       Archaeological Research
. Occasional Paper No. 23, Center for Archaeological Investigations,
       Southern Illinois University at Carbondale.


Sebastian, L. and W.J. Judge
  1988 Predicting the Past: Correlation, Explanation, and the use of Archaeological Models. In
       Quantifying the Present and Predicting the Past: Theory, Method, and Application of
       Archaeological Predictive Modeling,
edited by W.J. Judge and L, Sebastian, pp.1-18.
       U.S. Government Printing Office, Washington, D.C.


Thoms, A.V.
   1988 A Survey of Predictive Locational Models: Examples from the Late 1970s and Early 1980s. In
       Quantifying the Present and Predicting the Past: Theory, Method, and Application of
       Archaeological Predictive Modeling,
edited by W.J. Judge and L, Sebastian, pp. 581-645.
       U.S. Government Printing Office, Washington, D.C.


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The Mn/Model Final Report (Phases 1-3) is available on CD-ROM. Copies may be requested by visiting the contact page.


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MnModel was financed with Transportation Enhancement and State Planning and Research funds from the Federal Highway Administration and a Minnesota Department of Transportation match.


Copyright Notice

The MnModel process and the predictive models it produced are copyrighted by the Minnesota Department of Transportation (MnDOT), 2000. They may not be used without MnDOT's consent.