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Technological and Spatial Analysis: Fuzzy Data Mining on the North Coast, Peru March 31, 2015, CAA 2015, Siena, Italy
Kayeleigh Sharp
Southern Illinois University, Carbondale (SIUC), Dept. of Anthropology, U.S.A. [email protected]
Melissa Litschi Southern Illinois University, Carbondale (SIUC), Dept. of Anthropology, U.S.A. [email protected]
Abstract:
How can meaningful dimensions of social differentiation and complex social interrelationships be elucidated through archaeological data? Focusing on long-standing debates concerning the nature of Gallinazo (aka Virú) and Mochica (aka Moche) interrelationships during the first millennium, our long-term work has two key objectives: (1) to implement in-depth quantitative investigations of technological choices and activity patterns, and (2) to carry out high-level multivariate spatial data analysis to identify patterns and expose social asymmetries that may arise in situations of cultural coexistence.
We present preliminary findings from our integrated approach that combines quantitative and qualitative studies with spatial statistical analysis. Mixed-type data from various sources (GPS, pXRF, low-resolution microscopic, etc.) were integrated into our geodatabase and then used to identify technological constellations using a fuzzy taxonomic classification system. This system is based on technological rather than stylistic characteristics, and is useful in a broader range of applications (e.g., in studies that focus on standardization or the organization of production).
The resultant data will then be subjected to various data mining techniques to identify meaningful patterns and establish association rules that can be used to distinguish among a broader range of coexisting artifact assemblages. As our preliminary work shows, we have moved one step closer toward resolving critical debates concerning the Gallinazo-Mochica relationship by integrating high-powered tools borrowed from the geographic information and computer sciences. As a means for eliciting characteristics of social differentiation, the techniques applied herein have the capacity to generate nuanced feature classes within our geodatabase, which can then be used for higher-level spatial-statistical analyses.
The ultimate utility and efficacy of this integrated approach will be assessed based on the ability to detect patterned differences or similarities in the multivariate datasets generated through systematic excavations that are planned for the near future. In the meanwhile, the integrated approach we present has broader applicability for anthropologists working inside and outside of the Andes as a means by which to refine our vision of the past using state-of-the art archaeometric, spatial analysis and computer science techniques.
Keywords: GIS, fuzzy taxonomy, spatial analysis, geospatial data mining, archaeometry, technological
choices and activity patterns.
*All maps and images, unless otherwise specified are courtesy of Kayeleigh Sharp and Proyecto de
Investigación Arqueológica Zaña (PIAZ), Peru (2010-2015). Only for educational use, please cite
appropriately. ** Some content removed to protect copyrighted materials.
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Introduction:
Critical to understanding the past are the relationships between stylistically (or visually) diverse artifact assemblages, the very real contexts of past social interactions, and the broader social relationships these imply. While our abilities to address complexities of the past are stronger than ever before, our exploration and application of new techniques lags behind in many regional studies including the north coast.
Slide 2: Our work centers on theoretical issues of archaeological identity (e.g., Díaz-Andreu et al. 2005; Gosselain 2000; Graves-Brown et al. 1996; Insoll 2006; Stark 1998) and the relationship between people and things (e.g., Appadurai 1986; Gosden and Marshall 1999; Hodder 2012; Hoskins 2006; Jones 2002) that reside at the core of contemporary anthropological and archaeological thought. How can meaningful dimensions of social differentiation and these complex social interrelationships be elucidated through our archaeological data? Might a merger with soft computing techniques hold any answers? This paper explores
our endeavor to implement in-depth quantitative analysis of ceramic artifacts, an area of investigation often restricted to descriptive characterizations or morphological studies on the Mochica. Rather than focusing on stylistic, morphological or descriptive analysis, our work concentrates on technological choices and activity patterns to aid in the clarification of uncertainties and in the elimination of the strong biases that have become entrenched over many decades as discussed below.
Slide 3: In North Coast studies, the poor understanding of Gallinazo and Mochica coexistence limits the potential for understanding axes of differentiation. This is an unfortunate by-product of early works that focused almost exclusively on Mochica elites and funerary contexts (e.g., Baessler 1902-03; Larco Hoyle 1938, 1945; Tello 1938; Uhle 1913, 1915). For the most part, archaeological work retains this bias today, emphasizing only a small portion of Mochica archaeological sites (Shimada 2010), and overlooking the complexities of social interrelationships between
the Gallinazo, Mochica, and other contemporary groups. While recent studies have begun to critically re-evaluate our wide-held and long-standing assumptions (Millaire 2009; Shimada and Maguiña 1994), many sites and regions remain underexplored. Thus, the social contexts artifacts pertaining to these groups continue to evade clear understanding.
Slide 4: Based primarily on the stylistic interpretation of Gallinazo- and Mochica-style artifacts and architecture, traditional views have long held that the Mochica: (1) developed from earlier Gallinazo cultural antecedents (Bennett 1939; Larco Hoyle 1945), (2) conquered and replaced ethnically Gallinazo populations (Willey 1953), or (3) all-together doubted the cultural distinction of Mochica and Gallinazo (Ubbelohde-Doering 1957). Today, evidence of interactions among divergent yet contemporaneous North Coast peoples (Kaulicke 1992), and of Gallinazo and Mochica groups who lived side-by-side and actively competed for
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land and resources has forced a rethinking of these old ideas, yet the idea that the Gallinazo and Mochica were polar opposites still exists. Importantly, these views have been put forth without adequate study of non-funerary contexts, particularly in smaller settlements away from the capital. Largely lacking in studies of the Gallinazo more generally, is the systematic investigation of quotidian spaces where many of these artifacts have been observed. To add intrigue and complexity to this multi-layered problem, evidence of the Gallinazo-Mochica (ca. 100-800 CE) coexistence is present at many Early Intermediate Period (ca. 200 BCE-600 CE) sites, that still go overlooked. **
Slide 18-19: One of these is the site of Songoy-Cojal which is the focus of our long-term research.
Slide 20: New data from our preliminary fieldworks which focused on the collection of surface ceramics from both residential and funerary contexts, have revealed a much wider qualitative variation in Gallinazo ceramics than previously known. This critical finding has forced our reformulation of conventional models and ways of studying Gallinazo and Mochica coexistence as we now find them inadequate for addressing the new-found complexities of our data. **
Slide 23: Our solution for studying such diversity combines the study of technological choices and activity patterns using a suite of well-established archaeometric (e.g., Glascock and Neff 2003; Malainey 2011; Pollard et al. 2007; Shugar and Mass 2012; Sillar and Tite 2000) and spatial analysis (Fischer and Getis 2010; Fotheringham et al. 2002) techniques to extract characteristics of interrelationships between potentially diverse, yet coexisting archaeological groups. The investigation of ‘technological choices’ provides an excellent alternative to traditional studies of style in
material culture (some important examples include: Childs 1991; Chilton 1999; Gosselain 1992; Herva and Nurmi 2009; Lechtman 1977; Lemonnier 1986, 1993; Pfaffenberger 1992; Schiffer 2001; Shimada and Craig 2013). Attendant to this, the study of archaeological households, activities and their spatial dimensions provide a means by which to access underlying material and behavioral patterns, options and constraints (see Aldenderfer 1993; e.g., Ames 1995; Costin 2001; Deetz 1982; Feinman 1999; Hegmon 1998; Hillier and Hanson 1984; Kent 1984, 1990; Nash 2009; Rapoport 1969, 1990; Tringham
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1995), and the transformation of technological choices or ideas into material existence (Dobres and Hoffman 1999; Sillar and Tite 2000). While Gallinazo ceramics are commonly classified as either fine negative (presumably funerary) or Castillo decorated domestic-ware varieties, the reality is far more complex which has been illustrated visually in previous slides and is outlined quantitatively in the table. Although some data are incomplete, we followed Reedy and Reedy’s (1994) model to study technological choices in material cultural on three different levels (i.e., visual, techno-visual and purely technical), some of which are readily visible and others that remain invisible to the naked eye. By studying three technological dimensions of objects it is thus possible to identify meaningful suites of situationally-selected technological choices in the material record.
Slide 24: In the present work, we focus on visual and techno-visual technological choices that are fundamentally non-essential components of the chaîne opératoire (Lemonnier 1986, 1993; Reedy and Reedy 1994), which may influence the performance quality of artifacts (Schiffer and Skibo 1997), or be indicative of habitus and identity (Jones 1997). These include: how decorations were made (e.g., punctate or paint), paint color, exterior surface treatment, modeled vs. stamped designs, or rim lip shape, temper size and profile shape. We adopt this approach
as a means of moving beyond descriptive artifact-centered approaches and to make the data commensurable with complementary resources. Unlike many traditional applications of GIS in archaeology, which focus mainly on spatial distributions of metric data, our approach is designed to address anthropological questions such as those concerning the how activities were carried out by groups of individuals in the past, or even what types of social identity expression might be observed through archaeological investigation of spatial and non-spatial data using rule-based query systems (e.g., Bi et al. 2008; Chen et al. 2000; Khan et al. 2011; Smith et al. 2014) and clustering algorithms such as K-means (MacQueen 1967), or fuzzy c means or FCM (Bezdek 1981; Miniakhmetov 2011) which allows one piece of data to belong to multiple clusters (DIEB 2014). As such, the research design is a significant departure from traditional case studies that are fundamentally artifact-centered and typologically-conceptualized.
Slide 25: Because of the nature of the data at hand, there was a need to adopt great flexibility within the research design, in order to incorporate different datasets. From the onset, it has been recognized that the stylistic characterization of artifacts, which is fundamentally descriptive in nature, is insufficient for discerning how axes of social differentiation are represented archaeologically and equally unsatisfactory for identifying social interactions among or between them. However, it is also recognized that such descriptions are quite analytically meaningful. This is significant as
traditional artifact classification systems rely almost solely on the descriptive characterization of artifact attributes, to explore similarities and differences. Style and technology are, however, more nuanced and dynamic. In this regard analytical methods based on the concept of fuzzy logic is useful.
In its original form, Lofti Zadeh’s (1965) fuzzy logic computing permits the exploration of such
descriptions in terms of degrees of membership rather than binary oppositions. This gives meaning and
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significance to characteristics that directly as well as only partially, fit into one category or another.
With the ability to analyze degrees of membership, rather than binary oppositions, it is possible to speak
quantitatively about phenomena such as hybridized decorations, the partial borrowing of technologies,
the variability (or standardization) of manufacturing and even the outcomes of ‘stylistic’ or cultural
coexistence. With the implementation of a classification system which is inherently Fuzzy in nature, we
now have a means of quantifying choices, producing data from which to address the interplay between
things and people, and compare them to other lines of complementary data.
Slide 26: We adopted a relatively simplistic strategy borrowed
from ecology, to measure degrees of membership of technological
attributes in the archaeological record. Nominal (or categorical)
data on ceramic technology was assigned a degree of membership
by calculating matching coefficients which are generally used to
measure the closeness or diversity of relationships of nominal
variables (Qu and Zhang 2008:2), such as plant or animal species in
a given environment or area. The Jaccard index (Jaccard 1912; see
also http://ag.arizona.edu/classes/rnr555/ lecnotes/10.html), and
other types of similarity indices, can be easily adapted to assess variability archaeological contexts. In
this initial step, we calculated the matching coefficient (or Jaccard’s coefficient) to explore similarities
and differences between Gallinazo and Mochica technological attributes.
Slide 27: This map shows the estimated position of 195 ceramic
artifacts, collected from the surface of the residential sector of
Songoy-Cojal. In this visualization, ceramic artifacts were mapped
using traditional typological attributes (i.e., iconography). The
index of diversity was calculated for frequency counts of each
typological category using the Bray-Curtis index to compare the
samples. Based on the iconographic data alone, it is difficult to
identify meaningful patterns in spatial contexts and the more
subtle technological variations are obscured.
Slide 28: In the second map, averaged matching coefficients for
the same 195 ceramic fragments are visualized. Descriptive data
have been converted into a format that is meaningful in the
contexts of spatial-statistical analysis. The assignment of
matching coefficients numerically identifies the relationship
among variables, making it possible to explore the degree of
technological similarities, differences and overlap in each
architectural unit. It is also possible to understand precisely which
attributes contribute most to distinguishing one technological
suite from another, and mine for meaningful association rules. The key advantage of using the fuzzy
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classification system is that seemingly incommensurable nominal (or descriptive data) can be analyzed
in conjunction with other lines of complementary quantitative or semi-quantitative (e.g., INAA, pXRF)
and other metrical data, adding a detailed richness to the interpretation of technological choices.
Slide 29: While the persistent debate as to the nature of the
Gallinazo-Mochica coexistence has been heavily shaped by a
series of major biases and an overly simplistic dichotomous
perspective, there is far greater variability in Gallinazo ceramics
than previously recognized. Resolution of the debate calls for a
new approach that integrates multiple lines of evidence including
technological choice, archaeometric data, activities and their
spatial dimensions. Importantly, style and technology are not
binary but instead nuanced and dynamic which demands that we
study data with all its appropriate nuances. With this in mind, approaches which are designed to
explore uncertainty and relationships between datasets are appropriate and valuable. As our work
shows, the application of a fuzzy classification system reveals nuanced similarities and differences in the
north coast ceramic data that more closely approximate the observed technological diversity in the
artifacts under study. Ultimately, the approach advocated here holds much potential to be broadly
applied. The next step will be the implementation of our approach, integrating archaeometric data with
technological choice data derived from systematic excavations that are scheduled for 2015.
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Key topics:1. Background: The persistent debate as to the nature of the
Gallinazo-Mochica coexistence has been heavily shaped by a
series of major biases and an overly simplistic dichotomous
perspectives;
2. Challenges we face: Old interpretations but new data which
challenges previous assumptions;
3. Approach: Resolution of the debate calls for a new approach
that integrate multiple lines of evidence including technological
choice, activities and their spatial dimensions;
4. Position: Style and technology are not binary but instead
nuanced and dynamic
5. Application: Some preliminary findings of our multi-year research
project that might be applicable in other research contexts
6. Future Direction
2Geomatics Approaches in Latin American Archaeology: the State-of-the-Art in Spatial Archaeology, Kayeleigh Sharp ([email protected])
Foundational Questions and Concerns About
the Gallinazo and Mochica, North Coast, Peru
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Gallinazo(ca. 200 BCE – 700/800? CE)
Mochica(ca. 100 –800 CE)
http://discover.odai.yale.edu/
How can meaningful
dimensions of social
differentiation or the
complexities of social
interrelationships be
elucidated through our
archaeological data?
http://www.metmuseum.org/toah/works-of-art/1983.546.6
Geomatics Approaches in Latin American Archaeology: the State-of-the-Art in Spatial Archaeology, Kayeleigh Sharp ([email protected])
Background: The Gallinazo and Mochica:
Problem of Social/Political Designation
Throughout the first millennium, the Gallinazo and Mochica coexisted on Peru’s north coast
This has led to considerable debates over the nature of Gallinazo as an ethnic, political or even distinct social group
4
Principle Huaca
Songoy resembles
Castillo de Tomoval
Configuration at
Gallinazo capital
Evidence of
coexistence in the
residential sector
Songoy/Cojal Test CaseHuaca Songoy
Residential
Sector
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Looking for settings where the two ceramic styles coexist and where they are mutually exclusive
Practice-oriented perspective
Choices people make in doing things
Testing the
Competing Views in
the Residential
Sector
19
Residential
Sector
Meters
1. Addressing the primary questions of similarity, difference and convergence among technological attributes
2. The data are nominal and multivariate
3. Continuous rather than separate and distinct
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Some key differences and similarities between crisp and
fuzzy sets, a classic example:
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Figures from:
http://www.mathworks.com/help/fuzzy/fou
ndations-of-fuzzy-logic.html
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Similarity
PQUALfinlinEXTprssEXTpunchEXTpuncEXTstrwSFCpurple
PNTbrown-dkMODstampedVESbotSMHpolishVESmldPNTred-orange
SFCverydkgreyMODindSMHindSFCreddishFIRdkcorSMHrough
VESfigVESscuFIRbrnFIRredPFShemiRIMsquareVESgrt
APPotherPQUALdippedPFSotherSFCgreyCOTindRIMind
VESurnSMHburnishSFCbrownSFCburntGRNindPQUALrimpt
VESpotVESbwlEXTgpuEXTinsorgouPFSundMODhandmodeledAPPdecor
SFCorangeVESotherRIMflattenedPFSexpRIMtaperedCOTunc
FIRincRIMroundedPFSstrGRNmedSMHwipePFSincurv
RIMirregularGRNfinPFSindRIMnotrimPNTcream-beigePQUALbodydecSFCcream_ltbrown
FIRoxiSMHsmoothedCOTwshVESjarGRNcoarseCOTslp
MODunmPNTnopPQUALnopAPPnoaEXTnoxMODmoldmade
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6Similarity
COTslp
RIMtpr
EXTins
SMHwipe
EXTpunch
SMHsmoothed
COTunc
PFSexp
PFSsculp
PQUALdipped
RIMbvl
RIMlipped
RIMnotrim
GRNfin
SFCbrown
EXTgou
MODmld
APPeye
APPmodlug
APPnos
EXTpunc
MODstp
PFSfla
PFShemi
PFSincurv
PNTbrown-dk
PQUALbl
PQUALfl
PQUALsloppy
SFCgrey
SFCpurple
SFCverydkgrey
SMHpolish
EXTnox
PNTcream-beige
PFSdblinflx
PFSund
SFCburnt
APPnoa
MODmodled
PQUALstripe
PQUALarea
PQUALrimpt
APPother
GRNmed
PNTnop
PQUALnop
SFCorange
COTwsh
MODunm
RIMfla
SMHburnish
SFCltbrown
APPear
GRNcoarse
SFCcream
PFSstr
RIMrnd
SMHrough
PNTred-orange
RIMsqare
Closing Remarks:
1. Disparate datasets coming together for high-resolution analysis.
2. The persistent debate as to the nature of the Gallinazo-Mochica coexistence has been heavily shaped by a series of major biases and an overly simplistic dichotomous perspective;
3. There is a much greater variability in Gallinazo ceramics than previously recognized;
4. Resolution of the debate calls for a new approach that integrate multiple lines of evidence including technological choice, activities and their spatial dimensions;
5. Style and technology are not binary but instead nuanced and dynamic;
6. Approaches based on the fuzzy logic are appropriate and valuable;
7. Application of our approach reveals more nuanced similarities and differences in the north coast ceramic data, which more closely approximates the technological diversity observed in the artifacts under study.
8. The approach advocated here holds much potential to be broadly applied.
9. Future direction: Integration with high-precision archaeometric and excavation data, scheduled for 2015
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