Ch5 Methods

Research Methods and Data Recording Strategies

5.1. Introduction

This chapter reviews methods and equipment used in the course of the Upper Colca Archaeological Research Project, and it describes the creation of indices and measures that are used in analyses in subsequent chapters. Research at the Chivay obsidian quarry presented two principal challenges. First, the obsidian source itself is situated in a rugged, high altitude landscape that required working out of backpacker campsites and involved careful decisions regarding time budgeting during survey and excavation at the source. Second, raw material source areas present a challenge in the sheer volume of archaeological materials that are typically found in these areas. Sources require a modification of the established "site-oriented" survey paradigm for mapping and sampling during fieldwork, and for analysis during lab work. Anadditional goal of this project was to implement mobile GIS to record archaeological distributions in a digital format that integrates easily with a GIS database, and at a finer scale of resolution than is possible using traditional archaeological survey methods.

This chapter will review the research methods used both in the field during survey and excavation work, and in the subsequent lab analysis. Research design required an explicit selection of survey methods and these will be discussed below. Laboratory analysis of artifact collections was broad such that, for example, simple flakes were analyzed with nearly the detail of projectile points. This expansive analysis strategy required explicit sampling methods so that detailed analysis took place on representative samples from across geographical space and across artifact types, as sampling reduced the total count of artifacts requiring detailed analysis. Collections from survey and excavation were analyzed in two stages, (1) basic sorting and weighing of all collections, and (2) detailed analysis of selected artifacts from the larger population. The integration of field and laboratory digital data permitted the production of detailed summaries promptly for a Peruvian government report, and it allowed for the integration of GIS spatial analysis tools with the detailed data of lab analyses.

5.1.1. Locus rather than Site-oriented survey methods

Raw material sources are archaeologically complex features because, as foci for ancient procurement, there are frequently a large number of overlapping palimpsest occupations. A siteless survey approach is theoretically compelling (Binford 1992;Dunnell 1992;Dunnell and Dancey 1983;Ebert 1992;Foley 1981;Thomas 1975), however in practice most projects must balance detailed mapping against expediency and recording speed, as will be described below. The theoretical aims of this research were to detect and record meaningful variability in prehispanic artifacts and features throughout the study area, but also to focus on concentrations of lithic reduction activity and the variable material types that were evident in lithic scatters. The Upper Colca survey was not a "siteless survey" if that technique is taken to mean that the position of every artifact is recorded individually. Rather, it involved recording concentrations of non-diagnostic artifacts as loci in a mobile GIS system using a GPS polygon to delimit the loci (Tripcevich 2004;Tripcevich 2004). The result is a regional survey approach that is approximately as fast as traditional survey recording methods, but with much finer resolution and with greater detail on data that are more relevant to the theoretical goals of the research as determined by the field researchers.

5.1.2. Data recording in both categorical forms and field journals

The approach to data recording taken with this research project was essentially two-pronged where the mobile GIS archaeological recording system was complemented by more subjective notebook records written in a narrative form. These two complementary recording methods were employed, to some extent, in every data recording situation.

Method 1 - Digital forms with a spatial reference:The field crew aimed to record comparable and relatively objective data categories used digital forms linked to GPS-based spatial provenience in the mobile GIS data recording system. This system shares many of the advantages, and the limitations, of traditional, paper-based fieldwork forms.

Method 2 - Personal notebooks:Complementing Method 1, all team members had field notebooks where they were meant to record both data and more abstract observations on a daily basis at a variety of scales including the local, site-level scale, observations about regional patterns, and the relevance to larger goals of the project. Field notebooks may also take the form of digital audio recorded as one walks over a site.

While a two-pronged approach using both objective forms (traditionally, as paper forms on a clipboard) and field journals is not unusual, the distinction between the two systems is made more explicit by the use of mobile GIS. The digital recording system takes care of two aspects of archaeological feature and artifact recording: spatial positioning and data logging into comparable, form-based attributes. Conversely, the field notebooks fill the important role of capturing a range of other insights from fieldwork that are difficult and inefficient to capture in a digital form. In a notebook, observations and reflections on the contexts and patterns under study are more naturally recorded in narrative form, along with schematics, flow charts, or casual site sketches. The importance of reflection and observation has long been known to geographers and other field scientists.

There is at present enthusiasm for field mapping and their techniques… But map what and to what purpose? Is not this possibly another horn of the dilemma? ...Routine may bring the euphoria of daily accomplishment as filling in blank areas; the more energy goes into recording, the less is left for the interplay of observation and reflection (Sauer 1956).

Field notebooks provide an important contrast to the regular, systematized data notation of the mobile GIS system. Furthermore, many of the fieldworkers did not have access to the mobile GIS system, and as there was only one system in the 2003 Upper Colca team then only one person could be logging data into that system at a given moment. Months after the fieldwork was over, in the course of the analysis process, the various field notebooks provided daily observations, detailed notes on sites, and an overview that corroborated the geographically detailed and categorical organization of the mobile GIS database. Another valuable form of complementarity between the two systems is that one is geographical while the other is temporal in structure. Mobile GIS is inherently spatially focused, as it is a cumulative process of recording and improving spatial features with attributes and mapping detail, and when the GPS is activated the user is automatically viewing data layers in close proximity to the current location. Field notebooks, however, are temporally organized as they are logged chronologically as the field research progresses. Field notebook pages are easily photocopied and scanned into a file such as PDF, allowing the written logs can join the digital database albeit in an unsearchable form. Ultimately these two forms of data acquisition complement one another, and as mobile GIS increases in capability and in popularity, the dirt-smudged field journal will most likely continue to serve an important, material function.

5.2. Geographical context

As this research involves GIS analysis at regional, local, and intrasite levels of analysis, methodological issues related to spatial data organization will be described below.

5.2.1. Geographical datum and regional data sets

At the regional scale, data on obsidian consumption patterns in the central and south-central Andes were gathered from the original sources cited in summaries principally by Burger, et al.(2002;2000), as well as some more recent contributions from other sources. Regional studies of obsidian distributions require incorporating spatial data from a variety of maps and records. The bulk of these data consist of site locations and other archaeological phenomena, and these data derive from studies between 1960 and 2000 where spatial data in the Andean countries was referenced to an older coordinate system based on the Provisional South American Datum of 1956 - La Canoa, (PSAD56). Currently, the governments of these Andean countries are shifting their cartographic divisions to a modern datum based on World Geodetic System 1984 (WGS84) or the related Sistema de Referencia Geocéntrico para las Américas 2000(SIRGAS 2000) (Fortes, et al. 2006), a new continental reference system for South America. The Upper Colca Project functioned entirely in WGS84 in order to be consistent with global data sets such as satellite imagery and other forms of spatial data that have recently become available. The datum change from PSAD56 to WGS84 results in an incompatibility between historic datasets and recent work, when obsidian artifacts from older collections are chemically provenienced and the precise spatial origin of these artifacts can be difficult to ascertain. Ultimately all of these sites should be revisited and mapped with GPS using a modern system such as WGS84, but in the meantime a relocation of these site positions and transformations of the historic geographical data are required.

Spatial data in Peru and Bolivia are almost universally rely on the 1956 La Canoa (Venezuela) datum based on the International 1924 ellipsoid known as Provisional South American Datum (PSAD56). If data from PSAD56 and WGS84 data are inadvertently combined, the resulting misalignment of map features in the Arequipa area is approximately 300 to 700 m.



Semi-Major Axis (meters)


International 1924

Prov South American 1956



WGS 1984

WGS 1984



Table 5-1.The two reference ellipsoids used in Peruvian and Bolivian cartography(NIMA 1977).

Three parameter metric transformations that can be applied to UTM coordinates in the central Andes are shown in Table 5-2.


NIMA/NGA 1991 TR8350.2 report







(no error published)


PSAD56 ? WGS84

PSAD56 ? WGS84

PSAD56 ? WGS84

PSAD56 ? WGS84

?X (Eastings)

-279 m ± 6 m

-270 m ± 5 m

-288 m ± 17 m


?Y (Northings)

+175 m ± 8 m

+188 m ± 11 m

+175 m ± 27 m



-379 m ± 12 m

-388 m ± 14 m

-376 m ± 27 m


No. Satellites





ArcGIS Transformation

1208: PSAD_1956_


1202: PSAD_1956_


1201: PSAD_1956_



Table 5-2. Three parameter cartographic transformations for UTM coordinates from PSAD 1956 (La Canoa) to WGS 1984 (Dana 1998;Mugnier 2006: 496;NIMA 1977).

By using the WGS 1984 datum, the geographical data collected in the course of the Upper Colca Project research registered properly with newer spatial data from a variety of institutions such as international, US government, and private remote sensing sources. These datasets include global topographic data like SRTM, satellite imagery and DEM sources like ASTER, and these data are also consistent with web products like Google Earth. Furthermore, WGS84 is the native coordinate system of the Global Positioning System and therefore the dGPS data acquired during fieldwork in the Upper Colca did not require an additional geographical transformation. The majority of maps in the region will be released in WGS84 or SIRGAS 2000 in the coming years.

5.2.2. Regional datasets

Sources of spatial data were used in the regional analysis component of this project consist of digital raster and vector sources, and scanned paper-based maps. The first group include topographic datasets acquired from ASTER (Abrams, et al. 2002) and hole-filled SRTM (Jarvis, et al. 2006), and the second group are vector datasets acquired from Vector Smartmap and derivative datasets (NIMA 1995). Peruvian and Bolivian government maps were scanned from paper, georeferenced, and transformed (IGM 1986;Klinck and Palacios M. 1985;Palacios, et al. 1993) so that all now coincide with the WGS84 datum.

5.3. Data recording approach

5.3.1. Introduction

This research project encompassed three major zones: a river valley zone, a high punazone, and the obsidian source itself. A principal challenge in the archaeological evaluation of contrasting survey regions is the construction of meaningful categories that allow for comparison between these varied zones. Consequently, this project sought to strike a balance between the in-field assessment of sites and features based on the experience of archaeologists, and the significance and categorization provided by ancillary lab results and spatial analysis.

The 2003 field recording approach resulted in the presentation of data in this chapter that is a combination of two major forms of information. (1) Data that were recorded during survey work that described features and artifacts assessed by fieldworkers and quantified both in the field and in the lab, in the course of subsequent lab analysis. These categories are scalable, but the larger structure of the database is rigid in order to allow for comparison between different contexts. (2) Data were derived through inference and subjective assessment in the course of fieldwork by the project director (Tripcevich) and by five other experienced archaeologists who participated in segments of the fieldwork. The insights and notes of other project participants were also included into this subjective data category.

The result is a project that is integrated by using comparable quantitative measures, but that is informed by the subjective experience and interpretation of the archaeologists that conducted the fieldwork. The following data presentation is therefore based on quantitative measures, but the comments and assessments are informed by the insights from daily observations and personal notebooks.

5.3.2. Organization, sampling, and inference

Regional archaeological surveys must devise classification schemes that allow for meaningful comparison between features mapped throughout the survey area. In the Upper Colca Project, some artifact classes had radiating distributions that created special problems when devising comparable categories. This situation is best explained through an example that illustrates the challenges of consistent data recording.

The radial attenuation problem in artifact densities can be observed most dramatically in obsidian concentrations throughout the survey region as one departs from the Chivay source. In such situations, the human eye is easily misled by large concentrations of artifacts because the eye is attuned to the presence of contrasting or unusual materials, an issue that makes consistent sampling methods all the more important. In this example, the field crew would readily observe single flakes of obsidian far from the obsidian source, while at the source area itself a given obsidian flake was common place and obsidian densities were relatively deprioritized. Yet in the obsidian source area a flake of chertwas notable as it provided contrast. While these contrasts are meaningful: it is important that one might find a flake of chert at the obsidian quarry, far from the river where chert is usually found, such features should be mapped and observed separately with observations indicating that this is a-typical for the area. Ample effort should also be given to mapping features that aretypical to the area for faithful representation of general distributions.

This example illustrates a major methodological challenge for archaeological survey that exists in both conventional and digital recording systems. A systematic sampling strategy is the most cost effective way to describe the common features found in a region, because random samples can be extrapolated to the larger population. These distinctions in artifact identification correspond to two types of survey differentiated by Banning (2002: 27-38) as "statistical survey" and "prospection survey", as will be discussed in more detail below. Attributing the finds in the database based on the type of survey strategy that was employed permits a more consistent depiction of broader features of the landscape in later analysis.

5.3.3. Aggregating by Sites versus Loci

Spatially delimited "sites" were deprioritized in this project in an effort to capture changes in the continuous field of obsidian artifacts and related sites as one approaches the obsidian quarry workshop area. Rather than relying on the ill-defined concept of siteas the basic unit of analysis (Dunnell 1992), the Upper Colca Project survey team focused on recording lociof different artifact classes. Site boundaries were in fact recorded, however, because it was impractical to record isolates in the same detail as one recorded spatial structure in the concentrations conventionally thought of as a site.

For example, it was difficult to reconcile the density of the category High density lithic scatterbetween a workshop at the obsidian quarry, on one hand, and a residential base in the valley bottom; a problem that was resolved with sampling. Furthermore, pottery was almost non-existent in the lithic quarry area, even though on a regional scale there is ample evidence of consumption of obsidian by groups that possessed ceramic technology. In order to examine data in an integrated framework, broad categories such as "Site Type" were given minimal priority in favor of explicit and comparable categories based on features and on artifact concentrations described as loci. Broad site typecategories were assessed primarily for purposes of cartographic representation and to facilitate communication in the course of research, however analysis and interpretation focused on basic and comparable categories of data by artifact class.

5.3.4. Site and Loci recording structure

Archaeological sites and features were recorded primarily in terms of categories built around artifact classes while in the field. The site - isolate dichotomy is useful for expediency while doing fieldwork, but "Sites" and "Site Types" did not form the basic unit of analysis. Rather, in the course of survey, if a given group of artifacts was isolated or belonged to a "site", and if it was sufficiently large to be considered a site, then the recording strategy shifted to a more detailed analysis method.

When it came time for a site to be documented, attributes were recorded on numerous levels: on the level of the site, on the level of loci of three feature classes (lithics, ceramics, and structures), and finally on individual artifacts found in spatial association with that site. With these categories, the data and the lab results could be used to categorize the sites after-the-fact based on actual data and not only based on in-field intuition. In this manner, in-field impressions of archaeological features contributed to, but did not structure, the framework in which data was recorded. The structure provided by digital forms and GPS based mapping technology was complemented by interpretive notes and impressions that were written on field journals in daily narratives during fieldwork. The details of our Arcpad mobile GIS recording system are provided later in this chapter.

5.3.5. The Primary Key: ArchID

A stable, primary key ID number was assigned to all phenomena that were individually mapped. Every feature or artifact, including sites, loci, and individual artifacts that were mapped separately, were assigned their own "ArchID" number in a single number series regardless of the archaeological feature type. The ArchID primary key is a unique identifier integer that was value-free, as no further feature information was embedded in the number. For example, some archaeologists may classify sites by number range. In that system, rock shelter sites might be numbered between 100 and 200, and administrative structures might fall between 400 and 500, for example. However, that type of encoding of meaning into ID numbers is problematic for database design. The primary key approach adopted here is consistent with database normalization methods and the First Normal Form (Codd 1970) where one ensures that each table has a primary key that serves as minimal set of attributes that can uniquely identify a record. The First Normal Form further specifies that repeated fields be eliminated, and that each attribute must contain a single value and not a set of values. This kind of tabular organization is intuitive for those who have worked with computer databases, but the database normalization literature makes these features explicit.

The ArchID approach to numbering sites, loci, and artifacts in a single series is consistent with the low-interpretation field documentation system. The approach to survey provenience used in the Upper Colca survey is low-interpretation because an interpreted hierarchy is not encoded in the proveniencing system. For example, in some systems the Site ID# is primary, and structures and artifacts encountered inside that site are numerically subsumed by the site numbering, unless they are isolates. In other words, sites receive the principal numbering system, and any artifacts and features found "inside" sites receive index numbers from a secondary range that force the site assignment into the proveniencing of every artifact in that area. The weakness and spatial dependence of this system become more evident when features from different temporal occupations are recorded in a single, multicomponent site. In contrast, the upper Colca survey used a single number series so that the "site" assignment number did not intrude into the proveniencing of every feature inside the site, as features were mapped individually and thus were independent spatial entities.

The advantage to this approach, and to categorizing sites in later analysis rather than in the field, is that documentation and interpretation are distinct steps and data can be reinterpreted and individual loci reassigned to other time periods independently of the site context and spatial provenience in which they originally belonged. In other words, the GIS does the work of spatial provenience, proximity, and overlay, while numbering systems are dedicated only to the task of serving as a key for referencing records and tables in the database. Categories and types were used in this document for analysis, data presentation and summary, however, in the course of original data acquisition during fieldwork there was an explicit effort to document features based on simple artifact and feature characteristics rather than by a generalized typology or classification. There is no single file with all ArchID numbers represented, as they are distributed across the nine file types shown above in Figure 5-7a. However, in a post-fieldwork GIS processing step an "All_ArchID_Centroids" point file is created that serves as a single reference point for all ArchID numbers used throughout the season (see Section 5.10.1).

5.3.6. Site classification

Meaningful construction of site categories requires a combination of quantitative measures and qualitative assessment based taphonomy and other formation process issues. In the loose volcanic soils of the Upper Colca, relatively high erosion rates result from downslope movement and stream-channel migration, combined with wind deflation and seasonally-intense precipitation. For example, high density lithic loci are commonly found at the base of slopes due to the disturbance and erosion that are part of site formation processes (Rick 1976;Schiffer 1983). These artifact aggregations at the bottom of hill-slopes may appear to qualify as high-density loci when assessed quantitatively and through sampling, however a qualitative interpretation of the context reveals the formation processes at work.

In the Upper Colca project area anthropogenic effects, due to a high incidence of site reoccupation, include the direct and indirect effects of the later reuse of space. These include palimpsests, site maintenance and disturbance, interments, and reuse of construction materials for residential or mortuary structures. The indirect effects are primarily the result of intensive pastoral production that occurred in the Upper Colca in the last few millennia. These include erosion and trampling (although camelids have two digit pads instead of hoofs), and landscape modification for pastoral production such as corral construction and the modification of water distribution to enhance grazing opportunities at bofedales.

5.3.7. Linking Field and Lab data: an example

Every feature and artifact that was mapped individually received a unique identified key (ArchID number) that was used after the fieldwork was over to connect the GPS derived geographical location to associated attribute tables for recording numerical and text characteristics, and other field observations. The ArchID number links Arcpad GPS derived data to these other tabular data in a GIS through One-To-One or a One-To-Many relates. Collections were conducted during the course of fieldwork and in many of these spatial proveniences, a number of individual artifacts were collected and examined creating a One-To-Many relate situation.

An example would best illustrate this situation. In this example, a concentration of lithics is identified and it is mapped and described during fieldwork as lithic locus ArchID: 100. All field acquired data are linked through the number 100, including the polygon delimiting the concentration, environmental and cultural observations at the location, photograph numbers, the date and time of the mapping that is automatically logged. Bags of artifacts collected from that provenience, including ceramics, would receive the ArchID 100 spatial identifier. Furthermore the #100would be noted, or the range of numbers in that area, in the verbal field journal descriptions providing an explicit link between digital tables and interpretive description.

During a later phase of research, when bags of artifacts are opened and analyzed, an artifact-specific level of proveniencing occurs. A collection of artifacts that are spatially provenienced to a polygon: ArchID 100 results, however when it comes to time acquire detailed measurements on those artifacts, some of these artifacts should now be numbered individually. The solution in the Upper Colca Project was to create a catalog ID number, or "rotulo" in Spanish (RotID), such that each spatial provenience has lab numbers inside it numbered 1 to n.In practice, the bags would be tagged with a number and a decimal as inArchID.RotIDor100.15for the fifteenth artifact analyzed from spatial context 100, although digitally the two number series remain integers stored in separate fields. Lithics, ceramics, bone, and any other artifact class were stored together in a single second-level number series, yet in practice there was an attempt during lab work to keep all artifacts of a single class within a contiguous numbering range.

5.3.8. Theoretical relevance of the provenience system

These data recording issues are methodologically specific, but the issues have theoretical importance because later analyses are circumscribed by the units of analysis used in proveniencing. The above descriptions highlight an apparent contrast in the methodology:

Situation 1:Individually mapped and located artifacts and loci that are found at larger sites receive their own ArchID numbers. These are independent entities that may, or may not, belong to the larger site upon further interpretation. Importantly, the GPS derived coordinates store this spatial relationship and the numbering system is independent of spatial location.

Situation 2:Asin the example described previously, lithic locus ArchID 100 contains fifteen artifacts rotulonumbered RotID 1 through 15 that are analyzed and retained as 100.1 through 100.15. In this case, the artifacts are locked into their spatial container that is the ArchID number. While it would be ideal to have geographical positions for every artifact analyzed, it is not practical to spend so much time in the field and therefore, by necessity, the RotID is inside the ArchID spatial provenience. In other words, spatial provenience is the first level, while artifact provenience is the second level.

In sum, the object here is to allow the GIS to manage spatial information and use the ArchID number system not as a geographical hierarchy, but rather as a linking system to other forms of data be they attribute tables, artifact collections, or digital photos. On a theoretical level, when particular loci are subsumed within particular sites by the numbering strategy (as per the hierarchical system where loci are inferred to "belong" to sites), it is impossible to later extract the loci from within the site in the database because their numberings are inextricably linked. If later analysis reveals that the locus is likely a later occupation and not related to the site itself, there is no easy way to reverse the hierarchy in proveniencing. In the non-hierarchical proveniencing system used by the Upper Colca project, the location of that locus inside the site boundary is already conveyed in the GIS and that liberates the ArchID numbering system to serve as an effective primary key for the database.

5.4. Survey Strategy

5.4.1. Goals of Survey and Testing

The goals of the Upper Colca Archaeological Survey were to document the prehispanic use of the Chivay obsidian source and to record changes in obsidian processing evident at the source. Quarry research presents special challenges to archaeologists because prehistoric patterns are obscured by the sheer quantity of non-diagnostic materials from early reduction stages, and the compounded reuse of space over time (Ericson 1984;Torrence 1986). Working at the Chivay obsidian source involved additional challenges in its remote location at high altitude where roads and electrical sources were unavailable. The research design therefore had to maximize the time spent camping at the high altitude source using field methods that could be used effectively to detect variability in obsidian production at the source.

Very few archaeologists had visited the source area prior to this work, and therefore the research team had to accomplish basic documentation of the source area. However, detecting change in obsidian production required a relatively in-depth investigation, such as analyzing lithic production loci and excavating test units to acquire temporal control. Preliminary visits in 2001 and 2002 indicated that the rugged, high altitude terrain around the Chivay source precluded a systematic and extensive survey of contiguous lands near the source. While total coverage surveys are preferable in theory, it simply was not worthwhile to survey many square kilometers of jumbled rhyolite boulders and skree fields, terrain that were barely passable on foot, when the vast majority of all the locations for sizable sites could be targeted by the general criteria evident on maps and imagery. Furthermore, a comprehensive study of activities related to the source area demanded that time was budgeted for an investigation of the highly productive lands approximately one day's travel away from the obsidian source, in order to place obsidian procurement in the context of the local economy. A research strategy that approached the entire region in terms of survey and testing in three major contiguous blocks was deemed the most effective approach to documenting the source region.

5.4.2. Surveys types: Prospection, Statistical, and Spatial Structure

The principal goal of the survey work was to document archaeological distributions in source area and adjacent terrain. Banning (2002) describes the goals of archaeological survey in terms of three principal types of survey that emphasize different research goals.


Prospection / Purposive


Spatial Structure


For findingarchaeological sites.

For estimating population parameters, evaluating probabilistic hypotheses and constructing locational models.

For detecting spatial patterns such as settlement lattices, travel routes. Also good for documenting continuous phenomena.


Prioritize the locating of sites by incorporating background information, predictive models, and remote sensing.

Sampling strategies for documenting artifact density, diversity, and site types within stratified samples or numerical samples.

"Total coverage" or nonsite survey to identify spatial interrelationships that might be missed by sampling approaches.

Table 5-3. Types of archaeological survey described by Banning(2002: 27-38).

Banning makes the point that while sampling is a common approach to archaeological survey, sampling is actually a poor method for prospecting for sites or for characterizing settlement lattices because major clues can fall outside of the sampling window. Each method has specific strengths and weaknesses, and often archaeological surveys are a mixture of several types.

5.4.3. Surveyor interval and sampling

Following Banning's (2002) terms, the Upper Colca survey work was a combination of all three survey types. The survey was prospective because it attempted to document a little known region and find the majority of the large sites associated with the obsidian source, but it was also statistical because survey zones were deliberately stratified so as to permit predictive statements about the use of space throughout the study region. Finally, the Upper Colca research also involved survey for spatial structure because it consisted of three large blocks within which the land was thoroughly surveyed so as to document intersite relationships and travel routes.

Many contemporary archaeological surveys will claim to have conducted "100% survey" of large regions, but then they will have had a survey interval of 30m or more between surveyors. Surveys focused on documenting complex societies with standing architecture are particularly likely to refer to their widely-spaced surveys as "100% surveys". A wide surveyor interval is actually a non-explicit kind of sampling that de-prioritizes smaller sites and those lacking standing architecture, resulting in an often unstated bias in the results. Subsequently, the region is considered "surveyed" though many smaller sites falling between transects were surely missed. While smaller sites are found in these widely spaced surveys, it is only if the site happens to fall across one of the surveyor lines. More realistically, such a survey method is somewhat successful because the surveyors cover a lot of ground but then they will veer off their route to visit high likelihood locations for sites such as rock shelters and lake shores; a technique belonging to the realm of prospection survey.

5.4.4. Survey design

The Upper Colca Project survey goals emphasized investigating the Chivay source area, the geological contexts for obsidian formation, and the principal areas of human settlement within one day's walk from the source. In the implementation of the survey, high-likelihood areas in the obsidian source zone were evaluated using a prospection survey. This included a careful survey of the entire Maymeja area itself and large portions of the southern rim using a surveyor interval of 15m.



Scale / Res.

Comments / Application

ASTER imagery

NASA, JPL(Abrams, et al. 2002)


Visual and NDVI analysis


NASA, JPL(Abrams, et al. 2002)


Representation, slope calculation


NASA, USGS, CGIAR (Jarvis, et al. 2006)


Regional relief mapping

Aerial photos

Servicio Aerofotográfica Nacional, Perú


Historic aerial photos.

Topographic maps

Instituto Geográfica Nacional, Perú


Features, toponyms (PSAD56), scanned

Geology maps



Geology (PSAD56), scanned


NIMA (1995)


Regional map


NIMA (1995)

1:1 million

Continental map

Table 5-4. Digital data sources used in developing the survey strategy.

Selection of survey regions involved the use of a number of spatial data sources (Table 5-4), as well as interviews with local residents, personal visits, and consultation of previously published reports. Preliminary field visits with a Trimble Geoexplorer GPS in 2001 and 2002 involved collecting ground control points and GPS lines on major roads and other features. After post-processing using the AREQ base station (International GPS Service), these data permitted the georeferencing of aerial photos and scanned maps directly to the GPS acquired data.


Figure 5-1. Criteria in designing regional survey from three stage research proposal including obsidian source survey, testing program, and concluding with the river valley survey.

Survey in the area of the obsidian source, outside of the Maymeja depression itself, was selective as it focused on high likelihood areas. Survey coverage in the Blocks 2 and 3 zones was contiguous with a 15m surveyor interval although here the survey region was delimited by other criteria. First, in the Block 2 (San Bartolomé area) a particular strip of land was targeted that paralleled the terminus of a Barroso lava flow. The survey block was surveyed 100% at 15m intervals along this densely occupied region. In Block 3, a maximum steepness and distance to river criteria was used to concentrate survey efforts to the river corridor region. Thus, in Block 3, all lands were surveyed within 500m of the high river terrace above the principal drainage (Colca, Llapa, and Pulpera drainages), and terrain over 15° slope (33% slope) were not surveyed. This maximum steepness limitation excluded many eroded regions where preservation is poor, but it also excluded a number of areas that were perhaps occupied. In order to evaluate the survey criteria in Block 3, a swath of land 1 km wide by 3 km long was surveyed at truly 100% coverage at 15m interval, and these areas could then evaluated to gauge the effects of the survey criteria used elsewhere in Block 3 that excluded the high slope and non-riverside areas. This 100% survey test swath will be described in more detail below.


Figure 5-2. GPS tracks from edges of most survey routes showing emphasis on Blocks 1, 2, 3 and 6.

With GPS units the survey coverage and spatial sampling is made relatively explicit, permitting future researchers in the region to focus on areas that were under-investigated in the 2001, 2002 and 2003 survey efforts. With GPS track loggers becoming easily available, explicit coverage reporting will likely be more widely adopted in the future.

5.4.5. Testing the effectiveness of the B3 survey strategy

While survey criteria for coverage in Block 3 were relatively restrictive, a swath in the vicinity of Callalli with a diversity of topographic and ecological conditions was selected for conducting a "100% survey". The goal of covering ground at a 100% was to evaluate the effectiveness of the survey strategy that was being applied throughout the rest of Block 3. The survey coverage in Block 3 included only areas within 500m of the highest river terrace, and slopes under 15° (33.3%) incline.

The 100% survey revealed seven small sites, some lithic isolates, a lone broken vessel and a wall on a hilltop location that was undiagnostic but is probably a Late Intermediate Period pukara construction. The area of the 100% survey swath is 0.5 km wide by 3.5 km long (area = 1.7 km2) and if onlythe area outside of the regular survey model is included, the area is 1.1 km2. The sites located in the areas outside of the survey model fall into two major groups: pukaras on hilltops and small, eroding lithic scatters with no reliable temporal assignment found on steep open slopes. Other regional evidence points to a pattern of intensified pastoral production during the LIP and Late Horizon, and these dispersed sites may result from herders working while they monitor their flock during the wet season when the hillslopes of Callalli contain rich graze.




Feature type














Visib is +33





























Pukara. Visib is +3.12









Possible Pukara wall.

Table 5-5. Sites and isolates from 100% survey strip that would not have been encountered using the regular Block 3 survey strategy.

Given the high effort expended in completing the 100% survey, and the eroded condition of most sites on steep slopes, a slight modification of the survey strategy would have resulted in the group encountering virtually all the informative sites in the region. The improved survey model would be like the one that was employed (500m from the high terrace and < 15° slope), and furthermore it would include a visit to all the major hilltops in the region searching for pukaras. With prospection survey, it is often true that pukara walls can be identified with binoculars or on imagery (Arkush 2005), allowing for targeted climbs of only those hills with visible walls.

5.5. Mobile GIS for archeological survey

Mobile GIS can be incorporated into archaeological survey methods with varying degrees of change to traditional archaeological survey techniques. This section describes the methods implemented in 2003 where a predominantly digital recording method was used.

5.5.1. Standard survey practice

The standard survey practice in the 2003 season consisted of a single team of four to six surveyors spaced 15m apart. The team swept across hillslopes following contours, and at the end of each survey transect the team would sweep around and return towards the opposite direction in the adjacent transect following a boustrophedon configuration. The survey team would assemble to investigate sites when they were encountered, although the team would not necessarily assemble for isolated finds.


Figure5-3. An example of a pedestrian survey line following a river terrace at a 15 meter interval. In this survey, only one mobile GIS unit is used. GPS units carried by the surveyors at either end of the survey line mapped the extent of all surveyed areas.

The portable equipment carried on the survey by each team member included basic day hiking equipment such as personal gear, lunch, copies of maps and a compass, and everyone had a small FM walkie-talkie. In order to map the extent of daily survey coverage, and to quantify the distances between surveyors, those hiking on either end of the survey line carried a Trimble GeoExplorer GPS logging a polyline attributed with the side of the survey that they were walking (right or left) as well as the number of surveyors hiking that day. GPS datalogger tracking devices are more widespread today (in 2006) and pair of small USB based dataloggers such as the Sony GPS-CS1 for geotagging photos are also suitable for continuous data stream mapping on either end of a survey transect. A separate mobile GIS system consisting of Dell Axim PDA with a Trimble Pathfinder Pocket GPS receiver was carried for mapping archaeological sites into Arcpad as will be described in more detail below.

5.5.2. The contribution of mobile GIS

Current mobile GIS technology contributes to traditional archaeological survey methods in several ways. First, mobile GIS aids surveyors with navigation because the anticipated survey transects, and some other relevant guidance information, can be clearly indicated in conjunction with the current GPS location. Second, mobile GIS allows researchers to record new vector data along with attribute forms that are more flexible than those provided by GPS or by data dictionary approaches in the past. Finally, mobile GIS allows researchers to transport digital datasets into the field so that they can do error checking immediately, review the work of other research teams, and perform queries on large existing volumes of data in digital form.

When a surveyor encountered an archaeological feature the surveyor would first determine if the feature exceeded the specification for isolates and then, should the feature be a site, the surveyor would call a halt to the survey line. Site boundaries were established for two reasons in the 2003 fieldwork. First, in GIS it is generally required that a geographical feature be delimited and that a database record is created before it can be attributed. Thus, one cannot describe a site that has not yet mapped, unless some kind of more complicated work-around is employed such as the creation of a temporary attribute record. A second beneficial effect of delimiting sites as an initial step, however, is that the team is forced to travel over the site completely and assess the extent and variability before an attempt was made to describe it.

5.5.3. Hardware configuration

Mobile GIS systems permit surveyors to attribute spatial locations with a variety of data types. Currently the GPS unit is the primary digital input into the mobile GIS and this permits the mapping of point, lines, and polygons delimiting archaeological features. In the GIS the spatial data is attributed and once post-processing is complete the new data joins the larger GIS database.


Figure5-4. Mobile GIS implementation with ESRI Arcpad 6. New data sources from external instruments are shown in the top row. Where post-processing is needed new data is not integrated with other data until later. New and existing data can be summarized and displayed together.

Existing inputs to the mobile GIS system in 2003 are relatively limited. Important attributes that currently must be entered manually include the digital photo numbers associated with each archaeological feature, and relative measurements collected at the site. Additional instruments in the future might include wireless tapes that can transmit precise lengths back to the mobile GIS of features like the dimensions of a doorway. Alternately, for in-field lab analysis on non-collection survey, wireless calipers or scales could be used to transmit the size of an artifact to the mobile GIS linked to the spatial provenience.

5.5.4. Defining loci and sites

When an archaeological site is encountered during survey in this Arcpad system the site must be delimited first, using the Site-A polygon, and then loci located within the site are delimited and described as related in two accounts (Tripcevich 2004;Tripcevich 2004). The locations of individual artifacts of interest are mapped and bagged separately using a Lithic_P or Ceramic_P geometry type. These include diagnostic artifacts or other materials of specific interest.

Locus / Site

Min. Density Artifacts

High Density

10+ artifacts per m2

Medium Density

5-10 artifacts per m2

Low Density

1-3 artifacts per 2m2


2 artifacts per 10 m2

Table 5-6. Locus and Site artifact density definitions.

Pin flags were used to delimit these features of interest, and generally in the case of most medium and high density loci, the result is a "fried-egg" model of artifact density polygons. In recording these polygon features, one generally went from the geographically largest to smallest entity because, as is also true in desktop GIS, features that are created later appear "on top" of features created earlier and thus larger, later features would visually obscure earlier features. This condition has to be corrected back in the laboratory and thus it was simply easiest to map largest to smallest. When a feature is mapped in Arcpad with the GPS then, subsequently, an attribute form appears that allows for explicit description of the feature.

5.5.5. Attribute Forms

Aside from the site datum points and site boundary polygons, three dominant feature types characterized the archaeological data set in the mobile GIS. Each archaeological data type had an attribute form associated with it that recorded information appropriate for a given feature. Page One of the digital forms comprised a unique ID number generated from a script and a range of numbers for digital photos (JPEG files) documenting a feature.





Figure 5-5. (a) Arcpad screen showing a large site with loci and points. (b) Example of page two of a lithic locus form in Arcpad showing Category 1 and Category 2 columns; in the background, two sites and contour lines are displayed on top of a 15m resolution ASTER satellite image.

Page Two of the attribute forms (Figure 5-5b) contained specific information about the feature type, such as Site, Locus, or Point information. The third page contained eight pull-down menus with environmental attributes for geology, exposure, and other local variables. These values were usually the same within a given site so that the values were "sticky"; they were stored in temporary memory between recording events, and the editable form was repopulated automatically unless a new site feature was being recorded. The final page contained a "Comments" field that accepted up to 255 characters and included a button that would open Pocket Word application with a text file named for the unique ID #, allowing the entry of additional notes if necessary. A link to a separate application that permitted MP3 compression of voice-based comments was available as well, but because the processor demands of sound encoding overly hampered the functionality of the Pocket PC for the GIS application, the feature went unused.

5.5.6. Variability within a Locus

A basic complexity of archaeological survey is that artifact concentrations frequently contain a variety of artifact types, perhaps dating to completely different occupations. This variability presents a particular challenge for a fast, mobile GIS based recording system because in lieu of sampling, all that the archaeologist has time to do is to document his or her rapid assessment of the artifacts that are found within individual loci geometry mapped into the GIS. Additionally, despite of the variability present within the locus, the archaeologist must attempt to generate data over the course of the field season that are consistent and comparable. During the Upper Colca Survey this difficulty was addressed by estimating the characteristics of a primary and secondary attribute category, dubbed Category 1 and Category 2 (see Figure 5-5b), that best characterizes the locus using the custom interface developed for the project.

The problem: How does one evaluate and map a scatter of, say, 5,000 stone flakes in less than one hour, as well as estimate the percentage of obsidian to another material type, such as chert?

In order to achieve statistical rigor and reliability, a sampling strategy was needed. Sampling and collecting artifacts is time consuming, and sampling at every concentration of lithics near a quarry is also unrealistic because there are so many lithic artifacts in such areas. Sampling was therefore carried out at "High Density Loci" with artifact concentrations deemed most worthwhile given the research goals, while a less rigorous approach was applied for artifact distributions of lesser importance. A solution was devised that is geared for conducting cursory inventory, not an in-depth assessment. This solution captured variability by estimating the proportions of the two dominant attribute categories within a given polygon.


(a) (b)

Figure5-6. Maps for two different hypothetical sites recorded in less than one hour. (a) A conventional, low precision sketch map showing only major site features and perhaps subdivided into site sectors (b) Mobile GIS site map with 1-2m dGPS error. Internal distributions, such as the fried-egg density gradient model shown here, can be assessed and rapidly mapped.


(a) (b)

Figure 5-7. (a) Structure of the archaeological Shapefiles with names and descriptions. Each of the Shapefiles had a form associated with it that prompted the user with fields appropriate to that data type. (b) An example of a part of the ID # system that prioritizes spatial provenience in the field.

A hypothetical site description makes the site recording strategy more clear. This example takes place at a site with a large, low-density lithic locus (Figure 5-6b), where the concentration of stone artifacts was mostly obsidian material but also included artifacts made from chert, chalcedony, and quartzite. The mobile GIS user walks around the locus with the GPS running, and the area was recorded into the "Lithics-A" ShapeFile (Figure 5-7a). Lithic concentrations of medium and high density are found inside the locus, creating a 'fried-egg' density map. Subsequent to delineating the locus with a GPS, the custom form (Figure 5-5b) appears. Several steps are followed in filling out the form.

(1) The primary "axis" of variability is determined. In this case, it is stone material type.

(2) Using this variable, the largest group is characterized. This attribute category 1 (C1) was described as "Material: Obsidian," and other attributes of interest to lithic analysis such as amount of cortex, size of debitage, and artifact density in the attribute category, were rapidly estimated. In our case, the density was "Low."

(3) The second most represented group, attribute category 2 (C2), is characterized and its attributes are evaluated, again as quickly as possible. Any subsequent groups were disregarded for expediency and because of the error in estimation and low reliability of the method.

(4) The proportion of stone artifacts in the polygon estimated to meet the description of C1 is entered in the field labeled "C1% of Locus," and an estimate is also generated for category 2.

The method works for a rapid inventory, and it provides a general estimation of materials along with the characteristics and densities within loci. Using this system, archaeologists are encouraged to describe the variability between category 1 and 2 in terms of only one variable at a time. For example, if there were notable differences in both Material Type and Debitage Size in a particular locus, then a second polygon was created. Alternatively, the first polygon was copied, and the different "axes" of variability were distinguished independently. Instant types (i.e., attribute categories) were generated for each polygon by emphasizing the greatest variability within the locus, and this was considerably more spatially explicit than rapid archaeological survey had been in the past despite a relatively small investment in time. Time efficiency was a major objective of the Colca Survey with recording all but the very highest density lithic concentrations, and this approach allowed for rapid feature mapping. A variety of new possibilities for custom field applications are becoming available now that modern digital equipment, such as the mobile GIS used in this archaeological survey, can be modified and streamlined by the archaeologists to suit the needs of research without recourse to professional programmers.

5.5.7. Sampling High-density Loci

For the purposes of the Upper Colca project High density lociwere defined as areas where the density of the artifact scatter appeared to exceed 10 artifacts per m2. As with all loci, these concentrations were mapped using the mobile GIS interface, but then High-density loci were further characterized by collecting all artifacts within two or more 1x1 m sample squares for later analysis back in the lab. The Arcpad SampleDesignscript was used to pseudo-randomly place, using an unaligned-grid method, a sufficient a number of square sample units to cover at least 0.01 of the Shape Area (m2)of the locus as reported in Arcpad. This works out to a 1 m2collection area for every 100 m2of polygon area. The GPS indicator was used to navigate to the randomly generated point locations. When documenting each sample an overhead photo was taken of the 1x1m area from near-nadir for later georeferencing, and then artifacts were completely collected. One or more units were randomly placed somewhere within the polygon, and one unit was always placed right on the location of estimated highest density. During the 2003 season, such collections resulted in an average sampling fraction of 0.014 among the twenty-two samples that were collected during the course of the field season in this process of sampling high density loci.

5.5.8. Collection during survey

Traditionally, it has been impractical for archaeologists to retain precise spatial provenance for surface artifacts that are not particularly interesting or rare. Collected artifacts are aggregated by site, sector, or by locus. However, artifact collection is increasingly seen as a destructive practice. The collection strategy used in the Upper Colca Survey consisted of assigning a unique ID number (ArchID) from a single number series to all spatial proveniences, point locations, loci, or entire sites-very much like postal zip codes for street addresses. After four months of fieldwork, 1100 spatial provenance numbers had been assigned from the series. As described previously, individual artifacts collected from a given provenience were assigned key ID#s after a decimal point. An interesting alternative to handwriting the unique ID# on labels for sample bags collected in the field is to bring a sheet of pre-printed barcode stickers. As the sticker is placed on the sample container, a serial barcode scanning wand can scan the barcode value directly into the GIS record. The barcode scanner approach is somewhat restrictive, however, because the mobile GIS unit must to be available to scan every collection bag.

5.5.9. Other Data Types

As a systematic pedestrian survey of extensive areas, the Upper Colca Project survey presented an opportunity to collect other field data as well. During survey work a separate set of GIS data was collected that consisted of non-archaeological data. These included geological sources of stone material such as chert outcrops and natural obsidian flows. Similarly, fresh-water springs and other resources of use to past peoples were mapped in. Mountain summits, trails that may follow Prehispanic trade routes, and other such environmental features were also mapped. Thousands of digital photos were taken, including a number of stitched panorama photos. The location of these photos was mapped with the mobile GIS using a form to enter the JPEG file numbers, as well as the cardinal direction and an estimate of distance for photographs of distant objects. The variety of data types that were determined to be "worth recording" during this survey project underscores the need for individual flexibility in recording methods.

5.5.10. Processing steps with mobile GIS

The time investment in implementing the mobile GIS approach is still considerable, as it involved both pre-fieldwork and post-fieldwork processing steps. Pre-fieldwork tasks, discussed above, include acquiring and preparing regional datasets, and designing digital forms that are appropriate for the project. Post-fieldwork processing involved standard issues such as downloading all data to a laptop from various devices, tagging folders of digital photos with the associated ArchID, GPS post-processing, as well as analytical processing steps such as deriving meaningful indices from the digital data. Post-fieldwork tasks also involved some unanticipated and time consuming labor, such as cleaning inconsistent datasets to prepare them for general analysis, and other management issues. These inconsistent data include the records gathered during two periods when the system was not functioning smoothly as described in Table 5-7.



Resolution in future projects

Pre-fieldwork preparedness

During the first two weeks of fieldwork software and hardware debugging were still underway.

Allow sufficient time for designing and debugging fieldwork forms and equipment prior to work in remote locations.

Equipment failure

During the last 10 days of the field season the cable connecting the mobile GIS PocketPC to the GPS unit failed and all sites were recorded with older Trimble Geoexplorer GPS units.

Purchasing two similar mobile GIS units, with one acting as a backup device so that no break in data recording would have occurred in case of equipment failure.

Table 5-7. Sources of inconsistent data during the 2003 project, and means of avoiding these problems in future projects.

Some of the processing steps are the result of employing GPS based mapping, and are therefore largely inevitable. Despite post-processing, the polygons and polylines gathered using GPS in Streaming mode contained a lot of redundancy and required geometry validation. These redundant positions were especially abundant where the person mapping a feature had to slow down or stop in the process of delimiting the feature. In these cases a number of vertices would be gathered as a small cloud (within the positional error of the GPS) and this resulted in a line intersection and short segments. Processing the data to resolve these GPS derived problems was accomplished in ArcGIS 9 using the following processing steps

(1) The "Repair Geometry" function was used to resolve intersections in a single polygon or polyline.

(2) Polygons were converted to polylines and the ArcToolbox > Data Management > Features > Simplify Line operation with a 10 cm tolerance was applied the data. The 10cm range for simplifying the lines reflects the data quality. In this case, the post-processed accuracy of the spatial data was 1-2m as stated by Trimble Pathfinder Office.

More recent versions of Arcpad (v7) and other mobile GIS software such as Terrasync provide a movement filter for datalogging that allows the user to specify a minimum distance setting, so that data are not logged unless one continues moving. For example, during streaming mode in 2003 the GPS was logging a vertex from the average of every 2 positions for a smoother line (at a 0.5 second rate this resulted in a vertex every second). A feature of the newer Arcpad 7 allows the user to specify that a vertex should be logged only after 3m of movement.

This option would have resulted in cleaner data during the 2003 season, although with low real-time accuracy the movement filter is relatively coarse and it is probably accurate to only within 10-15m. In North America and Europe, where SBAS (WAAS or EGNOS) correction signals are available, real-time positions are approximately 5-10m and these movement filters for datalogging function adequately on low-cost receivers. However in South America SBAS correction will not be available in the foreseeable future and movement filters are probably most relevant for faster GPS mapping tasks such as mapping from a moving vehicle. It is conceivable that GPS movement filters will actually result in noisier data because exceptionally large position measurements are logged, but the majority of positions are filtered out because the distance of movement is not sufficient.

5.5.11. Implications ofMobile GIS for Fieldwork

For the Upper Colca Project the mobile GIS recording system produced both cartographic data for site report mapping, and GIS vectors for analysis. The results of these efforts are presented in chapter 6, where the cartographic output appears in local and regional maps. Automated cartography methods, such as one-to-many labeling through a VB script (discussed below), permitted the automated labeling of lab results based on field collections from polygons or points.

In the bigger picture, the incorporation of mobile GIS for scientific field research seems inevitable although the applicability of mobile GIS to specific applications depends largely on the extent to which mobile GIS meets research needs. Minor benefits of mobile GIS, such as the time and date stamp associated with every measurement, improve the data that are being gathered in unobtrusive ways. A more elaborate system might gather extensive metadata concerning research methods and data structure into an automatically generated digital log file. Additional tools, such as statistical summaries and visualization applications would have proved useful during the Upper Colca Survey but these are not yet available in a mobile GIS platform. The ability to estimate spatial variation measured on archaeological variables would have been useful for a more informed selection of sampling strategies, and perhaps for guiding the placement of test excavation units (Hodder and Orton 1976;Redman 1987). When researchers are able to investigate new spatial data in conjunction with existing datasets using the exploratory data analysis approach (Tukey 1977) while in the field it will open up original research strategies by combining information from new and existing digital datasets. Statistical indices, such as the degree of spatial autocorrelation among particular classes of data, would be useful to know in the field. Geostatistical methods such as kriging, familiar to archaeologists in lab analysis (Lloyd and Atkinson 2004), will have application in fieldwork contexts as well when these tools become available in future mobile GIS systems.

5.6. Test Excavation Methodology

Changes in the use of the Chivay Obsidian Source through time was a principal aim of this research project, and therefore test excavations were critical for acquiring temporal control. As per survey regulations in Peru in 2003, test excavations must be anticipated and planned, and specific permits acquired, prior to beginning the research project. A testing program was planned for the 2003 season at three significant sites in the research area targeting sites in each major ecological zone. A datum for each site was established either on a large, permanent rock or by placing a datum stake. A datum stake consists of a 1.5 x 20cm piece of rebar placed on the margin of each of the site being tested, and site datum stake locations appear in this document in the maps in Chapters 6 and 7. A subdatum was placed adjacent to each test exacavation unit to minimize the horizontal distance (and therefore the error) of measures during excavation proveniencing.

5.6.1. Excavation procedures

The test excavations were conducted in 1m x 1m units excavated in either natural or arbitrary levels of less than 15cm thickness. As a total station was not available for test excavation work in 2003, the team relied on string and line-levels for depth measurements from the excavation unit subdatum adjacent to each unit. In 2004 the group of returned to Q02002u2 and u3 (obsidian quarry) and to the Block 2 Pausa area with a Topcon total station and mapped in the positions of the site datum, subdatums, and the backfilled test unit corners with greater precision. This 2004 mapping effort also permitted the production of relief maps of features at each site such as the quarry pit, mounds, and rock ovals.

Prior to excavating each test unit level, the top of each level surface was cleaned and a digital photograph was shot from near-nadir with a visible nail in each unit corner in the photo that could be used for later georeferencing following Nathan Craig's (1999) method. Features were designated on the top of each level, and artifact proveniencing for each unit included level and feature. Carbon samples were point located from the south-west corner of the unit (in compliance with the UTM coordinate system), as well as in centimeters below the unit subdatum. Two liter soil and starch samples were gathered from each level, and from features of sufficient size. All dirt was screened through 1/4" framed metal screens loaned by CIARQ, and then through 1/16" fine green window screen except where noted in the unit description (a few levels of largely sterile soil in A02-26u1 were not fine-screened). Upon completion of the testing, all test units were backfilled.

5.6.2. Proveniencing of excavated materials

During the 2003 field season the Upper Colca team did not have digital proveniencing of sufficient accuracy (sub-centimeter accuracy) to permit digital records from excavation and therefore the team returned to using more traditional methods. Collections were bagged and tagged using the unit/level/feature spatial proveniencing that facilitates locating the units without referencing a computer database or a locus sheet. Arbitrary spatial units included levels (15 cm or smaller natural levels), 1x1m units, and 50cm quads with letters in reading orientation following a convention borrowed from Mark Aldenderfer's excavation methods.



U2 / 4d / F2
Proj. Point





10 Nov 2003 NKT

Figure 5-8. (a) Example of proveniencing for four 50cm quads within a 1x1m unit with an item in quad d, north is at the top of the page. (b) An example of a paper tag showing site name, unit/level and quad/feature number, artifact type, date, and excavator initials.

The advantage to these explicit field proveniences is that bags and artifacts being returned from screening can easily be relocated to their origin provenience in the field (in the excavation block) based on the unit coordinate system. The disadvantage is that these provenience values, such as U2 / 4d / F2 do not code easily into a database and while some method of geocoding of these addresses is conceivable, the technical advantage to doing so is slim. In such cases, the European locus system, or a lot number system based on integer key fields, is much more effective and computer-ready.

The Upper Colca project ended up using a system that resembles, in some ways, the postal service system: street addresses are akin to the unit/level/feature codes in the sense that they are field-useable (one can find a house without referencing a database). Street addresses are supplemented by a Zip code in the US, which in the form of a nine digit zip, is house specific. This small amount of redundancy minimizes error in the postal service, and in the Upper Colca proveniencing a similar system worked for minimizing error in collections from excavation.

Field proveniencing was supplemented by Lot numbers during the first phase of analysis and data entry in the laboratory. Consistent with the Arch ID - RotuloID strategy, each spatial provenience was given an integer LotID number, and then artifacts within each spatial provenience received a RotuloID number in a sequence ( Rotulois Spanish for index number). Thus, for surface materials collected in 2003 a A03- [ArchID].[RotuloID]code specifies spatial location and then item identity, whereas for materials from excavation in 2003 the L03- [LotID].[RotuloID]code specifies excavation provenience (Lot) and artifact identity. As mentioned, the LotID number system was redundant with the unit/level/feature proveniencing system written on tags, but in reality it referred directly to the database unique ID # which is unavoidable in database organization. This redundancy was actually useful in a few circumstances and it serves to minimize error, as the digital ID# system appeared on the artifact ID# tags, and vice versa. Ultimately the Lot# system became the one that is referenced in queries after lab work was complete; it is the most up-to-date system in the database.

5.6.3. Proveniencing for database management and quantitative analysis

During quantitative analysis, individual artifacts needed integer unique ID numbers that followed through the entire analysis process and could be tracked back to the original artifact. The solution was to concatenate the two series for each artifact by moving the decimal point three units to the right and converted it into a long integer string. For example, for surface materials from ArchID# 1050 the tenth artifact collected would be coded as A03-1050.10. This provenience then became 1050000+10 and therefore 1050010 (a preceding zero was added to Rotulo ID number because in a few cases more than 99 artifacts came from one provenience). The unique artifact ID# solution was applied to excavated LotID.RotuloID numbers, such that Lot# 215 and artifact #15 would be coded as L03-215.15, and this became 215015. In order to avoid confusion between numbering for ArchID# (surface materials) and LotID# (excavated materials), 2,000,000 was added to the Lot numbers. The highest ArchID# recorded in 2003 was 1120 and therefore these coded to unique artifact ID#s as 1,120,000. Thus, in order to not overlap at all with the surface materials in the database system, the LotID# began at 2,000,000. Therefore in the previous LotID# example, the L03-215.15artifact would be coded as 215015 + 2,000,000 and therefore 2,215,015 which falls in a range that has no risk of overlapping with the ArchID# artifact example that is 1,050,010. While these numbers are cumbersome to type, they are managed easily in a database.

5.7. Lab analysis

Laboratory analysis followed on fieldwork with the aim of inventorying the collections from survey and test excavations, the examination of all diagnostic artifacts, and comprehensive analysis of a sample of all flaked stone artifacts recovered during fieldwork. Lab analysis took place in two stages in spring and summer of 2004 in collaboration with Willy Yepez, Alex Mackay (ANU), Randi Gladwell, Saul Morales, and Javier Morales, with additional assistance provided by Adan Lacunza, Guillermo Flores, and Tamara Flores Ramos.

These lab data contributed to the quantitative analyses that are presented in Chapters 6 and 7 of this document. In addition to ArcGIS 9, the software packages employed in this analysis \were SPSS 12 for boxplots, cluster analysis, principal components analysis, and Chi-Squared tests; and for data organization and display, principally through the indispensable Pivot Tables feature, the analysis depended on Excel 2003.

5.7.1. Phase I Lab work

An initial review of all field collections was performed as a distinct phase of lab work. While this phase of lab work is preferably performed during or immediately after fieldwork, this was delayed due to a lack of lab facilities in the Colca valley in 2003 and the time constraints of quarry area research.

Phase I lab work consisted of reviewing all items from fieldwork and standardizing the tag and bag structure in the collections. Lithics and ceramics collections were entered into an MS Access database using forms designed for this purpose. Lot numbers were assigned to the excavated collections based on a link between the spatial provenience and the database unique ID#, as described in the Proveniencing discussion above. Most collections were counted, although due to time constraints some collections (those with many small artifacts) were not counted. Additionally, all collections, including lithics, ceramics, bone, and other miscellaneous collections were weighed in their aggregate (by spatial provenience) using a lab scale with 1 gram accuracy. While the weight of ceramic and bone is not typically a meaningful measure, it is nevertheless useful measure of relative size per provenience for collections management, and weight serves as an expedient proxy for counting in large collections.

During Phase I lab work obsidian and "non-obsidian" lithics were segregated as this distinction reflected the priorities of the obsidian source research. In order to be more expedient during Phase I the non-obsidian materials were not sorted by material type although performing this sorting is a priority for future lab work.

5.7.2. Phase II Lab work

Detailed analysis of lithics, ceramics, and excavated bone occurred in the lab in Phase II. During this phase of lab work, individual artifacts were examined and the Rotulo (Index) ID# system was employed to track these artifacts through the database and later analysis stages. This analysis took place at the CIARQ facility in Arequipa in June 2004.

Phase II Lithics Analysis

The lithics analysis conducted by A. Mackay, S. Morales, and N. Tripcevich is largely based on a modified version of the lithics analysis strategies that Mackay employs in projects at Australian National University under the direction of Dr. Peter Hiscock. This methodology was tailored to meet the needs of the Upper Colca project and to be comparable with lithics analysis in Andes that often have a more typological orientation.

During Phase II lithics analysis, the following measures were taken when possible. A custom MS Access form was designed for the Phase II lithics analysis that functioned with digital calipers to expedite gathering metric attributes digitally.





Obsidian, Chert, Chalcedony, Fine-grained Volcanics (Rhyolite, Andesite, Basalt), Quartzite, Fine-grained siliceous, Igneous

Class and Type

Flake, core, heat shatter, hammerstone, groundstone, non-diagnostic fragment (NDF). Projectile Point, biface, retouched flake, perforator.

Color / Shade

Shades of obsidian: Black, white, clear, clear-banded, grey, grey-banded, brown, brown_banded. Other material types also included: red, black-orange, black-tan, olive, orange, pink, red-blue, red-brown, cream, white-tan, white-pink.

Weight (g)



Transverse snaps

Proximal, medial, distal - referring to existing segment.

Longitudinal snaps

Left or right - referring to existing segment

Percussive Metrics

Distance from Point of initiation to termination


Percussive Width

[Proximal, Medial, Distal] Distance across flake at 90 to force of application


Percussive Thickness

Distance from ventral to dorsal at midpoint of percussive length and percussive width (medial)


Platform width

Lateral distance across platform


Platform thickness

Distance across platform from dorsal to ventral


Platform preparation

Presence of overhang removal, faceting, or both (Whittaker 1994: 101)

Platform angle

Angle formed by intersection of platform and dorsal face.

Cortex percentage

% of cortex coverage in increments of 10. Flakes: dorsal and platform only, cores: whole piece.

Cortex location

On flakes: Dorsal, platform or both.

Cortex type

Rounded, tubular, irregular,

Heat affected


Heat Type

Greasy luster, crazing, pot-lidding, shatter

Termination type

Feather, hinge, step, plunging (outre-passé)

Number of dorsal flake scars

Number of clear (with initation, termination or both) flake scars on dorsal surface of flake

Number of rotations

N-1 where N is the total number of different directions from which previous flakes have been struck, sensu Clarkson et al. (2006). Increments of 45°.



Retouch Type

Dorsal Ventral, both

# of retouched segments

Flake divided into 8 segments


Retouch data
(8 segments)

P, PL, PR, ML, MR, DL, DR, D: Degree and Angle of Retouch for each. Marginal = .5, Invasive = 1 following Clarkson (2002).


Table 5-8. Measures on flaked stone artifacts during Phase II lithics analysis with measures depicted in Figure 5-9 indicated in "Illustrated" column.


Figure5-9. Showing some of the percussive metrics, platform metrics, and measures of retouch invasiveness for ventral side (Clarkson 2002) used in the Phase II analysis. Dorsal features and platform angle not shown.

Metric measures (shown in Table 5-6 and Figure 5-9) were taken with digital calipers accurate to 0.01 mm. The percussive length measure, rather than total a flake length measure, was used. Approximately 3,050 flakes were analyzed with these criteria.

Projectile Point typology

The projectile point typology developed by Klink and Aldenderfer (2005) discussed previously (Figure 3-10) is applicable throughout the south-central Andean highlands. The authors state that in Arequipa the northern frontier of the region under consideration for this typology is the ríoOcoña or the Cotahuasi valley. The study used projectile points exclusively from excavated and dated contexts in order to identify elements of projectile points that changed through time. In Klink and Aldenderfer's (2005: 27-28) study, measures on points were measured in millimeters with a precision of 1/100thof a millimeter, and angles were measured with a goniometer. The measures used include Length (mm), Shoulder angle (°), Maximum tool width (mm), Haft length (mm), Blade length (mm), Haft angle (°), and Basal width (mm), as well as derivative ratios of these measures. The typology was thus constructed to differentiate stylistic features that reliably distinguish time periods. Following this typology, Series 1 through 4 projectile points types are (with two exceptions) diagnostic to the Archaic Period prior to the advent of predominantly pastoral economies. Series 5 projectile points are diagnostic to the later, pastoralist periods that include a wide variety of socio-political and economic changes in the Andes.

During Phase II analysis of the Upper Colca project lithics analysis, an emphasis was placed on Series 1-4 projectile points because these points are the only diagnostic temporal evidence from surface contexts for a broad swath of human history in the Chivay source area.


All Points

Advanced analysis

Projectile Points



Not series 5



Series 5






Table 5-9. Proportion of analysis by projectile point typological group.

These data show that Series 1-4 projectile points were analyzed disproportionately in the Phase II lab work and resolving this discrepancy is a priority for future analysis of the collection. However, in the exploration of the data concerning Ob1 vs Ob2 obsidian types (heterogeneity of material), as well as artifact color and other characteristics, the analytical bias does not overly skew the results and interpretations. During Phase II analysis diagnostic projectile points were illustrated by Yepez.

Phase II Ceramics Analysis

Ceramics analysis was performed by Yepez. Virtually all diagnostic ceramics were derived from surface collections during survey. The priority during Phase II ceramics analysis was in classifying these ceramics using a local ceramics typology, and vessel diameter estimates were attempted with rim sherds of sufficient size.

As part of his dissertation research Steven Wernke (2003, Appendix A) developed a ceramic typology that differentiates the later ceramic periods. Wernke's typology is derived from surface materials resulting from his extensive survey of the central part of the Colca river valley. Wernke incorporated ceramic data from prior research by Neira (1961), Malpass and De la Vera Cruz (de la Vera Cruz 1988;de la Vera Cruz 1989;Malpass and De la Vera Cruz 1986;Malpass and De la Vera Cruz 1990), and Brooks (1998).

Phase II Faunal Analysis

Randi Gladwell examined the only significant faunal remains in the collections, which were bones excavated from unit 1 and unit 1x at A02-26 (Taukamayo) near Callalli. These were primarily camelid bones and Gladwell focused on diagnostic elements in this collection. She had access to a comparative collection at CIARQ, where the analysis was performed.

5.8. Sampling in Upper Colca work

Archaeologists use sampling routinely in order to make inferences about larger populations. Sampling takes many forms, both in the field and in the laboratory, and there is therefore an attempt here to be explicit about the major forms of sampling that were applied in this research.

5.8.1. Sampling during survey

The three major types of survey strategy applied in the Upper Colca Project, described in Section 5.4.2, were forms of sampling. In particular, surveying with a widely spaced surveyor interval is a type of spatial sampling. In general, a 15m surveyor interval was employed which resulted in a tight enough interval to capture a large proportion of smaller sites as well as larger sites.

During survey work, field collections largely consisted of unsystematic "grab bag" collections of diagnostic or otherwise interesting artifacts together with some proportion of representative or typical artifacts from the site or locus. High density lithic loci were sampled in two specific ways using 100% collection units. During the first step, a 1x1m total collection unit was placed on the estimated highest density area of the locus and all artifacts were collected within that 1x1. During the second step, additional 1x1m units were randomly placed throughout the locus in the strategy known as cluster sampling (see Section 5.4.5).

5.8.2. Sampling during excavation

Test excavations at A02-26 and A02-39 involved screening all collections through 1/4" mesh and 1/16" window screen with the exception of material from level 1 (plow zone) that was only coarse screened. Furthermore, the excavation at A02-26 (Taukamayo) was into a landslide margin and therefore a profile was available revealing that the unit was effectively sterile until level 3. Thus, a coarse 1/4" screen was used in level 2, except for one quad in levels 1 and 2 that were fine screened.

At Q02-2, the obsidian source, more stringent sampling was required for three reasons. First, at a raw material source researchers will inevitably find a great abundance of material and sampling is the preferred means of reducing that abundance to a manageable quantity. Second, fieldwork while camping at high altitude at the Chivay source was constrained by available time. Finally, collections were constrained because they had to be hauled out on the backs of mules, including all artifacts and soil samples, limiting the quantity that could be transported.

At Q02-2u2 (quarry pit) and Q02-2u3 (workshop) the units were virtually all flaked obsidian, although at the quarry pit much of the material was non-culturally fractured obsidian. The solution devised was to excavate a standard 1x1m test unit, as described in this chapter, however collection would include non-diagnostic flakes from one quad of the unit, resulting in collection of 25% of the flakes from u2 and u3. The remaining three quads (75%) of non-diagnostic material were used as backfill. This allowed the recovery of diagnostic artifacts, retouched artifacts, cores, and organic materials such as charcoal, from throughout the 1x1m unit.

5.8.3. Sampling during lab analysis

During lab analysis of surface collections from survey all diagnostic materials were analyzed, however only non-diagnostic lithics from select regions were investigated with the detail of the Phase II lab analysis. Non-diagnostic lithics from the obsidian source area were examined in Phase II, however in the collections from Blocks 2 and 3 only non-diagnostic surface lithics from within 200m of the excavation units in each block were examined comprehensively. Additionally, collections of non-diagnostic materials were examined on a site specific basis outside of the 200m buffer.

Sampling in the lab was required of the collections from the Q02-2u3 (Maymeja workshop) excavation materials. Although only 25% of the 1x1m was collected, the unit was almost entirely made up of cultural material and consisted of flaked obsidian artifacts. At the lab facilities at CIARQ all cores and bifaces were removed that remained in the collections from Q02-2u3, and then the collection was further sampled down to 5% by weight of the 25% from the original 1x1m test unit. From this subsample (1.25% of the original 1x1m unit) all complete flakes were recovered and the resulting sample was between 150 and 300 complete flakes for each provenience from Q02-2u3, which was an appropriate number for the analysis.

5.9. Derivative indices

Field and lab measurements acquired during the Upper Colca project were transformed, in some cases, into indices and other measures that are more widely useable than the original measurement data. The indices that were generated using GIS data, primarily the vector to raster conversion and lab measurements, such as the Bifacial Thinning Flake index, are described below.

Fieldwork was largely conducted using a GPS receiver connected to a mobile GIS unit, resulting in a variety of vector datasets, however some of these phenomena are better examined using the raster data model. The vector ? raster transformation is a basic concern for scientific fieldwork because it makes explicit the process of deriving spatial generalizations from detailed observations. For example, distributions of temperature or rainfall are interpolated from point-specific measurement locations where empirical evidence has been gathered by instruments. Similarly, in this project specific observations were generalized to produce continuous raster surfaces.

The spatial extent of surface archaeological features were recorded as bounded vectors using mobile GIS, as was described in the preceding chapter. Because mobile GIS involves the use of GPS to delimit features, all spatial features had to be recorded as discrete vectors, such as polygons, even when they are better investigated and represented as raster data. The following methodological explanations are all cases where field data were recorded in a vector form and then interpolated to raster for analysis and review.

The analysis of these raster surfaces took place in two contexts. The first was direct spatial queries against the raster in a GIS context. The second form was derived from the first: it was a tabulated query of all 1200 spatial features recorded during survey and the Mean, Standard Deviation, Min, and Max of the raster value for each feature. These tabulated data were linked using the ArchID reference number to their respective GIS and lab database records, and used in SPSS 12 and MS Excel 2003 for analysis and display.




Destination Raster

GPS Vectors

Lithic Loci and collection: Density / material type variables





ASTER Imagery:VNIR bands


Distance from NDVI calculated large bofedal

Raster DEM

ASTER DEM(Spaceborne remote sensing platform)


Elevation (masl)

Slope (degrees)

Aspect (8 categories)

Cumulative viewshed index

Raster DEM

SRTM DEM Topography


Regional scale elevation

Table 5-10. Vector and raster layers used in analysis and derived raster output.

A variety of raster surfaces were created from source data layers used in the analysis. The creation of these non-standard raster layers is the subject of the next section.

5.9.1. Lithic density raster index

Concentrations of lithics and ceramics were delimited and recorded as low density loci when they exceeded 1 artifact per meter, medium density at 2-10 artifacts/m2and high density at 10+ artifacts/m2. The resulting GPS polygons describe regions of increasing lithic density, which is the familiar 'fried-egg' distribution of archaeological artifact scatters. These polygons are based on estimates of artifact scatter density but the resulting GPS vectors with firm boundaries are not a suitable way to analyze data that had its origin as estimated artifact density scatters (Figure 5-6b). In order to examine lithic surface distributions as a statistical model of artifact density, the mobile GIS derived polygon vectors were converted to a raster data model with a 1m cell size.


To allow for expedient field procedures, lithic loci were mapped in terms of two categories inside a given polygon: the dominant lithic artifact represented is attribute category 1 (C1), while the second most well represented lithic artifact group was attribute category 2 (C2). To separate lithic material types as recorded by the Arcpad interface the estimated percentage was used to differentiate, by weight, the calibrated material type for collections from that locus. Due to time constraints both in the field and in the lab only (1) obsidianand (2) non-obsidianwere differentiated for material types during Phase I lab analysis.

This is keeping with the relatively expedient lithic recording strategy because time was not available to bend over and check a large number of light-colored flakes to see if each one was chert, chalcedony or quartzite. However, one should be able to differentiate obsidian from non-obsidian relatively consistently since obsidian procurement is the subject of this research. During Phase I lab analysis, obsidian from non-obsidian lithic material types were separated for the entire collection. Materials were not sorted and weighed into finer material type groups until Phase II analysis, which occurred for only a select portion of the total surface collection. As a result, it is only possible to calibrate the lithic loci by material type at the obsidian / non-obsidian level of specificity and thus only obsidian lithic concentrations and non-obsidian can be differentiated (primarily chert, but also quartzite, chalcedony, and aphanic volcanics) from the surface scatters for mapping and spatial analysis purposes.


A site is identified and two low-density loci and one medium and one high density lithic loci are identified and mapped. The medium and high density loci are, by definition, inside one of the low-density loci in a layer-cake fashion. Additionally, as described above a 100% collection of two or more sample units (1m2) per high density locus was conducted. Thus, returning from the field data consist of

(1) A site boundary polygon, two low-density polygons, and a medium and high density polygon mapped and attributed according to Attribute Category 1 (C1) and Attribute Category 2 (C2) variability (see Section 5.5.6"Variability within a Locus").

(2) Representative surface collections from the site and from each of the loci were gathered by fieldworkers with common knowledge of the description being entered into Arcpad as attributes by the mobile GIS user.

(3) A minimum of two 100% collection units from each high density locus were gathered.

These sources of data were combined, when available, to produce an "obsidian" and a "non-obsidian" lithics density raster surface. When a locus has a C1 of Obsidian and a C2 of Chert (for example), the Estimated Percentage for C1 and for C2 of that locus could be used to estimate the density for each cell of the GRID, scaling the representation of C1 and C2 by the percentages of each returned to the lab.

However, if a locus has a C1 of Obsidian, big flakes and a C2 of Obsidian, smaller flakes (same material, different sizes), for example, the lab results can be examined to see if there are non-obsidian artifacts collected for that locus. If so, then the count of those non-obsidian artifacts, as a percentage of the count of the whole collection, is assumed to represent the percentage of, say, quartzite in that locus that was predominantly obsidian based on its description. In that way, the weight and percentage of a given material type from lab analysis was used to calibrate field recordings of artifact scatter composition.

Using the locus definition rules stated above (i.e., high density locus = 10+ artifacts /m2) the polygons were converted to rasters and the following values were placed in the cells. The vector to raster conversion and the attenuation on the edges of each class were resolved by the mosaic command which averages the differences between raster surfaces along the contact boundaries between classes.

Polygon type - Density

Stated range

Value assigned in raster

Lithic-A - Low

.5 to 1 artifacts per m2


Lithic-A - Medium

2 to 10 artifacts per m2


Lithic-A - High

10+ artifacts per m2


Site-A area (entire site is a low density scatter)

When site has Lithic-A Med density with no Low.


Table 5-11. Loci to GRID conversion values.

The Density value therefore ranks the polygons by their number of flakes per meter for variability in either Material Type, Reduction Level or Flake Size, and the raster creation focused on material type differences, but rasters could have been created, theoretically for the other characteristics as well.

5.9.2. Cumulative Viewshed Analysis and Exposure Index


A comparable measure of view was needed to better evaluate the locational properties of sites encountered on survey. Based on research for a Master's paper on Cumulative Viewsheds in the Ilave Valley (Tripcevich 2001), the work of Wheatley (1995) and Lake et al. (1998;Lake and Woodman 2003), and using methods suggested by Nathan Craig (2000 pers. comm.), a surface was calculated that quantifies the visibility and, to some extent, environmental exposure of a given location.


Figure5-10. Line of sight across hilly terrain results in specific cells and targets being in view or out of view.

The basic concept to cumulative viewshed analysis is that a large number of viewsheds for random locations are calculated and overlaid on one another, and the locations that have a high incidence of visibility from random locations are likely to have both broad viewsheds and an unusually high environment exposure. Comparability between viewshed and exposure has been addressed more thoroughly by Kvamme (1988: 335-336;1992: 26-27) where the exposure is considered in terms of the volume of a cylinder surrounding the point of interest, and thus exposure of a location to a greater variety of altitudes and directions is considered. This approach is more thorough than a visibility calculation, but given the relatively coarse resolution of the DEM, the index for Visibility was assumed to be essentially representative of climatic exposure.


Figure5-11. Viewshed is not necessarily reciprocal, as the individual and the left can see the person on the right, but the opposite is not true.

The assumption is that of reciprocal visibility: if person A can see person B, then person B can see person A. The resulting surface contains a value in each grid cell that indicates how many of the random observers can see that grid cell.

Procedure used in generating the mean Viewshed layer

(1) Defined the extent of the study area as 50 x 50 km square (2500 km2) that includes a buffer of at least 5 km around the Upper Colca Project survey blocks in order to eliminate edge effects.

(2) Five thousand points were generated using the pseudo-random point placement function in "Hawth's Tools 9" tool for ArcGIS 9. This produced approximately 2 observer points per kilometer in the viewshed study area. These points were converted to the Arc/info Coverage format.

(3) An observation target height of 1.5m (OFFSETB) and maximum distance of 10km (RADIUS2) were used.

4. The 30m resolution ASTER DEM was smoothed using FocalStatistics with a 3m kernel and converted to GRID.

5. The ArcWorkstation GRID command VISIBILITY was issued with the FREQUENCY flag which tallies the frequency at each location (this is, effectively, a cumulative viewshed analysis). Each view calculation took approximately 80 seconds on a P4 computer, for a total of 111 hours (4.6 days) of calculation.


Figure 5-12. Cumulative Viewshed using 5000 random observers and 10 km viewing distance.


In conducting a cumulative viewshed analysis in the Ilave River valley in the Lake Titicaca Basin (Tripcevich 2001), the maximum view distance was defined as 5 km because a principal goal was to model the observability of camelids (wild or domesticated) and other people traveling in the Ilave valley. It was estimated that a distance of 5 km was the approximate distance that such objects could be seen, with a consideration for the great deal of variability in such situations, including:

- the visual acuity of the observer

- atmospheric conditions

- the amount of contrast between the target and the background

- whether the target was in motion.

Similar criteria exist in the Upper Colca viewshed study, however in this case a 10 km maximum radius was selected for this situation. The rational for doubling the maximum radius from 5 to 10 km was as follows. The goal here is to model ancient visibility, but also environmental exposure. In the much higher relief terrain of the Upper Colca area, observation distances are potentially much greater, and exposure is similarly greater. In order to avoid artificially limiting the amount of view / airspace adjacent to high visibility sites, a 10 km buffer was issued to the RADIUS2 variable for all 5000 random points. Also, a OFFSETB (target) height rather than OFFSETA (viewer) height was raised to 1.5m to simulate the assumed eye level of an Andean adult because the reciprocal viewshed was being calculated in this process.


The visibility study output offered two forms of output data that were used with a Zonal Query in ArcGIS Spatial Analyst in assessing the visibility and exposure of archaeological features encountered during survey work, and these data presented with the survey results in Chapter 6.

5.9.3. Bifacial Thinning Flake index

An index of Bifacial Thinning Flakes (BTF), also known as a Flake of Bifacial Retouch, was developed for this project. This index is defined for complete flakes that have measures for both medial width and thickness. Additional characteristics of BTF that occur in relatively low frequencies, such as platform lipping, were not incorporated in this index (Sullivan and Rozen 1985:758).

In the course of lab work, twenty-three flakes were noted that were "possible thinning flakes" and these were used to assess the BTF index described above. Using the BTF index, 18 of the 23 flakes (78%) flagged as possible BTF were 7 or greater on the BTF index.

Being a general measure, a cutoff of 7 or larger is used to identify "possible BTF" in the following analyses.

When rotations > 0 and Cortex = 10%, BTF =/misc/image038.gif

This index was applied to general collections in the analyses presented in Chapters 6 and 7.

Calibrated projectile points counts, and why they were not used in this study

Time-sensitive projectile point distributions from survey can be used to produce "calibrated counts" for sites in a given region as has been applied in studies in the Titicaca Basin (Craig 2005:453-468;Klink 2005;Tripcevich 2001) and in other regions. Calibrated counts are valuable measures because time periods assigned to projectile point styles are of different durations, and thus direct comparisons between site counts for a given time period can be misleading. For example, the Middle Archaic is 2000 years in length while the Terminal Archaic is only 1300 years in length, therefore there were more years by a factor of 1.53 for sites to occur in a given time period.

However, in the Upper Colca study, site counts were not systematically calibrated for two reasons. First, as mentioned above, there were virtually no single-component sites identified in this survey. Due to the aggregation of settlement around water sources and sheltered places in the higher altitude portion of the survey, virtually all sites with more than two diagnostic artifacts were shown to be multicomponent sites. Second, this research was conducted in the vicinity of an obsidian source and the evidence from this study and work elsewhere in the region show that obsidian is strongly correlated with Series 5 projectile points belonging to the later Prehispanic time periods. Therefore projectile point style and length of time are not independent variables such that one could be used to calibrate the other, because obsidian was increasingly in demand and further circulated during the Series 5 time period.

5.10. Database structure and 1:M labeling

A basic challenge to organizational structure in GIS is database normalization. As per the first and second normal forms in database structure (Codd 1970), One to Many (1:M) table relationships should be used to eliminate redundancy in tabular data. The 1:M relationship is common-place in archaeological research because it is often very efficient to map many artifacts to geographical features represented as a single point or polygon. Managing data that are distributed through 1:M relates brings added complexity, however, both in locating particular references to data, and in graphical representation. Current capabilities of GIS are limited in the representation of data accessible through a 1:M relate, symbolically or through labeling, and most require a restructuring (make-table query) of the data for each mapping effort.

5.10.1. The All ArchID Centroids file

The added complexity of managing data through 1:M relates and distributing the recording of archaeological feature types among multiple GIS feature layers can be alleviated by creating a single file reference dataset. The concept is based on the ArchID numbering system described above (Section 5.3.5). Because all archaeological features that were individually mapped, including lithics, ceramics, and structural loci, shared a single number series, then a number list could be generated that is comprehensive and serves as a "backbone" against which to structure Many-to-Many relates.


Figure 5-13. The All_ArchID_Centroids table provides a single reference layer for all the ArchID numbers used in the project, and allows relates to occur between disparate data layers. This figure shows the structure of these different layers using artificial data.

Many-to-Many (M:M) relates consist of a series of 1:M relates linked through a file structure that contains key fields in held common by the different tables, and this structure allows for relates to move across different file types. An example of queries moving between different layers would include the following: one needs to know if there is a statistical association between the percentage of obsidian among simple flakes in lithic assemblages in Ichocollo (Figure 5-13), and the rim diameter of LIP painted ceramics at that site. All the lab results from the archaeological features within Ichocollo showing obsidian percentage can be selected and related to the ArchID numbers, which in turn are related to the Ceramic_p layer containing the rim sherd diameter information. The problem is that some of these features are lines and polygons, while others are points, thus a single lookup table reconciles all these differences and allows relates to occur.

This file, referred to as All_ArchID_Centroids, is a single point-type file containing the point locations, and in the case of lines and polygons, the centroids of these features. This GIS layer is a single comprehensive list of all geographical proveniences, although the spatial detail and topology of lines and polygons is obviously lost when centroids are used. The table from the All_ArchID file can accompany lab data into statistical software packages to allow very general geographical queries on lab analysis results, such as patterns by common Locus, by Survey Block, and other queries that do not require GIS.

In sum, this post-fieldwork processing step involves creating the comprehensive list of ArchIDs that were used throughout the field season from the multiple file types described above in Figure 5-7a and creating a single GIS point geometry file from the cumulative list. The All_ArchID_Centroids file proved to be extremely useful in later GIS and lab analysis. The point location centroids for all feature types contains sufficient detail for many of the later geoprocessing steps, and the All_ArchID_Centroids layer was used as single location for labeling and for providing a structure for linking to photos on external website URLs.

5.10.2. VB Script for One-to-Many labeling

Labeling through a 1:M relationship is not currently supported in ESRI Arcmap 9.1. To resolve this limitation, the script presented below iterates through all the artifacts collected from a given spatial location and places their values in a text box. Text boxes can then be converted to annotation and moved around the map as needed.

' This command is issued from the Label Properties "Text String" Expression, and with the Advanced button checked.

' In this case Feature:ARCH_ID and Table:Arch_ID are Strings and they hold an ID common to both records in both

' the Feature, and the Table Litico_II is the name of the table C:\gis_data\colca_data\colca.mdb is an MDB or GDB

'Recursive Label function for labeling 1:Many from a table in a Access / GDB file.

' Modified from code by Mohammed Hoque 2005 in

Function FindLabel ( [ARCHID] )

Dim strLblQry, strInfo

' strLblQry = "SELECT * FROM Litico_II WHERE ArchID = '" & [ARCHID] &"'"

' if ARCHID were a String then the line would look like the above

strLblQry = "SELECT * FROM Litico_II WHERE ArchID = " & [ARCHID]

Dim ADOConn

set ADOConn = createobject("ADODB.Connection")

Dim rsLbls

set rsLbls = createObject("ADODB.Recordset")

ADOConn.Open "Provider=Microsoft.Jet.OLEDB.4.0;Data Source=C:\gis_data\colca_data\colca.mdb;"

rsLbls.Open strLblQry, ADOConn, 3, 1, 1

'If no record is found, return label only

Select Case rsLbls.RecordCount

Case -1, 0 ' no matching records in table

strInfo = "A03-" & [ARCHID] & "." & trim(rsLbls.Fields("Rot_Inicio").Value) & ": No Artifact Data!"

Case 1 ' just one matching record

if cint(rsLbls.Fields("Diag14").Value) > 0 then ' checks before labelling this record

If cint(rsLbls.Fields("Rot_Inicio").Value) = 1 then

' Don't show Dot-Rotulo for only one label

strInfo = "" & [ARCHID] & ""

Else strInfo = "" & [ARCHID] & "." & trim(rsLbls.Fields("Rot_Inicio").Value) & ""

End if

' use the TRIM function so that no error is returned in case of NULL

End If

Case Else

'Loop through all records in Table with same Arch_ID

Dim i

For i = 0 to rsLbls.RecordCount + 1

if cint(rsLbls.Fields("Diag14").Value) > 0 then ' checks to see if we should label this record

strInfo = strInfo & "" & [ARCHID] & "." & trim(rsLbls.Fields("Rot_Inicio").Value) & ""

end if

i = i + 1



End Select

'closing connections, this is a must



Set rsLbls = Nothing

Set ADOConn = Nothing

FindLabel = strInfo ' This is where the string is returned for labeling

End Function

Table 5-12. Script for labeling through a One-To-Many relate in ArcMap 9.1.

Cartographic capabilities of future GIS software likely will include a similar feature in the symbology and labeling function. Table restructuring merely to represent relationships that are inherent to the database structure, as is currently required by the off-the-shelf software, is highly inefficient.

5.11. Conclusion

Methods implemented in this research project were complicated by the fact that the 2003 season combined a regional survey, test excavation work, and recently developed geographical technology into a single project in a remote location. This chapter discussed data recording, sampling, lab analysis methods, together with methods for integrating digital data from these distinct steps. The integration of GPS and mobile GIS technology permitted feature recording at a scale that was impractical in archaeology until very recently. The project used adaptable archaeological feature recording methods that emphasize the recording of artifact clusters by expediently mapping areas of similar material types, debitage sizes, or ceramic types. Many of the specifics of these methods will rapidly become obsolete as digital methods develop, however the larger issues surrounding objective and subject data recording, and the relative benefits of the vector and raster data models for analysis, relate to larger questions linking method and theory that will be remain important issues for years to come.