Geospatial Archaeology

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Geospatial archaeology is the study of spatial relationships and patterns in archaeological data using geographic information systems (GIS) and other spatial analysis techniques. It integrates geospatial technologies to analyze the distribution of artifacts, sites, and landscapes, enhancing the understanding of human behavior and cultural development over time.

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Geospatial Big Data and archaeology: Prospects and problems too great to ignore

by Mark D McCoy

2017, Journal of Archaeological Science

As spatial technology has evolved and become integrated in to archaeology, we face a new set of challenges posed by the sheer size and complexity of data we use and produce. In this paper I discuss the prospects and problems of Geospatial... more

Fig. 1. Geospatial big data on sites, ancient places, and radiocarbon dates. Sources: The electronic atlas of ancient maya sites: a geographic information system (GIS); pleiades — tl stoa consortium; Goldberg et al., 2016; Martindale et al., 2016; Russell et al., 2014; Silva et al., 2015; Vermeersch, 2016). Se ee ee ee Ee ee ee ee The integrative databases, as opposed to the archival databases, are especially good at exposing clear biases in what kinds of in- formation is currently accessible. For example, Fig. 1 shows a several kinds of geospatial data: Mesoamerican ‘sites’ (The Elec- tronic Atlas of Ancient Maya Sites), ‘ancient places’ of the Medi- terranean (Pleiades), and sample locations derived from several major radiocarbon databases and regional studies. Each uses co- ordinates to record location and so are easily converted in to the same data model (vector, points). The site database is a “pan-Maya registry of ancient Maya settlements” and each point has a corre- sponding assessment of “Site Rank” to allow for quantitative geo- spatial analyses. In contrast, ‘ancient places’ includes settlements and an extremely broad variety of other categories, such as place names, to aid in the qualitative analyses of historical texts. The third map shown, radiocarbon data from archaeological sites, is different again. Like the site database, radiocarbon databases are clearly built with quantitative analysis in mind, but like the ancient places database, it includes any and all kinds of phenomenon (i.e., evi- dence of settlements, foraging, farming, burials, etc.). It should also be noted that it is possible that some radiocarbon dates within a database reflect natural processes rather than human behavior. Nonetheless, even after combining some of the largest An even more difficult quality to evaluate in our largest geo- spatial datasets is geospatial-temporal coverage. Take for example recent studies from the Near East (Lawrence et al., 2016) and China (Hosner et al., 2016). The goals of each study are similar — to qualify and quantify paleodemographic and settlement pattern trends over the Neolithic through Bronze Ages — and both take a time-slice approach where site records are coded by cultural period (e.g., Bronze Age) and by absolute time (century or millennium scale) with a beginning/end (min/max age) and time period (e.g. 6 kya). The Near Eastern study is focused on urbanism and, using a mix of survey and remote sensing, each record includes an estimate of site size. Both studies compare their data to shifting climate regimes in their respective regions and other key variables. These are both ambitious and important undertakings using “the best available and most up-to-date coverage of archaeological sites obtained by salvage and research excavations and surveys” and recognize the underlying data has known limitations in terms of uneven geospatial-temporal coverage within the respective regions (Hosner et al., 2016: 1589, emphasis added). At first glance, the Chinese dataset appears to have much better coverage with +50,000 site records compared with less than 400 sites in the Near East. However, if we account for the total size of the regions

Fig. 2. General model for the spatial distribution of Maori fortifications in New Zealand. It is well established that the majority of fortifications and other pre-European contact era archaeological sites are found in the warmer northern region of New Zealand, especially in coastal environments, as shown in this example of site predictive model (Leathwick, 2000:Fig. 3).

Fig. 3. Geospatial Database Radiocarbon Dates in New Zealand. This image shows an archived geospatial database (kmz) created by Waikato Radiocarbon Lab.

Fig. 4. Average Modern Temperature and Distribution of Radiocarbon Dates. Temperature ranges from —6.0°C (black) to +16.2°C (white). Sources: LINZ, Waikato Radiocarbon Lak Data reproduced with the permission of Landcare Research New Zealand Limited.

Fig. 5. Distribution of Radiocarbon Dates: Population. This time series shows all radiocarbon dates as a proxy for spatial distribution of population (after Chaput et al., 2015). See te for description of periods. The estimate of fortification and population distribution yields three generalized phases. First, in the period before fortification, the population distribution was remarkably uniform across the islands, with indications that the South Island may have been more GBD of fortifications in New Zealand are a good example of the difference between professional (privately maintained and restricted) and publically available site records. As noted above, in New Zealand the spatial distribution of fortifications is not in dispute, nor is the value of science and scientific facts, and so it is largely unnecessary to make such a comparison. But, given that in the United State at the moment there is a real danger to the authority and value of scientific data, specifically when it is perceived as an impediment to economic development, it is in archaeology's interest to be able to clearly show that professional site records, while often kept from the public view to avoid looting, do exist and are the ‘tip of the iceberg’ when compared with sites that are better known.

Fig. 6. Distribution of Radiocarbon Dates: Fortification. This time series shows all radiocarbon dates as a proxy for spatial distribution of population (after Chaput et al., 2015). Se text for description of periods.

Fig. 7. Average Temperature at Locations with Radiocarbon Dates. When organized by time series, it appears that fortifications favored warmer areas from their first appearance and grew to include colder regions through time. The general trend for the proxy for population distribution appears to shift toward warmer locations over time with a broad range of environments occupied throughout.

Fig. 8. Distribution of Public and Professional Site Records of Fortifications. Note that site locations are masked by using a polygon layer representing the NZ map grid. Sources: LINZ ArchSite. The results show how surprisingly poorly a public geospatial database represents the actual geographic distribution and density of fortifications. Fortifications are present in ~20% of all map grid polygons. To put that in more meaningful terms, at any given place in New Zealand there is a 20% chance that a fortification is within a 1—2 h walk. The public records of fortifications alone would un- derestimate how common fortifications are across space

Fig. 10. Terrain Relief Visualizations of Puketona Pa. From clockwise from top left: slope (64 to —1), multi-hs, pca, srlm, open-neg, open-pos (150—58), svf-a (1—0.4), svf (255-0), and field map from site record (not-to-scale).

Fig. 11. Screen Capture from Video Fly Through of 3D Model of Puketona Pa. Areas of high slope (white) contrast with flat areas (black).

simply missing. Major academic journals, including the Journal o Archaeological Science, do not require publishing geospatial data alongside of new research. This is not a problem that is unique tc geospatial data, but one that could be fixed (see Horsburgh et al. 2016 for a similar critique regarding the lack of rigorous publica- tion of zooarchaeological data). examined (~0.3 million vs ~9.6 million square km), the Chinese dataset has only about four times as many records per million square km as the Near Eastern dataset, and if one considers coverage within contemporaneous time periods, the density of sites is even more similar. Between 3 and 4 kya, there are less than three times as many sites per million square km in the Chinese data than the Near Eastern data (19,837 sites/9.6 mil sq km vs. 222 sites/ 0.3 mil sq km). The lesson here is clear — these datasets are more alike than one would estimate just based on the number of records when one accounts for space and time. Examples of Geospatial Big Data in Archaeology. See full internet references in List of Web and Data Sources.

Estimated Storage Size of Visualizations. The digital products from this study for Puketona Pa would take the current 1 MB of files associated with the site up to 469 MB. Table 3

Suggested Questions for Standalone Quality Reports on the Use of Geospatial Big Data in Archaeology. The specific data qualities listed here were modified from Merino et al. (2016). This type of document is in line with the London Charter (Denard, 2012) principle of documentation for computer-based visualization of cultural heritage.

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In Pursuit of Māori Warfare: New archaeological research on conflict in pre- European contact New Zealand

by Mark D McCoy

2019, Journal of Anthropological Archaeology

In the landmark book Māori Warfare, Vayda (1960) used oral traditions and historical documents to characterize conflict in New Zealand as a cycle of fights over prime agricultural lands driven by population pressure. We examine the... more

Fig. 1. Archaeological Sites of New Zealand. Distribution of known sites as recorded in the ArcSite Database (http://archsite.org.nz/, accessed 2017)

Fig. 2. Archaeological Sites of the Bay of Islands Region, New Zealand. Locations of three fortifications on Urupukapuka Island discussed in this study shown

Fig. 3. Timeline of Maori Culture History. Here we highlight three possible scenarios for when we first see the cycle of warfare over agricultural lands outlined i Vayda (1960). There is a distinctive pattern regarding obsidian use associated with these early communities. There are a couple dozen natural sources of obsidian on the North Island, and none on the South Island. The dis- covery of obsidian artifacts in moa-hunter sites across the country suggests a great deal of mobility and led to the first geochemical sourcing studies in the 1970s (Leach, 1977). Those geochemical studies It has been recognized for more than a century that there was a distinct shift from life in the earliest villages to the distinctive Maori culture and society encountered by Europeans (von Haast, 1871). The culture history of Maori has been defined and divided a number ways; we present a brief summary following the scheme outlined in Smith

Fig. 4. Obsidian Transported from Mayor Island to the South Island. This ex- traordinarily large example is in the collections of the Otago Museum was discovered on the west coast of the South Island and has been matched by its geochemistry to Mayor Island.

3. Methodology 3.1. Meta-Analysis of radiocarbon geodatabase

Fig. 6. Changes in the Distribution of Fortifications: Continuity, abandonment, and expansion. In these series shows examples of how the process of expansion dominates early on (AD 1400-1600), but is replaced later by a roughly equal mix of expansion and abandonment. Here we show an example of century snap-shots of the distribution of radiocarbon dates in one small region. The top map represents the transition from AD 1400-1500 to AD 1500-1600; the middle map from AD 1500- 1600 to AD 1600-1700; and the AD 1600-1700 to AD 1700-1800. For consistency with our previous publication (McCoy, 2017), centuries are numbered 3 through 6. See McCoy (2017:82-85) for a detail description of how radiocarbon dates were sorted into century scale bins.

Fig. 7. Map of Whangapau Pa (Q05/86), Urupukapuka Island.

Fig. 8. Stratigraphic Sequence from Whangapau Pa (Q05/86), Urupukapuka Island. Thumata Pa is a fortification constructed on a headland high above Urupukapuka Bay (Fig. 9). Excavations at this site focused on three areas, the bank above the ditch (Area A), the highest platform (tihi) (Area B), and a second tier terrace (Area C). Area A excavation units were laid out ina 3 x 1m trench and two test units were excavated on

Fig. 9. Map of Ihumata Pa (Q05/87), Urupukapuka Island.

Fig. 10. Stratigraphic Sequence from Ihumata Pa (Q05/87), Urupukapuka Island. Top: First Stage of Fortification at Ihumata Pa (Q05/87), ca. AD 1750. At present, we have no archaeological evidence to confirm the presence or absence of a fighting stage at the site; and so we have illustrated what the defenses might have looked like without a fighting stage. But, a fighting stage like the one recorded on Moturua Island at Paeroa Pa by the French in 1772 would have given defenders an advantage. At Paeroa Pa, the fighting stage is represented on a map by an independent structure behind the inner palisade. Bellwood (1972) and Fox (1976:Fig. 24d) have suggested a fighting stage could have been erected over a double palisade. Bottom: Second Stage of Fortification at Ihumata Pa (Q05/87), ca. AD 1820. The improvements made to the site’s defenses would have allowed defenders with muskets to maneuver between the palisades, or if there was a fighting stage above, above the main defenses.

Fig. 11. Map of Pa (Q05/84), Urupukapuka Island. 5. Conclusion

Classification of Trends in the Location of Fortifications. Here we have tallied map grids with radiocarbon dates present in consecutive centuries (continuity), where radiocarbon dates at fortifications are present in one century then absent in the next (abandonment), and where dates are absent in the earlier century but present in the next (expansion). The onset of fortification construction (AD 1500) occurs all over New Zealand and is characterized by a great deal of expansion. The following centuries show slight increases in spatial continuity, but are distinctively different in the massive amount of reshuffling evident in abandonment and expansion. Table 1 Urupukapuka Island is the largest of the near shore islands within the Bay of Islands (Ipiriri) (Fig. 2). While much of the island’s eastern coast is comprised of steep cliffs, three-quarters of the coastline is easily accessible from sheltered beaches in bays framed by small headlands on The spatial patterning of the distribution of fortifications and the paleodemographic model allow us to identify broad trends in the his- tory of warfare. On the broadest geographic scale, there is an expansion of fortifications over the South Island as the relative population levels

Stratigraphic Sequence from Whangapau Pa (Q05/86), Urupukapuka Island. Table 3

densely covered in pits. We chose to excavate on a lower terrace with no signs of pits to try and recover domestic deposits. A 4 x 1m ex- cavation unit was laid out and again we began with two formal ex- cavations that were later expanded to include the entire trench (Area A). The stratigraphy in the back of the terrace was made especially complex by a series of difficult to interpret post-abandonment erosional events. The cut in the natural slope had cultural material immediately on top, including obsidian. A hearth was excavated from this context (Feature 3) as well as other less distinctive possible features.

Stratigraphic Sequence from Ihumata Pa (Q05/87), Urupukapuka Island. Table 4

Warfare, with attention to new archaeological research on a national scale and a regional study of fortification in the Bay of Islands. In our view, while the onset of fortification construction around AD 1500 is an important watershed in culture history, the nature of conflicts at this period are a poor fit for Vayda’s view of conflict caused by population pressure, food scarcity, and a paucity of undefended good farmland. Other environmental, cultural, and historical factors that have been identified with the Middle Period (AD 1450-1650) - especially a sense of environmental loss, ambiguity about who had the authority to mi- tigate the impacts of people on the environment, and other social fac- tors — may be more important to understanding the origins of warfare in New Zealand.

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Ethics and Best Practices for Mapping Archaeological Sites

by Cecilia Smith

2020, Advances in Archaeological Practice

Archaeologists are tasked with balancing a call to open data practices and maintaining conﬁdentiality of sensitive archaeological site locations. Low-resolution mapping and data aggregation are the methods most commonly used to hide site... more

MAdi is the divergence between the lengths of the major axes of the ellipses. In addition to the lengths of the observed and geomasked ellipses, the length of the longest major ellipse axis (Maximum.MA) that could be drawn through the entire study area is required. The length of the major axis for the geomasked ellipse is compared to either the difference between the maximum major axis length and the observed length, or the difference between the minimum major axis length and the observed length. Whichever difference is greater is used for the comparison. The minimum possible major axis length for the study area is so close to zero that it does not significantly impact the resulting index value and is therefore not included in the denominator of the second calculation below. Descriptive st atistics summarizing the distances separating observed and geomasked points are contained in Table 1. The greater the distance the more the observed site is obscured, however, increased displaceme Perhaps the most sur perturbation and Gau also place geomasked nt will also affect the spatial patterning of the resulting geomasked data set. prising result was that at the commonly used low-resolution method of using a map scale of 1:5,000,000 resulted in geomasked points were quite close to the actual points. Random ssian displacements without a minimum distance were not included as they woul¢ points in close proximity, or possibility in the same location, as the observed sites Mean and maximum distances for Gaussian donuts were relatively large. Random direction buffers with fixed radius provide t ocations because the he greatest control in terms of distance between observed and geomasked distance is defined by the user.

Figure 1. Sites aggregated to county boundaries. Darker color represents more sites. State boundary data from Esri and TomTom North America, Inc. (2019).

Figure 6. Locations of archaeological sites with the topographic basemap removed. Site data from Wyoming Student Atlas (2013).

Figure 9. Random Perturbation Donut: The geomasked point (blue) is placed in a random direction ina random distance within 1 km of the observed point (black) at a minimum distance of 250m. The browr color represents the area in which a geomasked point could be placed.

Table 1. A summary of the distances in meters between an observed point and the geomasked point for each geomasking method.

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Preliminary Results of Magnetic Gradiometry and Photogrammetric Imagery from La Playa, Sonora, Mexico (SON F:10:3)

by Hunter M Claypatch and

Covering an area of nearly ten square kilometers, La Playa (SON F:10:3) is one of the most important archaeological sites in northwest Mexico. While La Playa Las been best known for its Early Agricultural occupation, this research,... more

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Geospatial Archaeology

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