Figure 1. Plot of Blue MCDS by Dimension 1 scores for shovel-test-pit assemblages from the Papine Village.

Figure 2. Plot of ware types from Papine Village along CA Dimension 1 and 2. Note early types on the right, late ones of the left.

DAACS staff aims to produce a seriation-based chronology for each site using the same methods (see Neiman, Galle, and Wheeler 2003 for technical details). The majority of sites in the archive are comprised of data derived from deposits within quadrats. On these sites, only assemblages from features or stratigraphic groups with more than five ceramic sherds are included in these ceramic-based seriations. Plowzone contexts do not contribute to a DAACS seriation-based chronology.

The DAACS Caribbean Initiative focuses on exploring large-scale change on slave villages, or areas where enslaved individuals lived and labored, such as great house compounds, in the Caribbean through the use of shovel-test-pit surveys. For sites with extensive and standardized STP coverage, including the Papine Village site, a variation on our site-based seriation method is employed. This is because each STP is small (50 cm. in diameter) and provides a small artifact sample. As a result, STP assemblages are rife with sampling error. The samples from individual STPs are so small that variation among STPs is almost entirely statistical noise.

Successfully analyzing STP data, without first aggregating those pits into counting units called sites, requires methods to suppress sampling error. Here we use empirical-Bayesian methods. They offer a smart way to smooth both artifact density surfaces and relative frequencies of artifact types. To understand how these methods work, consider an STP - let's call it STP 12. The number of artifacts found in STP 12 is likely to be similar to the number of artifacts in the STPs within a certain distance of it. The information contained in the neighborhood of pits is combined with the actual number of artifacts from STP 12 to arrive at an estimate of artifact counts that are less influenced by sampling error (Neiman et al. 2008).

We use two forms of Bayesian smoothing in succession. First, to smooth counts of ceramic ware types in individual STPs, we use a gamma-Poisson model. The gamma-Poisson algorithm smooths counts of individual artifact types in each STP, based on the counts for that type in nearby STPs. We then use a beta-binomial model to estimate relative frequencies (percentages or proportions) of ceramic ware types in individual STPs. Together two forms of Bayesian smoothing provide smoothed, stable estimates of artifact-type frequency variation in individual STPs, allowing us to see overall site patterning that may otherwise be distorted using raw data (Neiman et al. 2008).

To infer a chronology from the STPs we used correspondence analysis (CA) of ware-type frequencies. We employ CA because with the numbers of STP assemblages in the hundreds, a traditional manual frequency seriation is completely impractical. CA converts a data matrix of ware-type frequencies into a set of scores which estimate the positions of the assemblages on underlying axes or dimensions of variation. MCDs are weighted averages of the historically documented manufacturing date for each ware type found in an assemblage, where the weights are the relative frequencies of the types. Measuring the correlation between CA axis scores and MCDs offer an indication of whether the CA scores capture time (Ramenofsky, Neiman and Pierce 2009).

Figure 3. Histogram of ceramics from Papine Village plotted along CA Dimension 1. Note dips in counts at .5 and 0 on the Dimension 1 axis indicate phase divisions.

Figure 4. Plot of Blue MCDs by Dimension 1 scores for phased shovel-test-pit assemblages from the Papine Village.

Figure 5. Plot of phased STPs from the Papine Village by STP northing and easting.

Bayseian smoothing and CA analysis can be used on STP data from the Papine Village site. The CA for the Papine Village resulted in three occupational phases for the survey area.  The Papine Village dates from the 1770s through the mid-19th century with materials dating from the post-emancipation East Indian laborer settlement.  The mean ceramic dates and TPQs for each of the three phases are provided below. 

The table also includes three estimates of the ceramic TPQ for each phase. The first TPQ estimate is the usual one - the maximum beginning manufacturing date among all the ware-types in the assemblage. The second estimate -- TPQp90 -- is the 90th percentile of the beginning manufacturing dates among all the sherds in the assemblage, based on their ware-types. The TPQp95 provides a robust estimate of the site's TPQ based on the 95th percentile of the beginning manufacturing dates for all the artifacts comprising it. These last two TPQ estimates are more robust against excavation errors and taphonomic processes that might have introduced a few anomalously late sherds into an assemblage.

The smoothed ceramic ware-type frequencies for the village fit the expectations of the seriation model well, witness the point configuration in the plot of STP assemblages on the first two CA dimensions (Figure 1). The corresponding plot of ware types reveals that CA axis 1 (Dimension 1) reflects a temporal trend, with early ware types on the right and later ware types on the left.  A histrogram of ceramic sherds from Papine by Dimension 1 shows the three phases. The plot of the Dimension 1 scores against BLUE MCDs demonstrates the strongly temporal association, especially visible when the STPs are coded with their Phase assigment. When the Phase assignments were mapped onto the shovel-test-pits, the earliest, Phase 1 occupation is seen along the western side of the site, just southwest of the mill.  Phase 2 is primarily on the western side of the aqueduct with Phase 3, representing the latest occupation, spreading far across the eastern half of the village.