Advancing Predictive Modeling in Archaeology - Supplementary Data

Creator(s): Peter Yaworsky; Kenneth Vernon

Year: 2020

Summary

The data provided here(Yaworsky_etal_2020_sdmdata.csv) accompany a set of archaeological predictive models created for the Grand Staircase-Escalante National Monument, Utah. The full dataset is comprised of 11,814 observations (1,619 presence points and 10,195 absence points), and ten predictor variables. Column 1 is a unique ID. Column 2 is a binary identifier of whether an observation represents an archaeological site/presence point (1) or pseudo absence point (0). The remaining columns represent environmental predictor values at presence and absence points extracted from spatial raster data. These include a decomposed east-west aspect (3), growing degree days (4), a decomposed north-south aspect (5), net primary productivity (6), mean annual temperature (7), slope (8), cost-distance to springs (9), cost-distance to streams (10), cost-distance to wetlands (11), and wastershed size (12). Detailed information about these data and how they were generated can be found in the Supplementary Information (S.I.) of the published paper.

The S.I. is a markdown document (.html) that walks through the analysis covered in the published paper. The primary findings can be replicated using the data provided here.

Cite this Record

Advancing Predictive Modeling in Archaeology - Supplementary Data. Peter Yaworsky, Kenneth Vernon. PLOS ONE. 2020 ( tDAR id: 457626) ; doi:10.6067/XCV8457626

Data Set Structure

Measurement Column
Count Column
Coded Column
Filename Column
Integration Column (has Ontology)

Table Information: Yaworsky_etal_2020_sdmdata

Column Name Data Type Type Category Coding Sheet Ontology Search
wetlands_cd Description: Using the DEM data, we generate the slope raster and then a friction surface. Next, using Tobler's hiking function, we create a cost-distance surface from lakes, springs, streams, and wetlands. Categorical Type: Resource Distribution Other Designation: 'lakes_cd', 'springs_cd', 'streams_cd', and 'wetlands_cd'. Included in Final Model: Yes (Springs, streams, and wetlands) Units: Minutes Original Resolution: 5 meter x 5 meter Dates: NA Source: Utah AGRC Springs, Streams, and Lakes: https://gis.utah.gov/data/water/lakes-rivers-dams/ Wetlands: https://gis.utah.gov/data/water/wetlands/ Citation: Utah AGRC. Utah GIS Portal, 2014.
DOUBLE  Measurement (other) uncategorized none none true
east_west_asp Description: Using the DEM data, we generate aspect which represents the direction in degrees that ground surface slopes. Because direction in degrees is non-linear, we decompose aspect into east-west aspect and north-south aspect (see Section 2.2 for more information). Categorical Type: Landscape Other Designation: 'east_west_asp' Included in Final Model: Yes Units: Degrees (but dimensionless after decomposition) Original Resolution: 5 meter x 5 meter Dates: NA Source: Utah AGRC: https://gis.utah.gov/data/elevation-and-terrain/ Citation: Utah AGRC. "5 Meter Auto-Correlated Elevation Models." Utah GIS Portal, 2014. https://gis.utah.gov/data/elevation-terrain-data/5-meter-auto-correlated-elevation-models/.
DOUBLE  Measurement (other) uncategorized none none true
GDD_corngrowing_dds_2005 Degree-days (DDs) (which are also referred to as "growing degree-days", "heat units" or "thermal units") are the summation of temperature over time. We use a standard 50 degree Farhrenit theshold temperature. We use six different calculation methods including, simple average, growing dds, single triangle, double triangle, signgle sine, double sine and heating and cooling degree-days. Information on each of the methods can be found here http://uspest.org/wea/mapmkrdoc.html. Only the growing dds method for corn is used in the final model. Units: days/year Original Resolution: 100 meter x 100 meter Dates: 30 year average Source: Oregon State: http://uspest.org/cgi-bin/usmapmaker.pl Citation: Coop, Len. "Documentation - US Degree-Day Mapping Calculator," 2014. http://uspest.org/wea/mapmkrdoc.html.
DOUBLE  Measurement (other) uncategorized none none true
north_south_asp Description: Using the DEM data, we generate aspect which represents the direction in degrees that ground surface slopes. Because direction in degrees is non-linear, we decompose aspect into east-west aspect and north-south aspect (see Section 2.2 for more information). Categorical Type: Landscape Other Designation: 'north_south_asp' Included in Final Model: Yes Units: Degrees (but dimensionless after decomposition) Original Resolution: 5 meter x 5 meter Dates: NA Source: Utah AGRC: https://gis.utah.gov/data/elevation-and-terrain/ Citation: Utah AGRC. "5 Meter Auto-Correlated Elevation Models." Utah GIS Portal, 2014. https://gis.utah.gov/data/elevation-terrain-data/5-meter-auto-correlated-elevation-models/.
DOUBLE  Measurement (other) uncategorized none none true
wtrshd_size Description: The area of a landscape feature within which water drains to a common outlet. Categorical Type: Landscape Other Designation: 'wtrshd_size' Included in Final Model: Yes Units: square meters Original Resolution: Derived from polygon shapefile, but raster is 5 meters x 5 meters. Dates: NA Source: Utah AGRC: https://gis.utah.gov/data/water/watersheds/ Citation: Utah AGRC. Utah GIS Portal, 2014.
BIGINT  Measurement (square meter) uncategorized none none true
streams_cd Description: Using the DEM data, we generate the slope raster and then a friction surface. Next, using Tobler's hiking function, we create a cost-distance surface from lakes, springs, streams, and wetlands. Categorical Type: Resource Distribution Other Designation: 'lakes_cd', 'springs_cd', 'streams_cd', and 'wetlands_cd'. Included in Final Model: Yes (Springs, streams, and wetlands) Units: Minutes Original Resolution: 5 meter x 5 meter Dates: NA Source: Utah AGRC Springs, Streams, and Lakes: https://gis.utah.gov/data/water/lakes-rivers-dams/ Wetlands: https://gis.utah.gov/data/water/wetlands/ Citation: Utah AGRC. Utah GIS Portal, 2014.
DOUBLE  Measurement (other) uncategorized none none true
springs_cd Description: Using the DEM data, we generate the slope raster and then a friction surface. Next, using Tobler's hiking function, we create a cost-distance surface from lakes, springs, streams, and wetlands. Categorical Type: Resource Distribution Other Designation: 'lakes_cd', 'springs_cd', 'streams_cd', and 'wetlands_cd'. Included in Final Model: Yes (Springs, streams, and wetlands) Units: Minutes Original Resolution: 5 meter x 5 meter Dates: NA Source: Utah AGRC Springs, Streams, and Lakes: https://gis.utah.gov/data/water/lakes-rivers-dams/ Wetlands: https://gis.utah.gov/data/water/wetlands/ Citation: Utah AGRC. Utah GIS Portal, 2014.
DOUBLE  Measurement (other) uncategorized none none true
slope Description: Using the DEM raster, we generate a slope raster using the 'terrain()' function in 'R'. Categorical Type: Landscape Other Designation: 'slope' Included in Final Model: Yes Units: Degrees of slope Original Resolution: 5 meters x 5 meter Dates: NA Source: Utah AGRC: https://gis.utah.gov/data/elevation-and-terrain/ Citation: Utah AGRC. "5 Meter Auto-Correlated Elevation Models." Utah GIS Portal, 2014. https://gis.utah.gov/data/elevation-terrain-data/5-meter-auto-correlated-elevation-models/.
DOUBLE  Measurement (degree (angle)) uncategorized none none true
PRISM_tmean_30yr_normal_800mM2_annual_asc Description: PRISM datasets provide estimates of six basic climate elements: precipitation (ppt), minimum temperature (tmin), maximum temperature (tmax), mean dew point (tdmean), minimum vapor pressure deficit (vpdmin), and maximum vapor pressure deficit (vpdmax). Two derived variables, mean temperature (tmean) and vapor pressure (vpr), are sometimes included, depending on the dataset. Categorical Type: Climate Other Designation: 'PRISM_ppt_30yr_normal_800mM2_annual_asc', 'PRISM_tdmean_30yr_normal_800mM2_annual_asc', 'PRISM_tmax_30yr_normal_800mM2_annual_asc','PRISM_tmean_30yr_normal_800mM2_annual_asc', 'PRISM_tmin_30yr_normal_800mM2_annual_asc', 'PRISM_vpdmax_30yr_normal_800mM2_annual_asc', and 'PRISM_vpdmin_30yr_normal_800mM2_annual_asc'. Included in Final Model: Yes (Mean temperature) Units: See documentation: http://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf Original Resolution: 800 meter x 800 meter Dates: 30 year average Source: PRISM Climate Group: http://www.prism.oregonstate.edu/normals/ Citation: PRISM Climate Group. "Oregon State University." PRISM Gridded Climate Data, 2018. http://www.prism.oregonstate.edu/documents/PRISM_terms_of_use.pdf.
DOUBLE  Measurement (other) uncategorized none none true
NPP_mean_00_15 The difference in total chemical energy produced by plants (gross primary production) and chemical energy invested in plant maintenance and growth (also known as respiration). Categorical Type: Environmental Productivity Other Designation: 'NPP_mean_00_15' Included in Final Model: Yes Units: Kilograms of Carbon per square meter per year Original Resolution: 1 kilometer x 1 kilometer Dates: 2000 - 2015 Source: NASA MODIS: https://modis.gsfc.nasa.gov/data/dataprod/mod17.php Citation: Numerical Terradynamic Simulation Group. "MODIS GPP/NPP Project (MOD17)." University of Montana.
DOUBLE  Measurement (other) uncategorized none none true
pa Binary indicator of whether the observation represents an archaeological site/presence (1) or pseudo absence point (0).
BIGINT  Count uncategorized none none true
Unique ID.
BIGINT  Uncoded Value uncategorized none none true

Temporal Coverage

Radiocarbon Date: 2100 to 650 (Basketmaker II, Pueblo, Fremont)

Spatial Coverage

min long: -112.797; min lat: 37.061 ; max long: -110.204; max lat: 38.382 ;

Individual & Institutional Roles

Contributor(s): Kenneth B. Vernon

Lab Director(s): Brian Codding

File Information

  Name Size Creation Date Date Uploaded Access
Yaworsky_etal_2020_sdmdata.csv 1.97mb Jul 13, 2020 Jul 13, 2020 11:08:51 AM Public
The data provided here(Yaworsky_etal_2020_sdmdata.csv) accompany a set of archaeological predictive models created for the Grand Staircase-Escalante National Monument, Utah. Column 1 is a unique ID. Column 2 is a binary identifier of whether an observation represents an archaeological site/presence point (1) or pseudo absence point (0). The remaining columns represent environmental predictor values at presence and absence points extracted from spatial raster data. These include a decomposed east-west aspect (3), growing degree days (4), a decomposed north-south aspect (5), net primary productivity (6), mean annual temperature (7), slope (8), cost-distance to springs (9), cost-distance to streams (10), cost-distance to wetlands (11), and wastershed size (12). Detailed information about these data and how they were generated can be found in the Supplementary Information (S.I.) of the published paper. The S.I. is a markdown document (.html) that walks through the analysis covered in the published paper. The primary findings can be replicated using the data provided here.
  • Translated version Yaworsky_etal_2020_sdmdata_translated.xlsx (1.59mb)
    Data column(s) in this dataset have been associated with coding sheet(s) and translated: