Snowvision: The Promise of Algorithmic Methods in Southeastern Archaeological Research

This article presents the contexts, methods, contributions, and preliminary findings of Snowvision, a digital archaeology project developed by faculty and students at the University of South Carolina and the South Carolina Department of Natural Resources. Snowvision uses computer vision to reconstruct southeastern Native American paddle designs from the Swift Creek period, ca. 100-850 CE. In this essay, we first present the context of the Swift Creek culture of the southeastern United States, along with broader related issues in prehistoric archaeology. Then, the relevant methods from archaeology and computer vision are introduced and discussed. We also introduce World Engraved, our public-facing digital archive of sherd designs and distributions, and explain its role in our overall project. We then explore, in some level of technical detail, the ways in which our work refines existing pattern-matching algorithms used in the field of computer vision. Finally, we discuss our accomplishments and findings to date and the possibilities for future research that Snowvision provides.

COVID-19 Wastewater Epidemiology: A Model to Estimate Infected Populations

ABSTRACTBACKGROUNDWastewater-based epidemiology (WBE) provides an opportunity for near real-time, cost-effective monitoring of community level transmission of SARS-CoV-2, the virus that causes COVID-19. Detection of SARS-CoV-2 RNA in wastewater can identify the presence of COVID-19 in the community, but methods are lacking for estimating the numbers of infected individuals based on wastewater RNA concentrations.METHODSComposite wastewater samples were collected from three sewersheds and tested for SARS-CoV-2 RNA. A Susceptible-Exposed-Infectious-Removed (SEIR) model based on mass rate of SARS-CoV-2 RNA in the wastewater was developed to predict the number of infected individuals. Predictions were compared to confirmed cases identified by the South Carolina Department of Health and Environmental Control for the same time period and geographic area.RESULTSModel predictions for the relationship between mass rate of virus release to the sewersheds and numbers of infected individuals were validated based on estimated prevalence from individual testing. A simplified equation to estimate the number of infected individuals fell within the 95% confidence limits of the model. The unreported rate for COVID-19 estimated by the model was approximately 12 times that of confirmed cases. This aligned well with an independent estimate for the state of South Carolina.CONCLUSIONSThe SEIR model provides a robust method to estimate the total number of infected individuals in a sewershed based on the mass rate of RNA copies released per day. This overcomes some of the limitations associated with individual testing campaigns and thereby provides an additional tool that can be used to better inform policy decisions.

Econometric Method for Estimating the Impact of Tourism Spending on County Employment in South Carolina

This article analyzes their university backgrounds. Tourism can promote job growth and income growth in regional economies. Policymakers in the regional government promote tourism to bring outside money into the local economy. Using accommodation tax revenue data published by the South Carolina Department of Revenue, this paper estimates the employment impact of tourism spending in South Carolina counties. The OLS regression results show that increased tourism spending, as measured by the accommodation tax, leads to increased total county employment, increased county employment in the accommodation sector (NAICS 721), increased county employment in full-service restaurant sector (NAICS 7221) and increased county employment in arts, entertainment, and recreation sector (NAICS 71).

Assessment of Crash Location Accuracy in Electronic Crash Reporting Systems

Over the past several years, traffic fatality rates in South Carolina have been consistently ranked among the highest in the country. Furthermore, South Carolina incurs an annual economic loss of over two billion dollars because of roadway traffic crashes. The South Carolina Department of Transportation, in collaboration with the South Carolina Department of Public Safety, has undertaken a series of initiatives to reduce the number of vehicle crashes, with a particular emphasis on injury and fatal crashes. One of these initiatives is the deployment of a map-based geocoded crash reporting system that has greatly improved the quality of crash location data. This paper provides an assessment of improvements in crash geocoding accuracy in South Carolina and how improved accuracy is beneficial to systematic statewide safety analysis. A case study approach is used to demonstrate practical applications and analysis techniques based on spatially accurate crash data. A survey of U.S. state highway agencies indicates that there are disparate crash reporting systems used across the country with regard to crash geocoding procedures and accuracies. Survey results indicate that not only does geocoded accuracy of crash locations vary by state, but accuracies often vary by jurisdiction within each state. Research results suggest that poorly geocoded crash data can bias certain types of safety analysis procedures and that many state safety initiatives, analysis methods, and outcomes can benefit from improving crash report geocoding procedures and accuracies.

Highway Cross-Slope Measurement using Mobile LiDAR

Ensuring adequate pavement cross-slope on highways can improve driver safety by reducing the potential for ponding to occur or vehicles to hydroplane. Mobile laser scanning (MLS) systems provide a rapid, continuous, and cost-effective means of collecting accurate 3D coordinate data along a corridor in the form of a point cloud. This study provides an evaluation of MLS systems in terms of the accuracy and precision of collected cross-slope data and documentation of procedures needed to calibrate, collect, and process this data. Mobile light detection and ranging (LiDAR) data were collected by five different vendors on three roadway sections. The results indicate the difference between ground control adjusted and unadjusted LiDAR derived cross-slopes, and field surveying measurements less than 0.19% at a 95% confidence level. The unadjusted LiDAR data incorporated corrections from an integrated inertial measurement unit and high-accuracy real-time kinematic GPS, however it was not post-processed adjusted with ground control points. This level of accuracy meets suggested cross-slope accuracies for mobile measurements (±0.2%) and demonstrates that mobile LiDAR is a reliable method for cross-slope verification. Performing cross-slope verification can ensure existing pavement meets minimum cross-slope requirements, and conversely is useful in identifying roadway sections that do not meet minimum standards, which is more desirable than through crash reconnaissance where hydroplaning was evident. Adoption of MLS would enable the South Carolina Department of Transportation (SCDOT) to address cross-slope issues through efficient and accurate data collection methods.

Marine Artificial Reef Research and Development: Integrating Fisheries Management Objectives

<em>Abstract</em>.—In support of the Magnuson–Stevens Conservation and Management Reauthorization Act of 2006, which tasked regional fisheries management councils with ending overfishing of numerous marine finfish species, the South Atlantic Fisheries Management Council established 8 deepwater (90–150 m [300–500 ft]) type II marine protected areas (MPAs) along the coastline of the southeastern United States. At the request of the South Carolina Department of Natural Resources (SCDNR), one of these MPAs was established on an undeveloped sand-bottom area previously permitted by SCDNR for artificial reef development. After monitoring the production potential of unfished artificial reefs for several years on shallower experimental reef sites, SCDNR staff proposed that a deeper location had the potential to become a highly productive spawning site, particularly for deepwater grouper species. Development of this permitted site began in 2014 when two 79-m (260 ft) barges with nearly 30 m (100 ft) of added profile were deployed. Subsequent monitoring of the site through remotely operated underwater vehicle video revealed colonization by several target species, including Warsaw Grouper <em>Hyporthodus nigritus</em>, Snowy Grouper <em>H. niveatus</em>, and Misty Grouper <em>H. mystacinus</em>. Due in part to the success of this deepwater MPA, the SCDNR was also granted spawning special management zone designation for its two previously established, undisclosed experimental artificial reef sites in federal waters off South Carolina in 2017.

Water-Level Trends in Aquifers of South Carolina

Groundwater levels are examined to document and evaluate short- and long-term trends observed in each of the major aquifers in the State. Data are compiled from groundwater-monitoring networks maintained by the South Carolina Department of Natural Resources (DNR), the South Carolina Department of Health and Environmental Control (DHEC), and the United States Geological Survey (USGS). The data are used in the support of groundwater management and allocation, assessment of droughts, groundwater-flow modeling, and resource assessment. Hydrographs from approximately 170 wells are reviewed with periods of record ranging from 1 to 56 years.