Alexandra Bazarsky
University of California, San Diego, Anthropological Archaeology, Graduate Student
- Brandeis University, Anthropology, Alumnusadd
- Alexandra Bazarsky is a first-year Ph.D. student who is interested in Maya settlement practices and socio-political interactions, particularly exploring and mapping the regional relations between larger kingdoms and their subordinates. S... moreAlexandra Bazarsky is a first-year Ph.D. student who is interested in Maya settlement practices and socio-political interactions, particularly exploring
and mapping the regional relations between larger kingdoms and their subordinates. She graduated from Brandeis University with a B.A. in Anthropology and Latin American, Caribbean, and Latinx Studies, as well as a minor in Art History. She researches and excavates with the Proyecto Arqueologico Busilja - Chocolja and the Proyecto Arqueológico Bajo Lacantún.edit
We present results from the archaeological analysis of 331 km2 of high-resolution airborne lidar data collected in the Upper Usumacinta River basin of Mexico and Guatemala. Multiple visualizations of the DEM and multi-spectral data from... more
We present results from the archaeological analysis of 331 km2 of high-resolution airborne lidar data collected in the Upper Usumacinta River basin of Mexico and Guatemala. Multiple visualizations of the DEM and multi-spectral data from four lidar transects crossing the Classic period (AD 350–900) Maya kingdoms centered on the sites of Piedras Negras, La Mar, and Lacanja Tzeltal permitted the identification of ancient settlement and associated features of agricultural infrastructure. HDBSCAN (hierarchical density-based clustering of applications with noise) cluster analysis was applied to the distribution of ancient structures to define urban, peri-urban, sub-urban, and rural settlement zones. Interpretations of these remotely sensed data are informed by decades of ground-based archaeological survey and excavations, as well as a rich historical record drawn from inscribed stone monuments. Our results demonstrate that these neighboring kingdoms in three adjacent valleys exhibit diver...
Research Interests:
A wealth of remotely sensed data has accumulated over the past several decades and now constitutes an analytical resource primed for archaeological applications. To date, remotely sensed big data (RSBD) analytics in archaeology have... more
A wealth of remotely sensed data has accumulated over the past several decades and now constitutes an analytical resource primed for archaeological applications. To date, remotely sensed big data (RSBD) analytics in archaeology have focused on filling spatial gaps in the distribution of sites and features, characterizing environmental landscapes, and monitoring cultural heritage sites. The scientific promise of these data to expand our understanding of past human-environment interactions has not been fully realized. Limitations of data access, sufficient analytical and computational resources, and methodological awareness and education on the appropriate use of RSBD have limited the adoption and widespread use of RSBD in archaeology, despite its ubiquity in the Earth sciences. Google Earth Engine (GEE) is a freely available planetary-scale cloud computing platform that addresses the perennial challenges of data access, analysis, and computing power that are particularly acute among archaeologists aiming to derive insights from RSBD. GEE lowers the barrier to entry for analyzing RSBD, expanding the potential for these data in the automated identification of archaeological features through deep learning; fieldwork planning and archaeological practice; modeling of past environments and environmental variability; and cultural heritage impact and risk assessments. In doing so, it also contributes to open science via increased access, transparency, and reproducibility.
Research Interests:
We present results from the archaeological analysis of 331 km2 of high-resolution airborne lidar data collected in the Upper Usumacinta River basin of Mexico and Guatemala. Multiple visualizations of the DEM and multi-spectral data from... more
We present results from the archaeological analysis of 331 km2 of high-resolution airborne lidar data collected in the Upper Usumacinta River basin of Mexico and Guatemala. Multiple visualizations of the DEM and multi-spectral data from four lidar transects crossing the Classic period (AD 350–900) Maya kingdoms centered on the sites of Piedras Negras, La Mar, and Lacanja Tzeltal permitted the identification of ancient settlement and associated features of agricultural infrastructure. HDBSCAN (hierarchical density-based clustering of applications with noise) cluster analysis was applied to the distribution of ancient structures to define urban, peri-urban, sub-urban, and rural settlement zones. Interpretations of these remotely sensed data are informed by decades of ground-based archaeological survey and excavations, as well as a rich historical record drawn from inscribed stone monuments. Our results demonstrate that these neighboring kingdoms in three adjacent valleys exhibit diver...
Research Interests:
We present results from the archaeological analysis of 331 km2 of high-resolution airborne lidar data collected in the Upper Usumacinta River basin of Mexico and Guatemala. Multiple visualizations of the DEM and multi-spectral data from... more
We present results from the archaeological analysis of 331 km2 of high-resolution airborne lidar data collected in the Upper Usumacinta River basin of Mexico and Guatemala. Multiple visualizations of the DEM and multi-spectral data from four lidar transects crossing the Classic period (AD 350–900) Maya kingdoms centered on the sites of Piedras Negras, La Mar, and Lacanja Tzeltal permitted the identification of ancient settlement and associated features of agricultural infrastructure. HDBSCAN (hierarchical density-based clustering of applications with noise) cluster analysis was applied to the distribution of ancient structures to define urban, peri-urban, sub-urban, and rural settlement zones. Interpretations of these remotely sensed data are informed by decades of ground-based archaeological survey and excavations, as well as a rich historical record drawn from inscribed stone monuments. Our results demonstrate that these neighboring kingdoms in three adjacent valleys exhibit diver...
Research Interests:
This thesis presents the findings derived from satellite and high-resolution airborne lidar data of the Upper Usumacinta River basin of Mexico and Guatemala. Multiple environmental and spatial models were made from the remotely sensed... more
This thesis presents the findings derived from satellite and high-resolution airborne lidar data of the Upper Usumacinta River basin of Mexico and Guatemala. Multiple environmental and spatial models were made from the remotely sensed data to look at the settlement patterns between the Classic period (250-900 A.D.) kingdoms centered on the sites of La Mar and Lacanja Tzeltal. ArcGIS Pro tools such as Stream Order, Basin, Least Cost Path, Kernel Density Estimation, and Geomorphon Landforms were used to define what factors most impacted the Maya’s preference for living in certain areas. The results establish that both environmental and socio-political factors heavily impacted ancient settlement preference. More broadly, this research emphasizes the strengths of remote sensing and GIS technology within archaeological study of settlement patterns, and it serves as a foundation for future studies to draw further connections between the relationships of landscape, politics, agriculture, the economy, and social organization.