Authors: Sarah E. Walters*, Oak Ridge National Laboratory, Lauryn N. Bingham-Bragg, Oak Ridge National Laboratory, Rohan Dhamdhere, Oak Ridge National Laboratory, Marie L. Urban, Oak Ridge National Laboratory, Dalton D. Lunga, Oak Ridge National Laboratory
Topics: Population Geography, Qualitative Research, Asia
Keywords: Mixed-Methods, Population Modeling, Cemeteries, Graves, Burials, Building Occupancy, Culture, Qualitative, Quantitative, Automation
Session Type: Paper
Presentation File: No File Uploaded
The objective of the Population Density Tables (PDT) project at Oak Ridge National Laboratory (ORNL) is to report occupancy (people per 1000 square feet) at both the National and Subnational level, globally. PDT does so through the creation of observation models that capture facility-specific use-dynamics. These models serve to update and refine preexisting baseline estimates to more accurately reflect use. While PDT is ever-expanding, not all facility-types currently can be equally reported due to a dearth of available open-source data. Cemeteries are one such category and present specific challenges. A strength of PDT is the ability to incorporate qualitative data – to include an ever-growing repository of culture-specific funerary practices. However, patterns of burial and rituals related to the disposition of the dead are driven by aspects (e.g., cultural belief, religion, geography) that, however informative, may not fit within existing observation models. In order to ensure that these models accurately reflect cemetery use, they must be updated and expanded to better exploit what data is available. Additionally, grave counts are fundamental for estimating use by living populations as well as interpreting activity patterns within burial grounds. However, this has proved problematic since cemeteries often contain large numbers of burials and the manual quantification effort required is resource intensive, making identification and subsequent updates challenging. Results of addressing both challenges will be presented, including the development of an automated feature counting tool that, within the confines of the designated test area, has detected and quantified graves – with little human oversight.
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