Zooarchaeological Faunal Identifiability: Using GIS Technology to Facilitate Analysis of Gracile Long Bone Specimens 公开
Penmetcha, Neharika (Spring 2019)
Abstract
Zooarchaeological analysis aims to identify the majority of represented skeletal elements within a given sample. Although research has shown that some fragments have areas with greater diagnostic value than others thanks to distinctive features, little research has been conducted to quantify that relationship. If proper analysis on how bone portion (location on bone where fragment came from) and relative size affects identifiability, then the zooarchaeological community can more accurately determine the frequency of specific skeletal elements at a site. This study attempted to quantify the ability to identify skeletal elements from bone fragments through its shape, size, and location on bone (bone portion) through GIS software and comment on the efficacy of such a strategy. Materials came from gracile goat limb bones from Stephen Merritt and Davis 2017 research on fragmentation and butchery. Each fragment was assigned to an identifiability ranking with 1 being the most identifiable to side, element and portion and 5 being the least to only class of animal such as mammalian. Each individual specimen was labeled and characterized on the geospatial template and then converted to pixelated IDcat values. Then each subsequent layer of the same element type was aggregated and averaged to visually assess which regions are more identifiable than others. Results suggest that relative specimen size (percent size of fragment relative to total bone) is positively related to identifiability, meaning the bigger the size of the fragment, the better it is to identify it. Most elements had more identifiable areas around the epiphyseal ends and less in the midshaft areas across all long bones. The data and methodology were spatial in nature, but certain calculations through the python coding language aided in the analysis of the question. Although the results obtained in the frame of this project are still at a preliminary stage, it still demonstrates the high potential of exploring and extending the methods and calculations in GIS to a possibly larger sample of specimens and case studies in zooarchaeological skeletal identifiability.
Table of Contents
Introduction……………………………………………………………………………………………………………………………….1
Zooarchaeology- A Vital Discipline……………………………………………………………………………………………..2
Related Fields…………………………………………………………………………………………………………………………….3
History And Development Of Zooarchaeology……………………………………………………………………………4
Important Terms………………………………………………………………………………………………………………………..6
Theory, Methods, And Scope……………………………………………………………………………………………………..7
Identifiability And Fragmentation………………………………………………………………………………………………8
What is it? How do we know? ………………………………………………………………………………………………..10
Quantification In Fragmentation………………………………………………………………………………………………11
Approaches To Calculation……………………………………………………………………………………………………….14
Using GIS as a tool……………………………………………………………………………………………………………………15
Faunal Analysis………………………………………………………………………………………………………………………..17
Bones……………………………………………………………………………………………………………………………………….18
Structure And Function Of Bones…………………………………………………………………………………18
Anatomical Directions In Osteology……………………………………………………………………………..19
Categories Of Bones……………………………………………………………………………………………………..20
Rational for using long bones……………………………………………………………………………………….20
Structure of Long Bones……………………………………………………………………………………………….22
Diagnostic Features and Zones for Long Bone Identifiability…………………………………………23
GIS……………………………………………………………………………………………………………………………………………24
Past Use……………………………………………………………………………………………………………………….25
Faunalyze Toolbox………………………………………………………………………………………………………..26
Methods………………………………………………………………………………………………………………………………….26
Assessing Identifiability………………………………………………………………………………………………..27
Reconstruction……………………………………………………………………………………………………………..28
Feature Maps……………………………………………………………………………………………………………….29
Feature Maps as coded layers…………………………………………………………………………..30
Work Flow……………………………………………………………………………………………………………………………….44
Loading Files…………………………………………………………………………………………………………………44
Rasters to Vectors………………………………………………………………………………………………………..50
Problems with Missing Fragments……………………………………………………………………………………………54
Data Analysis………………………………………………………………………………………………………………………56
Executing Raster Average…………………………………………………………………………………………………65
Python Code…………………………………………………………………………………………………………………57
Future Directions and Use of ArcPro……………………………………………………………………………......59
Results………………………………………………………………………………………………………………………………61
Raster Analysis……………………………………………………………………………………………………………..61
Graphical Analysis………………………………………………………………………………………………………..68
Discussion………………………………………………………………………………………………………………………………..81
Size in Relation to Identifiability…………………………………………………………………………………..82
Caveat………………………………………………………………………………………………………………………….83
Overshoot Bias………………………………………………………………………………………………….83
Future Directions……………………………………………………………………………………………………………………..83
Use of ArcGIS Pro…………………………………………………………………………………………………………84
Importance of Analogy…………………………………………………………………………………………………85
Conclusion……………………………………………………………………………………………………………………………….86
References……………………………………………………………………………………………………………………………….88
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