Abstract

During the late 7th to the first half of the 6th century BC a large urban center existed atop the Kerkenes mountain in central Anatolia. After a brief occupation that ended in a fiery blaze, the site saw only minimal activity until being visited by archaeologists first in the 1920s and then again in the 1990s. Archaeological work at Kerkenes has generated impressive digital datasets through remote sensing and traditional excavation that have identified the foundation of this once impressive city. These types of datasets have proven ideal for use with modeling and simulation methods that have successfully been used to understand the social organization of modern urban centers as understood through the spatial organization of their built environments. While using techniques from the modeling and simulation field is often used to understand modern cities, these methods also hold value in understanding ancient cities. This study applies these methods to the extensive Kerkenes dataset to provide insights into social interactions within the city that can be understood through the organization and location of built elements within the urban space. The popular naïve k-means clustering analyses is used to understand how elements within the urban space cluster around specific elements within the built environment. The output from these analyses is used to inform a series of space syntax analyses to elucidate how the arrangement of the city's built elements can be used to understand social interactions within the city. The methods herein used demonstrate the value of engaging in interdisciplinary research to answer questions related to ancient cities that can be used to address challenges within modern urban spaces, as well.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

Branting, Scott

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Anthropology

Degree Program

Integrative Anthropological Sciences

Identifier

CFE0009780; DP0027888

URL

https://purls.library.ucf.edu/go/DP0027888

Language

English

Release Date

August 2023

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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