Objectives <p>Analysis of the local and regional drivers of archaeological looting using diverse data, partially derived from satellite remote sensing (SRS) platforms and coded using a systematic data collection framework.</p> Methods <p>Employs a systematic data collection framework that leverages very high-resolution satellite imagery from 2015 to 2017 to create multiple measures of archaeological looting activity across 140 archaeological sites in the Nile Delta. Incorporates theoretically relevant site, local, and regional influences on looting activity across sociopolitical, environmental, economic, and infrastructural variables. Using models designed for handling rare events, it employs multilevel logit models with nested random effects for the site and governorate to examine drivers of archaeological looting activity over time.</p> Results <p>In line with other types of crime, prior looting activity is the strongest driver of new looting activity. Beyond this influence, conflict (across types), environmental stress, and economic stress all drive looting to varying degrees.</p> Conclusions <p>SRS data and methods designed to account for multiple levels of influence are very important for identifying drivers in crimes like archaeological looting. Accounting for local and regional conditions are essential when seeking to mitigate looting activity.</p>

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Drivers of Archaeological Looting in the Nile Delta: Leveraging Satellite Remote Sensing and Multilevel Modeling for Spatiotemporal Analysis

  • Michelle D. Fabiani,
  • Ashleigh Siciliano

摘要

Objectives

Analysis of the local and regional drivers of archaeological looting using diverse data, partially derived from satellite remote sensing (SRS) platforms and coded using a systematic data collection framework.

Methods

Employs a systematic data collection framework that leverages very high-resolution satellite imagery from 2015 to 2017 to create multiple measures of archaeological looting activity across 140 archaeological sites in the Nile Delta. Incorporates theoretically relevant site, local, and regional influences on looting activity across sociopolitical, environmental, economic, and infrastructural variables. Using models designed for handling rare events, it employs multilevel logit models with nested random effects for the site and governorate to examine drivers of archaeological looting activity over time.

Results

In line with other types of crime, prior looting activity is the strongest driver of new looting activity. Beyond this influence, conflict (across types), environmental stress, and economic stress all drive looting to varying degrees.

Conclusions

SRS data and methods designed to account for multiple levels of influence are very important for identifying drivers in crimes like archaeological looting. Accounting for local and regional conditions are essential when seeking to mitigate looting activity.