<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Conflict | Javier Pérez Sandoval</title><link>https://javierpsandoval.com/tag/conflict/</link><atom:link href="https://javierpsandoval.com/tag/conflict/index.xml" rel="self" type="application/rss+xml"/><description>Conflict</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Sat, 01 Mar 2025 00:00:00 +0000</lastBuildDate><image><url>https://javierpsandoval.com/media/og-image.png</url><title>Conflict</title><link>https://javierpsandoval.com/tag/conflict/</link></image><item><title>The Visibility Deficit Index: Triangulating Nighttime Lights and Event Data to Overcome Conflict Illegibility</title><link>https://javierpsandoval.com/project/visibility-deficit-index/</link><pubDate>Sat, 01 Mar 2025 00:00:00 +0000</pubDate><guid>https://javierpsandoval.com/project/visibility-deficit-index/</guid><description>&lt;p>&lt;strong>With Jorge Ruiz Reyes.&lt;/strong>&lt;/p>
&lt;p>Can we systematically identify places where violence is likely to go underreported? Conflict data are shaped by the same conditions that render violence difficult to observe, producing non-random patterns of incomplete documentation. We propose a triangulation strategy that combines ACLED with satellite-derived nighttime lights (NTL) to identify areas of conflict invisibility. Our core contribution is the Visibility Deficit Index (VDI), which measures the gap between NTL-detected disruption and reported conflict events. We apply this framework to cartel violence in Mexico, focusing on Michoacán since 2021. Results reveal a stark territorial gradient: municipalities under cartel control exhibit high visibility deficits, indicating severe underreporting. By leveraging divergence between data sources, the VDI provides a scalable tool to detect hidden violence and improve the legibility of conflict in otherwise opaque settings.&lt;/p></description></item></channel></rss>