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Position Mapping: Cartography, Intelligence, and the Third Battle of Gaza, 1917

  • Joel RadunzelEmail author
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

World War I saw numerous innovations in military cartography. In the Palestine theater as elsewhere, the British and Dominion forces leveraged new technologies, including aerial photography and wireless intercepts, to supplement their use of intelligence to map enemy troop positions. The creation and distribution of these position maps by the 7th Field Survey Company for the Third Battle of Gaza in late 1917 represented an innovative process of intelligence-gathering, map production, and knowledge distribution. This paper not only examines the Egyptian Expeditionary Force (EEF) along with its subordinate intelligence assets and cartographic organizations as a comprehensive mapping system, but also elaborates upon David Woodward’s cartographic framework to study the creation of the 7th Field Survey Company’s position maps as well as their utility, accuracy, and effectiveness. Woodward’s framework divides the map production process into four phases: information gathering, information processing, document distribution, and document use. Elements of the EEF were involved in each of these phases during the Third Battle of Gaza. This mapping system was cyclical insofar as the operations that these maps helped to facilitate also gathered further information that fed into the next cycle’s product. As the condition of the battlefield and the nature of the operations changed, so too did the value of various modes of intelligence gathering, with varying effects on the accuracy and utility of the position maps. The utility of the position map technique is apparent in its reintroduction prior to the EEF’s final offensive in 1918.

Keywords

Mobile Operation Turkish Formation British Unit Aerial Reconnaissance British Force 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Syracuse UniversitySyracuseUSA

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