A Cartographic Analysis of Soviet Military City Plans

A Cartographic Analysis of Soviet Military City Plans
Author :
Publisher : Springer Nature
Total Pages : 214
Release :
ISBN-10 : 9783030840174
ISBN-13 : 3030840174
Rating : 4/5 (74 Downloads)

Book Synopsis A Cartographic Analysis of Soviet Military City Plans by : Martin Davis

Download or read book A Cartographic Analysis of Soviet Military City Plans written by Martin Davis and published by Springer Nature. This book was released on 2021-09-29 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: The collapse of the Soviet Union has seen the emergence of its unprecedentedly comprehensive global secret military mapping project and the commercial availability of a vast number of detailed topographic maps and city plans at several scales. This thesis provides an in-depth examination of the series of over 2,000 large-scale city plans produced in secret by the Military Topographic Directorate (Военное топографическое управление) of the General Staff between the end of the Second World War and the collapse of the USSR in 1991. After positioning the series in its historical context, the nature and content of the plans are examined in detail. A poststructuralist perspective introduces possibilities to utilise and apply the maps in new contexts, which this thesis facilitates by providing a systematic, empirical analysis of the Soviet map symbology at 1:10,000 and 1:25,000, using new translations of production manuals and a sample of the city plans. A comparative analysis with the current OpenStreetMap symbology indicates scope for Soviet mapping to be used as a valuable supplementary topographic resource in a variety of existing and future global mapping initiatives, including humanitarian crisis mapping. This leads to a conclusion that the relevance and value of Soviet military maps endure in modern applications, both as a source of data and as a means of overcoming contemporary cartographic challenges relating to symbology, design and the handling of large datasets.


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