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Mapping and evaluating urban density patterns in Moscow, Russia
Choudhary K., Boori M.S., Kupriyanov A.V.

Samara National Research University, Samara, Russia,
American Sentinel University, Denver, Colorado, USA,
Image Processing Systems Institute – Branch of the Federal Scientific Research Centre“Crystallography and Photonics” of Russian Academy of Sciences, Samara, Russia,
Bonn University, Bonn, Germany

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DOI: 10.18287/2412-6179-2017-41-4-528-534

Pages: 528-534.

Abstract:
The defense of the notion of ‘compact city’ as a strategy to reduce urban sprawl to support greater utilization of existing infrastructure and services in more compact areas and to improve the connectivity of employment hubs is actively discussed in urban research. Using the urban residential density as a surrogate measure for urban compactness, this paper empirically examines a cadaster database that contains details of every property with a view of capturing changes in urban residential density patterns across Moscow using geospatial techniques. The policy of densification in chase of a more compact city has produced mixed results. Findings of this study signal that the urban densities across the buffer zones around Moscow city are significantly different. The Landsat images from 1995, 2005 and 2016 are classified based on the maximum likelihood to expand the land use/cover maps and identify the land cover. Then, the area coverage for all the land use/cover types at different points in time is combined with the distance from the city center. After that, urbanization densities from the city center toward the outskirts for every 1-km distance from 1 to 60 km are calculated. The city density on the distance of 1 to 35 km is found to be very high in the years 1995 to 2016. As usual, the population, traffic conditions, industrialization and government policy are the major factors that influenced the urban expansion.

Keywords:
density, compact city, land use/cover, buffer zones.

Citation:
Choudhary K, Boori MS, Kupriyanov A. Mapping and evaluating urban density patterns in Moscow, Russia. Computer Optics 2017; 41(4): 528-534. DOI: 10.18287/2412-6179-2017-41-4-528-534.

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