Smoothing of land use maps for trend and change detection in urbanization

Ventrucci, M., Cocchi, D. and Scott, E. M. (2016) Smoothing of land use maps for trend and change detection in urbanization. Evironmental and Ecological Statistics, 23(4), pp. 565-584. (doi: 10.1007/s10651-016-0354-y)

Abstract

Urban sprawl and its evolution over relatively short periods of time demands that we develop statistical tools to make best use of the routinely produced land use data from satellites. An efficient smoothing framework to estimate spatial patterns in binary raster maps derived from land use datasets is developed and presented in this paper. The framework is motivated by the need to model urbanization, specifically urban sprawl, and also its temporal evolution. We frame the problem as estimation of a probability of urbanization surface and use Bayesian P-splines as the tool of choice. Once such a probability map is produced, with associated uncertainty, we develop exploratory tools to identify regions of significant change across space and time. The proposal is used to study urbanisation and its development around the city of Bologna, Emilia Romagna, Italy, using land use data from the Cartography Archive of Emilia Romagna Region for the period 1976–2008.

• Item Type: Articles
• Additional Information: Massimo Ventrucci is funded by a FIRB 2012 Grant (Project No. RBFR12URQJ, title: Statistical modeling of environmental phenomena: pollution, meteorology, health and their interactions), for research projects of national interest provided by the Italian Ministry of Education, Universities and Research.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Scott, Professor Marian and Ventrucci, Dr Massimo
• Authors: Ventrucci, M., Cocchi, D., and Scott, E. M.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Evironmental and Ecological Statistics
• Publisher: Springer-Verlag
• ISSN: 1352-8505
• ISSN (Online): 1573-3009
• Published Online: 29 August 2016
• Copyright Holders: Copyright © 2016 Springer Science+Business Media
• First Published: First published in Evironmental and Ecological Statistics 23(4):565-584
• Publisher Policy: Reproduced in accordance with the publisher copyright policy