Building size modelization
- Published
- Accepted
- Subject Areas
- Data Mining and Machine Learning, Data Science, Graphics, Scientific Computing and Simulation, Spatial and Geographic Information Systems
- Keywords
- building geometry, Building size distributions, statistical modelization, R language
- Copyright
- © 2016 Antoni et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Building size modelization. PeerJ Preprints 4:e2264v3 https://doi.org/10.7287/peerj.preprints.2264v3
Abstract
New challenges in the efficient management of cities depend on a deep knowledge of their inner structures. It is therefore very important to have access to reliable models of cities characteristics and organization. This paper aims at providing and validating a stochastic modelization based on statistical data of buildings parameters which can be useful as an entry for many other models considered in a wide range of fields where buildings structure is a main factor of a thorough modelization of cities. The interest of such an approach is highlighted through the detection of errors in the data or as a tool for visual clustering.
Author Comment
“This is an article intended for the OGRS2016 Collection” Scientific Session : Spatial Statistics.
The major changes are an extension of the application and a general conclusion as required by the referees.
Supplemental Information
Buildings areas data of Vannes city
2 variables: building identifier, building area square meter