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:e2264v2 https://doi.org/10.7287/peerj.preprints.2264v2
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.
Author Comment
This is an article intended for the OGRS2016 Collection
Session: Spatial Statistics
Papers has been very lightly modified in order to mention the project in which work is involved. Furthermore, a figure title has been changed and a reference to R language added.
Supplemental Information
Buildings areas data of Vannes city
2 variables: building identifier, building area square meter