Integrated method for cities air temperature estimation
- Published
- Accepted
- Subject Areas
- Data Science, Scientific Computing and Simulation, Spatial and Geographic Information Systems
- Keywords
- methodology, urban heat island, geographic indicator, climatic indicator, empirical model, air temperature measurement
- Copyright
- © 2016 Bernard 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. Integrated method for cities air temperature estimation. PeerJ Preprints 4:e2240v1 https://doi.org/10.7287/peerj.preprints.2240v1
Abstract
Urban Heat Island (UHI) is defined as the air temperature difference between the city and its surrounding areas. This phenomenon varies spatially (depending on the type of urban fabric constituting each neighborhood) and temporally (depending on the time of the day, on the season and on the weather conditions). This contribution proposes a methodology to model the UHI spatially and temporally using simple models built with free and open sources softwares (orbisGIS and python language). Ten air temperature sensors have been implemented in several neighborhoods of the Nantes urban area (a west coast french conurbation). The difference of UHI is observed and modeled for each of those sites. Spatial differences are modeled according to geographical indicators characterizing the urban surroundings of each temperature station. Temporal variations are modeled according to weather conditions (such as wind speed, solar radiations, etc.) for different time scales : diurnal and nocturnal differences, daily variations and seasonal variations. The objective is to create a method which may be applied for any city in France. Geographical indicators are then calculated with OrbisGIS software from geographical data which are homogeneous and available at the french territory scale. Wheather conditions are recorded by MeteoFrance stations, which follow the same standard for the measurement of climatic parameters all around France. Climatic data analysis and modeling are performed with Python language using libraries such as Pandas and StatsModels. Modeled established according to the Nantes temperature dataset are verificated according to new air temperature networks implemented in the city of Nantes as well as other cities of west France (Angers, La Roche-sur-Yon).
Author Comment
This is an article intended for the OGRS2016 Collection.
The session tack related to this submission is "Modelling spatio-temporal processes using open source geospatial tools"