TY - DATA T1 - Contribution of urban environment parameters on the thermal environment of a University Campus PY - 2017/12/05 AU - Pedro Renan Debiazi AU - Léa Cristina Lucas de Souza UR - https://scielo.figshare.com/articles/dataset/Contribution_of_urban_environment_parameters_on_the_thermal_environment_of_a_University_Campus/5669053 DO - 10.6084/m9.figshare.5669053.v1 L4 - https://ndownloader.figshare.com/files/35766369 L4 - https://ndownloader.figshare.com/files/35766372 L4 - https://ndownloader.figshare.com/files/35766375 L4 - https://ndownloader.figshare.com/files/35766378 L4 - https://ndownloader.figshare.com/files/35766381 L4 - https://ndownloader.figshare.com/files/35766384 L4 - https://ndownloader.figshare.com/files/35766387 L4 - https://ndownloader.figshare.com/files/35766390 L4 - https://ndownloader.figshare.com/files/35766393 L4 - https://ndownloader.figshare.com/files/35766396 L4 - https://ndownloader.figshare.com/files/35766399 L4 - https://ndownloader.figshare.com/files/35766402 L4 - https://ndownloader.figshare.com/files/35766408 L4 - https://ndownloader.figshare.com/files/35766411 L4 - https://ndownloader.figshare.com/files/35766414 L4 - https://ndownloader.figshare.com/files/35766417 L4 - https://ndownloader.figshare.com/files/35766420 L4 - https://ndownloader.figshare.com/files/35766423 L4 - https://ndownloader.figshare.com/files/35766426 KW - Urban climate KW - Sky view factor KW - Urban vegetation KW - Geographical Information Systems KW - Artificial neural networks N2 - Abstract This paper investigates the influence of some characteristics of the urban environment on the air temperature by considering urban parameters such as occupancy coefficient (OC), urban vegetation coefficient (UVC), sky view factor (SVF) and cover coefficient (CC). The campus of the Federal University of São Carlos, in São Carlos, Brazil was used for spatial sampling. The method uses dataloggers for air temperature measurements in different collecting points, as well as the determination of urban indexes. The data of temperature and urban indexes are related to each other by the development of Artificial Neural Networks (ANN) models considering three radii around the sampling points: 25, 50 and 100 m. Among the prediction models developed, the one with the best performance is incorporated into a Geographical Information System (GIS), allowing data simulation of other points on the campus and leading to the creation of more detailed thermal maps. The results show that the UVC was the most significant element determining the thermal patterns in the campus. Furthermore, the ANN models associated with the GIS platform may be useful tools to support actions aiming at the thermal quality of the campus. ER -