A1 Refereed original research article in a scientific journal
Spatial prediction of urban-rural temperatures using statistical methods
Authors: Hjort Jan, Suomi Juuso, Käyhkö Jukka
Publisher: SPRINGER WIEN
Publication year: 2011
Journal: Theoretical and Applied Climatology
Journal name in source: THEORETICAL AND APPLIED CLIMATOLOGY
Journal acronym: THEOR APPL CLIMATOL
Number in series: 1-2
Volume: 106
Issue: 1-2
First page : 139
Last page: 152
Number of pages: 14
ISSN: 0177-798X
DOI: https://doi.org/10.1007/s00704-011-0425-9
Abstract
Spatial information on climatic characteristics is beneficial in e.g. regional planning, building construction and urban ecology. The possibility to spatially predict urban-rural temperatures with statistical techniques and small sample sizes was investigated in Turku, SW Finland. Temperature observations from 36 stationary weather stations over a period of 6 years were used in the analyses. Geographical information system (GIS) data on urban land use, hydrology and topography served as explanatory variables. The utilized statistical techniques were generalized linear model and boosted regression tree method. The results demonstrate that temperature variables can be robustly predicted with relatively small sample sizes (n a parts per thousand aEuro parts per thousand 20-40). The variability in the temperature data was explained satisfactorily with few accessible GIS variables. Statistically based spatial modelling provides a cost-efficient approach to predict temperature variables on a regional scale. Spatial modelling may aid also in gaining novel insights into the causes and impacts of temperature variability in extensive urbanized areas.
Spatial information on climatic characteristics is beneficial in e.g. regional planning, building construction and urban ecology. The possibility to spatially predict urban-rural temperatures with statistical techniques and small sample sizes was investigated in Turku, SW Finland. Temperature observations from 36 stationary weather stations over a period of 6 years were used in the analyses. Geographical information system (GIS) data on urban land use, hydrology and topography served as explanatory variables. The utilized statistical techniques were generalized linear model and boosted regression tree method. The results demonstrate that temperature variables can be robustly predicted with relatively small sample sizes (n a parts per thousand aEuro parts per thousand 20-40). The variability in the temperature data was explained satisfactorily with few accessible GIS variables. Statistically based spatial modelling provides a cost-efficient approach to predict temperature variables on a regional scale. Spatial modelling may aid also in gaining novel insights into the causes and impacts of temperature variability in extensive urbanized areas.
UTU Research Portal