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Spatial prediction of urban–rural temperatures using statistical methods

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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 ≈ 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.

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Acknowledgements

We express our gratitude to three anonymous reviewers for the critical and helpful comments which improved the manuscript. JS was funded by the Finnish Cultural Foundation’s Varsinais-Suomi Regional Fund and the Emil Aaltonen Foundation. TURCLIM project is maintained in collaboration with the Turku Environmental and City Planning Department, whose assistance is greatly acknowledged. The long-term reference weather data used in this study is provided by the Finnish Meteorological Institute.

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  1. Department of Geography, University of Oulu, P.O. Box 3000, FI-90014, Oulu, Finland

    Jan Hjort

  2. Department of Geography and Geology, University of Turku, Turku, FI-20014, Finland

    Juuso Suomi & Jukka Käyhkö

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  1. Jan Hjort
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Correspondence to Jan Hjort.

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Hjort, J., Suomi, J. & Käyhkö, J. Spatial prediction of urban–rural temperatures using statistical methods. Theor Appl Climatol 106, 139–152 (2011). https://doi.org/10.1007/s00704-011-0425-9

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