Место издания:University of Auckland, New Zealand Auckland, New Zealand
Аннотация:The main goal of this investigation is to develop a kind of “urban reanalysis” – the database of
meteorological and radiation fields under Moscow megalopolis with high spatial resolution. That’s
why is quite useful to calculate SVF (Sky View Factor) for obtaining losses of UV radiation in
complex urban conditions. Usually, the raster-based SVF analysis the shadow-casting algorithm
proposed by Richens (1997) is popular (see Ratti and Richens 2004, Gal et al. 2008, for example).
SVF image is obtained by combining shadow images obtained from different directions. An
alternative is to use raster-based SVF calculation similar to vector approach using digital elevation
model of urban relief. Chen et al. (2012) implemented it in this way mentioning that it ”has an
advantage in that it does not involve marginal error, i.e. once the radius is decided, the DEM layer
can be prepared accordingly so every calculated pixel in the Mask layer has a correct value”. We
used similar raster-based calculation of SVF. The data should prepared for analysis using the
following sequence of operations:
Select resolution value , to which building heights and surface elevations should be reduced.
Rasterize buildings layer with resolution r and using height field for raster values.
Resample digital elevation model to resolution r.
Sum building height raster with digital elevation model raster using map algebra.
So, using this algorithm, we can take into account shading capacity of urban canyons in city
landscape. This way allows to estimate additional attenuation of UV radiation in urban conditions.
References:
1. Chen, L., et al., 2012. Sky view factor analysis of street canyons and its implications for
daytime intraurban air temperature differentials in highrise, highdensity urban areas of
Hong Kong: a GIS based simulation approach. International Journal of Climatology, 32 (1),
121–136.
2. Gal, T., Lindberg, F., and Unger, J., 2008. Computing continuous sky view factors using 3D
urban raster and vector databases: comparison and application to urban climate.
Theoretical and applied climatology, 95 (1-2), 111–123.
3. Richens, P., 1997. Image processing for urban scale environmental modelling. In: J.D. Spitler
and J.L.M. Hensen, eds. th Intemational IBPSA Conference Building Simulation, Prague.
4. Ratti, C. and Richens, P., 2004. Raster analysis of urban form. Environment and Planning B:
Planning and Design, 31 (2), 297–309.
