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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
  
Moose density for Uusimaa, Finland 
Years 2001 to 2003 d Pu 
    
    
  
(ER RE) 
& m 
      
  
    
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d 
   
   
   
eno = versa Ursewevity of Tacrmenngy. Toa of Certbgnanny £ Geonatics 2 
  
  
  
Figure 8. Interactive animation of the 3D moose density maps 
for Uusimaa including the years 2001-2003. 
The interface for this system is the same as for the moose 
accident analysis tool. The maps intend to stress the information 
about the moose density by a color scale from red to green. 
4. CONCLUSIONS 
The highways with wildlife fences and other major roads with 
heavy traffic have sectored the southern Finland to dozen parts. 
The fences hinder the long distant movements of moose and 
cause changes in the functioning of moose metapopulations. 
The changes can be seen as different moose densities in the 
separate sides of the fenced highways. The gradual changes 
occur also when highway is fenced. The fenced highway 
without proper green bridges or underpasses cuts the winter 
pasture areas of the continuous moose population the moose 
density peaks start to move gradually away from the highway. 
Animals look for better pasture areas. At such areas where there 
is good under or overpasses in highways suitable for animal 
use, such change has not been discovered. The changes are 
gradual and they can be seen in decade long time series. 
To calculate the moose density for different years provides us 
with the basic data to show us changes in the moose density, 
which might be caused by the roads. The Kernel method proves 
to be a good tool to calculate the density patterns of the 
individual moose locations. By creating a smooth of density 
values in which the density at each*location reflects the 
concentration of points in the surrounding area, analysts are 
able to identify how densities vary across a study area. To 
overlay of the moose density and road network map can to 
identify “hotspots” concerning the location of a traffic wildlife 
accident. 
A four dimensional animation can help to visually analyze the 
moose density patterns over time. Integrating the maps into an 
interactive system makes it easy to navigate between the 
different maps created for each point in time. In our example 
the time span from 2001 to 2003 appears to be too short to 
identify significant changes in the moose density. Further 
research should aim to integrate data from a longer time period. 
The Finnish hunting association will continue to record moose 
data points with individual coordinates, so future research will 
be able to consider longer time spans. 
413 
4.1 Acknowledgements 
The Finnish Road Administration in cooperation with the 
Helsinki University of Technology — Department of 
Cartography &  Geoinformatics supported this research. 
Additionally we thank the Ministry of Transport and 
Communication for financing the MOSSE research project. 
5. REFERENCES 
Bailey, T.C. & Gatrell, A.C. (1995) Interactive Spatial Data 
Analysis Addison-Wesley, Reading. 
FinRA (2003). The animal accident statistics 1996-2003. 
Finnish Road Administration. 
Hággman, D. (1999) Hirvireittien GIS-Analyysi. Diploma 
Thesis, HUT - Technical University Helsinki, Espoo. 
Krisp, J.M. & Fronzek, S. (2003) Visualising thematical spatial 
data by using the third dimension. In ScanGIS (eds K. 
Virrantaus & H. Tveite), pp. 157-166, Espoo. 
MacEachren, A. & Kraak, M.-J. (2001) Research Challenges in 
Geovisualization. Cartography and Geoinformation Science, 28. 
Patterson, T. (1999). Designing 3D Landscapes. In Multimedia 
Cartography (eds W. Cartwright, P. Peterson & G. Gartner), 
Berlin. 
Raper, J.F. (1989). Key 3D modelling concepts for geoscientific 
analysis. In Three dimensional applications in GIS (ed Raper), 
pp. 215-232, London. 
Silverman, B.W. (1986) Density estimation for statistics and 
data analysis Chapman and Hall, London. 
Tórmánen, P. (2002) Finnish moose photograph. 
Woess, M. & Grillmayer, R. (2002) Migration corridors for 
wildlife -another network of mobility. In International 
Symposium on GIS, Istanbul. 
Vire, S. (2001). The ecological network in Uusimaa 
(Ekologinen verkosto Uudenmaan liiton alueella). The Council 
of Uusimaa, Helsinki. 
Väre, S. (2003) The follow-up research on moose and other 
wild animals at Pernaja, European highway E18. In 
International Conference on habitat Fragmentation due to 
Transportation Infrastructure COST action 341., Brussels. 
Vàüre, S. & Grenfors, E. (2004) The movements of animals at 
highway environment., The University of Helsinki, Helsinki. 
Vire, S. & Krisp, J.M. (2003) The ecological network and land 
use planning of the urban areas. In Proceedings 4th 
Environment Symposium, Espoo, Finland.