MATRIX PIXEL AND KERNEL DENSITY ANALYSIS FROM THE TOPOGRAPHIC MAPS
Complex networks with building density play a significant role in many fields of science, especially in urban sciences. That includes road networks, hydrological networks, computer networks and building changes into geo-space through some period. Using these networks we can solve the problems like the shortest path, the total capacity of networks, density population or traffic density in an urban or suburban area. In this paper for quantifying the complexity of road networks and a novel method for determining building density by using a matrix pixel analysis and Kernel distribution with a concrete example of the city of Belgrade. Both of them represent geo-spatial data. In this case we have analyzed road networks, building density, with the help of specially created software for analyzing pixels on the maps from 1971, including the properties of geo-spatial data we have analyzed from old topographic maps in ratio 1:25.000.
Chen, S. 1999. Beta Kernel estimators for density function. Computational Statistics & Data Analysis, 31(2), pp. 131-145.
Chen, S., Hanzo, L., & Wolfgang, A. 2004. Kernel-Based Nonlinear Beamforming Construction Using Orthogonal Forward Selection With the Fisher Ratio Class Separability Measure. IEEE Signal Processing Letters, 11(5), pp. 478-481. doi:10.1109/LSP.2004.826509.
Čolović, M. 1984. Topographic signs.Belgrade: Geographic-Military Institute of Serbia, Military book press., pp. 11-34.
Dibase, D., Maceachren, A., & Kryger, J. 1992. Design in Scientific Visualization. Cartography and Geographical Information Systems, 19(2), pp. 201-256.
Gudmundsson, A., & Mohajeri, N. 2013. Entropy and order in urban streets networks. Scientific reports, 3(3324), pp. 1-7. Retrieved from http://www.nature.com/srep/2013/131125/srep03324/full/srep03324.html
Helbing, D. 2003. A section-based queueing-theoretical traffic model for congestion and travel time analysis in networks. Journal of Physics A: Mathematical and General, 36(46), pp. 593-598. doi:10.1088/0305-4470/36/46/L03.
Hurvich, L.M., & Jameson, D. 1957. An opponent-process theory of color vision. Psychological Review, 64(6, Pt.1), pp. 384-404. pmid:13505974. doi:10.1037/h0041403.
Knežević, A. 2010. Social position, residential problems and characteristics of household and families of Roma population in Belgrade. Glasnik Srpskog Geografskog društva, 90(1), pp. 257-276.
Levkowitz, H. 1997. Color theory and modeling for computer graphics, visualization, and multimedia application.Norwell, Massachusetts: Kluwer Academic Publishers., pp. 27-36.
Munsell, A. 1975. A color notation, 12nd ed.Baltimore: Munsell Color Co.., pp. 131-156.
Muslims, A., Foody, A., & Atkinson, M. 2006. Localized soft classification for super-resolution mapping of the shoreline,. International Journal of Remote Sensing, 27(2), pp. 2271-2285.
Saks, R. 2008. Job creation and housing construction: Constraints on Metropolitan area employment growth. Journal of Urban Economics, 64(1), pp. 178-195.