Data Structures and Algorithms
Data Structures and Algorithms
Spatially modelling pathways of migratory birds for nature reserve site selection
International Journal of Geographical Information Science
Network density estimation: analysis of point patterns over a network
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Time-geographic density estimation for moving point objects
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Network based kernel density estimation for cycling facilities optimal location applied to Ljubljana
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
An ontology-based traffic accident risk mapping framework
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
A network based kernel density estimator applied to barcelona economic activities
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part I
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We develop a kernel density estimation method for estimating the density of points on a network and implement the method in the GIS environment. This method could be applied to, for instance, finding 'hot spots' of traffic accidents, street crimes or leakages in gas and oil pipe lines. We first show that the application of the ordinary two-dimensional kernel method to density estimation on a network produces biased estimates. Second, we formulate a 'natural' extension of the univariate kernel method to density estimation on a network, and prove that its estimator is biased; in particular, it overestimates the densities around nodes. Third, we formulate an unbiased discontinuous kernel function on a network. Fourth, we formulate an unbiased continuous kernel function on a network. Fifth, we develop computational methods for these kernels and derive their computational complexity; and we also develop a plug-in tool for operating these methods in the GIS environment. Sixth, an application of the proposed methods to the density estimation of traffic accidents on streets is illustrated. Lastly, we summarize the major results and describe some suggestions for the practical use of the proposed methods.