Name
Title | ||
|---|---|---|
A kernel density estimation method for networks, its computational method and a GIS-based tool |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1080/13658810802475491 | International Journal of Geographical Information Science |
Keywords | Field | DocType |
univariate kernel method,gis environment,computational method,unbiased discontinuous kernel function,density estimation,unbiased continuous kernel function,kernel density estimation method,gis-based tool,ordinary two-dimensional kernel method,traffic accident,network,hot spot,computational complexity,kernel method,kernel function,kernel density estimate,kernel density estimation,unbiased estimator | Multivariate kernel density estimation,Radial basis function kernel,Computer science,Kernel embedding of distributions,Polynomial kernel,Artificial intelligence,Kernel method,Variable kernel density estimation,Machine learning,Kernel density estimation,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
23 | 1 | 1365-8816 |
Citations | PageRank | References |
44 | 3.32 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Atsuyuki Okabe | 1 | 165 | 32.48 |
| Toshiaki Satoh | 2 | 89 | 10.52 |
| Kokichi Sugihara | 3 | 856 | 241.55 |