By Mikhail Kanevski
This ebook combines geostatistics and international mapping platforms to provide an up to date learn of environmental info. that includes a variety of case experiences, the reference covers version based (geostatistics) and knowledge pushed (machine studying algorithms) research concepts reminiscent of threat mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, man made neural networks (ANN) for spatial info, Bayesian greatest entropy (BME), and extra.
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Extra resources for Advanced Mapping of Environmental Data (Geographical Information Systems)
22. 6. e. which phenomena can be detected by the monitoring network and at what resolutions. Clustering (nonhomogenity) and preferential sampling already give rise to biased estimates of global statistics such as mean and variance values. Therefore, correct quantification of monitoring network quality and following selection of an appropriate declustering technique are extremely important both for exploratory data analysis and spatial predictions. 44 Advanced Mapping of Environmental Data In this chapter topological, statistical and fractal measures were introduced to quantify clustering of simulated and real monitoring networks.
Both indices are complementary, because they describe different clustering properties. Complementarities of the indices are shown in the two following “toy” examples. 13 shows two similar monitoring networks associated with the same numbers of samples: the first presents a cluster of samples in the middle, while the second is homogenously distributed. 13. 2 shows the values of fractal dimensions calculated with both methods on both MN considered; only the sandbox method is able to detect a spatial clustering, by the strong change in local densities, while the box-counting measure remains almost unchanged, because the 2-dimensional space is covered by both networks in the same way.
12 shows the results for the clustered and homogenous networks. Clustering of both networks is particularly distinguishable for small box sizes, where the effect of local clustering can be detected. As both networks are covering the entire space, the box-counting method cannot detect the difference between them at large scales (the curves are very similar for log (box sizes) greater than 4). e. if the network is appropriate to detect a D-dimensional phenomenon in a D dimensional Euclidean space: the first, the sandbox method, calculates a measure of the local densities of samples at different scales, while the second, the box-counting method, is based on an estimate of the spatial covering of the region under study by the network.
Advanced Mapping of Environmental Data (Geographical Information Systems) by Mikhail Kanevski