Read e-book online Advanced Mapping of Environmental Data: Geostatistics, PDF
By Pierre Dumolard(eds.)
This booklet combines geostatistics and worldwide mapping structures to provide an up to the moment research of environmental facts. that includes various case reviews, the reference covers version based (geostatistics) and knowledge pushed (machine studying algorithms) research innovations comparable to threat mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, synthetic neural networks (ANN) for spatial info, Bayesian greatest entropy (BME), and more.Content:
Chapter 1 complicated Mapping of Environmental info: creation (pages 1–17): M. Kanevski
Chapter 2 Environmental tracking community Characterization and Clustering (pages 19–46): D. Tuia and M. Kanevski
Chapter three Geostatistics: Spatial Predictions and Simulations (pages 47–94): E. Savelieva, V. Demyanov and M. Maignan
Chapter four Spatial info research and Mapping utilizing computing device studying Algorithms (pages 95–148): F. Ratle, A. Pozdnoukhov, V. Demyanov, V. Timonin and E. Savelieva
Chapter five complex Mapping of Environmental Spatial info: Case experiences (pages 149–246): L. Foresti, A. Pozdnoukhov, M. Kanevski, V. Timonin, E. Savelieva, C. Kaiser, R. Tapia and R. Purves
Chapter 6 Bayesian greatest Entropy — BME (pages 247–306): G. Christakos
Read Online or Download Advanced Mapping of Environmental Data: Geostatistics, Machine Learning and Bayesian Maximum Entropy PDF
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Additional resources for Advanced Mapping of Environmental Data: Geostatistics, Machine Learning and Bayesian Maximum Entropy
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.
On the other hand, the more constraining the VD is, the more the curves begin to be similar. 3) confirms these observations. 3. 18. 3. 1. 19) confirms the previous results: for real data, the index is higher than 1, which is the value of the index for a homogenous network, at all the scales. This shows a clustered distribution of points. The value of 1 is only reached asymptotically at larger scales, where one box covers the whole space, giving a value of I' of 1. Comparison with the simulated networks shows that the real data are distributed similarly to the random points over the populated regions VD, but also that the level Environmental Monitoring Network Characterization and Clustering 39 of clustering is significantly higher for every scale.
The index has been used in a wide range of environmental applications, from ecological studies [SHA 04, BON 07] to risk analysis [OUC 86, TUI 07a]. 7. 7. Morisita index for random (dashed) and clustered (solid) MN The K-function (or Ripley’s K) [RIP 77, MOE 04] can be used to calculate the degree of spatial randomness in the spatial distribution of samples. 2] where O is the density (number per unit area) of samples and I is an indicator function giving 1 if the considered samples are within a circle of radius R and 0 otherwise.
Advanced Mapping of Environmental Data: Geostatistics, Machine Learning and Bayesian Maximum Entropy by Pierre Dumolard(eds.)