By Pierre Dumolard(eds.)
This e-book combines geostatistics and worldwide mapping platforms to give an up to date research of environmental information. that includes quite a few case reviews, the reference covers version established (geostatistics) and knowledge pushed (machine studying algorithms) research options akin to danger mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, man made neural networks (ANN) for spatial info, Bayesian greatest entropy (BME), and more.Content:
Chapter 1 complex Mapping of Environmental facts: 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 laptop studying Algorithms (pages 95–148): F. Ratle, A. Pozdnoukhov, V. Demyanov, V. Timonin and E. Savelieva
Chapter five complicated Mapping of Environmental Spatial facts: Case reviews (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
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Additional resources for Advanced Mapping of Environmental Data: Geostatistics, Machine Learning and Bayesian Maximum Entropy
The principles of geostatistics were developed by Matheron [MAT 1963] and extended in later works [JOU 1978; CRE 1993; CHI 1999]. An independent contribution to spatial data modeling and interpolations (objective analysis of meteorological fields) was made by L. Gandin [GAN 63]. Geostatistics considers a spatial phenomenon as a random process Z(x), where the argument x denotes location in space (x=(x,y) in a two-dimensional space). Available measurement data (Z(x1), …,Z(xN)) from N locations (x1,…,xN) are treated as realizations of the random process Z(x).
Therefore, the area/frequency distribution of the polygons can be interpreted as an index of spatial clustering [NIC 00, KAN 04a, PRO 07]. 6. 6. Voronoï polygon area for the clustered (left, above) and homogenous (left, below) areas. 2. Statistical indices Several statistical indices have been developed to highlight the presence of spatial clustering, the most common probably being Moran’s index [MOR 50], a weighted correlation coefficient used to analyze departures from spatial randomness. Other indices can be used to discover the presence of clusters: – the Morisita index [MOR 59]: the region is divided into Q identical cells and the number of samples ni within every cell i is counted.
9. 10 shows the results of the sandbox method to both networks considered in this section. The slope of the regression function fitting the curves gives the fractal dimension. 197. The value of 2 is not reached by the homogenous network because the distribution of samples is not regular: the value of two can be reached by a regular grid of samples, while a real network is characterized by slight over-densities at small scales resulting in a small level of clustering and a decrease of the fractal dimension.
Advanced Mapping of Environmental Data: Geostatistics, Machine Learning and Bayesian Maximum Entropy by Pierre Dumolard(eds.)