Data analysis and mapping with geostatistics | Training course
Understand and master the use of geostatistics for data analysis and mapping.
At the end of this course, you will be able to use geostatistics to:
- better understand and value your data,
- produce better quality maps by integrating various types of data,
- quantify the uncertainties on the calculated maps,
- understand the underlying assumptions and choose the most suitable geostatistical technique for your data.
- Half of the course is devoted to methodological presentations, the second half to practical exercises on real-life cases to deepen the understanding of concepts. The focus is on illustrations and practical contributions of the covered concepts.
- Computer exercises with Isatis.neo software and air quality data. Depending on the participants’ profiles, additional case studies from other sectors of activity will be shown.
- Course material provided.
Who should attend
This course is aimed at those who work with spatial data and wish to understand and put into practice the geostatistical methods for mapping: academics, agricultural engineers, air quality engineers, climatologists, environmental consultants and engineers, epidemiologists, foresters, geotechnical engineers, soil scientists, etc.
Day 1: Analysing data and their variability in space
– Added-value of geostatistics compared to deterministic methods for spatial interpolation.
– Presentation of usual deterministic interpolation methods (nearest neighbor, moving average, inverse distance, etc.) and their application limits.
- Exploratory data analysis and validation:
– Use of statistics for data analysis and quality control, and identification of outliers: mean, variance, histogram, correlation coefficients, linear regression, etc.
– Data visualization in 2D and 3D.
– Spatial variability: concept and quantification through the calculation, interpretation, and modeling of the experimental variogram.
– The various theoretical variogram models. Fitting of the experimental variogram.
Day 2: Mapping
- Interpolation by kriging:
– Kriging principles and properties. The smoothing effect of kriging.
– Definition of the most appropriate neighborhood (single or moving, size, number of samples, etc.).
– Analysis of the kriging weights according to the sample positions, the neighborhood, the presence or not of a nugget effect.
- Cross validation to validate the variogram model.
- The different variants of kriging (simple, ordinary, with variance in measurement error, etc.).
Day 3: Refining the maps
- Multivariate geostatistics: to reduce interpolation uncertainty
– Analysis of the correlations between multidisciplinary data, quantitative and semi-quantitative (remote sensing data, DEM, soil occupation, physicochemical models, lithology, etc.): calculation of scatter plots and coefficients of correlation.
– Analysis of the spatial relationships between variables: multivariate variogram calculation and modeling.
– Integration of one or several secondary variables in the interpolation: cokriging principles and practice. Collocated cokriging.
– Analysis of the inputs of cokriging compared to kriging.
- Non-stationary geostatistics: when data show a drift or a trend.
- Introduction to simulations and risk analysis: added-value of simulations, examples.
The course does not require prior knowledge of geostatistics. However, it is recommended to have a basic knowledge of statistics.