1. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Developing ground water level map for Dinajpur district, Bangladesh using geo-statistical analyst Dr. A.K.M. Saiful Islam Institute of Water and Flood Management (IWFM) Bangladesh University of Engineering and Technology (BUET)

2. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Geo-statistical Analyst of ArcGIS This training will be on: 1.Represent data 2.Explore data 3.Fit a interpolation Model 4.Diagnosis output 5.Create ground water level maps Input Data Groundwater well data of Dinajpur district of Bangladesh

3. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Study Area and Data Study area –Seven upazillas of Dinajpur District of Bangladesh Data –Data from 27 Groundwater observation Wells as shape file “gwowell_bwdb.shp”. Weekly data from December to May for 1994 to 2003 –Upazilla shape file “upazila.shp”

4. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Activate Geo-statistical Analyst Turn on Geostatistaical Anaylst of ArcGIS Enable toolbar Enable Extension

5. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Add Data Add both shape files

6. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam 1. Represent Data

7. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Groundwater well data

8. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam 2. Explore Data a)Histogram b)Normal Q-Q Plot c)Trend Analysis d)Voronoi Map e)Semivariogram f)Covariance cloud

9. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam a) Histogram Select attribute: any data e.g. DEC05_1994 We can change no of bars or bin size Distribution is normal

10. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Transformation Log- transformation doesn’t change distribution pattern

11. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam b) Normal Q-Q Plot Normal Q-Q plot is straight line which represents normal distribution

12. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam c) Trend Analysis Shows trend in both X and Y direction since the projection lines (blue and green) are not straight.

13. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam d) Voronoi map Shows the zone of influence of known data points

14. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam e) Semi-variogram

15. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Shows search directional Exhibits directional influence in different angle (arrows)

16. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam f) Co-variance

17. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam 3. Fit interpolation model

18. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Inverse Distance Weighting (IDW) Inverse Distance Weighting (IDW) is a quick deterministic interpolator that is exact. There are very few decisions to make regarding model parameters. It can be a good way to take a first look at an interpolated surface. However, there is no assessment of prediction errors, and IDW can produce "bulls eyes" around data locations. There are no assumptions required of the data.

19. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Global Polynomial (GP) Global Polynomial (GP) is a quick deterministic interpolator that is smooth (inexact). There are very few decisions to make regarding model parameters. It is best used for surfaces that change slowly and gradually. However, there is no assessment of prediction errors and it may be too smooth. Locations at the edge of the data can have a large effect on the surface. There are no assumptions required of the data.

20. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Local Polynomial (LP) Local Polynomial (LP) is a moderately quick deterministic interpolator that is smooth (inexact). It is more flexible than the global polynomial method, but there are more parameter decisions. There is no assessment of prediction errors. The method provides prediction surfaces that are comparable to kriging with measurement errors. Local polynomial methods do not allow you to investigate the autocorrelation of the data, making it less flexible and more automatic than kriging. There are no assumptions required of the data.

21. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Radial Basis Functions (RBF) Radial Basis Functions (RBF) are moderately quick deterministic interpolators that are exact. They are much more flexible than IDW, but there are more parameter decisions. There is no assessment of prediction errors. The method provides prediction surfaces that are comparable to the exact form of kriging. Radial Basis Functions do not allow you to investigate the autocorrelation of the data, making it less flexible and more automatic than kriging. Radial Basis Functions make no assumptions about the data.

22. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Kriging Kriging is a moderately quick interpolator that can be exact or smoothed depending on the measurement error model. It is very flexible and allows you to investigate graphs of spatial autocorrelation. Kriging uses statistical models that allow a variety of map outputs including predictions, prediction standard errors, probability, etc. The flexibility of kriging can require a lot of decision-making. Kriging assumes the data come from a stationary stochastic process, and some methods assume normally-distributed data.

23. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Cokriging Cokriging is a moderately quick interpolator that can be exact or smoothed depending on the measurement error model. Cokriging uses multiple datasets and is very flexible, allowing you to investigate graphs of cross- correlation and autocorrelation. Cokriging uses statistical models that allow a variety of map outputs including predictions, prediction standard errors, probability, etc. The flexibility of cokriging requires the most decision- making. Cokriging assumes the data come from a stationary stochastic process, and some methods assume normally-distributed data.

24. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Kriging Geo-statistical method Select ordinary kriging

25. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Semivariogram modeling Select spherical method

26. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Searching neighbour

27. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Cross validation Root mean square error is 1.437

28. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Report of output layer

29. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Prediction map

30. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Extent of Map Set Extend as upazilla

31. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Export as Raster Select cell size as 100

32. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Prediction map as raster

33. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Zonal statistics Zonal statistics from Spatial Analyst

34. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Mean ground water level

35. Concept Course on Spatial Analyst @ Dr. A.K.M. Saiful Islam Change color for Mean ground water level of Dinajpur