Transcript Hands on exercise on Geo

Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Hands on training on developing
ground water level map using
geo-statistical analyst
Dr. A.K.M. Saiful Islam
Institute of Water and Flood Management (IWFM)
Bangladesh University of Engineering and Technology (BUET)
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Geo-statistical Analyst of ArcGIS
This training will be on:
1.
2.
3.
4.
5.
Represent data
Explore data
Fit a interpolation Model
Diagnosis output
Create ground water level maps
Input Data
Groundwater well data of Dinajpur district of
Bangladesh
Remote Sensing and GIS in Water Management @ 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”
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Activate Geo-statistical Analyst
• Turn on Geostatistaical Anaylst of ArcGIS
Enable Extension
Enable toolbar
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Add Data
• Add both shape files
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
1. Represent Data
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Groundwater well data
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
2. Explore Data
a)
b)
c)
d)
e)
f)
Histogram
Normal Q-Q Plot
Trend Analysis
Voronoi Map
Semivariogram
Covariance cloud
Remote Sensing and GIS in Water Management @ 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
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Transformation
• Log- transformation doesn’t change
distribution pattern
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
b) Normal Q-Q Plot
• Normal Q-Q plot is straight line which
represents normal distribution
Remote Sensing and GIS in Water Management @ 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.
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
d) Voronoi map
• Shows the zone of influence of known data points
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
e) Semi-variogram
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Shows search directional
• Exhibits directional influence in different angle (arrows)
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
f) Co-variance
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
3. Fit interpolation model
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Kriging Geo-statistical method
• Select ordinary kriging
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Semivariogram modeling
• Select spherical method
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Searching neighbour
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Cross validation
• Root mean square error is 1.437
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Report of output layer
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Prediction map
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Extent of Map
• Set Extend as upazilla
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Export as Raster
• Select cell size as 100
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Prediction map as raster
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Zonal statistics
• Zonal statistics from Spatial Analyst
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Mean ground water level
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Change color for Mean ground
water level of Dinajpur
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Thank you !
Remote Sensing and GIS in Water Management @ Dr. A.K.M. Saiful Islam
Glossary
Methods of Interpolation in geostatisticsal
Analysis
o Inverse Distance Weighting (IDW)
o Global Polynomial (GP)
o Local Polynomial (LP)
o Radial Basis Functions (RBF)
o Kriging
o Cokriging
Remote Sensing and GIS in Water Management @ 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.
Remote Sensing and GIS in Water Management @ 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.
Remote Sensing and GIS in Water Management @ 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.
Remote Sensing and GIS in Water Management @ 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.
Remote Sensing and GIS in Water Management @ 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.
Remote Sensing and GIS in Water Management @ 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 crosscorrelation 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 decisionmaking. Cokriging assumes the data come from a
stationary stochastic process, and some methods
assume normally-distributed data.