Rick Faber CE 513 Geostatistical Analyst Lab # 6 6/2/06

Transcription

1 Rick Faber CE 513 Geostatistical Analyst Lab # 6 6/2/06

2 2 1. Objective & Discussion: Investigate and utilize Kriging methods of spatial interpolation. This lab is meant to highlight some of the strengths and weaknesses of the Kriging methods of spatial interpolation. 2. Major Steps Utilized in this Lab Geostatistical Analyst tools: Histograms/ QQ Plot / Trend Analysis. Geostatistical Wizard used to produce a raster model. Produce an Error map for the resulting model. Calculate Zonal Statistics. 3. Flow Chart of Major Steps Start with Point Samples (i.e. monitoring stations) Exploratory Spatial Analysis: Look for normal Gaussian behaviour, tools include: - Value plots - Histograms - Normal QQ plot ( test for normal distribution) - Spatial Trend Analysis Create a prediction Map/Model/Raster Use the Geostatistical Analyst Wizard to perform the interpolation. Set parameters including Kriging type, transformation type, trend removal etc. Look at the Semivariance/Covariance, choose appropriate lag size Select parameters for the neighborhood of influence, shape, size, # etc. Crossvalidation tool shows plot of predicted vs. observed values. Check model errors Create a mean prediction error map with the Geostatistical Analyst. Aggregate the model for the features of interest, i.e. calculate Zonal Statistics

3 3 1. Make a quick print of screen shot of figures 3.0, 4.0, 5.0, and 6.0. This only requires title and brief description. All can be on one map (8.5 by 11 or 11 by 17). 3 pts Expoloratory Spatial Data Analysis Fecal Coliform Monitoring points Histogram of Log(FCU) Log(FC_mean) vs Standard Normal Value Spatial Trend Analysis for Fecal Coliform Concentrations The above series of plots show preliminary looks at the monitoring station fecal coliform measurements. A graphic representation of the raw data, then histogram & QQ plots looking for a normal Gaussian distribution, and finally, 3D spatial plot to glance for overall trends.

4 2. How does the predicted surface look? Does it capture the observed highs and lows? 4 The predicted surface captures the general aspects of the sample points but not the fine details. i.e. the highest monitoring station has a measurement of 179, but only ~130 in the predicted model. Cross validation shows higher values tend to be underestimated. 3. Make a professional map of showing the before and the after FCU. Provide a paragraph of text explaining how the predicted surface was obtained. See map on page 5 The map shows the sampled fecal coliform concentrations for the Galveston bay and the model of concentrations derived from these data. After inspecting the sample data and determining it looks like a normal Gaussian distribution, these points were used to form an interpolated continuous surface of mean fecal coliform concentrations. Interpolation was done using an ordinary Kriging method. This method uses the semivariogram (plot of semivariance vs distance) to model spatial correlation of the sample points. Surface values are then calculated weighted by the modeled influence of the surrounding data points. 4. Create a professional prediction standard error map. 4 pts See map on page 6 5. What is the average fecal coliform for each segment of Galveston and neighboring bays? Mean Fecal Coliform Counts for Galveston Bay areas NAME MEAN MPN/100mL Trinity Bay HSC-San Jacinto Riverl Burnett Bay Houston Ship Channel Scott Bay San Jacinto Bay Black Duck Bay Tabbs Bay Barbours Cut Galveston Bay Bayport Channel East Bay 4.52 Armand Bayou East Bay 4.52 Clear Lake Lower Galveston Bay 5.22 East Bay 4.52 Moses Lake Texas City Ship Channel 7.61 West Bay 9.03 Chocolate Bayou Tidal Chocolate Bay Bastrop Bay/Oyster Lake Christmas Bay Drum Bay 17.28

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7 7 6. Are the estimates of average fecal coliform for each bay that we get through kriging necessarily better than what we would get by simply averaging the observations for each bay segment? 3 pts Yes. The kriging (interpolation) smooths out any irregularities that might occur in the data due to simple spurious measurements, it also helps to provide a much better coverage when sampling points may not be evenly distributed. 7. Make a professional map of the average fecal coliform for the water body features, figure 17. 4pts See map on page 8

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