Lab 10: Using Regression to Predict Rainfall.

In this lab we had an assignment that involved predicting values for rainfall using regression analysis. We were given an Excel file containing rainfall data for station A(Y) and station B(X) ranging from the years 1931-2004. Unfortunately station A was missing measurements from 1931-1949. The goal was to predict those measurements. Using the regression analyst tool in Excel I got the slope(b) and intercept coefficient(a) of the regression. This allowed me to use the regression formula, Y = bX + a to calculate the missing values assuming that the stations would have similar values for that year and have a normal distribution of data.

Lab 9: Assessment of the accuracy of DEMs

One can determine the quality of a Digital Elevation Model (DEM) using statistics. In this lab I used Microsoft Excel to calculate the percentiles, Root Mean Squared Error (RMSE), and Mean Error (ME).
The percentiles tell you how many points fit within a given range. I used the 68th and 95th percentiles for this lab. This means, for example, that the difference between the DEM and the field report data for the 95th percentile of urban land cover is within 0.384 m 95% of the time.
RMSE tells you how similar one set of values is to another. The lower the number the more accurate the data is. RMSE does not tell you about the distribution of error. I found that bare earth and low grass land covers were the most accurate while fully forested areas were the least accurate.
ME tells you about possible bias in the data. A negative number indicates underestimation while a positive number indicates overestimation.  The urban area was the most biased with a ME of 0.164 while bare earth was the least biased with -0.005. I have attached below a table summarizing the values I arrived at during this lab.

Accuracy Metric	Accuracy (m)
	Bare earth and low grass	High grass, weeds, and crops	Brush land and low trees	Fully forested	Urban	Combined
Sample size	48	55	45	98	41	287
Accuracy 68th (m)	0.098	0.151	0.22	0.222	0.189	0.276
Accuracy 95th (m)	0.163	0.44	0.481	0.463	0.384	0.171
RMSE (m)	0.105	0.181	0.246	0.394	0.2	0.429
ME	-0.005	-0.069	-0.103	0.003	0.164	-0.006

Lab 8: Interpolation Exploration

In Lab 8 I explored various interpolation methods. The lab covered thiessen polygons, inverse distance weighted (IDW), regularized spline, and tension spline. The picture above is an example of tension spline used to interpolate water quality in Tampa Bay, FL. Thiessen polygons take each point and matches it to the closest data point. This creates uniform areas that don't easily display anything subtle. IDW weighs the distance from data points and uses that to interpolate. The farther away a data point is the less effect it has on how an area is interpreted. The two types of spline both use similar formulas to create a "sheet" that best fits the slope the data points create. Regularized splines create smoother more gradually changing surfaces but the values can lay outside of the data's original range. Tension splines are a little bit stiffer and values are more constrained by the data's original values.

Lab 7: An exploration of TIN models

This week I explored Triangulated Irregular Networks (TIN) and compared them to Digital Elevation Models (DEM). They are both used to model elevation but they are structured differently. DEMs are raster which basically store information in a grid while TINs are basically triangles created from nodes that have elevation data. One big difference is that TINs can easily show slope, aspect, elevation, and more without further processing. You have to make new rasters to display that data when using DEMs. Below is a picture of a TIN displaying edges, nodes, slope, and contour lines.

I also explored how to edit TINs. The triangles in the TIN sometimes don't create flat surfaces where they should be so you have to edit them in. In the example the lab provided I had a TIN that wasn't properly displaying the flatness of a lake. I have attached before and after pictures.

Before lake feature was added

After lake feature was added

Editing the TIN created a hard breakline (in blue) that told the model to make the area inside it a certain elevation and created nodes along the breakline so it could be modeled.

Lab 6: Allocation-Location Modeling

In Lab 6 we had to use network analyst to solve an allocation-location problem. This example features a company with 22 distribution centers and the desire to optimize how they serve customers. The allocation-location analysis used a network data set to determine the shortest distance between a customer and a distribution center. Then I looked at which distribution center served the most customers in each market area and assigned them as shown above. Most of the market areas did not change but the 28 that did I highlighted with a red outline. The new market area assignments allow the company to serve their customers but with less distance traveled than before.