Special Topics: Lab 14

This week's lab covered the topic of spatial data aggregation.  The lab this week looked into how congressional districts are within the United States.  The first part of the lab looked at how compacted the districts are throughout the country.  Compactness is based on the shape of the polygon.  For example, oddly shaped polygons are not considered compact.

To determine the top 10 worst offenders of least compactness, I had to calculate the area and perimeter of each polygon.  The odder the shape, the more likely the longer the perimeter.  As the screenshot below will show, the district has a weird shape that clearly shows the district is covering across a lot of space to get certain groups in the district.

Compactness

The other aspect of gerrymandering is community.  It is ideal to have the least amount of districts in each county.  Having more than one district to cover a county show that certain areas of the county could be select to achieve certain results.  I made sure to exclude counties with large populations which would need multiple districts and then looked at how many districts fell in a county.  Below are the results of the analysis.

Community

Special Topics: Lab 13

Week 13 lab focused on how scale and resolution can affect the details of an image.  The first part of the lab looked at vector data while part be focused on raster data.  The first part of the lab compared polylines and polygons at different scales.  The second part of the lab compared LIDAR and SRTM with cell size at 90 m.  SRTM is a very high resolution images.

To compare the two, I had to change the projection of the DEM and resampled it to have the cell size of 90 m.  After that process was complete, I used the Slope tool to find the average slope.  I compared the slope to the LIDAR slope.  I also visually compared the two images.  The LIDAR image has a larger slope and the low elevations are more noticeable in the LIDAR image than SRTM.

Below are the results of the analysis I performed.  The SRTM images shows less change in the elevation than the LIDAR.  As seen in the average slopes, the SRTM has the lower slope.

LIDAR

SMRT

Special Topics: Lab 12

This week lab focused on comparing the OLS model to the GWR model. The Geographically Weighted Regression model.  The model looks at small sets of data.  The model uses an equation to incorporate the independent and dependent variables.  The shape of the results are determined by bandwidth and kernel type.

This lab looked at housing data to determine the relationship.  First the OLS model was used.  Next, I used the same variables to create the GWR model.  After running the models, I looked at the results to see which variable is the greatest interest.  By running the analysis with the GWR, it strengthens the relationship.  By taking the spatial relationship into consideration, it improves the model by using spatial aspect into play.  The OLS does not look at the spatial relationship.  The OLS just looks at the variables and the results don't take into account the kernel type or bandwidth.

Special Topics: Lab 11

Week 11 of Special Topics continued the topic of statistics and regression.  The topic this week taught how to find the best model performance.  Using the Ordinary Least Squares tool, The tool generate results using one or multiple variables.  The results state the coefficients, p values, VIF, and Jarque-Ber.

In order to determine if the selected variables are correct, more are needed, or some should be removed, The a few of the checks are if the independent variables are helping, what are the relationships, and are the variables redundant.  A few other checks look at if the model is biased, if all the needed variables are used, and how well the variables explain the dependent variable.

The lab this week required using the OLS tool along with the Exploratory Regression tool.  The Exploratory Regression tool produces results of whether models pass and goes through all the various options.  The results also use the Adjusted R Square and Akaike's Information.  These numbers can be used to determine fit and explains variation.

Special Topics: Lab 10

This week's lab was an introduction to statistics which involved correlations and bi-variate regression.  Part of the lab consisted of finding missing data for 20 years of rainfall for a rain station.  To find the missing data, I used the regression tool from the Data Analysis Toolpak.  Once I had the regression summary, I found the slope and intercept values.  I used the values in the formula y = m*x+b.  The x variable was the data I had for rain station B.

By using  y = m*x + b, it assumes that whenever x is 0 y equals the slope plus the intercept.  The intercept tells us how much change is in each variable while slope tell us how much the variable will go up or down.  It assumes if I have x, I can figure out y.  The regression analysis looks at where station A and B for the years there is data for both.  It finds the relationship between the stations.

Special Topics: Lab 9

This week's lab covered vertical accuracy of a DEM.  I used data points on top of the LIDAR layer and used the Extract Values tool.  Once the values were extracted, I had to convert the values from feet to meters.  The next thing was to calculate the difference between the LIDAR values and the field points.  Once the difference was calculated, I squared the difference and then summed it.  The next step was to find the average and then take the square root to find the Root Mean Square Average.

Once the RMSE was calculated, to determine the 95th percentile accuracy, I multiplied the RMSE by 1.96.  To find the 68th percentile accuracy, I multiplied the RMSE by 1.69.  The lower the number, the more accurate it is.  To determine if there was a bias, I had to find the Mean Error.  To find that, I took the sum of the difference and then take the average.  Below are the results.  The results show the most accurate and that the urban area had the most bias.

Accuracy Results

Special Topics: Lab 8

This week's lab covered interpolation.  The example used was for water quality of Tampa Bay.  Four different techniques were used to display the Biochemical Oxygen Demand (BOD).  One technique used non spatial analysis.  The other technique was Thiessen interpolation.  This analysis used the Create Thiessen polygon tool.  The input features are the BOD points and the output is all fields.  A mask needed to be applied to only show the Thiessen polygon for Tampa Bay.  This used the points in each polygon to determine the whole value.

Technique three used Inverse Distance Weighting interpolation.  The IDW tool is used to perform this and I had to adjust the radius and power.  The last technique was spline.  The Spline tool was used for both regularized spline and tension.  Since there were points very close to each other, it caused high concentrations.  The points needed to be removed or the average to be calculated.

The results of the analysis show fairly similar for each technique except the regularized spline.  Below is an example of the tension spline.

Tension Spline