In the previous labs the groups made a topographic model in a sandbox. Within the sandbox groups took data points to accurately map and model the topography that they created within the sandbox. The data for each point collected was then put into an excel file. From here the data was imported into arc map and normalized. This means that the columns and rows were arranged based on their dependency on one another. This allows for the integrity and accuracy of the model to be accurate. Group 6 created their data points by making 19 6cm squares on both the X and Y axis. These points were then measured and placed on a Z axis giving us 3 dimensional points. These points were interpolated in arc map, this means that more points were assumed between the actual points. This allows the software to in a way, fill in the gaps.
Methods.
Five different interpolation methods were used. These methods were IDW, Kriging, Spline, Natural Neighbors, and TIN. These methods are all different interpolation methods and do different things.
IDW- Inverse Distance Weighted Interpolation
This form of interpolation says that closer points are more closely related than those that are farther apart. It uses points that are closer together to account for areas that have no data. This makes these areas more possible to have wrong values. Figure 1 shows the features that were created and from the points gathered these features are very rounded around each of the points. This method could be very smart to use on large areas.
Figure 1: This image shows the IDW method of interpolation. As you can see around each point is a slight bulge of data that was filled in by the software.
Kriging
This method is very similar to the IDW method in that it predicts areas that had no measurement. This method is based on statistical correlations. Kriging uses a mathematical function. This method would again be a good method if you wanted to map a large piece of land. The features in this image (2) are similar to those in the previous image, with some slight differences.
Figure 2: Kriging method of interpolation is similar to IDW with this image being less precise in such a small area.
Spline
The spline method of interpolation makes a surface that passes through as many data points as possible. This makes for a very flat image that is very precise and accurate. This made for the best looking map in my opinion. (Figure 3) This map looks much smoother because there are 400 data points for it to connect to.
Figure 3: This image shows the spline with very rounded and well defined features. This image best shows the detail.
Natural Neighbor
This method of interpolation uses the interpolation point and overlapping points around it. This is then given a weight. When these areas are overlapped it gives more of a weight to areas that are closer. As a certain point is within a polygon, the software takes other polygons that over lap this point and use the data from it. This is another way to have an accurate reading on a larger scale piece of land. This method is nearly as accurate as the spline method. (Figure 4)
Figure 4: This image shows the Natural neighbor method. In the valley as well as the ridge there is not as much detail, but this still does a great job of depicting the landscape.
The next of the images made in arcgis was a TIN. This is a Triangulated Irregular Network. This takes the amount of points into effect. These TINs take more points if the landscape is highly variable. For our group this interpolation method did not work the best, because we took such uniform points. (Figure 5) The image is still viewable, but is not as good as some of the others.
Figure 5: This image shows the TIN image that we collected. The area that this image captured the best was the plateau that was located right in the center of the image.
After all of these maps were created in arcGIS they were transferred into ArcScene. This allows the maps to be viewed in 3D. (Figure 6) The image that I used to transfer into arcScene was the spline method of interpolation. This image best captured the scene that we were trying to create in the sand. The final image that I created in arcScene is not north and south. There was a north arrow placed on the image for origin. I also took a lower angle view at the map, because I wanted to show the 3D effect of the map.
Figure 6: This image shows the final map that was created in arc scene. The lower areas are represented by the purple, while the dark green areas represent high spots.
Discussion
The images that were gathered really had all different kinds of results. The spline was the most successful in both arc gis and arc scene. The TIN was the least visually pleasing of the images. This was due to the fact that the points we took were all uniform. This is why spline worked the best. The next best was Natural neighbors method. These images looked very similar to the spline just without all of the detail. Kriging method did not look good either. There may not have been enough difference in the vertical elevation. This method did not show nearly as much of the detail as any of the other methods. IDW was right in the middle. This image showed some of the detail and it shows a great job showing how this software gets this data. There are small bulges around all of the data points. This is because again we took our points in such a uniform fashion.
Conclusion
This exercise did a good job in getting the students a slight background in surveying. This is obviously very small scale, but is very similar to how surveying is done. Professionals obviously use different equipment from tacks, string, and meter sticks, but this is how surveying is done. Surveying is calculating elevation differences and creating maps from certain data points. This is why interpolation can be so important to professionals. They obviously will not survey every square inch of land. This means that the interpolation methods that calculate the differences could come in handy, and also save some time. This type of survey is not realistic especially on a larger scale. There were many data points taken and this would not happen with a larger area.
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