Lab 2: Sandbox Visualizing

Introduction: 

In Lab 1, critical thinking skills were used to create a terrain that included specific landscape features (Ridge, Hill, Depression, Valley, and Plain) in a 114cm by 114cm sandbox plot.  Using systemic point sampling, the terrain was surveyed and measurements were recorded at an even distribution throughout the plot.  Thumbtacks were placed evenly every 6cm around the border of the plot and then string was laced through to create a (X,Y) grid. Measurements were recorded at every string intersection with a table labeled X,Y, and Z. The Z column being the elevation measurements. Sea level was set at the top of the sandbox. More information regarding this lab can be acessed Here.

For Lab 2, Data Normalization was a very important concept because it refers to organizing and normalizing data. This means that the data is organized in such a way that multiple individuals can look at the same data set and receive the same interpretation from it. Making refinements to tables and analyzing the correct columns that would fit with a data set is important with Data Normalization. 

The systemic point sampling method was used to complete this activity in Lab 1. Now, the Interpolation tool in ArcMap will help to visualize the collected data and the quality of the survey. The Interpolation tool provides multiple techniques that "predict values for cells in a raster from a limited number of sample data points. It can be used to predict unknown values for any geographic point data, such as elevation, rainfall, chemical concentrations, noise levels, and so on" (ArcGIS Pro). 

Methods:

The first step was to create a geodatabase for the correct folder for this Lab. Then, the necessary steps were completed to normalize the data in Excel by setting the correct decimal values and making sure the data was set as numeric. The Excel file was then imported into the geodatabase.

To project the data in ArcMac, it needed to be added by using the 'XY data' option and then converted to a point feature class. With this type of data, it is not required to have a projected coordinate system because there isn't a specific landscape associated with the data points. Instead, a cadastral coordinate system was used to show the locations of the data points in relation to each other but without needing a coordinate system. This data shouldn't be projected because the data points were only being analyzed in relation to each other, but not being compared to a real landscape.

After that, the Interpolation tool allowed the points to be represented as a continuous surface. There are five different Interpolation techniques that will be demonstrated in this lab:

• Spline: This tool uses a minimum curvature technique that results in a smooth continuous raster surface that will still end up passing through the data points. This technique leaves the data looking more realistic and works well when working with elevation, water table heights, or pollution concentrations. 
• Inverse Distance Weighted (IDW): This tool focuses on the assumption that data that is closer in proximity to each other is more related than data that is farther in proximity to each other when creating a raster surface. This technique looks at the surrounding data of a specific data point to predict the measurement of that specific data point. IDW relies on the inference that data points that are closer to each other have some sort of relationship and would best be used in a situation where there aren't any outliers in the data set because IDW does not provide prediction standard errors. 
• Natural Neighbors: This tool looks at the surrounding subsets of a data point to determine the interpolated heights. This technique will not represent ridges, peaks, pits or valleys that wouldn't already be represented by the the data points. This leaves the surface of the raster image to be overall be very smooth. Natural Neighbors would best be used where there is variance in different data points so there aren't as many duplicates.  
• Kriging: This tool looks at the spatial correlation between data points to explain variance in the raster surface. This technique also fits a mathematical function to a specified number of points, which will determine the outlook value for each data point. Kriging would be best used in a situation where there is a known spatially correlated distance in the data. 
• Triangulated Irregular Network (TIN): This tool is slightly different from the others in the fact that it is a vector-based surface. The image is more pointed and edgy because it is constructed by triangulating a set of points. The surface is basically made up of different sized triangles that represent nodes, faces, and edges. TIN would be best used when analyzing hgih-precision modeling of smaller areas. 

Then, the Interpolation data was opened in ArcScene which shows the terrain in 3D to offer another figure of reference. The 3D Scene image was exported as a 2D Portable Network Graphic (PNG). Scale was digitally drawn into the map to represent the 114cm by 114cm sandbox because as discussed previously, the terrain was not being compared to a real landscape so ArcMap was not able to insert a standard scale.


Results/Discussion: 

For each Interpolation technique, there were numerous color schemes that could have been chosen but a simple greyscale is what showed elevation differences the best. However, the TIN Interpolation looked best with a color scale. Each figure shows the image of the terrain produced from ArcMap and another 3D image on the bottom that was produced by ArcScene with both accompanied by a legend to explain the elevation. The Spline Interpolation technique shows a clean rounded finish of the terrain (Figure 1). The images show a very accurate representation of how the terrain looked in the sandbox plot. The darker and lighter areas correctly show the elevation changes. This technique really displays the ridges and the crater located on the hill in the upper right hand corner well.

Figure 1: Spline Interpolation 

The IDW Interpolation technique doesn't show as clean of a finish as the Spline Interpolation (Figure 2). Elevation changes can still be observed in the images but the terrain looks more bumpy instead of a smooth continuous elevated surface. This technique makes each data point more visible instead of connecting each gradually. 

Figure 2: IDW Interpolation

The Natural Neighbors Interpolation technique produces an almost too smooth of a surface to where it is hard to determine the differences in elevation (Figure 3). The image doesn't display the landscape of the terrain as well as the previous two techniques. The 3D image shows the terrain better than the ArcMap image does. 

Figure 3: Natural Neighbors Interpolation

The Kriging Interpolation technique also displays the terrain fairly accurately (Figure 4). The elevation is continuous and gradual and it portrays the terrain and its features (hills, ridges, depressions) similarly to the real life landscape. This technique is most similar to the Spline Interpolation. 

Figure 4: Kriging Interpolation

The TIN Interpolation technique portrays the terrain probably the least correctly out of all the Interpolation techniques (Figure 5). Some elevation is detected but the surface is not smooth. Because TIN images are constructed by triangulating sets of points, it leaves the terrain looking very edgy and pointy where the data points were collected.

Figure 5: TIN Interpolation

Summary/Conclusion: 

Each of these Interpolation techniques displayed a different image of the terrain. However, the Spline Interpolation technique portrays the terrain most accurately out of all five techniques. The surface is smooth like it was in real life and the elevation differences are very noticeable and continuous throughout. The systemic point sampling approach was shown best through the Spline Interpolation. This survey relates to other surveys because data normalization is a very important concept to remember when collecting data. Because the sandbox plot was only 114cm by 114cm, a grid based detailed survey was completed within a reasonable time. When surveying larger plots of land, there most likely wouldn't be enough time to complete as detailed of a survey. The Interpolation tool displays elevation well but can also be used for temperature, precipitation and noise levels as well.

Sources: 
Database Normalization - http://searchsqlserver.techtarget.com/definition/normalization
Comparing Interpolation Methods - http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/comparing-interpolation-methods.htm