baj23_GIS

  Learning all the different interpolation methods to take the known values to estimate values of other points gave me a different perspective on how to analyze datasets like elevation, rainfall, chemical concentrations, and water quality. A challenge I found between the different interpolation values in which interpolation method would be best to apply to the different datasets if I had data with fewer sample sites and how to correct them so the data can be better represented. Believe elevation data would be easiest to apply the Spline interpolation method due to the simple data values and best representation in elevation change. Data such as data points of chemical values would be better represented with Thiessen interpolation values.  1.      The difference between two interpolation methods; Spline and IDW (Inverse Distance Weighted) is that Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exact

 This week, we explored the applications and used for DEM ( Digital Elevation Models) and TINs ( Triangulated Irregular Networks). They are often used for imagery and elevation models but learning more in-depth about their advantages and disadvantages to showcase data.  TINs are not generalize compared to DEMs and can showcase more data with higher densities of data. TIN can form more accurate contour lines based on the terrain. TINs generate vector points from x,y, and z coordinates and then bind the three points creating the triangles which can form the triangulated patterned model.  DEMs on the other hand create smoother and rounded contour lines. DEMs oftentimes in projects are used to create a grayscale gradient of elevation and simple elevation models in the 3D application of Arc Pro. DEM has limitations in its presentation and since it generalizes its models it has the possibility to misses terrain factors such as hills and dipped along a mountainside. 

 How are roads monitored and maintained?  The United States has a very intricate and developed road system that has continuously evolved over the years. As changes occur roads may become worn down, unused, or discontinued. The road can be repaved and also adjusted to the new development of buildings in the years.  In this week's lab, we learned how to calculate road completeness and analyzing its completeness in excel using reference data to determine how much percentage difference between the analyzed data to the reference. We were given a segment of road data from the TIGER road network from the U.S Census Berua and compared it to the local road centerline dataset from the Jackson County orgeon. To prepare the data given we had to clip the Centerline roads and the TIGER roads to the Jackson, OR county borders. I then split both roads datasets by whichever GRID box they fell into so I could properly calculate the percentage of the difference between the roads per grid. I created a