Nathan Berger Portfolio: Geographic Information Systems

 Interpolation In GIS Why use interpolation in GIS?  Because we can do many of the following examples below! Derive surfaces of a study area based on elevation points for elevation We can derive weather surfaces such as rainfall or snow We can look at prediction surfaces to see the effects of the ozone layer on temperature In this lab, we are using different interpolation techniques such as Spline, IDW, and Thiessen Polygons to derive surfaces for the water quality of Tampa Bay, Florida. These tools create rasters of derived surfaces to explain data distribution across a study area. As you can see, these surfaces all differ greatly from each other, so we must calculate the statistics for each one to determine t which "surface" best represents the water quality (BOD - ) for Tampa Bay, Florida.  We set our grid zones to a cell size of 250 to support our raster extent of the Tampa Bay, Florida area. We are using BOD (Biochemical Oxygen Demand) and  MGL (Milligrams Per Liter ) to

 Elevation Models For GIS Today we are going to run through some analysis of comparing Digital Elevation Models (DEM's) to Triangular Irregular Networks (TIN's) using Arc GIS Pro.   One of the main differences between TIN's and DEM's, is that a DEM file is raster based, whereas a TIN elevation model is vector based. In GIS, we can use TIN's and DEM's to analyze elevation of local areas or global even to conduct slope analysis, aspect ratio, terrain mapping, and solve complex problems.  Using a DEM to Develop a Ski Run Suitability Map In the example below, we are identifying where an engineering firm would place a ski run for a resort in an area based on elevation, slope, and aspect parameters. We used the weighted overlay tool to combine 3 reclassified rasters of elevation at a high altitude, slope at a high pitch, and aspect for a SW positioning with given percentages of weight.   25% aspect  40% elevation  35% slope This was our result: As you can see, we c

 Tiger 2000 Roads VS. Centerlines Roads Today, we are comparing the Tiger Road network in Jackson County, Oregon, to the Centerline streets road network. We are trying to determine the completeness of the road networks by comparing them to each other. First we map the road networks, then overlay grid cells that are 1km by 1km across the county. we then calculate the road length of each road in each grid cell for both the Tiger Roads and the Centerline Roads. We then calculate the percent change of the centerline road in each grid cell to the tiger roads to determine which grids have more Tiger data and which grids have more centerline data. Below are our results... So, why is the Tiger Roads dataset above at 11,382.7 KM and the Center Street lines dataset at 10,805.8 KM? There are significantly more road networks for the Tiger roads dataset than the centerlines.  But we just did that the Tiger Roads 2000 dataset is almost impossible to use for mapping positionally accurate road network