Nathan Berger Portfolio: Geographic Information Systems

The map above displays the importance of using a tool such as multiple ring buffers to calculate multiple sets of distances around a point, polygon, or line. In this case, we were trying to identify the protected zones near a Eagle Nest Site east of the University of West Florida. By geo-referencing imagery, added geolocated buildings and roads, we were able to determine the location of the campus. We then added our geolocated nest site for the eagles and went to work.  We established tat the two protective buffer zones should be 330 FT and 660 FT around the eagles next site. This would protect the wildlife nesting grounds for the eagles and prevent any construction for new development from happening within these buffer ranges. The MRB process is very straightforward, you input your point layer, set your unit of measurement, and identify the multiple distances you want buffered around your point. We then built the essential map elements, therefore identifying that the eagles nest is ex

  The 3D scene depicted above is the location of the University of West Florida. I was tasked to build a 3D display of the campus, including buildings, imagery, newly developed roads, and take into account lidar data for elevation. This project began with geo-referencing the two image files using control points to building polygons that were already collected around the campus. After I was able to complete this task with a low RMSE (error) I quickly created a new building feature class for a building that was not in our buildings layer but was clearly constructed recently on campus. I added a new road as well using a engineering survey from the facilities department to identify the new road feature class.  For a more accurate terrain and elevation layer of the campus, I took the Lidar dataset from an LAS file and converted it from a DEM to a raster. I was able to display this layer as my elevation layer which then allowed me to layer the different datasets on top of it. The biggest tak

  Geocoding is a powerful tool in Arc GIS Pro. We can locate areas of interest using X and Y coordinates, or we can just take an address of a location to map a point. In order to do this, we need to run a tool called "geocode" and use an address locator. Arc GIS Pro with an organization account will have the Arc GIS locator caked in to find locations based on coordinates or address. For this map in particular, I created our own address locator unique to Brevard County, Florida. I then geocoded all of the schools located in the county. 16 were unmatched, which required me as the GIS analyst to rematch them by fining their correct coordinate. Now we have the map displayed above, with all of the schools located in Brevard County. We projected the data to a more local datum for NAD 1983 HARN State Plane East Florida in U.S. feet.  Please see the webmap below: https://arcg.is/1rCa4v

The map depicted above displays possible campsites based on multiple parameters in the De Soto National Forest near Hattiesburg, Mississippi. The map identifies campsites with a specific distance away from roads, rivers, lakes and wildlife conservation areas.  As a GIS analyst, I utilized spatial analysis of vector data to build the map above. First, I used  the buffer spatial analysis tool to buffer the roads (300 M) and water features (150 M for lakes and 500 M for rivers) within the area. I utilized a fixed buffer range and multi-buffer ranges based on multiple distance parameters to generate buffer rings. I then dissolved the features that overlapped to have a clear view of the data and then joined the two buffer feature classes together with a Union tool. I assigned the "insd_wbuf = 1 / insd_rbuf =1" variable to identify which polygons fell within the buffer ranges.  I used the overlay layer tool and selected the buffer union that I had created between the roads and wate

  This map layout is a perfect example as to why understanding projections in GIS and coordinate systems is extremely important. Depending on the coordinate system your map is defined at, and the projection used for your data, it can determine major differences in mensuration. This map shows an example of calculating square miles per county with different projections for the state of Florida. The table displayed shows varying square mileage per county depending on the projection the data is in. Data can also look rotated or shifted as it is drawn if the data has a source spatial reference of a certain projection, and the map is not displaying that projection. It is a good idea to have your data mapped to the same projection that your map is referencing. This will also allow you to share the map as a web map to your Arc GIS Online account. The 4 selected counties are depicted in color, and the three projections used are displayed in the 3 different views above.