Nathan Berger Portfolio: Geographic Information Systems

 Special Topics In GIS Today we are going to talk about 3 topics in GIS. One is vector data and its scalability. The second topic is going to be raster data and resolution. And the third topic is going to be a special topic in regards to elections... aka gerrymandering.  Vector Data and scaling As we already know, Vector data is made up of points, lines and polygons. Vector data is directly affected by the scale used to map the vector data. The scale is important because in order to "digitize" the data on a map, we want the data scaled appropriately. This can cause boundaries of lakes and rivers to not be accurate, or even the location of hospitals or residential homes. Maps have different scales in which the data is displayed, so we want to ensure the spatial accuracy of that data when projecting it on a map.  Below is an example of mapping waterways (rivers) of an area at three different scales. The red is at a 1,200 scale, the blue is at a 100,000 scale, and the yellow...

 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 ...

 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 roa...

 Today We Are Using Imagery and Street Data I am creating digitized test points in the city below to compare a dataset of streets to a more accurate, local dataset of city streets. We are doing this to calculate the horizonal accuracy of the streets, by dropping points on intersections that both datasets share in commonality.  This is what my Test data looked like with the new streets data. I am comparing these streets to the ABQ local streets (which are extremely accurate) to compare this sample of USA streets to the real data or "accurate street data".  The red points are the intersections in the city. As you can see, these points were scattered to all 4 quadrants of the city for unbias distribution. Yet there will still be some bias included in the data due to the user, me, picking the intersections and the points. I also was using very high resolution oblique imagery to make sure I was comparing my test data to the real center point of the intersection. There is bias ...

 Spatial Data Quality... What is spatial accuracy? Why do we care? When you build maps, or work with any kind of job in which you are presenting data, you want to be accurate and precise. Accuracy is essentially the inverse of error, or lack of error in a dataset. In regards to GIS, we are primarily focused on spatial accuracy. This is the positional accuracy of your point data.  If you have an xyz coordinate, such as a crime was committed at this LAT and LON, or address, we want that location to be correct when reporting it. You don't want to present to your boss at a marketing firm that Sally bought your product at a certain store, but in reality it was another store located 5 miles away because your coordinate data or spatial accuracy was full of errors! Think about SWAT teams responding to an active shooter and they end up at the wrong school. Or even something more simple such as trying to drive to a certain location and your GPS takes you to the wrong place.  There ...