Get the GIS of it

This week I learned how scale and resolution can affect the interpretation and analysis of data and how to utilize the Polsby-Popper score to determine which voting districts are less than ideal when looking at their compactness and the dividing of counties. Scale effects on vector data involve the level of detail in spatial features: at large scales (high detail), features like rivers and city boundaries are depicted with more precision, while at small scales (low detail), features are simplified, leading to potential data generalization.  Resolution effects on raster data refer to the size of cells: higher resolution provides finer detail, while lower resolution leads to loss of detail, affecting analyses like land cover or terrain modeling. When resampling LiDAR data, you need to consider what analysis you will perform to determine which technique is best to use. I chose bilinear interpolation since we were using the data for a DEM of a watershed area. The lowest and highest-res...

 This week  I learned that different interpolation techniques can produce varied results depending on the frequency of the data points and the contents of that data. A  single instance of coinciding data points can throw off the results of the analysis greatly when using spline interpolation. W hen working with continuous data with gradual changes spline generates a smooth spatial pattern that more accurately represents the data. Where preserving exact values is necessary IDW shows sharp transitions between data points. Thiessen represents the data as various zones using TINs.  Spline (regulated) Spline (tension) IDW Thiessen

 This week, I explored various techniques for creating, extracting, manipulating, and analyzing TINs and DEMs. One of the tools that we used, the "Spline" tool, was used to create a DEM from the given elevation data points. Afterward, I created contour lines using the resulting DEM with the "Contour" tool. The image shown below is the final result.

 For this lab assignment, we were to complete an accuracy assessment of the road network data from two different sources for Jackson County, Oregon to determine their completeness. My methods to complete this task were as follows: 1.      I started by using the “Pairwise Intersect” tool for each of the road shapefiles to isolate each of the road segments within the grids 2.      Within the attribute table of the two new shapefiles the previous step created, I added a new field and used the “Calculate Geometry” tool to get the length of each road segment in kilometers. 3.      I then used the “Summary Statistics” tool for each shapefile to combine the lengths of each road segment based on their grid code. 4.      I used the “Join Field” tool to join the two resulting measurement fields to the grid attribute table to compare them. 5.      I created a new field in the grid attribute table called “comparison” an...

This week I completed a data quality standards assessment using the National Standard for Spatial Data Accuracy to measure and report geographic data quality as described in the Positional Accuracy Handbook. In the two provided street maps, I selected 20 points at various intersections and their matching counterparts. Afterward, I put a point where the intersection should have been for each intersection point. I then populated the attribute tables with their x and y measurements in decimal degrees. After I had all of the x and y points I filled in the horizontal accuracy statistic worksheet as shown in the worksheet and completed the formal accuracy statement. Figure 1. The 20 intersections selected for the assessment. Formal Accuracy Statement: o    Horizontal Positional Accuracy: §   Using the National Standard for Spatial Data Accuracy, the ABQ_Streets data set was compiled to meet 4.39067E-05ft and 0.001008426ft for the StreetMapUSA data set horizontal accuracy at 95%...

 For this lab, I was to determine the horizontal accuracy and precision of various data points taken with a GPS. The horizontal accuracy is determined by finding the average point from the data points taken and measuring its distance to the reference point. To determine horizontal precision, buffer zones can be used to show the distribution of the data points around the average. The distance between the reference point and the average location point is 10.71 meters and the vertical accuracy is within 4 meters. My results used the accuracy of the GPS unit to find the point that would represent the precise point that they are centered towards but not the exact location where they should have been.  In general, the points taken with the GPS are accurate but not precise.

For this suitability assessment, I needed to find the areas that black bears would most likely move through to get between the protected areas. -          I began by reclassifying the landcover and elevation rasters with their suitability values. I then used the Euclidean distance tool for the roads raster before reclassifying it with its suitability values. After that, I used the weighted overlay tool to combine the 3 rasters using the specified percentages before inverting it using the raster calculator tool with the equation [Cost_Surface = 10 – “Weighted_Suit”]. Using the newly created cost surface and both park areas, I created 2 cost distance rasters using the cost distance tool and then combined them using the corridor tool. I then reclassified it to represent the best possible corridors and changed the symbology.