Mod 3.1 Scale Effect and Spatial Data Aggregation

 

Scale effects the number of features that are depicted in a map, the overall length of these features, and if the feature is a polygon the overall area and perimeter length. For example a 1:500000 map of river networks would likely include major rivers and tributaries, but leave out smaller streams that may be found in a 1:50000 map.

Gerrymandering is a term used to refer to the redrawing of political districts in order to benefit a specific party. It can be measured via measuring the compactness of a political district. The measuring of a political districts compactness is accomplished by finding the Polsby-Popper score. The closer the score is to 1. the more compact it is which is desirable. The closer to 0, the more gerrymander a district.The screenshot above represents, what I calculated to be the district with the worst gerrymandering, North Carolina District 12.

Mod 2.2 Interpolation

 

This week I used the Thiessen Polygons, Spline, and IDW(above) interpolation methods to make a water quality map of Tampa Bay. IDW took the points that measured the biochemical oxygen demand(BOD) and assumed that the closer a raster cell was to the point, the more similar it's BOD would be. Thiessen Polygons creates polygons where inside is closer to its associated point than to any other point input feature.Finally, Spline uses an interpolation method that estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. Regularized splines modifies the minimization criteria so third-derivative terms are incorporated into the minimization criteria. Tension on the other hand, option modifies the minimization criteria so first-derivative terms are incorporated into the minimization criteria.

Mod 2.1: TINS and DEMS

 

The above image is an example of utilizing a TIN for geographic analysis. The contrasting colors and elevation symbolize the ideal and subpar locations for a ski resort. By using 3D modeling one can infer why exactly a ski resort should be where it is (higher elevations mean better skiing peaks). 

The desirable parameter was determined by creating a weighted overlay of Aspect, Slope, and Elevation layers. The elevation layer used was the TIN and this was also used as the ground source for the 3D scene. The TIN was derived from a supplied DEM, by using the Raster to TIN tool.

Module 1.3: Assessment

 

The above map depicts the comparison of the level of completeness of the centerlines and TIGER road shapefiles for Jackson County, Oregon. From my numerical calculations, the centerlines data source is more complete, and this is reflected in the map, by there being more blue squares. This is important to determine which road network would be a better source for research. The map was created by joining a new field that measured the percent completeness of each grid square, and then creating a chloropleth map.

Module 1.2 Standards

 

Figure 1: Sampling Locations

Figure 2: Accuracy Results

The Sampling Locations were created by using a point layer to mark the intersections of the ABQ layer and the street maps layer. A reference point layer was created for where the intersection was on the provided rasters. From the results, the ABQ layer appears to be more accurate compared to the streetmap layer.

Module 1.1 Fundamentals

 

Accuracy is the absence of error and is determined by comparing a coded value in the database of interest to some independent reference value. Precision is the variance of measurement.

The precision for this study was calculated by creating buffers measuring the distance the way points are from the created average point. The respective buffers represent 50% precision, 68% precision, and 95% precision. The buffer distance was found by finding specific breakpoints for the buffers. To do this I used the averages generated by ordering the distance ascending, and then finding the average distance at the ObjectID breakpoints of 25, 34, and 48.

The accuracy for this study was calculated by first taking the average XY coordinates of all of the way points and using those values to generate a point. Then, we took the XY coordinates of the actual waypoint and measured the difference between the points. The results were that the two points were 0.68 meters apart. 

Module 6: Suitability and Least-Cost Analysis

 

Non Equal Weighted Map

Equal Weighted Map

The Maps above showcase the suitability of sites for development. Warmer colors denote less suitable whereas cooler colors denote preferable development sites. The maps were created by reclassifying raster data and then creating a weighted overlay map. There is an equally weighted map and a map with alternative non equal weights to showcase the difference in results.

The map above showcases potential black bear habitability corridors between Coronado National Forest. The corridors were generated by combining variables like distance to road, preferable landcover, and elevation. Then a weighted overlay was used to find the combine the variables. Afterwards, cost distance was used to find paths between the national forest polygons. Finally, a corridor analysis reveals the proposed paths for black bears.

Module 5: Damage Assessment

 

The map above details destruction in the New Jersey Study Area as a result of Hurricane Sandy. The map was created by first making mosaic images for both a prestorm and poststorm mosaic utilizing the respective rasters for each. Then a point feature was created to serve to collect buildings. Domains were created to detail the structure damage, wind damage, inundation, and structure type. The chart above details the findings from study. From the results most of the buildings fell within 100-200m of the shore line. Additionally, the closer buildings were to the shoreline the more likely they were to experience damage.

Story Map Link: (https://storymaps.arcgis.com/stories/cf4f451b1dc74a79b7fe22ac86c1e211)

Module 4: Coastal Flooding Analysis

 

This week I created two maps. The map above details the changes in coastline with red marking a decrease in land and the dark blue marking an increase in land. I used the LAS Dataset to TIN tool before using TIN to Raster tool to created two DEMs, one for pre Hurricane Sandy and one for post Hurricane Sandy. Afterwards I used Raster Calculator and subtracted the pre Sandy DEM from the post Sandy DEM in order to get the final layer necessary for the map. 

This map details which buildings were affected by flooding in Florida. Moreover it compares the accuracy between the supplied USGS DEM and LiDAR data. From the findings

Module 3 Visibility Analysis

 For this week's Module we were tasked with taking 3 ESRI online courses. These courses were Introduction to 3D Visualization, Performing Line of Sight Analysis, Performing Viewshed Analysis in ArcGIS Pro, and Sharing 3D Content Using Scene Layer Packages. Introduction to 3D analysis introduced applying 3D symology to a 3D layer and using illumination and shadow to enhance realism for global and local scenes. Performing Line of Sight Analysis introduced the Construct Sight Lines tool, which Creates line features that represent sight lines from one or more observer points to features in a target feature class. The Sharing 3D content using Scene Layer Packages course demonstrated how to share and publish a 3d scene as well as the Add Surface Imagery and Layer 3D To Feature Class tools. The Add Surface Imagery Tool attributes features with spatial information derived from a surface. The Layer 3D to Feature Class tool exports feature layers with 3D display properties to 3D lines or multipatch features. The last course I took was the Performing Viewshed Analysis tool, with that I used the Viewshed tool which determines the raster surface locations visible to a set of observer features.

Module 2: Forestry and LiDAR

 

This week I worked with LiDAR in order to investigate the tree canopy of Shenandoah, VA. From the LiDAR, I created a Digital Elevation Model or DEM to display the elevation of Shenadoah. Finally, the Canopy Density layer was derived from the LiDAR.