G.Hudson Mastering GIS

As seen in the title, this week's module and lab covered two topics. The first topic was scale effect. In a world where we have various interactive maps at the palm of our hand, scale effect may not have the most blatantly obvious issue with viewing data. However, scale effect can have a wide-ranging impact on data we are using and collecting. Vector data, for example, created from imagery or raster data is only as good as the finest detail that can be seen. This means at a small scale the resolution is considered low and small features and intricacies are not visible. Larger features are the only items detectable or discernable. As such, fewer vertices and segments are used to represent a feature, which can lead to less accurate calculations that tend to have a smaller numerical account of the feature. The opposite is true for large scales.  The second topic covered this week was spatial data aggregation. This refers to the combining of data into a boundary, which in turn gen...

 During this week's module we were introduced to and learned about interpolation. While we were made aware that there are many interpolation techniques available, we went more in depth when learning about Thiessen, Inverse Distance Weighted and Spline techniques. We were then tasked with performing these techniques.  We were required to use a dataset of water quality samples from the Tampa Bay area to show where there were concentrations of the Biochemical Oxygen Demand (BOD). The Thiessen interpolation method was used first and produced a raster on polygons. This was not deemed suitable based on the abrupt changes that take place at the edge of polygons versus the fluid and continuous nature of water. The IDW and Spline methods (regularized and tension) both produced a continuous surface which was more suitable. Additionally the spline method proved to be helpful because it made an anomalous area noticeable. Ultimately, however, I decided that the IDW was most suitable from m...

Having moved on from the modules regarding data quality, this week we were introduced to surfaces and surface models, more specifically Triangulated Irregular Networks (TINs) and Demographic Elevation Models (DEMs). TINs are based on vector data and are made up of a series of linked triangles that vary in shape and size, but display a 3D representation of a surface. DEMs are based on raster data, more specifically the spot heights associated with each grid part of a raster.  For this lab we were tasked with creating a TIN and DEM, as well as displaying contours based on both of them. We were then tasked with comparing the contours produced by both. Below I have included images of my results.  (a) Contour lines based on the TIN (b) Contour lines based on the DEM The contours produced as a result of the TIN appear to be more jagged, while the contours produced from the DEM have a smoother appearance. There is also a noticeable difference between the derived contours in areas...

Like the second assignment, this module continues to focus on data quality. More specifically, this week focused on data quality of road networks. As such, we were tasked with performing a comparative assessment to determine the completeness of a street centerline shapefile and a Topologically Integrated Geographic Encoding and Referencing (TIGER) road shapefile for Jackson County, Oregon.  To perform this assessment we utilized a non standard method, based on that used by M. Haklay in the "Comparative Study of OpenStreetMap and Ordinance Survey datasets" in 2010. This included using a grid, which encompasses the study area, to clip the road network into smaller sections. These smaller sections give more detailed insight into areas which may be considered complete or incomplete. Once the roads were clipped to each grid cell/section the lengths were calculated and then compared.  My analysis determined that 134 of the grid cells/sections contained parts of the street cente...

Continuing on from the first week of GIS5935, this week we were introduced to the more common data quality standards used for cartographic and digital geospatial data. These standards were put in place by organizations, such as the United States Geological Survey (USGS) and the Federal Geographic Data Committee (FGDC), to "provide a common language for reporting accuracy to facilitate the identification of spatial data for geographic applications" (FGDC, 1998). In particular, the lab allowed us to perform our own positional accuracy tests using the National Standard for Spatial Data Accuracy (NSSDA) standards.  The NSSDA standard follows a seven step process which includes the following:      1. Determining if the test should be for horizontal accuracy, vertical accuracy or both.      2. Selecting test points from the data set being evaluated      3. Selecting an independent data set of higher accuracy that corresponds to the data s...

 The first module of GIS 59395 - Special Topics in GIS taught students how to calculate metrics, such as accuracy and precision, as it relates to spatial data quality. We were tasked with calculating the precision and accuracy of waypoints collected using a GPS device. The following image is a map I produced displaying the waypoints and some off my results.  As included in the map, the horizontal precision based on the 68th percentile is 4.6m and the vertical precision based on the 68th percentile is 5.88m. The horizontal accuracy is 3.3 meters, while the vertical accuracy is 5.96m.  For the purpose of this blog I will focus more on the horizontal accuracy and precision. Horizontal precision takes into consideration the distance between the waypoints and the average waypoint, which was determined based on the waypoints. Once the distances are determined, we can determine how many waypoints fall within each percentile. Horizontal accuracy takes into consideration the dista...