GIS 5935 - Surface Interpolation

 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 my perspective. I believe this to be the case because the IDW result better highlights areas of concentration given that the sample values stay true to location in the interpolated surface, with estimates of unknown values made based on the distance from the known samples, and the samples are limited to a short timeframe, as stated in the exercise. Below you can see a screenshot of the IDW interpolation result. 

IDW Interpolated Surface of Water Quality in the Tampa Bay Area

 

Comments

Post a Comment

Popular posts from this blog

GIS 5935 - Surfaces - TINs and DEMs

Image
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...
Read more

GIS 5935 - Data Quality Assessment

Image
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...
Read more

GIS 5100 - Coastal Flooding

Image
During this week's module, module 4, we learned about Coastal Flooding, which can take place at various speeds, including rapidly via surge or slowly via sea level rise. This week we were tasked with performing analysis on the New Jersey shoreline and the coast of Collier County in Florida. In New Jersey we used LiDAR data to evaluate change post hurricane Sandy. This analysis included observing the raw LiDAR data, followed by the creation of Digital Elevation Models (DEMs) of pre and post hurricane Sandy for comparison. A more recent building vector file was also used to assist in the assessment of change, although using more recent data should be used with caution as it does not always accurately depict change as a result of a disaster event. This is particularly true as more time passes. The following map displays elevation change on the New Jersey Shoreline.  Map depicting elevation change post hurricane Sandy.   For Collier County, Florida, we were tasked with determ...
Read more