Photo Interpretation and Remote Sensing - Tone and Texture

Map displaying a variety of tone and texture references for this aerial photograph.

 

In the first module for this class we explored aerial photography using a variety of different criteria such as tone, texture, shape, pattern, shadow, and color. 

Special Topics in GIS - M3.1 Scale Effect and Spatial Data Aggregation

 Scale determines the amount of data captured in spatial display, and as such it has direct effects on spatial properties of data. In both vector and raster data the smaller scale of measurement and the higher resolution, then the more data is available. 

On road polylines for example; roads and paths measured accurate to the nearest centimeter will contain much more data - more twists and odd jogs captured- than roads or walking paths measured to the nearest kilometer. In the kilometer scenario there may even be some paths excluded form the dataset altogether if they are under 0.5km in length. 

In a raster dataset this rule also holds true, but the effect of scale on spatial data is reflected more in the resolution -or cell size- of the raster. In rasters with large cell sizes less fine detail can be captured, so the data within each cell experiences an "averaging out" compared to rasters with smaller, finer resolution cells. This means that elevation properties like slope will decrease as the scale gets larger. This is not an inherently negative thing, as large scale and coarse resolution can be vital to displaying large areas in a short amount of time. It is important to keep in mind however how spatial properties can be manipulated by changing the scale and resolution of the data. 

Gerrymandering is one method used to manipulate data based on spatial properties. In this case the border of a voting district is drawn in such a way that the votes within it are more likely to be weighted in favor of certain demographics - even if those demographics don't make up the numerical majority of the area. By drawing the districts in these ways, representatives are effectively manipulating the scale of the votes in that area. Generally, districts that have been gerrymandered are not compact, and may divide communities or counties into different districts. By measuring compactness with a Polsby-Popper test (invented for paleontology and refined for politics by two lawyers in the 1990s) we can use area and perimeter to determine which districts are likely gerrymandered, and to what degree. 

During this analysis, I calculated the Polsby-Popper score for congressional districts in the continental Unites States, and highlighted those that were especially badly affected by a lack of compactness. 

This congressional district on the eastern seaboard is one of the worse "offenders" for not exhibiting compactness. The blue highlighted area outlines a district that wanders, doubles back on itself, and squeezes carefully in-between and around communities in order to manipulate scale of the votes in the district.  

Special Topics in GIS - M2.2 Interpolation

 For this assignment I used observed water quality points in Tampa Bay and a suite of interpolation techniques (Thiessen, IDW, and spline) to create a surface of water quality in Tampa Bay. 

Thiessen interpolation creates a series of polygons based on the spatial distribution of observed points, then fills each of those polygons with one value. Though this produces a very rough-scale result, it can be accomplished by hand if needed, and can be useful for quickly identifying entire regions at a time. For this analysis I used the Create Thiessen Polygons tool in conjunction with the Feature to Raster tool, which filled those polygons with values. 

Like Thiessen interpolation, Inverse Distance Weighted (IDW) interpolation is bound by the actual values entered, but uses the assumption that things close together must be more alike than things far away from eachother, and fills in the cells between values with estimates based on the nearby values. In IDW, values nearby are weighted higher for informing the estimated values. IDW creates a smooth raster that may contain hotspot-like nodes. The IDW analysis was easily completed by running the IDW tool on my existing datasets. 

Inverse distance weighted (IDW) interpolation of biological oxygen demand in Tamp Bay

Spline was the last technique examined, like IDW it takes existing values and creates estimates to fill the space between them, but where IDW uses a weight based on distance, Spline uses a constant weight from nearby points. The surface is "fitted" to the existing points as smoothly as possible, and the weight determines the allowable deviation. This means Spline may return values outside the range of the observed points, and will perform best with an evenly distributed sample set.