GIS 5935 Lab 15: Dasymetric Mapping

Dasymetric mapping is essentially breaking down larger aggregations of data into smaller areas or units. It is often used in population density estimates. For example, it could be the process of taking aggregated statewide population data and breaking it down to the county level. This week's assignment was along those lines. For this analysis, I was to take raster data and use its imperviousness to estimate prospective student populations for eight high schools. This was done by joining the raster's zonal statistics table to census vector data, then running an Ordinary Least Squares analysis on that to determine the estimated population. From there, I had to clip the OLS result to each school boundary and determine its area and new estimated population. The results of my estimated population vs. a reference, "true" population can be seen below:

School	Reference Population	Estimated Population	Error	Abs(Error)
Hagerty	4706	4214	492	492
Lake Brantley	6313	6094	219	219
Seminole	11776	10881	895	895
Winter Springs	5693	3863	1830	1830
Lyman	7853	8477	-624	624
Oviedo	4780	4750	30	30
Lake Howell	8585	6561	2024	2024
Lake Mary	5014	4885	129	129
Total:	54720	49725	4995	6243
Accuracy (%):	11.40899123

GIS5935 Lab 14: Modifiable Areal Unit Problem

A prime example of the modifiable areal unit problem is the delineation of political districts. Gerrymandering occurs when boundaries for political districts are manipulated to help gain a particular advantage. The purpose of this analysis was to measure gerrymandering affected the boundaries of a set of districts. The two ways they are affected are compactness, where the geometric boundaries take on unusual shapes, and community, where counties or other communities are divided into multiple districts. 

In this analysis, to measure the compactness of the districts to find the ones with the oddest geometric properties, I created a ratio of the perimeter of each boundary vs. the area, and found the ten worst districts based on compactness. To measure the community, I had to determine which districts broke up the boundaries of the most counties, while excluding any districts that broke up a county but fell completely within a county to account for higher population densities. The first example below shows compactness, and the second shows community:

GIS5935 Lab 13: Effects of Scale

This analysis consisted of comparing two DEMs, one from LIDAR and one from SRTM. Both were set to the same coordinate system and the same cell size of 90 meters, then compared a number of ways. To compare the two DEMS, I first looked at the differences in elevation and slope. I checked the minimum and maximum elevation for each one, as well as the minimum, maximum and average slope. I also compared the aspect of each of them to find any major differences in the direction that each cell faced.

The slope and elevation differences can be seen here:

	LIDAR	SRTM
Maximum Elevation	1063.67	1053
Minimun Elevation	4.31	12
Maximum Slope	50.46	45.77
Minimum Slope	0.89	1.53
Average Slope	29.65	27.49

The differences in aspect can be seen in this comparison:

The comparison of the two DEMs resulted in a slight, noticeable difference in each area. For example, the maximum elevation for the LIDAR data was 1063 meters, while the SRTM data was ten meters lower at 1053. The minimum elevations were also 4.3 and 12 meters, respectively. The image above also shows the differences in aspect that can be seen throughout each DEM. These can possibly be explained by how each DEM was developed. Since the LIDAR data was developed from instruments much closer to the ground than the SRTM (which was collected from a space shuttle), subtle differences in aspect are bound to arise.

 There was also a slight difference in slope between the two, LIDAR having an average 29.65 degree slope and SRTM having a 27.49 degree slope. This again could probably be attributed to how each set of data was developed. Given how the ground is detected using LIDAR at a much more specific level, as opposed to SRTM, which was an entire earth-encompassing project, there is likely to be a little more generalization throughout the STRM data. The higher slope average of the LIDAR data suggests slightly less generalization and therefore slightly more accuracy.