Special Topics in GIS (GIS5935): Lab 15

In this week's lab, we learned about the dasymetric mapping. Dasymetric mapping is improving on the results of standard population density in areal weighting by using additional data. We applied this mapping technique to high school zone in Seminole County, FL. We were trying to predicted the number of students between the ages 5 through 14 will be placed in which school zone depending on the location.The imperviousness method seemed to increase the percentage of allocated incorrectly students. This may have to do with the increase area of the school zone not missing the area without the water features.In the areal weighting, the percentage was 44.88%: however, the imperviousness method's percentage was 45.62%.

Special Topics in GIS (GIS5935): Lab 14

In this week's lab, we learned about MAUP. MAUP can affected many different subjects in relation to mapping, such as political boundaries. Gerrymandering is manipulating boundaries of congressional districts to benefits the one's own political gain.

To measure compactness, I looked at the perimeter and length of the districts because the perimeter and length determine the space that the feature occupies in. I had to change the project of the map to projected coordinate system to allow me to utilize the Calculate Geometry feature in the attribute table. I added a new field called Perimeter which measure the area in meter squared. I then added another field called Compactness and open on the Field Calculator. In the Field Calculator, I used the formula (4 * 3.1415 * [Perimeter]) / [Shape_Length]^2*100  allow me to rate the compactness on percentage of compactness.


Congressional District 12 - North Carolina

Special Topics in GIS (GIS5935): Lab 13

The results of the comparison between the two DEMs were very similar yet they were somewhat different from one another. The maximum slope range was higher in SRTM than LIDAR. The possible reason why SRTM has higher value in the maximum slope is the two synthetic aperture radars that were utilized for this scan of the watershed. LIDAR had a higher average slope than SRTM which means that the pulsed laser is able to penetrate the vegetation of the study area and get a better measurement of the slope of the watershed.

To compare the two DEMs I used the slope tool on the SRTM Raster to allow me to note the change in the z-value of the raster image. Once the slope tool was used, the Zonal Statistic tool was used to calculate the average and maximum degrees that both DEMs had. Also, I open up the Data Properties of both DEMs and noted the different cell size of both DEMs.

Special Topics in GIS (GIS5935): Lab 12

In this week's lab, we delved more into how to utilize OSL analysis and GWR analysis on real life statistical data. Both analysis techniques allowed us to be able to visualize the areas that were more likely to call 911 based on the residents level of education and we are able to see if there was correlation in a particular crime based on certain variables, such as race and income.

OLS does not have any spatial component when performing a regression analysis unlike GWR which looks at the change over space the between the relationship of the the variables. I notice a major improvement in understanding the fluctuation of the prediction of calls to 911. The GWR was able to weigh the values in analysis to spatial location on the map.

Special Topics in GIS (GIS5935): Lab 11

In this week's lab, we got more in-depth with using regression analysis. The regression analysis can help determine our models by using the coefficient, P-value, Adjusted R-squared, AIC, and the VIF to see how the data correlates with our research question. If the VIF value is over 7.5, there is some redundancy in the data being used to correlate the desire result. Also, using the analysis of residuals can improve our model by seeing if the residuals are clustering in one areas. If residuals are cluttering, that means we need to add more variables to the analysis to get better results for the model.

Photo Interpretation and Remote Sensing (GIS4035): Module 10

In this week's lab, we learned about how to create supervised classification of land use. ERDAS was utilized for this lab assignment. In the AOI tab, you can draw a polygon feature with the area of the spectral signature or you can use Growing Properties tool and select the spectral area. Both tools have to use Signature Editor to finalize the spectral signature. The map above is the land use of Germantown, Maryland.

Special Topics in GIS (GIS5935): Lab 10

How I got this is estimation is looking at the year 1950 through 2004 and notices that the rainfall tend to be less at Station A than Station B. I subtracted the value of the intercept coefficient of the regression analysis from the values of Station B. I believe that slope are going to increase as the years progress since climate change has a major factor in the amount of rainfall that these stations are receiving. The intercept points are probably going to very closer to each other since the values are not far off from each other.