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.

Photo Interpretation and Remote Sensing (GIS4035): Lab 9

In this week's lab, we learned about Unsupervised Image Classification. We majorly utilized EDRAS for manipulate the pixels and sort them into their own classification groups. The image above shows the UWF Main Campus. There are five classifications which are Buildings/Roads, Grass, Mixed, Shadows and Trees.