Special Topics: Lab 15

In the final lab for the course, the focus was on Dasymetric mapping.  Dasymetric mapping uses additional information such as land type to improve determining where populations are allocated.  To help get a better idea of where the population is distributed, impervious data of roads to remove areas where people are unlikely to live.

To perform this analysis, I used the Zonal Statistics as Table tool to find the impervious areas of the census tracts.  Then I joined the new table to the census tract data.  Next I used the Intersect tool for the census tract and high school layers.  I added a field to calculate the area of the new layer.  I also used the impervious against the area to find a new area.  I used the new area multiplied by the population and divided by the before area to calculate the new population.

The reference population was 54,720 and the estimated was 54,661.  About 12% of the population is allocated incorrectly.

GIS Portfolio

This is my last semester in the GIS Certificate program.  Through my time during the program, my knowledge of GIS and how it can be applied has grown tremendously.  Please click on the link to view my GIS Portfolio.

GIS Portfolio

Along with my portfolio is a brief interview covering my favorite map created as well as how I overcame obstacles.

Interview

Below are a few examples of my work:

Special Topics: Lab 14

This week's lab covered the topic of spatial data aggregation.  The lab this week looked into how congressional districts are within the United States.  The first part of the lab looked at how compacted the districts are throughout the country.  Compactness is based on the shape of the polygon.  For example, oddly shaped polygons are not considered compact.

To determine the top 10 worst offenders of least compactness, I had to calculate the area and perimeter of each polygon.  The odder the shape, the more likely the longer the perimeter.  As the screenshot below will show, the district has a weird shape that clearly shows the district is covering across a lot of space to get certain groups in the district.

Compactness

The other aspect of gerrymandering is community.  It is ideal to have the least amount of districts in each county.  Having more than one district to cover a county show that certain areas of the county could be select to achieve certain results.  I made sure to exclude counties with large populations which would need multiple districts and then looked at how many districts fell in a county.  Below are the results of the analysis.

Community

Special Topics: Lab 13

Week 13 lab focused on how scale and resolution can affect the details of an image.  The first part of the lab looked at vector data while part be focused on raster data.  The first part of the lab compared polylines and polygons at different scales.  The second part of the lab compared LIDAR and SRTM with cell size at 90 m.  SRTM is a very high resolution images.

To compare the two, I had to change the projection of the DEM and resampled it to have the cell size of 90 m.  After that process was complete, I used the Slope tool to find the average slope.  I compared the slope to the LIDAR slope.  I also visually compared the two images.  The LIDAR image has a larger slope and the low elevations are more noticeable in the LIDAR image than SRTM.

Below are the results of the analysis I performed.  The SRTM images shows less change in the elevation than the LIDAR.  As seen in the average slopes, the SRTM has the lower slope.

LIDAR

SMRT

Special Topics: Lab 12

This week lab focused on comparing the OLS model to the GWR model. The Geographically Weighted Regression model.  The model looks at small sets of data.  The model uses an equation to incorporate the independent and dependent variables.  The shape of the results are determined by bandwidth and kernel type.

This lab looked at housing data to determine the relationship.  First the OLS model was used.  Next, I used the same variables to create the GWR model.  After running the models, I looked at the results to see which variable is the greatest interest.  By running the analysis with the GWR, it strengthens the relationship.  By taking the spatial relationship into consideration, it improves the model by using spatial aspect into play.  The OLS does not look at the spatial relationship.  The OLS just looks at the variables and the results don't take into account the kernel type or bandwidth.

Special Topics: Lab 11

Week 11 of Special Topics continued the topic of statistics and regression.  The topic this week taught how to find the best model performance.  Using the Ordinary Least Squares tool, The tool generate results using one or multiple variables.  The results state the coefficients, p values, VIF, and Jarque-Ber.

In order to determine if the selected variables are correct, more are needed, or some should be removed, The a few of the checks are if the independent variables are helping, what are the relationships, and are the variables redundant.  A few other checks look at if the model is biased, if all the needed variables are used, and how well the variables explain the dependent variable.

The lab this week required using the OLS tool along with the Exploratory Regression tool.  The Exploratory Regression tool produces results of whether models pass and goes through all the various options.  The results also use the Adjusted R Square and Akaike's Information.  These numbers can be used to determine fit and explains variation.

Special Topics: Lab 10

This week's lab was an introduction to statistics which involved correlations and bi-variate regression.  Part of the lab consisted of finding missing data for 20 years of rainfall for a rain station.  To find the missing data, I used the regression tool from the Data Analysis Toolpak.  Once I had the regression summary, I found the slope and intercept values.  I used the values in the formula y = m*x+b.  The x variable was the data I had for rain station B.

By using  y = m*x + b, it assumes that whenever x is 0 y equals the slope plus the intercept.  The intercept tells us how much change is in each variable while slope tell us how much the variable will go up or down.  It assumes if I have x, I can figure out y.  The regression analysis looks at where station A and B for the years there is data for both.  It finds the relationship between the stations.