Blog Post #3

 Blog Post #3

The interview I watched was GIS in Natural Resources with Sarah Bellchanber. The reason I chose this one is because I work with some people in Operations who do Forestry and I wanted to see if there was something she used to help her team. The first important thing she said is learning about Python is very helpful for her job. I know we learned about that for our certificate and it is nice to see that it could help find future jobs. The second important thing was learning more about the apps we might need to use for the field and making sure people know how to use them. This is helpful because I have created manuals but I should probably do a learning session before people go out in the field. The third important thing is to tell people your specialty when you apply for jobs. Since I have been doing archaeology it would be good to tell someone that and my emphasis in GIS. After listening to the interview it was nice to hear that the GIS certificate we are getting could help us get jobs in other careers in GIS.

So far, I have created shapefiles for different studies. Also, I have been helping other people on my team on how to use GIS and what analysis they can do with the study. Finally, I have worked with other divisions on online mapper and getting all the data we need to do a project into one area. One life lesson I have learned is that you are not going to love every job you do but that you can make the most out of every job. Another life lesson is that even if you work hard, it does not always mean you will get the promotion, but that should not stop you from working hard. A final life lesson I learned is that you do not have to speak at every moment and it is okay to just listen. I have definitely felt academically prepared for the internship. Knowing how a geodatabase works, creating shapefiles, and using online mapper has helped me for the internship. Also, being able to help other people has been very nice with all we have learned while getting the GIS certificate.

The approach was to have me update my profile because I created mine back in 2017 when I graduated from the University of Oklahoma and updated after graduating from the University of Mississippi. It was good I added more information because I had not updated the information in over 5 years. I wanted to make sure I had my current job but also add in the different things we learned while taking classes for the GIS certificate. It was hard to remember everything but luckily I had updated my resume to where I could just copy and paste onto my profile. LinkedIn Profile: www.linkedin.com/in/emily-demontalvo-2a0142113.

Module 3.1: Scale & Spatial Data Aggregation

 Module 3.1: Scale & Spatial Data Aggregation

This weeks module was about the Modifiable Areal Unit Problem (MAUP) which involve the scale and zonal effects of data. According to Manley (2014), the scale effect shows that there are hierarchies that are put into our work that if we are not aware can skew the data. Also, the scale effect is attributed to variation in numerical results owing strictly to the number of areal units used in the analysis of a given area (Openshaw and Taylor 1979). Lastly, when thinking about MAUP it is important to know that your data can be skewed but if you make changes in how you correlate your data such as with scale it can help make sure you are getting answers that make sense.

Another issue that arose this week was the basic resolution effects on raster data. When talking about raster data the most common are digital elevation models (DEM) which focuses on the grid cell size that has significant effects on derived terrain variables such as slope, aspect, and curvature. Knowing that the grid size changes the look of the maps helps to choose the correct raster grid cell size. Such as when trying to carry out realistic terrain analysis is limited by the quality of the DEM applied (Kienzle 2007). There are three important aspects to keep in mind when trying to use DEM to analysis data: 

• "The accuracy and distribution of the elevation points used to interpolate the DEM.
• The interpolation algorithm used to generate a continuous DEM.
• The chosen grid cell size."

Knowing all this information can help the GIS analyst understand how to use the data to get the results they might want for terrain analysis (Kienzle 2007).

The last issue that was in this weeks module was how gerrymandering of political boundaries can cause issues because of boundary definition. These boundaries can change to make it to where one political party has more of an advantage in that congressional district. When trying to determine the compactness of a district I learned about the Polsby-Popper score and the formula. First, we needed to get the area and perimeter of all the districts. I did it in kilometers to keep it consistent. The formula was not working for me to use it all at once, so I split it to three different attributes. I did the formula for the top (4 * pi * Area). Then another formula for the bottom (Perimeter^2). Lastly, I created a formula where I divided the top number by the bottom number. The worst offender was North Carolina Congressional District 12 with the score 0.029476. It is seen in the picture below.

Module 2.2: Interpolation

Module 2.2: Interpolation

This weeks module was about using different interpolation techniques to represent different data points. The different interpolation techniques we used were Thiessen, Inverse Distance Weighted (IDW), and Spline (Regularized and Tension). Each one of the techniques shows a different way to represent data and it all depends on what the person wants to represent changes which technique you are going to use. The techniques were used to represent water quality in Tampa Bay, Florida.

The first technique was the Thiessen or Nearest Neighbor interpolation. One advantage of using Thiessen interpolation is that it assigns a value for any unsampled location that is equal to the value found at the nearest sample location. This is helpful because it makes each point its own polygon. Another advantage is that it provides an exact interpolator which means that the interpolated surface equals the sampled values at each sample point. It means that each location is preserved and there is not a difference between the true and interpolated values at the points. A major disadvantage is the sharp edges between the different points. It causes the image to look harsh with the edges. A final disadvantage is that the areas there are no points causes bigger polygons than what might actually be in the area. An example would be if they were on a terrain that changes quickly and there is no point in which the polygon would not represent that change.

The second technique is IDW. When looking at the data distribution of the IDW it is based on the assumption that the points that are close to one another are more alike than the points farther away. This could mess up the areas that have significantly different elevations next to each other. The IDW does not provide prediction standardized error which makes the use of the DEM a little problematic. Also, the IDW is oversensitive to outliers which could cause “bullseye” areas that have one point as a high or low point.

The third technique is Spline (Regularized and Tension). Spline, it is helpful because it is smoother and looks like it could be more accurate than the IDW. It is important to note that the DEM only works if the data is very accurate. The use of Spline is more adaptable because they may be used for lines or surfaces, and they may be estimated. Also, Spline is points that is used are considered “guides” that helps make the lines smoother. Lastly, a major problem to consider is that it can create unpredictable surfaces (overestimation or underestimation) in areas with low data density or high topographic change.

What I learned about surface interpolation is that there is not one method that is better than another. When choosing the technique all depends on different factors such as cost of sampling, available resources, and accuracy. Also, it was interesting to see that the sampling techniques can change how you try to represent your data. The results that surprised me were how spline interpolation can cause a big change just because one extra point was in the area. All I did was change the spline regularized interpolation by taking away one point. The way I would decide on a different technique is if I knew the sampling techniques that were used. I feel like the systematic technique would help make all the interpolations better whereas the cluster and random sampling would need to be more with spline interpolation. Lastly, when doing the adaptive sampling pattern would work best with the spline tension interpolation because it seems to be like the original data points.

The image below is Spline Tension interpolation. This image to me represented the data points the best. The reason is because it seemed to be the most like the non-spatial data that was represented. Also, the spline interpolation is smoother when it is representing the data points. Lastly, I chose this technique because they can be changed to represent the data points, and they cab ne used for lines and surfaces unlike the other techniques.

Module 2.1: TINs and DEMs

 Module 2.1: TINs and DEMs

This weeks module was using continuous surface in TIN and DEM models to represent elevation. One would use TIN for vector data models and DEM for raster data models. You can use either one to represent your data it just depends on what you are trying to present.

When doing the exercise for the module there were different ways we tried to represent the data but the one that I am going to talk about is how the contour lines were presented by using TIN or DEM. TIN stands for Triangulated Irregular Network and is created out of elevation points. A Digital Elevation Model or DEM is a digital representation of a topographic surface. This is also created from the same elevation points. The image below shows how the elevation points were able to show the different contour lines from TIN and DEM. It is important to note that the black and red lines are from the TIN data set and the gray lines are from the DEM data set.

The key differences between the two contour lines are that the DEM contour lines are smoother whereas the TIN contour lines are very sharp. Another difference is that the DEM contour lines showed more in the higher points than the TIN contour lines. It looks like the greatest difference is where the higher numbers were located. There are more DEM contour lines. The smallest difference was where the lower numbers are located for both the DEM and TIN contour lines. I would say these differences were because there were more points in the lower numbers compared to the higher numbers. Lastly, of the two sets of contour lines I would say the DEM is more accurate.

When trying to know if you should use TIN or DEM it depends on how you are getting your data. TIN is usually not widely available and has been made for the specific study. Whereas, DEM have various resolutions and used for larger study areas. Luckily with new data collections such as LiDAR it makes the differences between DEMs and TINs obsolete. It is all based on how you want to represent your data.

Blog Post #2

Blog Post #2

I searched for jobs where I can use archaeology and GIS. The job title is a GIS specialist for an archaeological cultural resource management firm. I chose this job because I have worked for this company in the past and they were really good but I needed to find a job where I could work from home. Also, it makes it a dream job because I get to use both archaeology and GIS to do my job which is the reason I wanted to get the certificate. Based on all the requirements I have all of them because of what we have been learning in our GIS classes and already having both a BA and MA in Anthropology with an emphasis in Archaeology. Also, since I have worked with the team in the past I know how their maps looked and what they would need in order to do field work. The only thing was they did not show a range of pay and that would make me worried they are trying to hide how much they would be willing to pay.

Job announcement: https://www.terraxplorations.com/gis-specialist

While doing the job search the biggest thing I noticed is that the GIS jobs that paid the best were the senior level jobs but you needed 5 plus years of experience in GIS. The entry level jobs sounded that I could apply for currently but I would need to get paid more to live in today's economy. It is interesting to find the line between what you are qualified for and what you need to get paid. 

Module 1.3: Data Quality-Assessment

 Module 1.3: Data Quality-Assessment

This weeks module was trying to see the completeness of the roads separately and compare them to each other. There were two road networks that were used, TIGER and Jackson County street centerlines. TIGER stands for Topologically Integrated Geographic Encoding and Referencing which was collected by the Census Bureau. The TIGER data that was used is from 2000 and it does have some major errors, but in 2010 the government tried to fix these issues. The other data is from Jackson County street centerlines that is more accurate than the TIGER data but it is not more complete. Knowing where your data comes from is important because it will help see the completeness of the road.

First, I needed to make sure that the roads were only within the grids. The way I did this was by using the select by location and choosing the input features as the roads. I made sure to use the relationship as Intersect and the selecting features as the Grid shapefile. Then I hit apply to get only the lines within the grid polygons. Next, I used the Intersect geoprocessing tool and each road as the input feature. I made sure to do it separately because I did not want all the information be put together. I made the Output feature class as “Grid_Street” and “Grid_TIGER” so I would know that was different than the other shapefiles. Then in the Attributes to Join I wanted All Attributes because it would make it easier in the future to get the information for each grid. Finally, I made sure in the Output Type to put Line so that it would make the line features that intersected the grid. Lastly, I added a new column called “New_Length” to get the new information of each polyline. Once it was created, I did the calculate geometry to get the new length in kilometers. I did this to make sure that there were actually new lengths.

After getting all the data it showed that the TIGER 2000 data  were more complete than the Jackson County street centerlines data. Also, that even though the data is open to the public and done by the Federal Government it does not mean it will always be accurate. This indicates that it is necessary to have two datasets to help compare against one of each other. The image below is a choropleth that shows the percentage of differences between the two dataset length within each grid. It indicated that there were major negative and positive differences between the two data sets. The negatives indicated that the TIGER data had more total length than the Jackson County street centerlines. The positives indicated that the Jackson County street centerlines had more total length than the TIGER data. Lastly, the little change is approximately from -3 to 3 on the number scale.

Module 1.2: Data Quality-Standards

 Module 1.2: Data Quality-Standards

This weeks module is looking at positional accuracy of road networks. The data for the roads came from Albuquerque and TeleAtlas for Street Map USA. The Albuquerque dataset is considered pretty accurate while the TeleAtlas is distributed by ESRI in ArcGIS. We were supposed to look the different street views to see where they were similar and where they diverted from each other. It was interesting to see the ESRI Street Map USA seemed further away from the "true" data points. One would think the street views that ESRI produces would be very accurate. The way we put the "true" data points in were but doing a close up of the orthoimages to see where the streets seemed to be located. The image below shows data points and street views of both Street Map USA and Albuquerque data sets.

I did 40 data points because it was hard to get the sampling rules of 20% in each quadrant and >10% of diameter apart at the 20 data points. The way I did the accuracy assessment was using the table on Page 5 in the Positional Accuracy Handbook within excel. In order to make sense of the data I switched it to UTMs to get the Easting and Northing in meters. In the handbook we are supposed to see the difference between the test points and the "true" points of both the x and y coordinates. Also, we had to square both of the x and y coordinate differences. Then we would add together the squared differences and then add it all up to get the sum. After that we would get the average which is the sum/number of points. We would get the RMSE which is the average^1/2. Lastly, we would get the NSSDA which is 1.7308 * RMSE.

Once we got all that information we needed to write our final accuracy statement. This statement does change between a tested and complied to meet. The tested statement is used when the accuracy was determined by comparison with an independent data set of greater accuracy (Positional Accuracy Handbook, pp. 5). The compiled to meet statement is used when the data has been thoroughly tested and that method produces a consistent accuracy statistic (Positional Accuracy Handbook, pp. 5).  Based on the way these points were collected we would use the test statement. For the Streets Map USA the statement is Tested 306.9 meters horizontal accuracy at 95% confidence level and the Albuquerque Streets is Tested 16.7 meters horizontal accuracy at 95% confidence level.

By creating the different data points it helps know the positional accuracy of your shapefiles. It is important to try to get a higher accuracy data set to compare. After doing all the data points and getting the math correct it seems that the Albuquerque data set is more accurate than the Street Map USA data set. This is interesting because the Street Map USA dataset is used within ESRI ArcGIS and one would think that is more accurate then other datasets.