Exercise 8: Raster Modeling

Goals

The goals of this assignment were to use various raster tools to build models for both sand mining suitability as well as sand mining impact in the form of environmental and cultural risk in Trempealeau County, WI. In this exercise we were required to:

• Build a sand mining suitability model
• Build a sand mining risk model 
• Overlay the results of the two models to find the best locations for sand mining with minimal environmental and community impact

Data Sources Used

• Trempealeau County Geodatabase
• UWEC mgisdata
• NLCD geology raster

Methods

The first part of the assignment was revolved around creating a suitability model for mining locations in Trempealeau County. There were five different criteria which were used to create the final suitability model. These criteria were, railroads, land cover, slope, water table, and geologic criteria. When creating these separate models, we had to use many different spatial analyst tools in order to create a raster the properly showed the data we were looking for. The specific criteria for each suitability model will be shown in an Excel spreadsheet labeled Figure 2. Figure 1 below, shows the model used to create the suitability model. Some of the tools that were used in creating these models were, Euclidean distance, Reclassify, Polygon to Raster, Topo to Raster. Euclidean distance was used to show distances from different points of interest. Reclassify was used to simplify our ranking system from more suitable to less suitable. Polygon to raster was used to convert polygon features to a raster that we could then use in our suitability model. Topo to raster was used once to convert a topographic map to a raster.

Figure 1. shows the model that was created to execute all of the tools used to create the suitability model.

Figure 2. This table shows what the ranking system for each suitability category was.

In the next section of part one, we were to create a similar model but instead of suitable land, we wanted to map potential areas where there could be environmental or social impacts. For this environmental impact model, we assumed that things like residential areas, schools, farmland, and rivers were all important things to make sure that the mine couldn't impact negatively. We were also told to add another feature that we thought would be bad if the mine negatively impacted. I chose to add trails because generally, trails are a nice recreational area that are generally supposed to look and feel peaceful. Most of the same tools were used. Figure 3 below, shows the model that was created to execute all of the tools to see the potential areas of environmental impact. Figure 4. shows the ranking system that was used to determine what areas were potentially going to be effected. 

Figure 3 shows the model that was used to execute the tools necessary to create the environmental impacts maps.

Figure 4. shows the table that explains the ranking system used to create the environmental impacts model.

Results

The final results for this assignment were very interesting. All of the individual models that we created were combined into final models using raster calculator. Figure 5 below shows the final maps created when creating the suitability model. Figure 6 below shows the final maps created when making the environmental impacts model. Finally Figure 7 shows a raster calculator result from combining the suitable land and the environmental impacts. 

Figure 5. Suitability models and the final combined map. Ranking follows the table in the methods section. Red is not suitable land and blue is more suitable land. 

Figure 6. Environmental impact areas and the combined impacts map. Red areas represent areas that would create more environmental or social impacts were a mine to be put there. Blues represent areas that would be more environmentally friendly based on the data that was used. 

Figure 7, The final overlay map that combines the suitable land area and the environmental impacts map. Blue areas represent more ideal areas to put a mine and the red and orange areas indicate spots that would not be ideal for a sand frac mine.

Conclusion

Exercise 8 was a very interesting way to end the GIS 2 class. Working with rasters provided a lot of interesting information and I feel like a learned how to interpret areas in a different way. Before this assignment, I didn't realize the amount of tools that could be run on rasters. This was both a challenging and rewarding assignment to showed us again that things do not always go as planned when working in GIS. I feel like this assignment really challenged my critical thinking and provided me with new skills in ArcMap. The end findings showed that there were a decent amount of areas where a mine could be placed without having too much of an impact on Trempealeau County.

Exercise 7

Goals

This blog post will go through the processes and results of the second part of exercise seven. The first part of exercise seven was covered in the recent python blog post. The goals of the second part were to perform network analysis. The given scenario was as follows: Transportation of frac sand from the mines to a railroad terminal will result in significant impact on local roads. In this exercise, you will route the sand from the mines to the nearest railroad terminal. We will then estimate the number of trips the trucks will take and the cost of the traffic on the local roads. 

Methods

First off, the data that was used in this exercise came from ESRI. They provided a street map in order to do the network analysis. 

Another note before we get into the methods, All of the data and cost results of trips to and from the railroads is hypothetical. The network analysis is simply being done to give us experience using network analysis.

All of the methods done in this exercise were done using model builder. This allows for an organized data flow model so that it is easy to visualize all of the processes being done. The first thing that needed to be done was create a Closest Facility Layer. Once the layer was added, the mines and rails were then put into the layer. The mines were classified as the incidences and the rails were the facilities. Once those were added, then the closest facility  was solved to show where the closest railroads were to each mine. 

Once the routes were copied into a new feature class, a tabulate intersection was preformed to basically join the tables of the routes and the Wisconsin counties. Once the new table was created, a field was added to convert the total route distances per county from meters to miles. There was then another field added which calculated the total cost per county of all of the trucking distance traveled. For the hypothetical situation, we assumed that each truck route is run 50 times to the railroad per year. This is multiplied by two to account for the travel back to the mine. We also then assumed that the cost per mile of a truck driving is 2.2 cents. And with this calculation, we were able to add another field that represented the total cost per year for each county. Figure one below is the model created to preform all of the tasks in exercise 7.

Figure 1

Results

The results of this exercise are represented by a final map. Figure 2 below shows the final map. I have also provided the final table showing the data that was output by the model above. Figure 3 shows the table. The final figure, figure 4, shows a graph giving another visual representation of the counties costs per year. We can look at all of the charts and data and see that Chippewa County has the highest yearly cost for trucking sand. This means that they have the most traffic from heavy trucks on their roads. This heavy traffic can result in the need to repave or redo streets in order to keep them usable. 

Figure 2

Figure 3

Figure 4

Conclusion

I would like to again point out that all of the data and calculations used in this exercise are purely hypothetical. That being said, it is very interesting to be able to apply network analysis to find something out like how much traffic areas are receiving from sand mine trucks. It shows that network analysis has a wide variety of uses. As the results show, Chippewa county had the highest hypothetical cost per year. If we take that knowledge and look at the final map, you can see why that may be. There are plenty of mines in the county but the rail lines that need to be used to transport the sand aren't located in a very convenient location. 

Data Normalization, Geocoding, and Error Assessment Sand Mining Suitability Project

Goals

The goal of this assignment is to geocode the locations of all the sand mines in Wisconsin and compare our results to the actual locations and the geocoding results of our classmates. Each student was given around 16 mines to geocode to ensure that we would be comparing to multiple peoples geocoding.

Methods

The data that was provided was in the form of an Excel table. Each student was assigned around 16 mines to geocode. The first step before geocoding was to normalize the address table. We needed to do this because some of the mines were given in a PLSS address, some were given in a street address, and some were given in both. In normalizing the table, the addresses were separated. Some of the street addresses were missing data like a zip code or street name or city. Because of this, the addresses were normalized further.  

Using ArcMap, we signed into the enterprise account for UWEC. This allowed the geocoding to be done. The first step in the geocoding was to add the table to ArcMap. The software is then able to use its geocoding tools to find addresses within the table. The original geocoding of the table was very messy because of the in-completion of the data. Before manually finding the mines, the mines not assigned were separated out. Once the assigned mines were all that was left, it was time to go through each and every mine to find its actual location. 

First, looking for mines with complete street addresses were checked for their accuracy. Ideally, the geocoding was successful. In a couple cases, the address locator couldn't find the address. The next step is to manually find these addresses. In most cases, Google Maps can in handy finding the mines. It is easier to navigate and cover ground faster. If there was an address that was not complete, and no PLSS was given, all of the mines locations were given in latitude and longitude. Once all of the street address mines were located, then the PLSS mines needed to be located. 

The PLSS shapefile was added to ArcMap. All of the PLSS addresses were given and relatively easy to find, but finding them was a long and tedious process. The PLSS address were given in a series of directional units and numbered squares. 

The second part of the assignment was to gather the data from the students who geocoded the same mines as us. First, it was necessary to add all of their mines into ArcMap and observe the data. There were visible differences in the geocoding. The next step was to merge all of the different classmates data into one feature class. 

Once the data was merged, the actual mine locations shapefile was added into ArcMap. After comparing my own and my classmates geocoding to the correct locations, I generated a near table. This gave the distances from my geocoding to the correct locations and also the distance from the classmates geocoding to the correct. The results are shown in the next section through two maps and tables. 

Results

The following map shows the differences between the mines that I mapped and the mines that my classmates mapped. As you can see for the most part, the mines are in the same general area. But in reality, there is quite a difference once we look at them at a larger scale. You can also notice that there is a point located on the East side of the state. Figure 2 is a table that maps the difference between all of our data points in meters.

Figure 1. Map of Wisconsin showing geocoded mines

Figure 2. shows the distance between mapped points in Figure 1.

Below, Figure 3 shows the difference between my mapped mines and the actual location of the mines. For the most part, my mines are in the general location with some of them located in the correct spot. However, like the map above, at a larger scale the errors will be shown more. Figure 4 shows a table with the distances from my mapped points to the actual mapped points in meters.

Figure 3. Shows my mapped points compared to the actual mine locations.

Figure 4. Shows the distance in meters between my points and the correct points. 

Discussion

After doing this assignment, it is easier to look at data and think about potential errors in the data. From the start we were told the the data was going to have some errors in it regarding the addresses. Some of the addresses would be incomplete. I even found some addresses where the street numbers just had two numbers that were switched. Errors like these can come up at anytime when working with GIS data. The majority of the errors in this assignment would be classified as operational errors. Operational errors are mostly contained in the collecting and managing of data. The main issue of this lab was to geocode data with operational errors inside the data. The inherent errors appeared in the results of our geocoding. It goes along with looking at and interpreting the results. Because the real world is far more complex than our mapping capability, inherent errors occur. For example there were a couple of mines that, when looked at through an imagery base map, didn't look like they were actually located where they were supposed to be. This is all part of GIS, knowing that not everything is going to work perfectly is very important. 

Conclusion

This exercise really showed the difficulty with geocoding and working with error filled data. It showed that no matter what, things are not going to always work smoothly. We need to learn to work around errors and know how to fix them. It also showed the importance of keeping data in an organized fashion. 

Python Blog

Python Blog

In this blog I will keep an updated page showing all of the python that I use throughout the spring semester in GIS 2 Geog 337. 

The first python script that I created was for our Exercise 5 assignment. The purpose of the script was to take the rasters that we downloaded from an internet source and put them into a geodatabase. We had three rasters and all of them came from different sources. This means that they were all in different coordinate systems. Because of this the script needed to project each raster. The next step was to clip the raster. We had a study area of Trempealeau County in Wisconsin so we clipped each raster to the county boundary shape. The last step was to load the clipped and projected raster into the geodatabase. Below is a screenshot of the final script that successfully ran and accomplished all of the above tasks. 

Exercise 7 Python Script

The purpose of this script was to prepare our data for part two of exercise 7. The goal of this script was to select all of the mines that will be used in network analysis in part 2. To set up the data we needed to locate the mines that meet the following criteria:

- The mine must be active

- The mine must not also have a rail loading station on-site. 

- The mine must not be located within 1.5 km of a railroad 

In order to find this data using Python, it was necesary to set up SQL statements that would select the mines by being active and not having a railroad on site. The next major step was then to select these mines by location to make sure that they were not within 1.5 km of a rail road. My end results came up with a total of 44 mines that were not within 1.5 km of a railroad. Once the script ran, I went into ArcMap to explore the data to see if the script ran correctly. I created a buffer around the new output class to make sure that it truly wasn't within 1.5 km. The data appeared to be correct. Below is a screen shot of the script that I wrote to complete the tasks.

Exercise 8 Python Script

In exercise 8, the final part was to use python to create a weighted index on the environmental impacts. I decided that using residential areas as the weighted feature was the most important. That would be something that would be hard for the mine to get around so I made it more important. This was a relatively quick python script where I just set variables to all of the rasters used in the model. I then took the residential area and multiplied it by 1.5 in raster calculator in order to make it more important. THe last step was to take the weighted value and add it to all of the other rasters in raster calculator. The result was a weighted index. The python script is located below. 

Data Downloading, Interoperability, and Working with Projections in Python

Goal

The goal of this assignment was to become familiar with the process of downloading data from different sources on the internet, importing the data to ArcGIS, joining the data, projecting the data from these different sources into one coordinate system and building and designing a geodatabase to store the data. Finally, create maps showing our results of our python script that we wrote to accomplish the previous actions.

Methods

In this assignment, data collection was a huge section. The first half of the assignment was going out to different sources on the internet and downloading data sets from them. These were all government sites, so we know that they are all trusted data sources. Before we started downloading data, I went ahead and set up my data management. I made sure that I had an exercise 5 folder and a working folder inside of that. The TEMP folder on the university computer was used to hold the initial downloaded zip files. From there, they were extracted and put into our working folder which was further broken down into folders for each website where data was downloaded from. 

The first site that we visited was the US Department of Transportation. They supplied a transportation data set. In that data set were things from railway and road files. The AOI that was used was Trempealeau County. The next site that provided data was the USGS National Map Viewer. This provided data for the land cover of the Trempealeau County area. The two DEM rasters that resulted in one of our final maps was from this site. The next site was the USDA Geospatial Data Gateway. From this site, I obtained information on Trempealeau Counties cropland data layer. The source of data was directly form the Trempealeau County land records them self. This was probably the most important piece of data. It contained an entire geodatabase of the county and all of its relevant data. The last site that was used was the USDA NRCS web soil survey. This site provided another of the rasters that will be shown in the results. 

After the collection of data, it was time to import the SSURGO Data that we got from the soil survey. These were a large collection of tables. To import it, we used Microsoft Access. It imported it to a format that could be used in ArcMap. Next, I created a python script that was used to project, clip, and load all the data into the geodatabase. The script brought in all of the rasters that we gathered from the data sources, put them all in an appropriate coordinate system and then clipped them to the shape of the Trempealeau County border. Figure 1 below is a screenshot of the script that was run. 

figure 1. Python script used to project clip and load collected rasters into the geodatabase.

Data Accuracy

Final Maps

Conclusion

After completion of this assignment, I think that I have gotten a much better idea of how gathering data can be both extremely helpful and sometimes very frustrating. I am glad that we got to experience going out on the internet and gathering real data from different government sources. The use of python for basically the first time also shined a light on the many different ways to accomplish tasks in ArcMap and GIS in general. 

Frac Sand

What is it?

Frac sand is a very important resource in Wisconsin. It is specifically sized grains of sand that can be used in extracting oil. The near pure quartz sand grains are put into fluid that is injected into oil at high pressure. The high pressure opens up fractures and helps to keep them open once the fluid is pumped out. The specific sand is found in many areas of Wisconsin but mostly in western and central Wisconsin.

Issues With Fracking

There are a couple of issues surrounding fracking. A lot of the issues are public health concerns. Many of the people in the area are scared of the mines because of the silica dust that they produce. There have been dangers to some of the workers in the mine that cause fear in the general public. Exposure to silica can cause chronic silicosis which can be fatal.

There are also some issues when it comes to local water sources. When processing the sand, there are certain chemicals that are used. These chemicals called flocculants can end up seeping into the ground and infiltrate the ground water. This leads to more concerns to the public close to the mines. Long term exposure of drinking water with these chemicals can lead to many health risks. 

GIS and Fracking

GIS and the frac sand mining industry can go together very well. GIS gives the ability to monitor and keep data on all of the mines. It can also use spatial analysis to monitor things like the proximity of mines to largely populated areas. GIS data that shows information and the location on local wells could help to prevent the contamination of local water supplies. GIS can also help to track the shipping and movement of frac sand to see areas where the sand has been. GIS has so many applications that could help to better manage the mines. 

Sources

http://isthmus.com/news/news/the-fight-over-frac-sand-mining-in-wisconsin/

https://wgnhs.uwex.edu/wisconsin-geology/frac-sand-mining/

http://apps.startribune.com/blogs/user_images/sand2.JPG

http://wcwrpc.org/frac-sand-factsheet.pdf