Raster Modeling

Objectives

    The goal of this lab was to use various raster processing tools to build models for both sand mining suitability as well as the impact on the environment it could potentially have for Trempealeau County, Wisconsin. These models would take a look at the best locations for sand mining with minimal environmental and community impact.

Data sets and sources

    The data sets that were used for this project came from the Trempealeau County geodatabase along with the USGS. To narrow down the output of these models I only concentrated on the southern 2/3's of Trempealeau County. This would give me a more distinct accuracy of areas that this portion of the county could hold, and flourish with a frac sand mine. 

Methods

    The first model I created was the suitability model for the region. The data that was included in this model was the NLCD land cover type, geologic unit, slope of the region, distance to rail terminals, and the water depth table. I had to convert all of these from a vector dataset to a raster, which would allow me to calculate the suitability much easier. I also had to reclassify all of these raster datasets. The reclassification was a scale from 1 to 3, with 1 being low suitability and 3 being high suitability (figure 2).

Fig. 2 Suitability model for Trempealeau County

    The first criteria that I looked at was the NLCD land cover type. By using the reclassify tool, I was able to determine what types of land cover were best for a sand mine. The best land cover for a sand mine was the barren land or herbaceous land cover. These lands were mostly open land which wouldn't take much to put a new mine in these areas. (figure 3a,b)

Fig. 3a Reclassify tool for land cover

Fig. 3b land cover suitability for Trempealeau County

   
    The second criteria of the suitability model was the geologic unit for Trempealeau County. The best Frac sand in the area is found in either the Trempealeau group or the Wonewoc formation. There are many other different distinct groups in this region, but they are considered not suitable. To classify these two formations, I only used a numbering system of either (1) for suitable or (0) as not suitable. (figure 4a,b)

Fig. 4a Reclassify tool for the geologic unit

   

Fig. 4b Suitable land for Trempealeau County

    The third criteria that I took into consideration for the model was the slope of the area. Seeing as it wouldn't be feasible to put a sand mine on the side of a steep slope, I had to take this into consideration when calculating this criteria. The most suitable slope for a sand mine in this area was less than 10 degrees. Anything above 10 degrees and it would be difficult to have a sand mine in that area. It would cause stress on everyone involved if the sand mine was located in a steeply sloped area. (figure 5a,b)

Fig. 5a Model breakdown of the reclass slope

 

Fig. 5b Suitable land based on slope breakdown

    The fourth criteria on the suitability model is the distance to a rail terminal. Seeing as the frac sand is loaded onto railways for easy shipping, it would make sense to have a new sand mine located in close proximity to a railway. The most suitable railways for a new sand mine would fall within 12,500 meters of the sand mine. Although it would awesome if the sand mine could be located directly next to a railway, this isn't always the case. With a railway within the highly suitable area, trucks can always transport the sand to these railways, or if it is close enough they can all use a conveyor system to transport the sand to the rails. (figure 6a,b)

Fig. 6a model used to create rail distance

   

Fig. 6b suitable land based on distance from rail terminals

    The final criteria of the suitability model is taking the water table depth into consideration. Water table depth is important because once the sand is mined it needs to be washed to remove fine particles. This means that the closer to the surface the water table is to the surface, the easier the access is to water for washing the sand. A high suitable water table would have a depth from 0-820 meters deep. This would allow for companies to drill a well for the wash plants. (figure 7a,b)

Fig. 7a model used for the water table depth

   

Fig. 7b Suitable land based on water table depth

    After taking all of these criteria into consideration, I could then use the raster calculator tool allowing me to generate a suitability index for the entire model. (figure 8a,b)

Fig. 8a raster calculator tool used to create suitability model

   

Fig. 8b Suitability Model

    The second model that I created was the sand mine risk model index. There were four different sets of criteria I used when creating this model, distance to streams, areas of prime farmland, distance to residential neighborhoods, and distance to schools. Just like the first model, I also had to reclassify all the criteria into categories of 1 being high risk, to 3 being low risk. This would allow me to create the risk model index much easier. (figure 9)

Fig. 9 Risk Classification Ranks

    The first criteria I used was the distance to streams. This would allow me to show areas that are within close proximity to streams. If a mine is located to close to a stream, it could pose a problem with contaminating the stream. The first step I did when calculating the stream distance was a select by attributes tool. This allowed me to only look at the streams I wanted to base my model off of. I decided to only look at perennial streams as they are around year long and pose the highest threat of being contaminated. After creating a feature class of just of the perennial streams, I could then run the Euclidean distance tool. I wanted to have this tool broken down into three categories. The closest category would have a value of "1", which would pose the highest threat. The farther from these streams the better. (figure 10a,b)

Fig. 10a Risk model based on streams

   

Fig. 10b Risk based on stream proximity

    My second criteria of the risk model was looking at prime farmland. This data came from the Trempealeau County database. This would allow me to see which areas in the county were considered to be prime farmland, and which areas are not. A sand mine shouldn't take priority over prime farmland seeing as it is just as important if not more important to continue to have agriculture in the region for producing food and income. First I looked at the attribute table and create a new field for the re-class values. I wanted all the prime farmland to have a value of "1" and all other areas to have a value of "0". This would allow me to show the areas of the prime farmland. I used the select by attributes tool to do this conversion. After selecting all the prime farmland, I had to then use the polygon to raster tool to create the raster for this dataset. Since it was in a different projection then the rest of my datasets, I then used the define projection to change its coordinate system. (figure 11a, b)

Fig. 11a Risk model based on prime farmland

   

Fig. 11b Risk based on prime farmland

    The third criteria that I used in this model was the distance to residential neighborhoods. I used the zoning distance feature class from the Trempealeau County database to base my findings from. Once again, I added a new field and used the select by attributes tool to give all the zones of residential a "1" value and all the other zones a "0" value. I then used the feature to raster tool, which I can run Euclidean distance from. This allowed me to run the distance tool based off of this new field. I then had to reclassify the zoning distance into these two new value fields. (figure 12a, b)

Fig.12a Risk model based on residential neighborhoods

   

Fig. 12b Risk based on residential neighborhoods

    The fourth criteria that was used in this model was looking at the distance to schools. Since there is no schools feature class in the Trempealeau County database, I had to use the parcels feature class to look at them. I built a query statement to locate the parcels that were owned by a school district. After selecting those schools I used the polygon to raster tool to create my raster of the parcels owned by the school districts. I was then able to use the Euclidean distance tool to create a map showing which schools are within close proximity. (figure 13a,b)

Fig. 13a Model used to show the proximity of schools



Fig. 13b Risk based on Proximity to schools



Conclusion
    This assignment taught me a lot about the proper ways to use raster tools and how to create different models for rasters. Some of these tools were hard to run as the computer continued to stop working at times, but the overall end result allowed me to show areas of land that would benefit from a Frac sand mine in Trempealeau County. By looking at not only the geological make up of the land, but taking into account streams, rail lines, schools, slope, and much more, I was able to determine areas of land in the county that these sand mines would be best situated in. 

Data Normalization, Geocoding, and Error Assessment

Sand Mining Suitability Project

Objectives

    The goal of this assignment was to geocode the locations of sand mines in Wisconsin and compare the results to the DNR data provided. We were given the locations of sand mines throughout the western part of Wisconsin. The data that was provided was not normalized in a working fashion allowing us to pin point the locations of these mines. The data was given to us in addresses and PLSS codes, meaning that we would have to find some of these mines on our own and adjust some of the locations for these mines.

Methods

    Before we could start geocoding and selecting the locations of the mines, we first had to normalize the data that was provided. The mine locations were provided in an Excel spreadsheet, with plenty of problems to give one a headache. Some of the mine locations provided addresses while some provided just PLSS information or a combination of both. The goal here was to pick through the addresses and make a new spreadsheet that would make sense for the geocoding tool to understand. Figure 1 shows the table that provided all the addresses for the mines that we wanted to geocode before normalization.

Fig. 1 Excel table before normalization.

After looking at the table, the easiest way to normalize the data was to break down the address into PLSS, city, street, county, and zip. This would allow for the geocoding tool to be able to run and make sense of the addresses provided. Figure 2 shows what the table looked like after the normalization.

Fig. 2 Excel table after breaking it down and normalization.

By breaking down the address, the goal was for the geocoding tool to be able to place location with address in ArcMap relatively close to the actual location. If the location was off, it could always be manipulated to the right location using Google maps, the base map, or even the PLSS code.

Results

    After geocoding the addresses, it was now time to check data accuracy with our fellow classmates. By merging data of my classmates I would be able to compare the mine locations. My mine locations had two other classmates with the same mines, along with some others. After the merge tool I had to use a query to seperate out only the mines that matched with mine. Next the near tool was used to create a table containing the distances between my geocoded addresses and the mines of my classmates. This is displayed in figure 3. The final results of my geocoded addresses compared to my classmate's is also located with figure 4.

Fig. 3 shows the distances to the nearest mines of my classmates. Using the near_fid, I then can relay that back to the fid to see which mines are the closest.

Fig. 4 My geocoded addresses compared to the addresses from my classmates

Discussion
   When it came to the results of the distances in figure 3 and the point table, there could be two types of errors involved, inherent and operational errors. One type of inherent error that has came into play with this data is that the base map images are relatively old and don't reflect current actual positions of the mines. Another type of inherent error could be when two different sets of data are in different coordinate systems. These errors tend to gradually build up until your data is so skewed and doesn't make sense.
    One type of operational error that has occurred with this data is not selecting the correct mine locations. Operational or processing errors occur during the procedures of collecting, managing, and processing the data.
    When it comes to original sources of the map both types of errors can occur for coding, measurements, and photogrammetric measurements. With data automation and completion, it is mostly inherent errors but both types can occur with digitizing and attribute data input. The last source is data processing and analysis. This again is mostly inherent errors, but both can occur with inappropriate use of a tool (point distance tool).

Conclusion
    This assignment was made to teach us how to properly normalize and geocode data. Without properly normalizing the data in Excel, we would have never been able to find the locations of the mines with the address coder. This was a major step with being able to complete this lab. Upon proper normalization, we could then geocode the addresses or PLSS information.

Data Downloading, Interoperability, and Working with Projections in Python

Objectives

    There were two major goals dealing with this assignment: the process of downloading data from different sources, and becoming familiar with Python Scripter. I have downloaded data before from different sources so I am familiar with the process and how much "massaging" of the data that needs to occur, but I am fairly new to Python at this stage. By continuing to grow and learn Python will allow me to further my knowledge and understanding of the capabilities of ArcGIS.

Methods

    There were five basic steps when it came to data for this lab exercise: 1. download the zip files from the sources to a temporary directory, 2. extract the zip files to a working folder, 3. project the data, 4. load data into geodatabase through Python, and 5. getting rid of the redundant data. By following these steps with all my data obtained in this exercise I would be able to create the maps on sand mining in western Wisconsin.

    The first set of data I obtained was from the USDOT. This data included rail lines, Amtrak lines, and rail nodes. The website I obtained this data from was United States Department of Transportation website: http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

This data came from 2015, and had the three shapefiles already established upon un-zipping the data. The second set of data I obtained was from the USGS National Map Viewer. There was three different sets of dataset included. One was a general large view topographic profile of my AOI or Trempealeau County, with the other two sets being DEM's of the county. With the raster and DEM's I will be able to focus on Trempealeau County. The website I downloaded this data from was The National Map website found here: http://nationalmap.gov/about.html.

The third data set I downloaded came from the USDA Geospatial Data Gateway. This data included land cover and cropland data, my focus being Trempealeau County. This data gave me all the different kinds of land cover and croplands that I would be able to find in the county. The website was: https://gdg.sc.egov.usda.gov/.

My fourth data set that I wanted to download was of the Trempealeau County land records division website. This would give me all kinds of different information, 85 different types of information, to create maps with of the county. The website I downloaded this data from was: http://www.tremplocounty.com/tchome/landrecords/.

The fifth set of data I downloaded came from the Web Soil Survey website. The data I was able to download was of Trempealeau County again, focusing on the different types of soils that have been collected and mapped out for the county. This will become very useful when talking and creating maps on Frac Sand Mining. The website I obtained this data from was: http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm.

Data Accuracy

    Dealing with downloaded data, it is important to look through all the metadata. Metadata tells us what scale the data is in, how it was obtained, and along with accuracy. I was only able to find portions of metadata for three of the data sets I downloaded.

Figure 1 shows the metadata that I was able to find.

Conclusion

     There are many concerns that have came to me when dealing with these datasets I downloaded. One thing that isn't shown in figure 1 is all the different projections that the datasets are in. This means that I will have to change them to make the data work. Another concern is that I wasn't able to find all the different metadata parameters. This could be operator error, or it could be that this metadata wasn't entered in. Not being able to understand all the different parameters of the metadata can cause my maps to be inaccurate or have false information. This is a major concern of mine.
Data Maps
   

These maps reflect the data I downloaded in this lab activity. I was able to combine different datasets allowing me to show more in-depth detail about Trempealeau County. This data will come in handy in future maps allowing me to visually show the Frac sand mining occurring in this area.



Sand Mining in Western Wisconsin

Introduction-

   Wisconsin sits on a hot bed for natural resources that are in demand, and Frac sand is the resource that is in high demand currently. Frac sand is used by oil and gas companies to extract the natural resources that they are after. Frac sand is quartz sand of a specific grain size and shape. This sand is pumped into the ground cracks that hold the natural resources allowing for the oil companies to extract the oil. The sand mostly holds open these cracks allowing for the extraction. Wisconsin holds some of the best Frac sand due to the geological formations found here. Frac sand comes from sandstone formations, which western Wisconsin is known for.  (Wisconsin Geological and Natural History Survey, 2012).

Figure 1. Shows current Frac mines in Wisconsin, alone with sandstone geological formations, (Wisconsin Geological and Natural History Survey, 2012).

    Frac sand mining isn't a new entity, but has recently gained attention from the demand for oil and gas. In fact, Wisconsin's sands have been used for the past 40 years in the petroleum industry. Currently, Wisconsin has approximately 60 mining operations along with 20 more new Frac mines already proposed. With the current mines in operation and processing plants, Wisconsin reaches about 12 million tons per year of Frac sand (WDNR, 2012, p.3).

Figure 2. Shows were the best Frac Sand is in the midwest, clearly Wisconsin (WDNR, 2012, p.4).

Troubling Issues-

    Although Frac sand mining seems to be a great resource for the state, it's not free and comes with a cost. There are many environmental issues that these mines produce. Two major issues are air quality, and road quality. These mines produce lots of emissions from the equipment that is needed to extract the sand, and also produces lots of dust into the air. Although these mines would have regulations on the emissions and dust they produce, the pollutants would still be in the air. For example, the WDNR holds a 10% opacity level on the air in the minds. Opacity is defined by the degree to which these omissions reduce transmission of light. 10% may not seem like a lot, but imaging having one of these mines in your communities. Long term health risks would come into play. The second major issue is road quality. These mines tend to dwell in smaller communities where they don't always have the funds to maintain the roads to high standards. With dump trucks filled with sand driving constantly over the same strips of roads, you will be bound to have cracks and pot holes throughout the community roads. (WDNR, 2012, p. 14/15)

Frac Mining and GIS-

    There are many pros and cons to Frac sand mining, and it all depends on who you talk to it about what their view point is. Frac mining does produce jobs for small Wisconsin towns, but also has negative impacts on the environment surrounding these communities. GIS can be used to show some of these impacts that the mining operations can have on these towns, and can also help to find better ways to reach the Frac sands through use of the GIS technologies. 

Sources-

    Wisconsin Geological and Natural History Survey, 2012, Frac sand in Wisconsin. http://wcwrpc.org/frac-sand-factsheet.pdf

    WDNR. 2012. Silica sand mining in Wisconsin. http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

    Journal Sentinel. Milwaukee, WI. Wisconsin's Frac sand industry booms, Thomas Content. http://www.jsonline.com/business/wisconsins-frac-sand-industry-booms-b99509220z1-305394131.html