Sand Mine Suitablity Using Raster Analysis



Goals and Objectives

In this lab, our goal was to show the suitability and risks of frac sand mining within a section of our study area Trempealeau County (Figure 1).  This is accomplished through building suitability models using raster analysis.  Using spatial analysis tools including reclassify, Euclidian distance, and raster calculator it is possible to assign rankings to a variety of data, and then combine them together into single maps that show a range of rankings that correspond to the best and worst places to build a new sand mine, based on a variety of different criteria.

Figure 1. Location of Trempealeau County in Wisconsin

As the prevalence of frac sand mining increases, it becomes necessary to look at how negative impacts can be mitigated, reduced or avoided.  Furthermore, as citizen concerns grow about environmental, economic, public health concerns associated with frac sand mining(Midwest Environmental Advocates, 2014), preemptive analysis and avoidance of high risk area is essential to the industry.  Raster analysis provides a way to critically look at suitability and an understanding of raster analysis tools can be beneficial to have when looking at complex problems with many factors. 

Figure 2.  Photograph of a sand mine from (Midwest Environmental Activists, 2014)



Data

A variety of publicly available data was utilized for making our suitability model.  Most of the data came from feature classes in the Trempealeau county geodatabase and rasters that we had downloaded from various websites (Figure 3).  A more detailed description of this data collection is available in a previous blog post.  Additionally we downloaded coverages of a water elevation map for Trempealeau county.  The sources for the data at the end of the blog.

Figure 3.  Selection of rasters utilized for analysis in sand mining suitability model.

Multiple characteristics were chosen as criteria to evaluate suitability and risk (Figure 4)  The type of geologic formation, the current land use of the area, the distance to rail terminals, the slope of the land were all used to evaluate suitability of particular areas.  The sand mine would ideally be in a place that has geologic formations where frac sand is occurring with a land use that could be altered (for example, it is unlikely a lake would be drained to build a sand mine). Additionally access to rail terminals for transportation of the sand, a slope that isn't too high, and a water table that isn't too low so that water can be accessed to process the sand. 

The distance from streams, residential areas, schools, landfills, and the occurrence of prime farmland were all used to evaluate risks associated with sand mining in different areas.  Ideally a new sand mine would be located away from schools and residential areas to reduce social impact, away from prime farmland to reduce economic impacts, and away from both streams and landfills to reduce environmental impacts that could be associated with both.  I personally chose landfills to add to my raster analysis, because I think there would be pollution concerns if landfills needed to be moved to accommodate sand mines. 

Figure 3.  Visual representation of different criteria utilized in sand mining suitability analysis

Methods

For each of the individual factors, rasters were made and reclassified so that they would give a rank of the data, which could that be added together using raster calculator.  The ranking system that we had ranged from 1-3 with 1 being low suitability and 3 being high suitability.  There are 10 factors that we are looking at, meaning that the maximum value for suitability that should be visible in the final raster in 30.

The majority of the processing was achieved with three spatial analysis tools, Reclassify, Euclidean distance, and Raster Calculator.

Reclassify

Reclassify allows you to change values within a raster.  Functionally this can allow you to do many things, such as reduce the amount of distinct values you have in a raster or create an inverted version of a raster that you have.  In this lab reclassify was frequently used to produce rasters with rankings. 

Euclidean Distance

Euclidian distance calculates a number for each cell within the raster that corresponds to its distance from the closest input source feature.  For example, if the input source feature was a road, Euclidian distance could generate a raster where each cell gives the distance from the road.  This tool was useful when settings ranks for risk based on how close it is to a certain feature.

Raster Calculator

Raster calculator allows you to write equations for rasters that will be performed on every cell, generating an output raster based on your equation.  Using this tool to add rasters together can give you a combined rating based on multiple rasters.  This tool was useful when deriving our final suitability rasters.

Other tools such as, Topo to Raster, Polygon to Raster, Slope other spatial analysis tools were also utilized throughout the activity.

Ranking Determination

In the lab, each of the ten criteria that were used as part of the final model had to ranked from 3-1 with three being the highest suitability and 1 being the lowest suitability.  The rankings for the datasets can be seen in Figure 4, and are explained below:

Geology

The Jordan and Wonewoc formations are the most sought after for purposes of sand mining (Dustman et. al., 2011).  These formations were given a ranking of 3, and the other formations were given a formation of 1.  The geology ranking raster was produced by converting a geology feature class into a raster, and then performing reclassify (Figure 6.)

Land Use

Land use was ranked for suitability based on what was most practical.  For example, it is not practical to drain a lake, or move an apartment building in order to build a sand mine.  The most impractical areas were removed by reclassifying them as NoData, and the other land uses were given rankings.  Barren land, pasture, and grasslands were given the highest ranking due to practicality in terms of overburden removal.   The land use ranking raster was produced by reclassifying the National Land Cover data raster (Figure 7.)

Railroads

The railroad ranking was based on how far away a given place was from a rail terminal.  This is because it can be both expensive for the companies to truck the sand across roads, and can also have wear out the local roads (Hart, et. al., 2013).  The railroad ranking raster was produced running the Euclidian distance tool with rail terminals in the state of Wisconsin as an input source, and then reclassifying this into a ranking. (Figure 8.)

Slope

The slope ranking was based on how steep the slope was, with lower slopes having the highest ranking.  The slope ranking was produced by taking an digital elevation model from the USGS, using the slope tool, and then reclassifying this into a ranking (Figure 9)

Water Table

The water table ranking was is based on how deep the water is in the ground.  Sand mines need large quantities of water for processing (Prengaman, 2013), so areas where  water is closer to the surface received the highest ranking.  The water table ranking was produced by taking water elevations contours, converting this into a raster, and then reclassifying this into a ranking (Figure 10.)

Streams

Streams located near sand mines may be negatively impacted by storm run off events, and degradation of riparian ecosystem can cause loss of wildlife(Wisconsin DNR, 2012), therefore areas that are farther from streams are given a higher rank than areas nearer to the streams (the rank corresponds to suitability to mining).  The distance from streams ranking raster was produced by selecting streams that were greater than 2 in order, using them as a input source feature to run euclidean distance and then reclassifying this into a ranking (Figure 11.)

Prime Farmland

There are some studies that reclaimed sand mines put back into use as agriculture may still produce crop yields of similar volume (Orr and Krumenacher, 2015), but it is still better to find mine sites at places that are not prime farmland, therefore areas that were rated as prime farmland were ranked as low suitability.  The farm ranking raster was produced by converting the farmland feature class into a raster, and then reclassifying into a ranking (Figure 12).

Residential Areas

Noise, and air pollution are both two reasons why it would be better to keep sand mines further away from residential areas ((Wisconsin DNR, 2012), therefore areas close to residential areas were given the lower ranking, and areas further away were given the higher ranking.  The residential ranking raster was taken by reclassifying the land use raster for residential areas, and then running a euclidean distance and then reclassifying into a rank.

Schools

For similar reasons as the residential areas, it would be better to keep the sand mines further away from schools, therefore areas closer to schools were given the lower ranking and areas further away were given a higher ranking.  The school distance ranking raster was made by selecting land parcels owned by school districts and then running a euclidean distance and then reclassifying into a rank (Figure 13)

Landfills

I chose this site because there are sometimes issues with contaminated runoff associated with frac sand mining.  There could some potential for greater environmental harm if a sand mine was located near a landfill, not to mention the economic costs as properties that are both near a sand mine and a landfill may be very low in value.  Therefore areas farther away from landfills I ranked as high, and areas near the landfill I ranked as low.  The landfill ranking raster was made by running a euclidean distance with landfills as the source feature, and then reclassifying this into a rank (Figure 14)

Figure 4.  Values associated with the ranks of various datasets

Figure 5. Geologic suitabilility dataflow shown on left, and map shown on right

Figure 6.  Land use suitability dataflow shown on left, map shown on right

Figure 7. Distance from rail terminal suitability data flow shown left, map shown on right

Figure 8. Suitability of slope dataflow shown on left, map shon on right

Figure 9.  Suitability of water table dataflow shown on left, map shown on right

Figure 10. Stream risk dataflow shown on left, map shown on right

Figure 11. Farmland dataflow shown on left, map shown on right

Figure 12. School risk dataflow shown on left, map on right

Figure 13. Lanfill risk dataflow shown on left, map on right

The data flow models were split into two sections, suitability and risk, shown in figures 14 and 15 respectively.  Afterwards those models were run, the two outputs of each of the respective models were added together using raster calculator.

Figure 14.  Mining suitability dataflow model

Figure 15.  Mining risk data flow model

Results

The results of the mining suitability data flow model can be seen in Figure 16, and the results of the the mining risk data flow can be seen in Figure 17.  The combined overlaid outputs of both suitability and risk can be seen in Figure 18.  The final has a ranking of classification that goes.  There was an issue with the raster being clipped to a smaller extent, but it still can be used to show the different areas of high and low suitability.

Figure 16. Output of the mining suitability data flow model

Figure 17. Output of the mining risk data flow model

Figure 18. Combined output of both models

Discussions

Using the map, it is easy to see that different areas have different rankings in terms of risk and suitability for sand mining.  The final map does offer some usefulness, but could be improved by making adjustments to the model.  If this map was intended for actual use by a mining company, it would be of greater importance to do more detailed research on the qualities of land that make them suitable or risky places to mine.  This would help set the rankings better reflect the reality of conditions.  Additionally, in a more advanced model, it would not be assumed that all of these factors are of equal importance, and therefore different weightings could be assigned (This was shown in a Python Script).

Conclusions

In this lab, the suitability and risk of frac sand mining was evaluated for areas within our study region.  This lab, and the previous lab on network analysis, can offer a critical evaluation of sand mining, and can be useful in several ways.  It can be useful for the frac sand mining industry, as it helps search for appropriate places to build mines, and it could also be useful to policy makers looking for ways to regulate sand mining.  It further shows the usefulness of GIS techniques in helping solve real world problems, and specifically how raster analysis can be useful.  The tools of raster analysis can be used to design a wide array of suitability analyses which can help facilitate effective decision making.

Data Sources

Geology, Natural Resources Conservation Service

Land use, National Land Cover Dataset

Railroad network, Department of Transportation

Slope, National Elevation Dataset

Water Table, Wisconsin Geological Survey

Stream, Trempealeau County Land Records

Prime Farmland, Trempealeau County Land Records

Residential areas, National Land Cover Dataset

Landfill, Trempealeau County Land Records

County boundaries, Trempealeau County Land Records

Information Sources

Dustman, J.E., Gulbranson, B., Bell, P., Gregg, W., 2011: Characteristics of high quality frac sand, and where to find it in the upper Midwest., Geological Society of America Abstracts with Programs, Vol. 43, N 5.

Hart, M. V., Adams, T., & Schwartz, A 2013. Transportation Impacts of Frac Sand Mining ;CFIRE

Prengamon K. 2013 New studies measure air, water impacts of frac sand mines, Wisconsinwatch.org

Wisconsin DNR, 2012 Silica Sand Mining in Wisconsin, dnr.wi.gov

Image Sources

Sepp, S.  Fine grained sand (2015).  Retrieved:

https://commons.wikimedia.org/wiki/File:Sand_from_Gobi_Desert.jpg

Nasa.  Satellite photograph (2009). Retrieved:

https://en.wikipedia.org/wiki/Agriculture#/media/File:Precision_Farming_in_Minnesota_-_Natural_Colour.jpg

Sturmovik, Railroad photograph (2008).  Retrieved:

https://en.wikipedia.org/wiki/Pennsylvania_Railroad#/media/File:NS_Buffalo-Line-Signal-304-3042-APPROACH.jpg

O’Neil,, A.  Sloping hill photograph (2011). Retrieved:

http://www.geograph.org.uk/photo/2424923

Dilmen, N.  Water texture (2013). Retrieved:

https://commons.wikimedia.org/wiki/File:Water_texture_1390020_Nevit.jpg

St. John, J. Sandy River photograph (2009). Retrieved:

https://www.flickr.com/photos/jsjgeology/20035367951

USDA NRCS, Corn in Residue with Healthy Soil (2013). Retrieved:

https://www.flickr.com/photos/nrcs_south_dakota/9314025147

Hodgson, A.  Residential Area (2005). Retrieved:

https://commons.wikimedia.org/wiki/File:Residential_area_off_Tileshads_Lane,_Cleadon_Village_-_geograph.org.uk_-_6409.jpg

Public domain photo, second grade writing class (2010). Retrieved:

https://www.flickr.com/photos/wwworks/5074150006

Network Analysis

Background

One negative impact that sand mining can have, is deterioration of roads from the heavy trucks carrying frac sand.  For numerous counties, this is a serious concern as it could lead to higher road maintenance costs.  The additional costs to the counties could potentially be avoided through road upgrade maintenance agreements (RUMA) between sand mining companies and county governments (Hart, Adams, and Schwartz, 2013)

Figure 1.  On the left is shown elements of a good road use agreement (Hart, Adams, and Schwartz, 2013) 

 

Part of understanding costs, is understanding the routes being used to transport sand from mines to rail terminals.  This can be done using network analysis, a set of tools that can be used to calculate the best routes in terms of distance, expense, and time.  Utilizing network analysis is often referred to as logistics, and can help companies and individuals transport more efficiently.

 

 

Goals and Objectives

In this lab, we will perform a network analysis to find the routes that would be used to transport sand from mines to rail terminals.  We will then use this estimation to produce an estimate of how much counties would have to spend on road maintenance annually.  We will be using theoretical numbers provided by our professor on how often the roads are used and what the average cost of a truck driving over a section of road is.  Because of this, our results our not intended to be an accurate representation of actual costs.  They are meant to show the process by which you could use network analysis to generate these estimates.

The general work flow for this lab is shown in Figure 2.  We prepared data by removing mines within 1.5 km of a rail road.  This was done using Pyscriptor, (code can be viewed here).  Then we calculated routes based on closest facilities using network analysis and built a model to incorporate theoretical costs given to us by our professor and calculated our final answer.

 

Methods

 

Step 1.  Prepare data using queries

Using a Python Script, mines that were within 1.5 kilometers of a rail terminal were eliminated.  This was done, because these mines would likely load the sand directly unto trains, and therefore would not likely have a great impact on the road.  The feature class produced from the python script was used in the following steps.  The mine feature class produced can be seen in Figure 3 below.

Figure 3.  Map of locations of Sand Mines and Rail Terminals in Wisconsin.  Mines within 1.5 km of rail terminals not shown.

Step 2.  Calculate routes based on closest facilities

 

After enabling Network Analysis, Esri street data was imported.  Then using the Closest Facility tool we calculated the closest routes from each sand mine to a rail terminal.  The mines were loaded as incidents and the terminals were loaded as facilities. The resulting routes can be seen in Figure 4.

Figure 4.  Map of routes from sand mines to rail terminals derived using network analysis Closest Facility tool

 



Step 3. Generate hypothetical estimates of cost per county

 

Our professor gave us the following theoretical information to use to calculate the annual cost per county that would result from hauling sand on the roads.

• Assume each sand mine takes 50 truck trips per year to the rail terminal, and that the truck has to return to the sand mine after each trip
• The hypothetical costs per truck mile is 2.2 cents

Model builder was used to organize the tools needed to perform the spatial and data analysis needed for this calculation.  The model can be seen in Figure 5.  A description of the process is written in numbered steps below:

 

1.  Used Closest Facility tool in Network Analysis to solve for routes.  Mines were loaded as incidents and rail terminals were loaded as facilities

 

2.  The routes were selected and copied into a geodatabase.

 

3.  The routes were projected in NAD 1983 Wisconsin TM (Meters) so that distance could be calculated

 

4.  The Intersect tool was used for the routes and the Wisconsin counties that were in the same projection.

 

5.  A field was added to calculate the distance of the routes in miles

 

6.  Summary statistics were used to get the sum of routes in each county

 

7.  A new field was added and calculated to account for the number of truck trips annually

 

8.  A new field was added and calculated to account for the cost of these truck trips

Figure 5.  Model used to organize tools and estimate the hypothetical annual cost per county

Results

The hypothetical annual costs of sand mining associated with road maintenance varied greatly between different counties.  Some counties with sand mines, like Buffalo and La Crosse have less than 50 dollars in associated road costs, while other counties like Barron and Chippewa have over 400 dollars in associated road costs.  The differences between counties can be seen in the map in Figure 6 and the graph in Figure 7.

Figure 6.  Map showing hypothetical annual costs of road maintenance due to hauling sand in various Wisconsin Counties

Figure 7.  Graph showing hypothetical annual costs of road maintenance due to hauling sand in various Wisconsin Counties

 

 

Discussion

 

Network analysis is incredibly useful, and can be used to make smart and efficient choices in transportation.  They can also be useful in helping determine the impact that certain businesses might have on local roadways, and can be used to make sure that they are held accountable for this.

Model building was also useful in this activity, in that it helped organize the tools used, and could be used again even if certain parameters or data was altered.  For instance, it would not be too difficult if we were to receive an updated list of mine locations, to simply adjust the model for this and run it again.  It is a great time saver when you are performing a series of operations that may need to be run again with new and updated information.

 

Conclusion

 

In this lab we prepared data using queries, we calculated routes using network analysis, and we build a model to derive hypothetical annual road maintenance costs associated with sand mining.  We went through the steps that could be used when performing research on the associated costs of sand mining.  Actual analysis on these costs are very important for counties who may need to negotiate road upgrade maintenance agreements (RUMA) with sand mining companies. 

 

Sources

 

Data

Mine Locations: Wisconsin DNR

Railroad Terminals: Federal Railroad Administration

Basemaps: Esri

Streets for Network Analysis: Esri

Wisconsin Counties Feature Class: Trempealeau county geodatabase

 

Background Information

Hart, M. V., Adams, T., & Schwartz, A. Transportation Impacts of Frac Sand Mining in the MAFC Region: , CFIRE.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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

Goals and Objectives

The purpose of this lab was to geocode the locations of frac sand mines in Wisconsin.  The locations of the sand mines will be important in further parts of this project when we perform network analysis on the frac sand mines. 

Geocoding is when a spatial location is mapped using a description of the location, such as an address or latitude/longitude coordinates.  In this exercise, our professor gave us descriptions of the frac sand mine locations originally provided from the Wisconsin DNR.

A table from the Wisconsin DNR was normalized and then used to geocode for the locations of frac sand mines.  This offers good experience working with data that is not in the format that we need for our project.  After geocoding, we will compare the spatial locations of sand mines with other members in the class, and with the accurate locations of the mines.  This will be done as a way to evaluate how the geocoding went, and to calculate error.  A workflow for the activity can be seen in Figure 1.

Figure 1.  Workflow for the exercise, provided by Professor Hupy

Methods

Table Normalization

The table with descriptions of the Sand Mine locations needed to be normalized before it could be geocoding could be done.  The process of normalizing the DNR table included

• Removing any commas between words
• Separating Address into different fields for street name, town name, zip code, etc.
• Separating the PLSS descriptions from the street address descriptions

In Figure 2, a selection of addresses that were normalized is shown. There was little organization kept throughout the table to begin with, and it was necessary to go through all of them to adjust the formatting.  Additionally many of the sand mine locations did not have any street addresses and had only a PLSS description.  

Figure 2.  Example of table normalization

Geocoding the Mines

The first step of this was to log in to ArcGIS online using the UWEC enterprise log in.  Geocoding requires credits, and will not run if it is not logged into a correct account.  After selecting the normalized table, the mines were geocoded using the World Geocode Service (ArcGIS Online).

Then using the View Address Inspector, check to make sure the ones that are matched are in the correct place.  13 out of the 18 sand mine locations I was geocoding were correctly matched automatically, and 5 of them were tied (see Figure 3). For many of them, it was simply an issue of moving the points so that they were near the driveway.

Figure 3. Screenshot taken after the automated geocoding process

For the locations with only PLSS descriptions, they would end up being placed in the center of the town.  These locations had to be found without the help of the automated geocoding.  

The PLSS quarter quarter sections from the Wisconsin DNR 2014 geodatabase.  From these, query statements could be used to locate where they were on the map.  For some of them, it was helpful to look at the same locations using google maps in order to locate the mines.  After I had checked the positions of all of my mines, and adjusted the positions of the ones that were off and then exported it as a shapefile to be shared with the rest of the class.

Then I made a new geodatabase, and uploaded the shapefiles from my classmates, as well as the shapefile from the DNR showing the actual locations of all of the sand mines.  Before I began, I projected all of my data in NAD_1983_Wisconsin_TM_US_Ft.  I chose this because there were mines across all of Wisconsin, and this was the best projection for the given study area.  It was essential to project the data, because I would be calculating distances in the next steps.

After looking through classmates, ones without a Mine ID field were deleted, because without the mine numbers, they could not be properly compared.  The mine IDs were sometimes recorded under different headings, so I created a new field in each of my classmates table with the field title ID, so that when I merged them, all of the mines would be in the same field and would be easy to query.  I first merged the shapefiles of my classmates’ mines together, and then I queried to find the Mine IDS that I also had found.

Using a query, I selected the same mines that I had geocoded from my classmates and used the generate near table tool to find how closely we had placed the mines to each others.  A near table was also used to compare the distance between my points and the DNR verified sand mine locations, and to compare our entire classes estimates to the DNR locations.

Results

For the most part, the geocoding of myself and my classmates had high amounts of error.  The average error (taken as an average distance from the near distance table) between my geocoded mines and my classmates was 36,874 feet.  The average error between my geocoded mines and the DNR mine locations was 33,889 feet, and the average error between the entire classes geocoded mines and the DNR locations was 14,215 feet.

Figure 4. Comparison of my geocoded mine locations with my classmates.  Example of estimates that were close together shown top right, example of locations that were far apart shown bottom right  

Figure 5.  Comparison of our classes geocoded sand mine locations compared to actual locations provided by the DNR.  

Discussion

The following table (Figure 6.) is from a class textbook that systematically lays out the different types of error possible when generating geographic data.  For the most part, I believe that mostly inherent errors were the reason that we had such large error values in our geocoding.  There was limitations with how up to date we could find aerial photography for (using either basemaps in google or ESRI), and this prevented us from accurately locating many of the sand mines.  Also, the data that was originally described using PLSS descriptions proved to be challenging to find, and was often hard to correctly get.  

Figure 6.  Table with classifications of GIS errors.

An example of a more operational error that may have occurred is a few  incorrectly geolocated mines are lowering the average accuracy.  When looking at the statistics of the near table for comparisons between myself and classmates, myself and the DNR, and the entire class and the DNR, I noticed that most of the distances were actually reletively small, and that a few of the larger distances were likely a reason that our average error was so high.

Figure 7.  Generate Near tables shown with frequency distribution tables to their right.  

Conclusion

Locations of Sand mines in Wisconsin and Trempealeau County

As a result of this lab, we now have a sense of where the frac sand mines are located in Wisconisn, and although we now have that spatial data from the DNR, it is important to know how to derive that information in case it was not available.  Geocoding can be a very difficult process, particularly when there are problems with the initial data sets being used.  However, it can provide extremely useful information when done correctly.  It is always good to have ways to evaluate errors that exist in your data, and to determine what the sources of those errors are.  

Data Gathering

Goals and Objectives

This semester we will be working on a project evaluating the suitability and risk of sand mining in western Wisconsin in our chosen study area of Trempealeau County. The first step any GIS based project is to acquire data, either by personally collecting data in the field, or by accessing pre-existing data sets.  In this project, we will be using publicly available data, downloaded from a variety of organizations. 

 The goals of this lab are to:

• Download data from various organizations
• Keep data organized and properly labeled
• Find metadata for what we downloaded
• Use Python to project and clip rasters

The objectives of this lab are to: 

• Gain familiarity with a wide array of data sources
  
  
• Develop skills to be more organized and efficient when downloading data
• Become more familiar coding in python

Methods

Figure 1. General data flow model for downloading data, provided by Professor Hupy

The steps in the data flow model shown in Figure 1 offer a good outline for the process of data collection and data management. Below I list main parts of the exercise

• Project and clip/extract data and load data into a geodatabase, then delete redundant data
• Produce a table showing metadata

Download zip files to a temporary directory and extract files to a working folder

• We downloaded the following data from the websites that are respectively listed:

National Elevation Data: http://nationalmap.gov/viewers.html

Trempealeau County Land Records Geodatabase: http://tremplocounty.com/landrecords/

Soils Data: http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

National Land Cover Data: http://datagateway.nrcs.usda.gov/

Railroads:  http://rita.dot.gov/bts/sites/ridta.dot.gov.bts/files/publications/national_transportation_atlas_database/2013/index.html

• The zip files downloaded from these websites were then into a temporary folder, and then extracted into our working folders.  This was done to reduce the amount of files we would have in our working folders.

Project and clip/extract data and load data into a geodatabase, then delete redundant data

• Projecting and clipping rasters was done utilizing python.  Redundant data was deleted to save space and keep folders organized.

Data Accuracy (Produce a table showing metadata)

It is important to have metadata for the data used in this project.  It is needed in order to understand how valid any results that we drive from analysis can be.  For instance, if a data set that we have downloaded is particularly old, we may not be able to make many accurate statements about what the current condition of a feature might be.  Also, it is important to have a sense of how accurate and precise the data is, so that we can describe how precise and accurate our results are.  

For these reasons, a metadata table (Figure 2.) was produced in order to have a simple reference that we can look back on as we continue forward in this project

Data	Scale	Effective resolution in meters (Approximation)	Minimum mapping unit (acres)	Planimetric Coordinate Accuracy	Lineage	Temporal accuracy	Attribute accuracy
Railway Network	1:100,000	50	N/A	N/A	U.S. Department of Transportation	2015	N/A
National Land Cover Data set	1:60,000	30	1	N/A	U.S. Geological Survey National Map Viewer, Multi-Resolution Land Characteristics Consortium	2011	N/A
National Elevation Data	1:60,000	30	N/A	N/A	U.S. Geological Survey National Map Viewer	2011	N/A
Cropland Data	1:60,000	30	N/A	N/A	U.S.D.A. National Agricultural Statistics Service, National Resource Conservation Service	2006	85-95% accurate, data standard
Soil data	1:12,000	10	3	N/A	National Resources Conservation Service	2004-2014	N/A
Trempealeau County Land Records geodatabase	The geodatabase downloaded from the Trempealeau County Land Records website contains 71 feature classes organized into 6 feature datasets.  Instead of listing all of these out in this metadata table, I will check metadata periodically for individual feature classes that are utilized in the project

r     Figure 2.  Table showing metadata for various data sources
   

     Results

      The following map (Figure 3) shows the rasters that were downloaded, projected and clipped during this activity.  The elevation is shown in meters.  The crop cover map from the National Agriculture Statistics Survey, and the land cover map from the Multi-Resolution Characteristics Consortium show different land use in different colors.

Figure 3.  Maps showing various rasters

     Conclusions

      Successfully downloading and organizing data is a fundamental part of any GIS project.  Understanding what the data is, and being able to find metadata characteristics are essential if you want to be able to make any claims on your results after analysis.  In this activity, we downloaded data from various different organizations that utilized different ways of formatting their download process and also had metadata of variable completeness and accessibility. We also projected and clipped data using python, and learned the problems that can be associated with improper coding or labeling of files. 

Understanding where this data came from, how its accuracy has been evaluated, and how to utilize python are all valuable to the project of evaluating suitability and risk in sand mining in Trempealeau County.  

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