To begin this project we need to have data (figure 1) on our area of
interest. The data we are interest in will be for Trempealeau County, Wisconsin
and the surrounding area and will be associated with varying aspects of frac
sand mining. This will require gathering data from several different sources which
will result in varied formats and procedures associated with gathering and
implementing the data. Some of the data of interest are land use, railroad
lines, local geology and soils and the location of sand mines.
The first data we downloaded
was on the railroads from the national atlas website (http://www.nationalatlas.gov/).
The file was an eOO format, railroad map for the 48 conterminous states. The eOO
format is an arc info coverage map. Next we visited the United States
Geologic Survey (USGS) national map website (http://nationalmap.gov/viewers.html.) where
we obtained land cover data for Trempealeau County and the national elevation
dataset. The elevation dataset is a 1/3 arc second arcgrid digital elevation
model. It was received as two individual tiles that we were able to mosaic together
in arcmap. Next we went to the United States Department of Agriculture’s
geospatial data gateway (http://datagateway.nrcs.usda.gov/)
where we gathered data on cropland for Wisconsin. Last we used the Natural
Resources Conservation Service SSURGO (http://soildatamart.nrcs.usda.gov/)
site to gather soils data. All of these data were received as zip files which
were saved into a project file, here they were unzipped. After all of the data
were downloaded and unzipped we opened Arccatalog where we built a new file geodatabase
and imported the data. The SSURGO data was a problem here. This data came as a
database not compatible with arc. To overcome this we used Microsoft access to
read the file into arc. Now that we have all of our data in Arccatalog we can
project the data into the most appropriate format for the area of interest. I
chose to use UTM zone 15N for my data due to the majority of the data being centrally
located in this region. I did not use the state system because of the location
of Trempealeau County and the surrounding area in relation to the borders of
the state system.
| Fig.1. Data layers gathered from various sources for use in our study of the effects of frac sand mining on local roads. |
The next step was to locate the mines. We have record of
about 120 locations of mines or other related facilities that are either in use
or proposed obtained from Wisconsinwatch.org. To minimize the time to locate
all of these locations we formed teams of three to four people and divided the
locations among us. We began by removing all of the sites with good addresses that
should geocoder. We imported a Bing baseman with road names. Then we attempted
to use a geocoder to locate the rest of the sites, by connecting to a local
server. The first attempt yielded what appeared to be good results, of the 29
addresses that I began with I had 22 matches and 7 that did not match. However,
on analysis all of the site were placed within a town. We went back to our excel
table and attempted to normalize the address data, but when we ran the geocoder
again we got a similar result. After doing some research we found a fusion
table with latitude and longitude locations for all of the mines. We downloaded
the table saved it in an excel format and added it to Arcmap as an event theme.
We then had to save it to Arcmap as a feature class and set its projection, now
we were able to use this as a reference in locating the sites. To locate the
sites we used the review/rematch tool in the geocoder. We zoomed to each
individual site to verify it from our original table, then we would zoom out until
we found a mine, when we found one we could verify its location with either its
street address or by the PPLS system, which we overlaid on the map by
connecting to a remote server. This process
took several hours to complete. After completion, I was able to identify 35 of
39 sites (figure 2); the remaining four sites did not have enough information to locate
them. There may be some uncertainty due to a large number of sites being
proposed and not completed and also outdated aerial imagery.
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| Fig.2. This map, with a Bing base map, shows the location of all 35 sites that I was able to match. The symbolization of the sites varies because the matching was done in two smaller groups. |
| Fig.3. The data table of all the sites I was involved in locating. I matched 35 sites while 4 had insufficient data to make a match. |
