Objectives: As part of the technical report following the
Geocoding and Table Normalization portion of our fracture sand mining project,
a few overarching objectives were outlined to give students clear understanding
of the techniques and skills involved in Geocoding using a GIS program. For
this project, we were supplied with a data table of the facture sand mines located in Wisconsin from the Department of Natural Resources
(DNR). Presented with this table of facility and locational information, we needed to organize
the structure of rows and columns into separated address attributes to allow
for efficient loading in the correct manner into the Esri program ArcMap.
The activity of organizing is called table normalization and is a critical part to begin to locate the mines via geocoding. By separating out the address and PPLS
information we allow Esri software to place the mine location on the map more accurately.
Along with geocoding, Table normalization will prove to be a valuable tool as
this project moves forward.
Methods: The next
portion to build towards the overall project goal is to process the mine locations with the end result of geocoded mines in the correct location.To begin, the normalized table is loaded into Esri ArcMap, followed by loading a base map which to aid in location the mines along with PPLS quarter sections. As the geocoding tool bar is activated, Esri automatically matches the mine
attribute to a location on the map based on ether the local street address or
PPLS address. The automatic matching is often not every accurate, especially when only the PPLS address is given as Esri's system doesn't function with PPLS information. Concerns with accuracy of automated matching will be
addressed upon completion of the project. Once all the mines are plotted students went through the strenuous
and sometimes confusing task of negotiating if the mine is actually located in
the spot, or not. Students have a tool box of ways to accomplish this, some of
which include a GoogleMaps search, using a Ersi Base map, or even a PPLS finder
online if the mine is particularly hard to locate. Once the most accurate
approximation of the location is made, the student manually matches the mine to
the location using the Geocoder toolbar. For each mine the steps are repeated.
However the great variety of address information made the task more of a
challenge. Some of the mine information did not contain a street address or only possessed a PPLS address. This did provide an added element of complexity, but nothing
that time and diligence could not overcome. After all the mines have been geocoded, the objective is to move to a dataset of high accuracy and check your geocoded locations against the actually mine locations.
Results:
By dividing the various elements of each mine into its own unique field, the information could be used to geocode the location of the mines. The resulting Table below has been appropriately normalized. 
Figure 1: Non-Normalized table contains
multiple pieces of attribute information in each cell.
By dividing the various elements of each mine
into its own unique field, the information could be
used to geocode the location of the mines.
The resulting Table below has been appropriately normalized.
used to geocode the location of the mines.
The resulting Table below has been appropriately normalized.
Figure 2: This table has been normalized in such a manner that allows for efficient loading into Ersi ArcMaps. Each column contains one type of information. A normalized table contains individual attributes separately to maximize clarity of address.
A few of the mines only contain a street address, while others have only a PPLS address. These types of inconsistencies make it hard to automatically plot all of the mines accurately. Steps must be taken by the individual to facilitate accurate geocoding. Tools like Google Maps, and online PPLS locators can aid in locating the mine if the individual is not able to find the mine in ArcMap using a PPLS address feature class.
After time has been taken to individually place each location of the mine to the best ability, the mine actual locations are imported into ArcMap after being obtained from a facility member of the University. It is always best practices to test for accuracy using a dataset with a higher degree of accuracy then the data set you are working with. To analyze how closely my geocoded mine locations matched the closeness of coordinate data of the true positions of the real world feature mines, I used a native Ersi data management tool "Near".
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| Figure 3: Locations of assigned mines vs true locations of mines. Base map source: Esri Online 2013 Esri.com |
This tool located the mine with the corresponding unique Mine_ID which each mine feature class contains. The Near tool created a new feature class as a result of the analysis in the source table, which was my mapped/geocoded mines. The new field contains the closeness of my geocoded mine to the true mine locations. Figure 3 shows mines locations in green and yellow, my mines being the former, and the true locations the latter.
Figure 4 below is the attribute table after I ran the near tool. The highlighted field shows the distances in meters of the mines. Some were placed quite close the true locations, while others missed altogether, as is the case of the last two mines. Although the accuracy was not very stellar for the set as a whole, lessons learned are very important. Computer programs often require getting "dirty" with the data, using and manipulating while aiming to learn best practices requires time and experience. This dataset provided great experienced in the class to help gear students work in real world situations and data.
Figure 4 below is the attribute table after I ran the near tool. The highlighted field shows the distances in meters of the mines. Some were placed quite close the true locations, while others missed altogether, as is the case of the last two mines. Although the accuracy was not very stellar for the set as a whole, lessons learned are very important. Computer programs often require getting "dirty" with the data, using and manipulating while aiming to learn best practices requires time and experience. This dataset provided great experienced in the class to help gear students work in real world situations and data.
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| Figure 4: Fields in source table (my mapped mines), highlighted in blue is the distance field on how closely my mines matched the true mines. |
