Lab 6: Geocoding

Goals and Objectives:

                The goal and assignment for Lab 6 is to geocode the locations of all of the sand mines in Wisconsin. We will do this by completing these objectives; normalizing the mines in Excel, Connecting to the ESRI Geocoding service, connecting to the department ArcGIS Server, Manually locating all the mines that only have a PLSS location, and comparing our results with the rest of the class.

Methods:

                We were given this data by the Wisconsin DNR and we quickly found out it was going to be a pain. The reason why it was going to be a pain is because it wasn’t normalized. What I mean by normalized is having the full address separated out into single fields for each feature. For example a street address needs to be separated into Address and Street, City, State, and Zip Code. You cannot have all of those in one single field because the geocoder won’t be able to pull apart the separate items. So it was up to us to go through the easy but time consuming process of normalizing the tables for the mines that we were assigned (18 mines each).

Once we completed that task we had to start the geocoding process by connecting to the ESRI geocoding service for one type of geocoding while also downloading the PLSS layers from our departmental server for the other type of geocoding. What are these two types of geocoding you ask? Well one is for those locations that have a normalized address associated with it that can be easily picked apart to find the exact address. The other type is for those locations that don’t have addresses associated with them but instead have PLSS locations which then have to be interpreted by ourselves to find the general location and then referencing other imagery to see where a mine may be currently.

This task was very time consuming as we had some problem locations that took many different referencing images to find the mine. When we had all the mines geocoded we exported them as a feature class so that we could merge them with the rest of the class. Here came a whole other can of worms as not everyone in the class had the same table structure so we had to go through every single persons attribute table and edit or delete data that was restricting us from using the merge tool to combine all of our geocoded mines. This took many problem soving skills and knowledge of Arc to figure out what was needed in order to merge two or more layers together. One common problem is that we couldn’t merge layers that had different types of fields. For instance some people for some reason had a string field that needed to be merged with a double field (doesn’t work).

LONG STORY short, the merge tool finally ran. With this new layer we put it into a spatial join with a layer that contained the actual mine locations (Thanks a lot Dr. Hupy….). Understanding that one of the reasons this lab is in place is to frustrate us so that we never give other people bad data is a hard lesson to learn but must be learned none the less. Anyways, the new layer created from the spatial join now had a field that gave us the distance between where we geocoded our mines to and where the actual mines were according to their lat/long. I compared my geocoding skills to the rest of the class and found that the class’s average distance that they were away from the actual mines was 1857 meters while my average was 306 meters closer at 1551 meters. I guess that means I did something right.

Results:

Figure 1: Comparison of a Table that is normalized versus a table that is not.

Figure 2: Map showing the correct actual locations of the mines versus the incorrectly geocoded ones.

Figure 3: Final table with a field showing the distance between the actual locations and the incorrect locations in meters.

Discussion:

                We can know which points are actually correct by referencing other images of the locations and by using the exact GPS coordinates. This way you don’t have any error resulting from incorrect PLSS interpretation.

Conclusion:

                The overlooking lesson from this lab was DATA INTEGRITY and why we should do our absolute best to never do this to anyone in our professional lives. Geocoding is an essential part of GIS especialt when you’re dealing with people and you want to make sure and get it right so you don’t but Joe in Bobs house. You can also see how even when you put a lot of time into trying to geocode all these addresses you can still be off as nothing is exact as the actual Lat/Long. However, this lab was instrumental as we move forward with our semester project. From here we can now take a look at network analysis and how we can route the sand from those mine locations to railways where they can be further shipped off.