By Will Harrison
Student in Community GIS, Spring 2022
The process of georeferencing is very important and useful in many different disciplines of geography. Georeferencing is the process of giving a raster image a geographic reference by overlaying that image to the same geographic reference on the basemap so that the two images align. To do this, you will need to open up a geographic software. I’m going to be using ArcGIS Pro as an example.
Once you start a new project, you will see a basemap of the world in front of you. The first thing you want to do is change the projection to a more precise projection. I have changed mine to NAD 1983 UTM Zone 16, because I am dealing with georeferencing within Georgia, which is in zone 16. I would also recommend you change the basemap under the map tab if you are dealing with buildings or parking lots. If you are just dealing with streets, keep it on the topographic basemap.
Next, you need to find the raster image. A raster image is a graphic that represents a two dimensional image as a grid of pixels. I am choosing a JPEG campus map of the University of North Georgia. I put the JPEG in the same folder as my ArcGIS project, so when I click add data, I know where to find it. Once it is added, your basemap won’t change. If I go to the UNG campus on the basemap, it will not be there, because the JPEG has no geographic reference.
On the left of your screen, you can see the JPEG underneath the contents section. Right click on the JPEG and click “zoom to layer” to find your raster image. If you zoom in or out, you can see it is in a random spot. My image is in the Galapagos Islands. So, now you need to put it in the right spot.
Under the imagery tab, click the “georeference” button to get started. There should be a box indicating so in the top right of your basemap. Then under the georeference tab, click “add control points” to identify a specific point on the raster image. It should show a red square where you clicked. There should also be a dashed line following your cursor. You need to then zoom into where that exact point is on the basemap. It will then show a red circle with an “x” through it to symbolize the completed control point created. Street intersections or building corners are a good reference to use.
Once you do this, the map should now be in the same general area, except now your raster image is covering your basemap. To get around this, go to the appearance tab at the top. Mess around with the transparency of the image. This way, you can see both images at once. The more control points you create, the more accurate it will be. Trying to add control points on opposite sides of the imagery helps to place it more accurately, quicker.
In just three control points, most of my map is placed correctly. If you go back and forth between transparencies, you can see how accurate it is. The buildings, parking lots, and roads will start to align perfectly. If they aren’t lining up, try looking at your control point. If there is not one created near where it is not aligned, try and create one. If there is a control point near and it is still not lined up, you may want to see if a feature has changed over time.
Once again, this can be useful in many different geographic ways. The use of control points, for example, is used by a surveyor on AutoCAD. AutoCAD is another geographic program. The surveyor needs control points to geographically reference himself in the real world to put points on a job site. In our class, we just use it to reference a boundary line JPEG that doesn’t have geographic information attached to it, but it is a relatively simple process if you’d like to do it yourself.
By Matt Cassada
Student in Community GIS, Spring 2022
When we initially started the 1958 Athens Census Mapping Project, I had initially a good idea of what I wanted to do since I noticed two distinct features with the Athens Census Mapping Data: population of the entire area along with businesses around the area. Next, I considered the timing of when this mapping took place which was in 1958. During this time, segregation was still a lingering issue across the Deep South, and this was true in both major urban and rural areas. Finally, I noticed that this mapping data also included major businesses around the Athens area and this included pretty much everything that was a business: federal businesses, food, entertainment, religious groups, and many more.
With these central ideas already placed for me, I decided upon the following observations:
With this plan, I decided to then form my central question/argument for the project which is: "Based on the location/concentration of either colored or non-colored owned businesses, does the population distribution of colored and non-colored residents seem different across Athens-Clark county area? Do you also see a population distribution difference based on if the particular resident was either a colored or non-colored resident? Is their a particular business that could be made out where we see the strongest correlation and is their one with the lowest correlation in relation to Athens population data and business location’s?"
With these questions in place, I then progressed into making my own maps. We all started with a excel/point data of the Athens 1958 Census Data, which we got thanks to cleaning out the initial data. I knew that I had to create two initial data sets just for the residents of the area I made two different point data sets, one for colored residents and one for non-colored residents. This proved to be easy since each resident is listed out if they are colored or non-colored residents. I then inputted the point data into ArcGIS and I then signaling out/deleting the data that was needed for each point set. Thus, when I did this, I ended up with two different point data sets for both colored and non-colored residents.
Initially, you do see some distribution differences in the point data. First, we see that the non-colored residents are more spread out across the Athens area and they are not as centralized. This is different for colored residents who were more centralized near downtown Athens and east of Athens. Colored residents were also less dispersed and more organized compared to non-colored residents. They we more organized in that they were more grouped together.
When it came to making the business points, I must first start off and say that given that data wasn't 100% complete. We still had some business points that were not complete and plenty of businesses that were not registered in the excel data. But I still pressed on with my maps since I still had just about over 300-400 businesses I could look at.
To make the point sets for the businesses, I first needed to make some distinctions between businesses. Since I wanted to look at what kind of businesses had the greatest correlation for residents, I needed to focus businesses out of each point data set and give them their own distinction. After looking through each business, I noticed four different business categories:
With these distinctions in place, I implemented the same overall process I did when it came for the residents in Athens. I cross referenced the points that were businesses and centered on those. I then went through each point individually to look at what category they would fall under based on the distinctions I made. I then repeated this process for all four different business categories.
Finally, when it came to patterns that I noticed with the businesses and its correspondence to residents, the one business I noticed that had the most central concentration was near religious businesses, like churches. The one business that had the least appeared to be food businesses, with very little shown correlation between the two for either resident.
In conclusion, this mapping project proved to be insightful to me. As someone who has never lived in Athens, its interesting to see how the demographics and businesses across the city has changed since the late 1950’s. This project showed not only how residential demographics has changed, but also how businesses across downtown Athens have shifted: from more of a rural area to a more college-themed town.