Burning Down Davidson

In this exhibit, I use Heganoo, which advertises itself as a “personalized interactive mobile map” to create a story about the fires that have occurred on Davidson College’s campus. Although Heganoo is limited in terms of overlaying historical images and maps on to current day scenes and creating a data-thick map, it is a useful tool to provide spatial context to a story that has an ordered plot. As can been seen below, the basic  Heganoo event map provides numbered locations for a storyline:

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Heganoo Basic Event Map

I think that story lines are Heganoos speciality. Some of the alternatives to Heganoo are Neatline, which specializes in compiling information and allowing the user to explore independently,  and History Pin, which makes historical comparisons easy by overlaying historical images onto Google Maps.  Heganoo does not incorporate ambiguity or allow user interaction, which Bethany Nowviskie considers to be important attributes of  Neatline. Instead, similar to Charles Cunningham’s “21 Steps,” Heganoo guides the user along a constructed story path and compartmentalizes information in order for it to be easily understood.

Not only could the stories of Davidson fires be told using another mapping tool, they could also have been told without maps altogether. However, as Farman argues in his article, “Site-Specificity, Pervasive Computing and the Reading Interface,” there is an importance in site-specificity and including the locations of the fires adds another dimension to the experience of the stories. In my exhibit, in addition to learning the details of each fire and some specifics about each building, knowing the location of each building allows the user to get a better sense of campus. For example, the location of older fires allows the user to get a sense of the old campus and the newer fire in Belk informs the user about how the college expanded down the hill. Additionally, the map enables the user to locate other buildings on campus, such as the church from which people ran to save the burning Watts dormitory.

In conclusion, I feel that Heganoo is the best application for presenting project similar to the exhibit I created that features the locations and stories about burning down Davidson College.

PA 5: Evolution of Campus Workout Facilities

Heganoo Exhibit

I have taken an in-depth peek into the construction and destruction of gyms on campus throughout Davidson College’s history to form an argument stating that the increase in importance of physical fitness as well as the increase in the college’s financial capabilities resulted in more fitness centers existing on campus. Heganoo, which we decided was great for events and the life of a subject, proved to be effective at depicting my narrative. Seth Long, in Digital Maps and Social Data addresses that maps (such as poverty maps) are “a mashup of spatial and non-spatial data”. His point resonates in my Heganoo exhibit because my narrative would be much weaker if it lacked images and description as well as the interactive aspect. As the map maker, I can ensure that the reader has an understanding of my argument since I can accompany the map with non-traditional map features such as text and pictures.

Furthermore, in Mobile Stories, Farman states in “Site-Specificity, Pervasive Computing, and the Reading Interface” that ““Stories tend to offer the illusion that they present the events in their entirety (and if they leave out anything, the omitted portions are simply not relevant)” (Farman, 9).  With Heganoo, I was able to shape an argument with all relevant information because of the tools that I had in hand. As a viewer of maps, it is helpful to see change in location accompanied by dates, explanations, and images that can further educate the user.

The college’s history involves the ‘birth and death’ of buildings; some ‘die’ faster than others, and the ‘birth rate’ for certain types of buildings is higher than other types. For example, as expected, academic buildings typically outlast and are more frequent than athletic buildings.  Until, 2001, only two workout facilities (not places where teams compete, but places where students can workout) ever existed on campus at the same time. Previously, the need for more dorms or academic buildings took priority over athletic facilities (rather appropriately), which is evident by the demolishing of the first two gyms on campus. The reason for one type of building taking priority over another can be explained by Lefebvre when he mentions in the Production of Space that “(Social) space is a (social) product” (Lefebvre, 27).  For Davidson College, at one point in time, there wasn’t enough need for workout facilities to be a top priority for campus updates, but as the social structure of Davidson changed so did the structure of space.  The increase of fitness centers is a result of necessity, capability, and social change on campus; as time goes on Davidson College is more capable and aware of providing students with fitness centers (and this could apply to other types of buildings as well).


PA 5 – Davidson College in the Medical World


Today, Davidson College is well known for its liberal arts education that prepares students for a variety of future careers and graduate schools. Although Davidson’s “College” status prohibits graduate programs such as a medical school, Davidson College’s history is linked closely with the medical world. Using Omeka’s plugin for Neatline I have created a visual narrative depicting just a few of the important medical advances that have occurred in throughout Davidson College history. As Farman argues in his Chapter in Mobile Stories, stories do not have to be linear and cohesive. In fact, the discontinuity of Davidson College’s medical history is exactly what I intended to capture in this exhibit. In Bethany Nowviskie’s article, she describes Neatline as unstable – “extensible, never fixed or complete”. This idea is perfect for describing the Medical History of Davidson College. With small facts here and there, Neatline helps to piece together a more general view of the impacts Davidson College has had on the greater medical world.

By using Neatline I was able to overlay the points in my Medical History story on a 1915 planned map of Davidson College. This allows me to blur the line between the physical world and narrative world (as discussed in Ritchie’s Chapter 4 of Mobile Stories). Like Ritchie discusses, the technology of Neatline can make the story, in this case not the artifacts found in Davidson’s Archives but the story of Davidson influencing the medical world, appear more real. By overlaying my pins on a planned historical image I address Ritchie’s claims and attempt to create a narrative describing the historical moments that give Davidson College recognition in the medical world today.


An Hour of Free Time Places Wrestlers Where?

I have used my geo-referenced map from 1939 with an overlay of data applied by QGIS to create a chloropleth map that shows were the wrestlers will most likely be during Common Hour on Tuesdays and Thursdays (if limited to a certain area, as shown on my map).

The wrestlers chose where they would most likely be in this area during Common Hour.
The wrestlers chose where they would most likely be in this area during Common Hour.

I gathered my data by simply sending a message to the wrestlers asking where they would most likely be during Common Hour if they could only pick buildings that are considered to be in the same area of Chambers.  This includes the Chambers, Freshman dorms, the Union, the Library, the Doe Weight Room (behind library), and all of the buildings on the “D”.  The reason I only inquired about this range of area is because my 1939 map doesn’t include all of campus.

My data strengthens the information contained on the map because it provides understanding besides simple location of buildings.  This map does two things effectively.  First, it shows location of new and old buildings on campus; the fact that the 1939 map cannot cover the current campus shows how much the campus has grown. Second, it provides a narrative of where the wrestlers decide to place themselves when they have an hour of ‘free time’.  Most of the wrestlers chose to go to the weight room while some went to the Library or Union, and others went to their dorms.  From this we can figure that some wrestlers might have work or desire sleep, some may take this time to eat while most would try to work out.  This data might be a good start to predicting the behaviors not only of wrestlers but of Davidson athletes. and even Davidson non-athlete students.

My greatest challenges were working with the limited 1939 map, acquiring answers from all of the wrestlers in a timely manner, and navigating QGIS.  I limited the wrestlers’ choices to the buildings that the 1939 map covered in order to remedy my situation.  As for QGIS, using the program is the best way to figure it out.  Learning in class is helpful but actually working through mistakes is a true learning process.  I’m beginning to understand the learning curve of QGIS; only “sort of” knowing it renders it rather useless, however, becoming familiar with QGIS opens up many doors to mapping.

La Connaissance: Acquaintances around Campus

As a Freshmen, getting to know people is an important part of life. Therefore, I have mapped out how many people I know in various dorms on campus.


One of the first issues I ran into in the creation of this map was the dissonance between the two styles of maps that were in the overlaying process. The given map of Davidson had a very large focus on portraying the buildings correctly, while the roads were simply one-dimensional lines. Conversely, the map of Davidson I used was very focused on portraying the roads accuracy, while the buildings had a lot of variation in their accuracy of scale. Therefore, in the first overlay of the map I did the points were very far off as I had attempted to approximate reference points on the roads as well as the buildings, which made the map very inaccurate. By focusing solely on the buildings for reference, I was able to get an overlay that lined up much more cleanly.

The second problem I encountered was the fact that when attempting to make a gradient with QGIS, my shapefiles would disappear. Since the shapes are rather important for the message of the map, I had to work out a workaround by using the categorized style. After inputting my data values into the categorized system, I set the colors of each category individually so as to represent a gradient. The downside to this workaround is that the gradient may not be equally spaces in its color values.

Interestingly, from this map I can see that my social web seems to either stretch to include a particular dorm or not. It is no surprise that Cannon has the largest amount of people, given that it is my home dorm, but the inclusion of Richardson is interesting, given the lack of proximity between it and Cannon. However, it is important to note that this map does not include those people with which I am friends or acquaintances whom I do not know where they reside. Despite that, I think this map presents an interesting picture of my social reach at Davidson and I would be very interested to see how it changes over the course of my time here.

QGIS: The Good and The Bad

For those who have the know-how on using it effectively  (or the patience to figure it out), QGIS can be an extraordinarily useful tool for combining layers of data, geographic information, and images. Although the map below presents a clean, simple combination of all three, the process in making it was by no means easy.

In the example below, the base layer is geographic data contained in the shape files of buildings in Mecklenburg County (marked Davidson_buildings on the legend) to which I have added a georeferenced map of Davidson College’s campus dating from 1974. These two layers are interesting juxtapositions in of themselves especially for those who wish to conduct a historical comparison of a certain space. However, in this example, the map stretches beyond mere historical comparison and the combination of building shape files and the map of Davidson College’s campus becomes the background for the shape file data on the number of  international students’ currently living in the dorms.

PA #3 Final MAP
QGIS Output Map: Background features a 1974 map of Davidson College campus. Data displays the number of international students’ housed in dorm.

While this QGIS map does not present a unique or insightful comment about the history of Davidson College or the composition of its student body, the map does exhibit the capabilities of the QGIS software and also provides insight into the difficulties that can arise when using this tool.

First, using QGIS, the user can georeference (i.e. embed geographic data points in the image) images and the latitude and longitude lines to align images, shape files and data. In the map above, I aligned the 1974 map of Davidson College visually with the building shape files; however, the latitude and longitude points were useful it that they connected the location of buildings with the data about dorms. Second, I combined the geographic information of the dorm data with the previous layers and formed a new shape file layer that is seen in the green/blue tinged dorms. Here, the shade of the  color of the building represents how densely each dorm is populated with international students. As can be seen on the legend, the more international students living the dorm, the darker the shade; thus, the viewer is easily able to see that Sentelle has the most international students.

Although I stumbled a few times in the process of getting to this finished product (as can been seen in the wonky orientation of Knox’s polygon shape file and the frustrated file name “Output Hopeful”), the ultimate outcome is a clean, visually simplistic representation of both historical and numerical data and a testament to the useful possibilities of QGIS.

Assigning Value to Davidson Buildings

As you walk around the main quad of Davidson College it’s hard to distinguish which buildings are old, and which are new. The continuity of the architecture, with red bricks and white facing, gives the college a consistently quaint feel. Although it seems like each building has been here from the beginning, GIS can help determine which buildings actually have historical value, and which do not. Because historical value isn’t the only measure of value, I have also included my own personal value in this analysis (measured by weather or not I have been to the building).

To measure value I have georeferenced a map of Davidson College in 1915 with Mecklenburg county buildings data. I then clipped the shape file so only Davidson buildings were included. I assigned each building a value from 1-4 depending on their combined historical and personal value. Buildings with a value of 4 have been there since 1915 and I have visited them. Buildings with a value of 3 have not been there since 1915 but I have visited them. Buildings with a value of 2 have been there since 1915 and I have not visited them. Finally, buildings with a value of 1 have not been there since 1915 and I have not visited them.


The above image shows the effectiveness of Davidson College planning. Of all of the buildings, only two have no historical value or value to me. Although I do not accurately represent all Davidson Students, I have been here for four years, so hopefully visited more buildings than not. Using GIS I show that the main campus of Davidson College is highly populated and contains many buildings with high levels of value. It would be interesting to compare this map to a similar map of the newly acquired areas of Davidson’s campus. It would also be interesting to map these measures of value of different students involved in different areas around campus. These maps might show us weather or not Davidson is designed effectively for the diversity of students. The historical map demonstrates how Davidson has managed to keep numerous buildings that are frequently visited (renovated or not) for 100 years.

Here is the map with google maps as a background 


Once you’ve overlaid the data you want on your map and assigned labels to your data points or polygons, you need to export the image as a JPEG.

First, situate your map in the frame as you would like it to appear in your final image.

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Then, click on the “New Print Composer” icon in the toolbar

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You’ll be prompted to enter a title – name your map something that clearly identifies it.

You’ll next be faced with an empty screen:

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Click on the “add new map” tool in the left-hand sidebar

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Then, drag as large a rectangle as possible over the blank canvas.  Your map should appear.

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You can add additional information to this map.  For instance, a legend explaining the color values in your choropleth map.

Click the “add new legend” button, and drag a rectangle where you would like your legend to be.

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After you’ve added your elements, go to the composer menu>export as image

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Save your image as a jpeg file, and upload to the course blog, with your commentary.

QGIS data tutorial

For PA4 you’ll be overlaying data onto a georectified map.  This tutorial shows you how to upload the vector shapefile for Davidson College, how to create your own vector shapefiles, and how to attach non-geographic data to those shapefiles.

Importing the Davidson shapefile

In the description for PA4, you’ll find a link to Davidson shapefiles.  Download them all, and place them in a folder called Davidson.

Open QGIS and add a vector layer:

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Select Davidson_buildings.shp and also upload Davidson_roads.shp

Once you’ve uploaded your shapefile, it should appear in the main area of QGIS.  I’ve projected these shapefiles in WGS84, but check to make sure that the CRS is set properly.

First, right-click on the Davidson shapefile.  Scroll down to Set Layer CRS, and select WGS84.

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Your map might disappear, but fear not! You also have to set the project CRS to match the layer you just imported.  Right-click on the layer again, and select  “Set Project CRS from Layer”

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If you still don’t see your map, click the check-box next to your layer, go to View>Zoom to Layer.  Your map should appear.

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Overlaying your map

Now that you have a base map, you can overlay your own map.  If you need a refresher in georectifying in QGIS, go back to the QGIS tutorial from earlier in the semester.  You can either input Longitude and Latitude coordinates or, if your map has features that are recognizable today, you can try to match them by hand, as we did with MapTiler.

For instance, if since the Davidson shapefile has Chambers clearly marked, I could insert a reference point in the center of ChambersScreen Shot 2014-10-01 at 3.19.05 PM

But instead of entering longitude and latitude points, I could click on “From map canvas” and estimate the same place in the middle of the Chambers on my base map.

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Once you have georeferenced your map, it should automatically appear on the canvas, on top of the Davidson shapefile.  If it doesn’t check to make sure that all of your maps are using the same CRS.

When you’re finished, you can play with the transparency of your georectified map by double clicking and adjusting the transparency slider bar.  When finished, you should have something like this:

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Adding Data

There are a few ways to overlay data on to your map: adding points, assigning information to shapefiles that someone else created, or assigning information to shapefiles you create.  We’re going to walk through the first and last approaches here – for a refresher on the middle one go back to the choropleth map tutorial.

Adding Points

One way to get data on to your map is simply to upload a spreadsheet that contains some kind of non-geographic data paired with Lat/Long data.

You can use the dorm data linked in PA4, or create your own spreadsheet.  Save as a comma separated file – the file type should be .csv

(If you’re working on a mac, sometimes the .csv files don’t upload properly.  To fix the problem, save your file as “Windows Comma Separated”)

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In QGIS, go to Layer>Add Delimited Text Layer and select your file.

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Set X field to Longitude and Y field to Latitude.  If you don’t see those options in the drop-down menu, make sure that “Number of header lines to discard” is set to 0.

Click OK, and select the appropriate CRS.

Congrats, you’ve now put data on the map!

Double click on the layer name to play around with labels, colors, etc.

Adding polygons

But, say you wanted to do more than just put some points on a map.  Say you wanted to represent the percentage of African-American students in each dorm.  To do that, we need to create some shapes to fill with different colors.

To do that, you need to create a new shapefile layer.

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This is going to be a layer composed of polygons, so select polygon, make sure the CRS is set to WGS 84 and add whatever attributes you think are important.  In this case, you could either hand enter all of the data from the Dorm Data document, or create a linking category, that allows us to merge the dorm data table with our shapefile.  Let’s create a category called BLDGNUM and add it to the attributes list.

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Create a folder and name the shapefile something like dormdata.

Now we can start making some polygons.

Highlight the layer we’re going to be making.  Then right-click and toggle editing.

Now click on the add feature tool.

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Use this tool to define the corners of a shape.  So, for a dorm, click on all of the corners until the red area covers the shape you want.  Then move the cursor inside the red area and right-click.

You’ll be asked to enter information.  This is where we need to make sure that each building has the right code associated with it.  So we would open the dormdata .csv file, and when we traced Belk, for instance, we’d make sure that the BLDGNUM is 4.  Same for the others.  When you are finished making these shapes, toggle editing off.

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Once you’ve created all of the shapefiles for all of the dorms, you can merge data like we did with the U.S. unemployment data.