Module 3- Cartography- Cartographic Design

             The purpose of this week’s lab was to implement effective map making strategies that we learned in lecture. We studied Gestalt’s principles of visual hierarchy, contrast, and figure ground as well as learned techniques for maintaining balance among the map elements. The assignment was to use what we learned to make a map depicting the location of public schools in Ward 7, Washington, D.C.

I used ArcGIS pro and the data provided, which was obtained from District of Columbia Open Data. I knew I needed to make two maps for my layout- a map of Ward 7 as well as an inset map to show the location of Ward 7 in relation to greater Washington D.C. I maintained a streamlined color scheme through both maps and chose a darker gray for the background of Washington D.C. while emphasizing Ward 7 with a lighter gray color. I clipped my schools to those located within Ward 7 and chose to make my school symbols bright red to visually emphasize them. I chose a pushpin symbol that made them look like they were closer to the reader.

 I chose to make major roads and highways/interstates a medium gray color that would show up on both images well, but still not take away from the primary focal point- the schools. I displayed all the minor roads in Ward 7 on the main map but made them lighter. I included parks and water features although I clipped the parks to show only those within Ward 7 on the main map and left them off the inset map. I maintained visual hierarchy by emphasizing key features, titles, and my legend but significantly decreasing the emphasis scale bars and source information.

Here is my final map.

Module 2- Cartography- Typography

     This week we learned proper typographic guidelines when map making. In addition to standard feature labeling, we explored annotation as a more versatile option. This was of particular interest to me as it solved some of the issues I had when I was trying to adjust my labels during my Intro to GIS final project last year. We were given data of Florida counties, major cities, rivers, and other water features and told to create a map labeling/featuring specific areas of interest.    

    Using ArcGIS, I created a map using the data and used the select for attributes option to only select the features I wanted to highlight from each category. I used a mix of traditional labeling and annotation labeling. I used annotation to label the water features- this was particularly helpful for the rivers. One of the rivers had an odd angle at one point in the lettering using the labeling option that I was able to smooth out by adjusting the vertices once I converted it to annotation. I made sure all my water features were italicized appropriately and added a halo effect to the swamps since there wasn’t a consistent color option that made them both clearly legible. I didn’t like how the halo obscured so much of the Okefenokee Swamp, so I made them partially transparent. I made the capital city distinguishable from the other cities with a star. I kept the serif font suggested in the lab exercise for the rivers and made sure it was consistent among all the water features and used a consistent sans serif font for the cities, title, legend and credits in order to limit my map to 2 fonts as recommended.

 Here is my final map:

Module 1- Cartography- Map Critique

    The focus of Module 1 was to find two maps, one well-designed and one poorly-designed, to critique. The questions provided to guide us in our critques were insightful and I actually ended up choosing a different well-designed map after beginning my well-designed map critique because my initial choice had glaring issues despite my first instinct that it was a well-designed map.

   Well-Designed Map

For my well-designed map I chose this one. Offered on the Alabama State Parks website, this map gives park visitors the location of each of the parks relative to major cities and highways. The map is supplemented with additional contact information for each park as well as the Alabama State Parks overall division. 

 I appreciated how the mapmaker chose to order the parks alphabetically in the legend, which provides a logical order to the listing of the parks, they then coordinated this with the green triangles representing each park. This allows the reader to quickly look up the location of a specific park by name but also alternatively allows readers to look for parks in their area in general and then quickly discover the name/contact information without prior park knowledge.

The layout of the legend wrapping around the shape of the state uses the empty space efficiently and is aesthetically appealing to the reader. The color green was chosen to represent the parks and since green is often associated with nature, I feel like this choice makes sense. It also stands out very well with the neutral colors of the other map elements, drawing the eye immediately to the subject of the map- the parks.

     I evaluated the map using the 20 Tuftisms provided in the lecture. Some of the indications that this was a quality map are as follows:

 “Clear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity.” (Tufte,1942)

The labeling was clear and easy to follow. Highways were labeled at multiple points along their route, which allowed the reader to identify the name of their highway quickly. The biggest 3 cities in the state were enlarged and bolded, but smaller major cities were still included in a smaller, not-bolded font. County lines were drawn and with the addition of cities this allows the reader to easily orient themselves on the map, however they were not labeled which would have led to the map being over labeled and cluttered. Overall labeling was effective for clearly visualizing the necessary information for the map reader to find state parks. 

“Forgo chart-junk.” (Tufte,1942)

The choice to number the parks on the map and give further information in the legend rather than label them with words made the map cleaner and less cluttered.. Titling the parks themselves would have been an option with fewer parks but with the number and closeness of them it was a good choice for the map’s readability. Additionally, as mentioned above, the choice not the label the counties themselves kept the chart clean and focused on the intended use.

    Overall I feel like this map is a good example of an aesthetically pleasing, well designed map with consideration for the map readers overall experience.

Poorly- Designed Map

For my poorly designed map I chose a map provided in our student resources folder. The purpose of this map is to give a visual representation of the population density for each of the state capitals. Immediately upon seeing this map I found it overwhelming. The choice to color all of the circles the same shade makes them less distinct, and if I chose to keep the graduating circle theme I would also use a color gradient to further distinguish them, especially since many of the capitals overlap.

 The map is noticeably missing the two non-contiguous states, but no information is given to explain why. Either the map needs to be re-titled to reflect this absence, the states need to be added, or an additional note included explaining their absence.

On the east side of the map, it is very difficult to make out the locations of the individual state borders are many are covered by the expanse of the state capitals. West Viriginia, notably, is completely encompassed by Columbus, Ohio. With the addition of a color gradient such dramatic sizing used for the largest capitals wouldn’t be necessary. I would play around with different circle sizes and possibly make the circles representing the capitals more transparent to allow the state lines to be more visible.

Again, using Tufte’s criteria to evaluate this map I found the following design principles not met.

“Clear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity.” (Tufte, 1942)

I found the labeling to be ineffective. There were several circles representing capitals that I was not able to determine the name of without considerable deliberation because the labels were freely floating around the circles and not following a logical positioning. I would rectify this by using some sort of leader line to allow the map reader to quickly identify the capital represented by each circle. Additionally, due to the lack of units on the legend the circles themselves have no quantitative meaning in reference to the population.

“Graphical excellence consists of complex ideas communicated with clarity, precision, and efficiency.” (Tufte, 1942)

I found this map very inefficient, multiple times I had to stop and try to understand what I was looking at and why it was visualized the way it was. I did not feel like I had a good grasp on which states had the highest population density in their state capitals. I also think that if this map is directed to the lay person it is not a fair assumption to presume the reader knows all the state capitals. With this this in mind I think a system like the “well-designed” map- numbering the circles and providing further information with the state and capital along the bottom might be an option to reduce the map clutter and visual distraction that would come with labeling the states separately. Alternatively, providing the state abbreviation in the label following the capital title would be a better way to clearly communicate with the map reader.

 Tufte, Edward R., 1942-. The Visual Display of Quantitative Information. Cheshire, Conn. :Graphics Press, 2001.

     One thing I noted while looking for my "well-designed" map was that many maps intended for the general public lack the scale bar- one of the areas I was supposed to critique. I ultimately chose to evaluate a map that lacked this map element, but I do see how the map maker decided it was irrelevant to the reader since the map wasn't showing the size of the parks, only the location. I feel like this lab exercise has altered the way I relate with maps. Before I didn't really have the understanding of what went into good map design, I had never considered all the ways a users experience could be considered. I find myself with a new appreciation for this process.

Introduction- Computer Cartography- About Me

My name is Brittany LaPointe and I am currently a full time stay-at-home parent. I enjoy the outdoors and would love to work in an environmental or conservation position. I have both my Bachelor of Science and Master of Science in Biology with an environmental concentration and I am currently working on my GIS certification because I recognize that it is a valuable skillset in environmental work. It can be a bit daunting to consider transitioning from being a full time caregiver to a career after almost a decade but I am determined to research and obtain the necessary experience and hard skills needed in this field. I have really enjoyed my GIS journey so far and I look forward to continuing to learn and improve.

Here is a story map tour of some of my favorite trails I've visited this past year in my area. 

https://storymaps.arcgis.com/stories/7a33fe0f607043958bf178b6f0a5a170

Module 5- Photo Interpretation and Remote Sensing- Supervised & Unsupervised Classification

This week we focused on learning how to  perform supervised and unsupervised land use classification in ERDAS. As always I feel like I learned a lot. After practicing the basic we were tasked to create our own land use classification map. Upon examining my final map I feel like my road area is likely overestimated. It seems like some of the regular "urban" area overlaps with the road pixels based on my examination of the histograms of all the spectral signatures I added, but I was unable to find a layer that they were distinct enough on to provide a more accurate map. 

Supervised classification of current land use in Germantown, MD. Image depicts 8 different land use categories as well as area in hectares for each one. An inset map shows the distance file, with areas of likely misclassification displaying as brighter and areas of likelier accuracy showing darker. 
     

Module 4- Photo Interpretation and Remote Sensing- Spatial Enhancement

 This was another exercise I spent way more time than I should have on. I am definitely a “hands-on” learner and while the concepts in the book make sense, it’s not until I’m in the lab and using the tools myself that it really begins to come together for me. This week we learned different spatial enhancement methods and how they could be performed in both ERDAS Imagine and ArcGIS Pro. All was going well until the map making portion of the lab. I could not figure out why my band combinations were not transferring over between programs, I would set it to False Color and it would open looking completely different in ArcGIS. Since the Intro to GIS course focused mostly on vector data and I had no experience with GIS before this program the raster data has been a bit problematic for me. So often I go to adjust my image in some way and have no clue how to do it. I often can find a work around but usually there is an even better option or my option would cause another issue to arise. I probably should’ve just asked my question and waited but I know if I can find a solution myself it usually sticks more and I often learn a lot of other stuff in the process so after almost an hour of trying all different methods of saving and opening my documents (I probably created 15 or so files trying to save in all different forms), the only one that worked was jpeg. I could have used a jpeg but it doesn’t come with the raster data and isn’t as clear of an image as the .img or .tiff files. I had the idea to type “false color” into the tools and used the create color composite tool that popped up to adjust my bands. This gave me the desired effect- a false color image, but what I didn’t realize until I brought it up in office hours was that this completely reassigned the bands, so what was band 4 was not just displaying as band 1- it became band 1. This means that had I continued to use this method of altering the colors, each photo I made would have the same band combination as I re-assigned the layers: Red: B1; Green:B2; and Blue:B3, and it wouldn’t have been clear which band from Landsat 5 I was actually using. Fortunately, as the professor showed me, I was able to rectify this by using the original file and simply changing which bands were displayed using the “Raster Layer” tab. A much simpler solution to my problem and something I will now definitely remember how to do going forward!

Anyway, I was finally able to create my maps which were of 3 unnamed features in an image of a mountain range in Washington State. We were given tips to identify these features using some of this weeks new skillsets.

I identified the first feature as water and chose to display the river in a false color photo because it really brings out the distinction between the water and the vegetation.

Feature 1 is depicted as a river (black) contrasted against red vegetation in a false color image

The second feature I identified was the snow on top of the mountains. I had to do some digging to figure out how to best display this feature. I almost went with true color but read that clouds and snow were indistinguishable in this band combination. I also knew band 5 and 6 had large pixel spikes in this region so I thought to incorporate them somehow. I found lots of information about how short wave infrared (band 5) is useful for distinguishing snow from clouds but I wasn’t sure where to put it and what bands to use with it for my combination. I found a research paper on Landsat 5 and glacier analysis that said the 5-4-3 combination was useful for studying glaciers so I tried it. The clouds were a pinkish hue while the snow was clearly blue with this combination so it was my ultimate choice.

Feature 2 shows snow (blue) capping the mountains 

Finally, the 3^rd feature was to show variation in brightness in the water throughout the map. I chose true color because it is best for viewing aquatic environments.

Feature 3 shows water brightness variations in a selected water feature on a true color satellite image

Module 3- Photo Interpretation and Remote Sensing- Introduction to ERDAS

 This week’s lab exercise was focused on familiarizing ourselves with the program ERDAS Imagine, learning how to do necessary functions, creating attribute tables with necessary data and exporting our raster image and data to open in ArcGIS. We took a subset of one of these maps and calculated the area for that part of the image and made our final map in ArcGIS pro. I also learned that you cannot add field to a raster image brought in with an attribute table from ERDAS and to build your own attribute table for the raster file you have to clear all the data you brought in. So I was able to get experience troubleshooting that issue in ArcGIS Pro.

Map created in ArcGIS Pro using image and data processed in ERDAS Imagine. Depicts a subset of a larger image containing land classification of part of a forest in Washington. The legend shows how many hectares of each type of land are portrayed in the image.