Module 6- Cartography- Isarithmic Map

 For this week’s exercise we focused on two methods of presenting data on an isarithmic map: continuous and hypsometric. We used PRISM annual precipitation data obtained from the USDA NRCS National Geospatial Management Center in coordination with the PRISM Group at Oregon State University for the state of Washington from 1981-2010.

First, we created a map presenting the precipitation data in continuous form. We also created our own hillshade layer to add to our map display elevation since this effect hasn’t been added to ArcGIS pro yet. Then we created a second map in which we converted the data to a hypsometric tint to better visualize the precipitation totals. To do this we used the Int (Spatial Analysis) tool to convert the raster data to integers. We then created 10 manual intervals to classify the data. We added our hillshade layer and added contour lines using the Spatial Analysis Contour List to add contour values. Finally, we were tasked to create a map layout and to include a description of how the data was interpolated.

When reviewing the differences in the continuous and hypsometric maps and answering the process summary questions I was reminded of a recent map I’d viewed on my local weather station’s website. Sure enough, both the tornado risk and predicted precipitation totals were shown on a hypsometric isarithmic map similar to the one I created.

 

 

 

Module 5- Cartography- Choropleth and Proportional Symbol Mapping

 For lab 5, I was tasked to make a map depicting the population densities of European countries as a choropleth and wine consumption for those countries as a graduated or proportional symbol using ArcGIS Pro. I incorporated data classification and map design principles learned from the past several modules.

Using Data from Eurostat and the Wine Consumption institute, I constructed a map meeting the appropriate parameters. I hit two major snags that took me awhile to resolve. At first, I could not figure out why the labels I wanted to exclude from my map were not excluded. After almost an hour I realized that I was supposed to use an “and” clause rather than the “or” clause we used earlier for data exclusion. This confused me because I understood “and” to require the associated feature to meet all the parameters listed, but I had never worked with a negative statement before and when using “is not equal to” then “and” is the appropriate choice. I anticipate that the programming course offered this summer will help me better understand this aspect of the software.

The second snag I hit was moving my symbols on the map. I didn’t realize that even though the wine consumption data was symbolized using graduated circles, the feature class itself was classed as polygon data. One of the module leaders found an article that helped clear this up for me. To be able to move the symbols I had to convert the data to a point feature. This solved my issue, but it was a headache because all my previous work (setting the classes, excluding the appropriate data, creating my labels, etc.) had to be redone on the new feature class.

I used the histogram to study the intervals made with the classification data. I used Natural Break classification for the population density data, but this posed an issue, one that I foresee would have been an issue for any classification method other than equal interval. For my inset map I needed to exclude the data from the Balkan region on my main map. By excluding this data it “altered” my natural breaks. Then, on my inset map which included those countries, the Natural breaks were representative of Europe as a whole, which meant some of the countries were classified differently and the break points were not the same for both maps. I ended up using the break points of the totality of Europe and set manual break points on the main layout.

I used equal intervals to classify my wine consumption data. Looking at the histogram I felt like this split the data in a way that was meaningful.

Overall, I found this project very insightful and learned many new strategies I feel will be helpful going forward. Even though I had several struggles that added significant time to the process I know that this further reinforced the information I took in.

Here is my map:

 

Module 4- Cartography- Data Classification

     For this week’s lab we compared four different data classification methods and identified the most appropriate method for visualizing the spatial data provided. Our data source was the 2010 US Census Tract in Miami-Dade County obtain by FGDL, and we were tasked with representing the population of seniors (65 and up) by both percentage of the population for each tract and by normalizing the data to show the number of senior citizens per square mile. To do this we created two maps, both comparing four classification methods- Natural Break, Equal Interval, Quantile, and Standard Deviation.

We were tasked to explain how each classification method differs as well as which method of classification (by percentage or per square mile) was a more accurate representation of the distribution of senior citizens. I concluded that since census tracts were specifically designed to have uniform populations the percentage of senior citizens was the best choice for representing this data set. Normalizing the data by square mile favors smaller census tracts even if the senior population is significantly less than a larger tract. In order to obtain a more accurate “per square mile” comparison I proposed that the county should be equally divided by area to get a more accurate visual of population density.

Map 1 depicting the four classification methods representing the distribution of citizens by percentage of the population

 

Of the four classification methods, I believe that the Natural Break is the most accurate representation of the data. The data contains an outlying tract consisting of 79% of the population being 65 or older. This skews the data slightly to the right. The Natural Break method accounted for this outlier while still breaking up the classes into categories that are a cleaner representation of the data. The equal interval method contained an empty class due to the presence of an outlier and grouped the majority of observations in the lower classes. It failed to provide insight into which tracts other than the outlier contained a higher density of senior citizens. The quantile method grouped the outlier with significantly lower population categories which masked its status as an outlier and might lead to inaccurate assumptions about the population represented in those tracts. Standard deviation is not the best representation due to skewedness of the data as well as the presumed audience.

I found this exercise very helpful in solidifying my understanding of the lecture material, how each classification method works, and under which circumstances they are best suited for creating an accurate representation of spatial data.

 

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