Mapping Aesthetics
On Thinking Geographic Representation
Course: SCI 6322 Mapping: Geographic Represention and Speculation
Instructor: Eric Robsky Huntley
Individual Work
Date: Jan.- Apr., 2021
Maps both represent reality and create it. It is in the context of this contention that this course presents the fundamentals of mapping, spatial analysis, and visualization. In a design process, the act of mapping selectively narrates site conditions. By choosing what features, forces, and flows to highlight—and which to exclude—the designer creates the reality in which their intervention will be situated. This is only becoming
more true, as urban space and populations are ever-more pervasively measured, monitored, and categorized by innumerable institutions. Such representations are often
a designer’s primary means of responding to a site. Designers are in the difficult position of approaching spatial datasets critically and as sites of contestation while also employing them in their work.
KEYWORDS:
Spatial Analysis, Data Visualization, Information Highlight, Criticality, Representation and Speculation
OBJECTIVES
Over the course of a semester, students will work extensively with techniques of spatial analysis. Using desktop GIS software, we will explore data sources, data models, overlays, map algebra, spatial statistics, terrain analysis, and suitability modeling, among other techniques of spatial representation. Students will learn to embed these techniques, recursively, within larger design workflows.
01 Representation and Positionality
In lab and lecture up to this point, we’ve emphasized that the construction of a cartographic representation is always somewhat wrapped up with the position of the mapper, and the position implied by the map. We connected the latter to the map projection: the implied perspective of our map can inform (and be informed by) the map-maker’s interpretation and ideology.
As such, we ask that you make two separate maps using the same datasets that take different positions and imply different perspectives. For simple examples of this, you can take a look at “Same Data, Different Stories”, a section from the Bending Lines exhibition, put on by the Leventhal Maps and Education Center at the Boston Public Library.
02 Magnitudes and Categories
Over the past couple of weeks, we’ve been experimenting with NLCD (National Land Cover Database) data, which tells us about what, in a given area, is the most widespread use of land, according to a very simple, but still useful, ontology. Your task is:
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To decide on a study area (unfortunately, the NLCD only covers the United States, so we’ll have to be a bit parochial this time around…)
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Recategorize the NLCD dataset based on a topic of interest - urban heat island effect! Deforestation! Urbanization! Loss of agricultural land!...
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...and aggregate these categories to an areal unit, telling us about the prevalence of a certain category of land cover, the ration of two different types of land cover, etc.
Download the NLCD data here. Also explore the additional derived datasets made available by the MRLC. These are fair game as well!
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You should turn in at least two maps: a map in which the NLCD areas are aggregated and a map in which your recategorized land cover is visible. These should arrive at a proposal or proposition, depicted both visually and through textual exposition, where necessary.
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Finally, we ask that you submit a short reflection on how the categories deployed in the service of this project may limit your results, or be subject to certain forms of bias.
03 Trajectories and Territories
This module has focused on how we think trajectories - as lines of mobility, mental maps, and representations of relations - and territories - as analytical artifacts and areas for the exercise of authority.
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Over the past five years, your predecessors in prior iterations of this class have created paths describing their repetitions and rhythms in space; these are, for obvious reasons, centered on Cambridge, Somerville, and Boston in Massachusetts. Your task is to either:
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Use a ‘trajectory’ from a previous year as a ground for analysis, producing aggregate analysis of urban form along these paths; or…
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Record your own trajectory using Strava, download it from the web interface, and use the GPX to Features tool to bring the path into Arc.
Along this path, you should play with aggregation by e.g., generating regular grids of varying resolutions, using various administrative boundaries (perhaps those used by the US Census), and incorporating other data sources that may illuminate certain aspects of the adjacent rhythms, forms, and metabolisms. You may also explore absence and things that cannot be captured: as Gieseking reminds us, there are many forms of relating to our trajectories through our places and many of them are irreducible to quantitative or absolute description.
04 Surfaces and Overlays
A. In this lab, we will be producing surficial representations of demographic data drawn from the U.S. Census. Where previously, we’ve used an estimated population grid to count populations, here, we’ll be using a standard source of social and demographic data for the United States: the U.S. Census bureau, as sourced from Social Explorer.
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B. In this lab, our goal is to locate a new housing development based on a land cover analysis of where would be best to build. Our new development has several restrictions. One of these is the slope of the land. We would also prefer to build on clear land, if possible. For instance we can’t build where there are already other buildings or on unreasonable surfaces, such as water. In fact, the further from water the better. To find the optimal area for our new development we will rank our data on a scale of 1 to 5, where 1 is the worst and 5 is the best.
05 Criticality and Counter-mapping
A. Today, we’ll be working through a georectifying process that uses historical artefact - namely, a landscape plan for the Charles River - and places it into coordinate space. This means, basically, that following this map, you’ll be able to use historical imagery (or non-georeferenced contemporary imagery) as the basis for GIS mapping practices. For a pretty fascinating application of georeferenced historical imagery, see the Leventhal Map & Education Center’s Atlascope tool, which allows the user to overlay historical atlas imagery onto contemporary basemaps in Greater Boston.
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B. Geocoding is our answer to this very common problem: how do you take information that we know identifies a location (say, 48 Quincy St, Cambridge, MA 02138) and turn it into a coordinate that is easily plotted using a GIS (say, 42.37570 dd, -71.11398 dd). Both of these pieces of information identify the same location, but only the second can be easily mapped.
