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# System prepended metadata

title: Session 1 Teacher Notes
tags: [Session1-Reverse-Engineer-a-Map]

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---
tags: Session1-Reverse-Engineer-a-Map
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# Session 1 Teacher Notes

#### Key learning goals

* Be able to look at a map and think about what kinds of sources were required to create it
* Understand what we mean by “geospatial data,” and the difference between the data and the map itself
* Critically evaluate what happens when observations of the world are encoded into computer data

*Stuff we’ll also be trying to communicate*

* Insights of Harley, “Deconstructing the Map” and critical cartography
* “Raw” data versus visual frames around the data
* Data ontologies

### Before Session 1

*  ask participants to bring in a map they find (if they want to/have time) - go into breakout rooms to discuss at some point in the sesssion (maybe at the end to bring everything together?) 


### Exercise 1 outline
* work through exercise, emphasizing and discussing the following:
    * BLS Map: 
        * Where did these boundaries come from? How does the computer know where they are? 
        * Why is data for Puerto Rico missing for the map from Arpil 2020?
    * City Health Dashboard:
        * Why do you think the cartographers chose to include a basemap?
        * Discuss census tracts vs. census block groups?
    * Sleep Maps:
        * Which map makes a stronger/more effective argument?
        * What is the purpose of each map? Are both trying to make an argument?


More discussion questions
* What does making a map interactive do for the argument it makes and the way the viewer interprets the data? When might we want to choose not to make maps interactive?
* What might be particularly dangerous about considering and presenting maps as objective representations of the world?
* Questions/thoughts from participants?


### Exercise 2 outline
* What is a map?
    * Walk thorugh exercise as written 
    * A bit of background about critical cartography maybe?
* In-class map-making exercise 
* Denver heat temp vs. income maps:
    * Is there a spatial pattern with these two attributes? 
    * Key terms: reference maps vs. thematic maps
* What is data? 
    * Giorgial Lupi Dear Data project
        * Non-geospatial data; observations of the world recorded and visualized 
    * Geospatial data
        * John Snow map of Broad Street pump
            * Geospatial data allow us to see patterns of occurances across space.
            * Recall the Denver maps here! 
        * Mention GIS software that reads this type of data
        * Key terms: features and attributes
            * BLS map callback: have participants identify features and attributes data 
        * Key terms: vector data and raster data
            * Minor terms to mention continuous and discrete
        * Key terms: point, line, polygon

* Breakout rooms
    * Discuss types of data that are on the hand-drawn maps; how would the information you drew be transformed into data? 
    * Discuss maps that participants brought in
* Quizlet 
    * Participants can do this after the class ends if we don't have time


### Exercise 1 Outline After Rework (1 hour session)


| Section | Time| Format|
| -------- | -------- | -------- |
| Intro to Course + What is Map    | 10 min    | Verbal   |
| Activity - Draw Map    | 10 min (5 min draw, 5 min discuss)     | Breakout Rooms     |
| How to Look at Maps   | 15 min        | Verbal
| Critical Thinking about Data + Deep Dive + How to Approach Data| 20 min     | Verbal    |
| Wrap Up + HW for Next Session    | 5 min    | Verbal     |

* What is a Map?
    * grammar analogy
    * Lewis Carroll
* Draw Your Own Map
    * coordinate sharing of maps - breakout rooms?
    * discuss what kinds of knowledge participants have through personal connection to the places they are depicting
    * the algorithm behind the map - automatic creation of buckets
* How Should We Look at Maps?
    * BLS Map:
        * Where did these boundaries come from? How does the computer know where they are? 
        * Why is data for Puerto Rico missing for the map from April 2020?
* Diving Deep into Data
    * How did the steps shown in the diagram play into your own mapmaking? What about in the BLS maps?
    * How do you decide what information to keep track of in your own life?
        * What data exists about you - health data, sleep data, food data etc. allude to making your own dataset
* Identify Types of Data (moved to session 2?)
* Data: What Gets Lost in Translation?
    * How might you want to use the LMEC Public Data Portal?
* What Questions Should We Ask of Data?
    * What are some potential harms of failing to ask these questions of data?
    * Data is like any other written source - has author, purpose, context etc which you shouldn't overlook
* Exercise: Find Your Own Data Set
    * List some examples of places participants can look for a dataset?
* How Do We Approach Data?
    * City Health Dashboard:
        * Why do you think the cartographers chose to include a basemap?
        * Discuss census tracts vs. census block groups?
    * Sleep Maps:
        * Which map makes a stronger/more effective argument?
        * What is the purpose of each map? Do they make the same argument?
* Conclusion
    * How might you use and view maps in the future?
* Session 2 Preview
    * Answer any logistical and content questions
    * Assign the find a dataset homework


More discussion questions
* What does making a map interactive do for the argument it makes and the way the viewer interprets the data? When might we want to choose not to make maps interactive?
* What might be particularly dangerous about considering and presenting maps as objective representations of the world?
* Questions/thoughts from participants?
