# Final session 1 teacher notes
###### tags: `Session1-Reverse-Engineer-a-Map`

If you remember nothing else:
* Come to us for questions!
* Data are structure observations about the world
* Maps are one way of showing data

## Logistics (approx. 11:10)

* Welcome to our course!
* LMEC staff introduce what LMEC does and the resources it offers
* Instructors introduce themselves
* Have participants introduce themselves to one another in the main room or in small groups, depending on class size, and have participants talk about why they took this course.
* For Zoom session, option to have participants type this in chat

## Overview (approx. 11:13)

* This page summarizes what we will be going over today.
* This first session is all about asking questions! Indeed, this course will probably leave you with more questions than answers. Learning which questions to ask about maps and data is the first step to building your own projects!
* Today you will learn how to look at a map and consider the data behind it, what distinguishes geospatial data, and how to question maps and data in a productive way
* Today is mostly about guiding you through your own thought patterns when it comes to maps. It will include a lot of discussion and a lot of asking you questions.
* We will go through material in the order it is presented in Cartinal.
* Asides give extra insights and questions
* "show this" tabs are extra reading if you want to know more about topic.
* This is the first offering of this course, so you will be helping us solve bugs along the way! Please ask questions if you get lost or stuck!

## What is a map? What is data? (approx. 11:18)

* How do you think maps and data are related? Consider the questions listed on the screen.
* Would anyone like to share your response to one of the questions?
* Maps are a visual interpretation of data; data itself is a recorded observation
* Qualitative data could be verbal responses to a survey
* Quantitative data coule be miles driven in a day.
* Maps often include qualitative and quantitative data. We will dive into this more later, but the key idea for now is that the relationship between maps and data is the following: maps represent an interpretation of data and are subjective, data is mere observation (but is still subjective). The ways in which we process data enable it to make arguments.

## Exercise: Draw Your Own Map (approx. 11:28)

* You probably know more about maps than you think you do! Let's put this to the test.
* We are going to take 5 minutes for you to draw a map of somehwere important to you
* town, street, school - anything that comes to mind!

**5 minutes drawing time**

* Now that you've had some time to draw, let's see a couple of your maps! (option to go into small groups)
* How did you make choices about what to draw?
* What was difficult about this?
* How do you think your experiences influenced your drawing?
* Now click on the link to OpenStreetMap and find the same location.
* How are the maps different?
* Which is more "accurate?"
* Is anything missing from either map?
* This illustrates the many forms a map can take. Two maps of the same location could look completely different.
* Every map is made for some purpose, and that purpose drives its structure. Just like we learn what languages makes the most convincing arguments, mapmakers learn what techniques make the most convincing arguments.

## Introducing geospatial data (approx. 11:33)

* What do you think makes maps important?
* Looking at space at different scales and through different lenses helps us understand more about the world
* Dennis Cosgrove thinks of maps as a tool for looking at the world in ways we normally cannot.
* 2 kinds of maps:
* reference maps - generally help us understand where we are and how to get around
* thematic maps - show us how some phenomenon is spread across space
* Both reference maps and thematic maps are made up of data. We will mostly talk about thematic maps in the class because they provide insight into both spatial and non-spatial data.
* In the "show this" tab, there is an excerpt from Lewis Carroll's novel *Sylvie and Bruno Concluded*
* We assume maps are smaller than the places we represent.
* What else do we assume about maps?

## Geospatial data: the what and the where (approx. 11:36)

* Geospatial data is information about phenomena and space.
* You just made geospatial data! Computer might understand parts of your drawings, but other parts need human insight.
* Only geospatial data can be visualized on a map.
* Take a look at John Snow's map of the cholera outbreak around the Broad Street pump. What are the data? Are they geospatial?
* The cases and corresponding locations make up the geospatial data.
* Geospatial data is made up of feature data and attribute data.
* Feature data are the "where" (eg. where cases occcured )
* Attribute data are the "what" (eg. the cases themselves)
* Feature data is often physical features, attribute data is usually not visible.
* Let's look at the Bureau of Labor Statistics map. Try the questions at the bottom. Ask about anything giving you trouble

**pause for participants to try questions**

## The human role in mapping: from data to maps (approx. 11:45)
* How do humans influence how data becomes a map? What changes about information when it is mapped?
* As we mentioned earlier, data and maps are related by the interpretations that turn data into a visualization.
* As the drawing shows, humans are between data and maps.
* Because humans are between data and maps, they have power over how viewers see maps.
* When looking at a map, consider how the cartographer analzyed the data and how the cartographer may have misrepresented the data.
* Many times, misrepresenting data happens unintentionally! layers of representational abstraction:
* who gets surveryed
* what questions are asked
* how people respond to questions
* how data is recorded
* how data becomes a map
* cartography comes at the end of a long process
* Take some time to look at the two maps below. Consider the arguments made by each. What do you take away from these maps? What kinds of choices created these maps?
* discussion
* Both were made using the same data. How did the same data turn into two maps that are so different from one another?

** pause for 1 minute for participants to look at maps**

* Would anyone like to share their observations?

## Learning to Question data (approx. 11:48)
* Just as we have been asking you lots of questions today, it's important to ask lots of questions of data. Not only do humans come between data and maps, but we also come between the world and data itself.
* Not everything gets recorded; people consciously and subconsciously prioritize what should be counted. We will talk more about this in Session 3.
* The questions listed here can help you approach a new data set!
* The world changes overtime, and thus, data sets need updating! One example is shown in the coastline changes in the map shown here. Questioning data is an ongoing process.
* LMEC has developed a Public Data Portal to help you ask and answer these questions. It includes many Boston-centric data sets and is great way to support your mapping explorations. We will jump into this more next week!
* Take a minute to try the true/false questions at the bottom. Let us know if you have any trouble!

## Concluding thoughts(approx. 11:51)
* It can be hard to keep all the things we've talked about today in mind because a lot of the time, maps are sneaky!
* J.B. Harley's quote: "the map is a silent arbiter of power."
* Maps are convincing; they reflect out own lives back at us.
* They are also convincing because they look objective
* Learning the tools behind mapmaking empowers you to be a conscientious viewer and maker of maps.
* We hope you learned a lot and enjoyed this session! Next week we will talk more about types of data, projections, joining spatial and non-spatial data, file types, and the LMEC Public Data Portal. It sounds like a lot, but we will help you through it!
* Check out the Bending Lines exhibition!
* We will stick around to answer any questions you have. Have a great week!

## Our refrain:
Data are structured observations about the world;
Maps are visualization of this data