Syllabus updates 2025 (#23)

* wip webmap part 1

* add more webmap

* update web map

* web mapping part 1

* update syllabus pt.1

* reorganize weeks

* wip updates

* update syllabus and reorg

* more updates

* more cleanup

* update nb 1

* update reqs

Author: mariogiampieri

.gitignore

@@ -12,3 +12,4 @@ _toc.yml
 _build
 /node_modules
 /.history
+/Tutorials/Data

Tutorials/00_Getting_Started.md Assignments/00_Getting_Started.mdTutorials/00_Getting_Started.md renamed to Assignments/00_Getting_Started.md

@@ -1,7 +1,8 @@
-# 00. A Note on syntax
-Throughout this site, you will see code snippets that are formatted in a specific way. This is done to make it easier to read and understand the code. Some examples include:
-- `# This is a comment`: This is a comment in Python. Comments are used to explain what the code does and are ignored by the Python interpreter. It is good practice to leave useful notes in the code to help others (and yourself) understand what the code does.
-- `[some-reference-here]`: Items in the code surrounded by brackets are cases where you should replace the contents, *including the brackets*, with another value. For example, if you see `[pick-a-name]`, you should replace it with a name of your choice, such as `gis` or `cdp`. The brackets are not part of the code and should not be included in your code.
+# 00. Getting Started
+> ### 00. A Note on syntax
+> Throughout this site, you will see code snippets that are formatted in a specific way. This is done to make it easier to read and understand the code. Some examples include:
+> - `# this is a comment in python`. Similarly, `// this is a comment in javascript`. Comments are used to explain what the code does and are ignored by the Python interpreter. It is good practice to leave useful notes in the code to help others (and yourself) understand what the code does.
+> - `[some-reference-here]`: Items in the code surrounded by brackets are cases where you should replace the contents, *including the brackets*, with another value. For example, if you see `[pick-a-name]`, you should replace it with a name of your choice, such as `gis` or `cdp`. The brackets are not part of the code and should not be included in your code.
 
 
 # 01. Setting up your environment

Assignments/01_Loading_Visualizing.md

@@ -2,4 +2,4 @@
 
 Load and visualize a dataset of interest, either from [NYC Open Data](https://opendata.cityofnewyork.us/) or elsewhere. (If you already have an idea of what your final colloquium project topic will be, this would be a good opportunity to get a head start on that).
 
-Explore its metadata and attributes, and hone in on one or two attributes in particular of interest. Make map and non-map visualizations that explore these attributes. Provide in-line notes that describe your thought process in choosing which attributes to visualize, and what they mean.**** Provide explanation (numeric, graphical, textual) of what the attribute can and cannot tell us. Save the dataset out to the file format of your choice (but be able to explain why you chose that format).
+Explore its metadata and attributes, and hone in on one or two attributes in particular of interest. Make map and non-map visualizations that explore these attributes. Provide in-line notes that describe your thought process in choosing which attributes to visualize, and what they mean. Provide explanation (numeric, graphical, textual) of what the attribute can and cannot tell us. Save the dataset out to the file format of your choice (but be able to explain why you chose that format).

Assignments/03_Web_Mapping.md

@@ -0,0 +1 @@
+# 03. Web Mapping

Assignments/03_Networks.md Assignments/04_Networks.mdAssignments/03_Networks.md renamed to Assignments/04_Networks.md

@@ -1,4 +1,4 @@
-# 03. Networks
+# 04. Networks
 
 Define a network, and calculate the distance between different elements of it. Discuss the experiential differences between Euclidean and network distance for the objects in question. Bonus points (in the form of kudos) for quantitative exploration of the network as described in Xin et al. (2022).
 

Assignments/04_Web_Mapping.md

@@ -11,4 +11,4 @@ Repository for CDP Mapping Systems summer 2025 course
 
 ## Getting started
 
-Visit the [01. Setting up your environment](Tutorials/00_Getting_Started.md) tutorial notebook to set up your programming environment and get started with the course.
+Visit the [01. Setting up your environment](Assignments/00_Getting_Started.md) tutorial notebook to set up your programming environment and get started with the course.

README.md

@@ -6,42 +6,48 @@ Instructor: Mario Giampieri (mag2382@columbia.edu)
 
 ## Introduction
 
-_Mapping Systems_ will introduce CDP students to programming concepts and methods for spatial analysis, as well as their role in the production and interpretation of spatial data.
+_Mapping Systems_ introduces CDP students to programming concepts and methods for spatial analysis, using urban planning challenges as a basis for learning. The course will also explore the social, political, and ethical implications of mapping technologies, as well as our role as practitioners in the production and interpretation of spatial data.
 
-The course will focus on building proficiency in Python-based workflows focused on finding, describing, and visualizing spatial data; manipulating and drawing meaning from data layers; understanding distance and spatial relatedness; and measuring change over time. While a primary goal of this course is to introduce students to practical tools and workflows and build fluency in their use, the course will also introduce students to some historical and conceptual context, as well as case studies.
+The course will focus on building proficiency in Python-based workflows focused on finding, describing, and visualizing spatial data; manipulating and drawing meaning from data layers; understanding distance and spatial relatedness; and measuring change over time. The course will also introduce web-based methods for visualizing and interacting with data. While a primary goal of this course is to introduce students to practical tools and workflows and build fluency in their use, we will maintain a critical perspective and also introduce students to some historical and conceptual context, as well as case studies.
 
-The course will require students to complete exercises to gain proficiency in spatial analytic tools in service of being able to use said tools in their computational design practice.
+The course will require students to complete weekly exercises to gain proficiency in spatial analytic methods in service of being able to use said methods in their computational design practice. Students will be asked to further develop one exercise into a final project, described in more detail below.
+
+## Final Project
+
+The final project will expand upon one of the exercises and further explore the methods and tools used. This may mean conducting a more in-depth analysis of a dataset, applying the methods to a different (and/or multiple) dataset, or extending the functionality of web-based visualizations. You will be asked to diagram your project, write a short description of your goals, and reflect on how you intend to further explore these methods through the following semesters.
 
 ## Learning Objectives
 
-At the most basic level, the goal of this class is to introduce students to mapping in Python and demonstrate how to explore, analyze, and visualize spatial data. By the end of the course, students should be able to:
+At the most basic level, the goal of this class is to introduce students to mapping in Python and Javascript and demonstrate how to explore, analyze, and visualize spatial data. By the end of the course, students should be able to:
 
-- Load, explore, and visualize spatial data in Python
+- Load, explore, and visualize spatial data in Python and Javascript
 - Understand and apply basic geoprocessing techniques
 - Measure distance and spatial relatedness
-- Analyze change over time
+- Understand the role of web mapping and APIs in spatial data visualization
 
 Furthermore, students should develop a deeper understanding of how spatial data is used in decision-making, and challenges associated with using data to inform arguments (agency in mapping; objective vs subjective / abstract vs experiential).
 
+This is primarily a methods course, however students will be expected to complete weekly readings and come prepared to discuss them in class. There are several optional readings listed for each week; it is not expected that students will read all of these, but they are provided for those who wish to explore the topics in more depth.
+
 ## Course Organization / Communication
 
-Class meets on Tuesdays and Thursday in 300 Buell South from 6-8pm. Weeks will generally be organized as follows:
-**Tuesday**: Lecture, reading discussion, review of technical concepts
-**Thursday**: Tutorials, desk crits for exercises and colloquium final project
+Class meets on Tuesdays and Thursdays in 200 Fayerweather from 6-8pm. Weeks will generally be organized as follows:
+- **Tuesday**: Lecture, reading discussion, review of technical concepts
+- **Thursday**: Technical tutorials and desk crits
 
-Conversation topics that pertain to the entire class, such as meeting time/location or technical difficulties / troubleshooting can live in the course Discord channel. All other questions can be sent to me directly via email at mag2382@columbia.edu.
+Conversation topics that pertain to the entire class, such as meeting time/location or technical difficulties / troubleshooting should live in the course Discord channel. All other questions can be sent to me directly via email at mag2382@columbia.edu.
 
-All slides and tutorials will be posted to the course's [Github repository](https://github.com/mapping-systems/cdp-mapping-systems). All exercises will be saved and managed via Github (details below).
+All reading materials and slides will be posted to Canvas, and tutorials will be posted to the course's [Github repository](https://github.com/mapping-systems/cdp-mapping-systems) (i.e. this website). All exercises will be saved and managed via Github (details below).
 
 ## Office Hours
 
-Office hours are by appointment, and preferable on Tuesday or Thursdays before or after class. Email me to schedule a time to chat.
+Office hours are by appointment on Fridays, or before class on Tuesday or Thursday. Email me to schedule a time to chat.
 
 ## Schedule
 
 ### Week 01
 
-Getting started- IDE, environment, loading and visualizing data
+Introductions + getting started: IDE and environment setup, loading and visualizing data
 
 #### Class 01: Introductions
 
@@ -51,35 +57,36 @@ Getting started- IDE, environment, loading and visualizing data
 - Projections
 - Vector data types
 
-  **Exercise** [Getting Started](/Assignment_Descriptions/00_Getting_Started.md) (to be completed by next class)
+  **Exercise** [Getting Started](../Assignments/00_Getting_Started.md) (to be completed by next class)
 
   **Readings**
 
   - (optional) Edwards, P.N., 2010. Introduction, in: A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. The MIT Press.
 
 #### Class 02: Loading, exploring, visualizing data (Tutorial)
 
-- Finding open data for NYC
-- Explore spatial and non-spatial attributes of tax lot dataset, MapPLUTO
-- Create static and interactive visualizations of dataset
+- [Tutorial](/Tutorials/01_vector_data_apis.ipynb) on loading, exploring, and visualizing data using `geopandas`, `pandas`, `matplotlib`, `lonboard`, and `folium`
+- Finding open data for New York City
+- Explore spatial and non-spatial attributes of tax lot dataset, `MapPLUTO`
+- Create static and interactive visualizations of the dataset
 - Saving data
 
-  **Exercise:** [01_Loading and visualizing data](/Assignment_Descriptions/01_Loading_Visualizing.md)
+  **Exercise:** [01_Loading and visualizing data](../Assignments/01_Loading_Visualizing.md)
 
 ### Week 02
 
-Geoprocessing / vector data analysis basics using `geopandas`, `pyogrio`, `pandas`, `matplotlib`, and `lonboard`
+Geoprocessing / vector data analysis basics using `geopandas` and `shapely`
 
-#### Class 03: Why we map
+#### Class 03: Mapping as a critical and creative process
 
 - Mapping as creative process, critical practice, and counter-narrative
 - Case study: Environmental Justice in New York City and New York State
 
   **Readings**:
 
-  - Miller, H.J., 2004. Tobler’s First Law and Spatial Analysis. Annals of the Association of American Geographers 94, 284–289.
   - Iconoclasistas, 2016. Manual of Collective Mapping: Critical cartographic resources for territorial processes of collaborative creation.
   - Wilson, M.O., 2018. The Cartography of W.E.B. Dubois’ Color Line, in: Batlle-Baptiste, W., Rusert, B. (Eds.), WEB Du Bois’s Data Portraits: Visualizing Black America. Princeton Architectural Press.
+  - (Optional) Miller, H.J., 2004. Tobler’s First Law and Spatial Analysis. Annals of the Association of American Geographers 94, 284–289.
   - (optional) Entrikin, J.N., 1991. The Betweenness of Place, in: Entrikin, J.N. (Ed.), The Betweenness of Place: Towards a Geography of Modernity. Macmillan Education UK, London, pp. 6–26. [https://doi.org/10.1007/978-1-349-21086-2_2](https://doi.org/10.1007/978-1-349-21086-2_2)
   - (optional) Maantay, J., Ziegler, J., 2006. Spatial Data and Basic Mapping Concepts, in: GIS for the Urban Environment.
   - (optional) Corner, J., 2011. The Agency of Mapping: Speculation, Critique and Invention, in: Dodge, M., Kitchin, R., Perkins, C. (Eds.), The Map Reader. Wiley, pp. 89–101. [https://doi.org/10.1002/9780470979587.ch12](https://doi.org/10.1002/9780470979587.ch12)
@@ -88,75 +95,79 @@ Geoprocessing / vector data analysis basics using `geopandas`, `pyogrio`, `panda
 
 - Manipulate, reshape, and combine datasets together using spatial and non-spatial characteristics using `geopandas` and `shapely`
 
-  **Exercise:** [02_Geoprocessing](/Assignment_Descriptions/02_Geoprocessing.md)
+  **Exercise:** [02_Geoprocessing](../Assignments/02_Geoprocessing.md)
 
 ### Week 03
 
-Ways to think about and measure distance and spatial relatedness
-
-#### Class 05: Distance, Adjacency, Networks
+Web mapping, interactive visualization, and crowd-sourced information
 
-- Euclidean and network distance
-- Introduction to graph theory
-- Different kinds of adjacency
-- Case study: CitiBike usage before and during the COVID-19 pandemic
+#### Class 05: Web mapping part 1
 
-  **Readings**:
+- Introduction to web mapping
+- Web 2.0 and the rise of interactive mapping
+- Web map basic components
 
-  - Barabási, A.-L., 2016. Graph Theory, in: Network Science. Cambridge University Press, Cambridge, United Kingdom.
-  - Xin, R., Ai, T., Ding, L., Zhu, R., Meng, L., 2022. Impact of the COVID-19 pandemic on urban human mobility - A multiscale geospatial network analysis using New York bike-sharing data. Cities 126, 103677. [https://doi.org/10.1016/j.cities.2022.103677](https://doi.org/10.1016/j.cities.2022.103677)
+  **Readings**
 
-#### Class 06: Measuring Distance (Tutorial)
+  - NEOGEOGRAPHY AND THE PALIMPSESTS OF PLACE: WEB 2.0 AND THE CONSTRUCTION OF A VIRTUAL EARTH - GRAHAM - 2010 - Tijdschrift voor Economische en Sociale Geografie - Wiley Online Library [WWW Document], 2010. URL https://onlinelibrary-wiley-com.libproxy.mit.edu/doi/full/10.1111/j.1467-9663.2009.00563.x (accessed 6.15.25).
 
-- Introduce `osmnx`, `networkx`, `libpysal`, `h3` to calculate distance from Avery to local points of interest
-- **Desk crits** on final colloquium projects
+#### Class 06: Web mapping (Tutorial part 1)
 
-  **Exercise:** [03_Networks](/Assignment_Descriptions/03_Networks.md)
+- Use `leaflet` to create interactive web maps
+- Load data via API
+- Launching a basic web map
+  **Exercise:** [03_Web Mapping](../Assignments/03_Web_Mapping.md)
 
 ### Week 04
-Web mapping, interactive visualization, and crowd-sourced information
-#### Class 07: Web mapping
-- Introduction to web mapping
-- Web 2.0 and the rise of interactive mapping
+
+APIs and website deployment
+
+#### Class 07: Web mapping part 2
+
+- Data production and governance
+- Elements of an API
 - Case study: OpenStreetMap and the Humanitarian OpenStreetMap Team
-- Web map basic components 
+- **Desk Crits** + checking in
 
   **Readings**
-  - NEOGEOGRAPHY AND` THE PALIMPSESTS OF PLACE: WEB 2.0 AND THE CONSTRUCTION OF A VIRTUAL EARTH - GRAHAM - 2010 - Tijdschrift voor Economische en Sociale Geografie - Wiley Online Library [WWW Document], n.d. URL https://onlinelibrary-wiley-com.libproxy.mit.edu/doi/full/10.1111/j.1467-9663.2009.00563.x (accessed 6.15.25).
-  - Haklay, M., Weber, P., 2008. OpenStreetMap: User-generated street maps. IEEE Pervasive Computing 7, 12–18. [https://doi.org/10.1109/MPRV.2008.80](https://doi.org/10.1109/MPRV.2008.80)
+
   - Schröder-Bergen, S., Glasze, G., Michel, B., Dammann, F., 2022. De/colonizing OpenStreetMap? Local mappers, humanitarian and commercial actors and the changing modes of collaborative mapping. GeoJournal 87, 5051–5066. https://doi.org/10.1007/s10708-021-10547-7
+  - (optional) Haklay, M., Weber, P., 2008. OpenStreetMap: User-generated street maps. IEEE Pervasive Computing 7, 12–18. [https://doi.org/10.1109/MPRV.2008.80](https://doi.org/10.1109/MPRV.2008.80)
 
-#### Class 08: Web mapping (Tutorial)
-- Use `leafleft`, and `maplibre` to create interactive web maps
-- Loading data via API 
-- Launching a basic web map
-**Exercise:** [04_Web Mapping](/Assignment_Descriptions/04_Web_Mapping.md)
+#### Class 08: Developing an API and site deployment (Tutorial)
+
+- Use `supabase` to create a simple API
+- Deploy a simple website using `github pages`, `cloudflare`, or `render`
+- **Desk crits**
 
 ### Week 05
 
-Raster analysis, STAC specification, change over time
+Conceiving of and measuring distance and spatial relatedness
 
-#### Class 09: Measuring Change
+#### Class 09: Distance, Adjacency, Networks
 
-- Introduction to raster data
-- Historical context for measuring change over time
-- Case study: National Land Cover Dataset
-- **Desk Crits** + checking in
+- Euclidean and network distance
+- Introduction to graph theory
+- Different kinds of adjacency
+- Case study: CitiBike usage before and during the COVID-19 pandemic
 
-  **Readings**
+  **Readings**:
 
-  - Couclelis, H., 1992. People manipulate objects (but cultivate fields): Beyond the raster-vector debate in GIS, in: Frank, A.U., Campari, I., Formentini, U. (Eds.), Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, Lecture Notes in Computer Science. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 65–77. [https://doi.org/10.1007/3-540-55966-3_3](https://doi.org/10.1007/3-540-55966-3_3)
-  - Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., Gass, L., Funk, M., Wickham, J., Stehman, S., Auch, R., Riitters, K., 2020. Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database. ISPRS Journal of Photogrammetry and Remote Sensing 162, 184–199. [https://doi.org/10.1016/j.isprsjprs.2020.02.019](https://doi.org/10.1016/j.isprsjprs.2020.02.019)
+  - Barabási, A.-L., 2016. Graph Theory, in: Network Science. Cambridge University Press, Cambridge, United Kingdom. Available online [here](https://networksciencebook.com/chapter/2)
+  - Xin, R., Ai, T., Ding, L., Zhu, R., Meng, L., 2022. Impact of the COVID-19 pandemic on urban human mobility - A multiscale geospatial network analysis using New York bike-sharing data. Cities 126, 103677. [https://doi.org/10.1016/j.cities.2022.103677](https://doi.org/10.1016/j.cities.2022.103677)
 
-#### Class 10: Supervised classification using earth observation (EO) data (Tutorial)
+#### Class 10: Measuring Distance (Tutorial)
 
-- Use `leafmap`, `rasterio`, `ipyleaflet` to find, download, classify, composite, and analyze raster data
-- **Desk crits**
+- Introduce `osmnx`, `networkx`, `libpysal`, `h3` to calculate distance from Fayerweather to local points of interest
+- **Desk crits** on final colloquium projects
+
+  **Exercise:** [04_Networks](../Assignments/04_Networks.md)
 
 ### Week 06
 
-Preparing for final colloquium presentations + presentations on August 14th
+Preparing for final colloquium presentations + presentations on August 6th
 
 #### Class 11: Desk crits / work session
 
 - Work session for final projects
+- Wrapping up and looking forward