Course Syllabus

Environmental Data Analysis

Course number | Plan 6009

Course Location | 412 Avery Hall

Course Date/Time | Mondays 3:00 – 5:00 pm

Instructors | Peter Marcotullio + Kaz Sakamoto

 

Course Description

Planners are increasingly in need of analyzing environmental data to curb and anticipate the effects that come with climate change for adaptation and mitigation. This course introduces methods of environmental data analysis across varying geographic scales and underlying planning issues in the context of climate change. The structure of the course will be defined through four modules (Global, National, Regional, and City), to introduce students to the variety of environmental data and analyses for different geographies. Each module will underscore planning approaches to climate change, including addressing rising temperatures and urban heatwaves, energy supply vulnerability and the challenge of mitigating greenhouse gas emissions through compact city simulation and planning for urban heat island adaptation.  Students master different skills including spatial suitability, data management, scenario development and machine learning to answer scale-specific research questions. The course will use analytics that will propel planners into the world of big data and help model the complexities of climate change-related environmental processes.

 

Course Goals

The objective of this course is to introduce students to the utilization of growing sources of environmental data available for answering planning questions. The experience from this course highlights technical skill development in the context of contemporary urban environmental planning issues.  For each assignment students will also explore planning challenges in more depth and apply newly acquired skills to extend the given analysis. The course will thus prepare students to take on future computational and quantitative research tasks.

 

Learning Objectives

By the end of the semester, students should be able to:

• Understand data needs and analytics for environmental planning solutions across multiple geographic scales
• Construct and manage large datasets in a “tidy” way, a framework for easy and effective data management, to facilitate modeling and visualizations
• Perform simulations (transportation sector) for GHG emission reductions using a number of different policies including compact city design
• Analyze and visualize spatial correlations between urban land use and climate change heat events
• Evaluate machine learning algorithms, for selection of optimal land uses within cities for heat wave adaptation

 

Course Expectations

Attendance

Classroom attendance is mandatory for this class. We expect an e-mail or in-person explanation on any absences at least 24 hours before class. Students will be responsible to catch up on class materials by themselves and other classmate’s help and are expected to complete assignments at the same deadline as the rest of the class.

Grading Schema

Deliverable	% of Final Grade	Deadline
Assignment 1: R Review 1	10%	20 Sep 2026 (Sun)
Assignment 2: R Review 2	10%	27 Sep 2026 (Sun)
Assignment 3: Global Analysis	20%	18 Oct 2026 Sun)
Assignment 4: National Analysis	20%	14 Nov2026 (Sun)
Assignment 5: Regional Analysis	20%	6 Dec 2026 (Sun)
Assignment 6: City Analysis	20%	20 Dec 2026 (Sun)

 

All assignments are due 11:59AM ET of the deadline date in Canvas.

Grades for late assignments within 24 hours will be discounted by 50%. If assignment deliverables are used in class discussions, late ones may not be included. Anything submitted beyond a week will not be graded and instructor feedback is not guaranteed.

System Requirements

• R
• Excel *if needed
• ArcGIS / QGIS *if needed

Readings

• Robin Lovelace, Jakub Nowosad and Jannes Nuenchow (2019) Geocomputation with R (Chapman & Hall/CRC The R Series) 1st Edition (fully online)Links to an external site.
• Other reading as pdf links in Canvas

Weekly Schedule

Class Date	Topic	Assignment to Handout
14 Sep 2026	Module 0: Technology Review	Assignment 1
21 Sep 2026	Module 0: Technology Review	Assignment 2
28 Sep 2026	Module 1: Global	Assignment 3
5 Oct 2026	Module 1: Global - Rmarkdown
12 Oct 2026	Module 1:  Global
19 Oct 2026	Module 2: National	Assignment 4
26 Oct 2026	Module 2: National
2 Nov 2026	Election Holiday - No Class
9 Nov 2026	Guest Lectures
16 Nov 2026	Module 3: Regional	Assignment 5
23 Nov 2026	Module 3: Regional
30 Nov 2026	Module 4: City
7 Dec 2026	Module 4: City	Assignment 6
14 Dec 2026	Module 4: City

Module Descriptions

The course includes four modules that address environmental urban planning issues at different scales, require different skill sets to explore, and have different clients.  We start at the global scale and work down to the local (city) scale and examine related, but different environmental challenges associated with climate change.  Each module spans several weeks.  In the first week of the module, a lecture is provided on overall concepts and issues.  Readings provide the background to lectures, based upon the environmental planning literature that addresses the issues at hand and provides ways in which planners can address challenges.  The data are uploaded to Canvas and available to students throughout the semester.  The analyses cover suitability, simulation, projection, network, statistical, and spatial methods. 

Module 1: Planning for future urban heat wave exposure at the global scale

1. What are potential planning measures to mitigate impacts in these cities?

Lecture

Instruction focuses on climate change and urbanization in the 21^st century including projected temperature changes, population increases, and urban land use expansion.  Topics covered include both the human (urban-related) and natural causes of climate change and planning remedies to make cities more resilient to heat.  We cover projections for changes in heat wave intensity and frequency as well as the variety of mitigation and adaptation options for urban residents. 

Methods

Data provided includes UN population projections (2010 – 2100)[1] and urbanization projections (1950-2030).[2]  From these data, students will calculate a logistic urbanization trend for each country to 2100.  Calculations for the 2100 urban population include the 2100 urbanization level and the UN projected population for each country.  Urban spatial data includes the Global Human Settlement Layer for 2015 and accompanying population data.[3]

The analysis includes projections based upon the assumption that the national proportion of the population within the different cities remains constant throughout the century.   We apportion the population in 2100 based on the share of the urban population for each city in 2015 and the 2100 urban population calculated above.      

Then 5- or 15-day heat wave data provided to students for the year 2100,[4]  identifies the heat waves.  The literature helps to define the level of “very warm” heat waves. We encourage using ranges of temperatures (i.e., < 32 C, 32-36 C, 36-40 C, 40-44 C, 44-48 C, > 48 C).  Further analysis includes a spatial and suitability study.  Students overlay urban maps with population data and heat wave data and identify the number and location of urban populations exposed to very warm heat waves.

A write-up with maps, scripts, and text is due at the end of the module.  Student papers must include recommendations for urban planning actions for this heat in locations of high exposure. 

Module 2: Energy supply planning at the national scale 

1. What are the changes in energy demand in the future given current levels of consumption or in meeting a minimum level for human well-being in our cities?
2. Given the current composition of the national energy supply, how big is the challenge for meeting requirements using renewable fuels?
3. Where might these renewables come from? Given predicted climate change, what are the vulnerabilities to the energy supply system?
4. What are the urban planning measures to address these mitigation and adaptation challenges?

Lecture

The lecture for this module overviews the current use of fossil fuels and the connection of fossil fuel use to climate change and includes current projections for the future energy supply.  Renewable sources are examined and limitations of the use of these sources are reviewed.  Challenges and solutions for the use of renewables at the urban scale are presented. 

Methods

Energy data for this analysis will come from the OECD, International Energy Agency (IEA) and the US DOE Energy Information Agency (EIA).[5]  Energy resources information is provided through the World Energy Council’s World Energy Resources publication (most recent 2018, data online).[6]  Maps of solar and wind potential are available from the UNEP World Solar and Wind Energy Assessment.[7]  Students will choose unique nations.  We encourage covering developing countries. 

The urban population growth estimates in the first exercise and the current energy supply forms the bases of the analysis.  Students estimate the increase in energy supply necessary for the population to meet minimum levels of well-being or based on annual demand trends (last 10 years).  They compare these values with current source levels of total primary energy supply (fossil fuels versus renewables).  The change in demand is the proxy for the new supply necessary, which is compared to the current supply. 

Students examine maps of wind and solar power to identify the ability to substitute fossil fuel energy with renewable energy. Students develop plans for the transition to renewable energy in cities of the country. 

Module 3: Planning for transportation energy and GHG emission reduction at the regional scale

1. What energy and GHG reduction benefits can be obtained with compact city development?
2. Does compact city development bring greater energy and GHG reduction benefits than moving employment away from the city (towards residential housing)?

Lecture

The lecture for this module includes the urban planning challenges of increasing mobility and potential land use planning solutions.  The importance of density, accessibility and choice is reviewed.  Special attention is provided to the compact city debate, which includes the concept of density.  Planning solutions to transportation energy use and GHG are reviewed. 

Methods

The transportation-related data for this exercise is based upon information from a small island state (Grenada) that can also substitute for an urban region.  It includes the spatial data for housing and a road network and the table information of the number of motor vehicles in the country during 2015 by make, model and year.  Data for fuel efficiency is obtained from the EPA’s miles per gallon fuel economy data.[8]  Population and urbanization figures are from the UN, as per above. 

The analysis includes the development of scenarios based upon a housing and road network within the region.  The national fleet of vehicles in 2015 provides for the ability to calculate the kpl (kilometers per liter) national average.  The road network allows for an origin – destination analysis and population figures from the UN allow for the projection of population spatially disaggregated with a housing database. 

Scenarios are developed to 2050 using different parameters including changes in vehicle fleet size, vehicle usage and vehicle efficiencies.  Spatial parameters include compacting a city (densification) and or moving employment to other locations outside the primary city location. 

Student reports include a summary of the results and the evaluation of different potential transportation policies on energy use and GHG emissions. 

Module 4: Land Use Planning for Urban Heat Island at the local scale
A phenomenon of excess heat compared to rural areas, known as the Urban Heat Island (UHI), has been observed for cities of different sizes, locations and development status. Researchers have identified a number of different factors that affect UHI and concluded that the urban size is a good predictor of UHI and that intra-urban development patterns, identified through local climate zones (LCZs) and population densities are good indicators for variation of UHI within cities. The analyses cover projections, machine learning and spatial methods. The mayor of a city chosen by students would like answers to two questions:
a) Which types of development design typologies provide the lowest urban excess heat compared to rural areas?
b) What types of planning policies can be used to help cities mitigate UHI in terms  of urban designs

Lecture
This lecture focuses on specific land use characteristics and urban heat. It introduces  students to some of the details of UHI. It further develops student understanding of potential future threats to urban residents from UHI  and focuses on changes in the urban climate. The lecture presents the determinants of UHI and green solutions to  this local phenomenon through a variety of planning solutions.

Methods
The temperature database for this project is from mean temperatures identified in the Global Historical Climatology Network (GHCN).9 The variety of classified images of cities around the world provides information on the local development patterns as defined by local climate zones (LCZs) through the World Urban Database and Access Portal Tools (WUDAPT) project.10 There are over 60 cities with LCZ classifications. The classifications of 11-14 different LCZs follows planning designs and urban structure (i.e., high-rise dense development, low-rise dense development, low-rise sparse development, etc.).

The analysis includes the calculation of UHI intensity by comparing mean temperatures in the different intra-city LCZs with those in rural locations. Students overlay temperature records from the GHCN on classified urban LCZ data. The temperature records includes historical observations from stationary weather instruments in over 100,000 locations outside and within cities around the world. Comparisons of temperatures from recent records form the basis of the analysis. Cluster analysis identifies urban and rural location. The lowest temperatures within the different LCZ maps form the solution to the problem. After identification of the differences in temperature, student reports will also include measures to mitigate UHI in their specific location.  

Extra Module: Satellite Images for Causal Inference and Environmental Sciences

This week we will have two guest lecturers talking to us about the use of earth observation data.

Lecture

We will have two guest lectures this week:

TBD

 

[1] https://population.un.org/wpp/Download/Standard/Population/ (Links to an external site.)

[2] https://population.un.org/wup/Download/ (Links to an external site.)

[3] https://ec.europa.eu/jrc/en/global-human-settlement-layer (Links to an external site.)

[4] See for example, http://worldclim.org/version2 (Links to an external site.) or https://tntcat.iiasa.ac.at/RcpDb/dsd?Action=htmlpage&page=welcome (Links to an external site.)

[5] https://www.iea.org/ (Links to an external site.) (through Columbia Library access), https://www.eia.gov/beta/international/ (Links to an external site.)

[6] https://www.worldenergy.org/publications (Links to an external site.) and https://www.worldenergy.org/assets/downloads/Issues-Monitor-2018-HQ-Final.pdf (Links to an external site.)

[7]https://library.stanford.edu/libraries/stanford-geospatial-center

[8] https://www.fueleconomy.gov/feg/download.shtml (Links to an external site.)

[9] https://www.sciencedirect.com/science/article/abs/pii/S0012825218306421

[10] https://dro.deakin.edu.au/view/DU:30016728

[11] https://ieeexplore.ieee.org/abstract/document/5417425

[12] https://www.nytimes.com/2008/05/13/world/europe/13iht-spain.4.12853501.html