Week 1: Introduction to GIS

PPOL 6805 / DSAN 6750: GIS for Spatial Data Science
Fall 2026

Jeff Jacobs

jj1088@georgetown.edu

Wednesday, August 26, 2026

Welcome to The Wonderful World of GIS!

Your Final Project

Unit 1: Maps

• Your least favorite part of the course (per survey 😜)
• My favorite part of the course (because I love overthinking things)
• My goal given survey results: Let’s think of this unit like learning languages for expressing spatial information:

Temporal Information	Spatial Information
\(\Rightarrow\) 22.5 seconds	\(\Rightarrow\) POLYGON ((2 1, 3 1, 5 2, 6 3, 5 3, 4 4, 3 4, 1 3, 2 1),(2 2, 3 3, 4 3, 4 2, 2 2))

• I think you’ll be surprised at how, complexity of geospatial/spatio-temporal data \(\implies\) need for programming-language-independent representations

Unit 2: Using Code to Make Maps

• (More on this in Prereqs section below!)
• Given representations from Part 1, the task of coding becomes task of finding “best” library for loading/manipulating/plotting them
  • Where “best” = best for you!
• In R: sf and friends (tidyverse)
• In Python: geopandas

Unit 3: Spatial Data Science

• Drawing inferences about spatial phenomena
• The meat of the course
• How can we write code (Unit 2) to analyze a map (Unit 1) so as to…
  • Discover patterns (EDA: Exploratory Data Analysis) or
  • Test hypotheses (CDA: Confirmatory Data Analysis)

Unit 4: Applications / Final Project

• Take everything you’ve learned in Units 1-3 and use them to learn something about the world!
• Public Policy: Which counties are most in need of more transportation infrastructure?
• Urban Planning: Which neighborhoods are most in need of a new bus stop?
• Epidemiology: What properties of a region make it more/less susceptible to infectious diseases? Where should we intervene to “cut the chain” of a disease vector?

Who Am I? Why Am I Teaching You?

• Started out as PhD student in Computer Science
  • UCLA: Algorithmic Game Theory
  • Stanford (MS): Economic Network Analysis
• Ended up with PhD in Political Economy
  • Columbia: “Computational Political Theory”

My GIS Adventures

• High school project: mine defusal in Indochina
• As a Telecommunications Engineer for Huawei (HKUST)
• As an Urban Economist at UC Berkeley
  • Used, e.g., Google Maps API to evaluate effects of Suburbanization of Poverty

My GIS 🤯 Moment

• Horrors of “Vietnam War” did not end in 1975…
• Casualties from unexploded ordnance (cluster bombs) continue to devastate the region, over 220,000 victims:
  • >105K in Vietnam
  • >50K in Laos
  • >65K in Cambodia

From Robert (2016)

Huawei: Optimizing Cell Tower Placement

From LTE 4G/5G Self-Organizing Networks  

The Suburbanization of Poverty

• Since 2008, a person living in poverty in the US is more likely to be in a suburb than an “inner city”
• What does this mean for…
  • Access to Food / Public Services?
  • Finding a job \(\leadsto\) Commuting?
• My job: computing “suburban accessibility indices”
• Does commuting = straight line distance?

“Distance” vs. Distance!

You’ve just been hired as a fine art curator at The Whitney… Congratulations!

Commuting 1 mile to the Whitney
Also commuting 1 mile to the Whitney

The Spatial Data Science Universe

• We’ll cover key pieces: GDAL (Geospatial Data Abstraction Library), PROJ to convert between projections, GEOS for computational geometry

Why Should You Care About GIS?

• As a Human
• As a Data Scientist
• As a Public Policy Expert

As Humans

• To understand the world around you!

Charles Dupin, Carte figurative de l’instruction populaire de la France (1826)

• \(\implies\) Crucial landmark in the genesis of social science

As Data Scientists

• All data scientists are expected to know how to analyze “standard” types of data: tabular, numeric data (think spreadsheets)
• However, you can differentiate yourself in the scary scary job market by developing a particular focus on some “non-standard” type:

Hello Mrs. Google Meta OpenAI, yes, indeed, I have a wealth of experience working with [text data / temporal data / signal processing / geospatial data]. This job will be no problem for me.

As Public Policy Experts

• Oftentimes, all it takes is one map to see why a policy has failed 😱

Who can guess what this map represents? (Source)

http://www.radicalcartography.net/index.html?chicagodots, then adapted to DC: “[Eric Fisher] was astounded by Bill Rankin’s map of Chicago’s racial and ethnic divides and wanted to see what other cities looked like mapped the same way. To match his map, Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000. Base map © OpenStreetMap, CC-BY-SA” https://commons.wikimedia.org/wiki/File:Race_and_ethnicity_map_of_Washington,_D.C..png

References

Robert, Amélie. 2016. “At the Heart of the Vietnam War: Herbicides, Napalm and Bulldozers Against the A Lưới Mountains.” Journal of Alpine Research | Revue de Géographie Alpine, no. 104–1, 104–1 (April).