Spatial Data Science (ECON 494)

2022 Spring
Faculty of Arts and Social Sciences
Ivan Lopez Cruz,
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Formal lecture,Interactive lecture,Seminar
Interactive,Communicative,Project based learning,Task based learning
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This course's central goal is to introduce the student to the analysis and employment of spatial datasets in the social sciences realm. It begins with a thorough description of R's tools and methods to manipulate and visualize geographic data. After becoming acquainted with the construction of spatial variables, the student learns how economists exploit the latter to uncover the causal mechanisms determining the link between historical developments (e.g., the colonization of America) and today's regional development levels. The course also deepens into various statistical models that incorporate parameters governing a given phenomenon's spatial diffusion, thereby tackling questions such as: how intense is the dissemination of violence across space following the outbreak of civil conflict? Will one municipalities' improvements in educational levels spill to adjacent localities? A discussion on estimation techniques, hypothesis testing, and an introduction to Machine Learning methods for spatial data marks the course's end.


In this course, students will learn how to analyze and use spatial datasets in social sciences. The discussion begins by providing a comprehensive overview of R's tools and techniques for manipulating and visualizing geographic data. Students will learn how economists use spatial variables to uncover the causal mechanisms behind the relationship between historical events like the colonization of America and today's regional development levels. In addition, the course delves into various statistical models that incorporate parameters governing the spatial diffusion of a phenomenon, answering questions like how violence spreads across space following the outbreak of civil conflict or whether an improvement in educational levels in one municipality will affect adjacent localities. The course concludes with a discussion on estimation techniques, hypothesis testing, and an introduction to machine learning methods for spatial data.



1. Understand the world, their country, their society, as well as themselves and have awareness of ethical problems, social rights, values and responsibility to the self and to others. 3

2. Understand different disciplines from natural and social sciences to mathematics and art, and develop interdisciplinary approaches in thinking and practice. 4

3. Think critically, follow innovations and developments in science and technology, demonstrate personal and organizational entrepreneurship and engage in life-long learning in various subjects; have the ability to continue to educate him/herself. 5

4. Communicate effectively in Turkish and English by oral, written, graphical and technological means. 5

5. Take individual and team responsibility, function effectively and respectively as an individual and a member or a leader of a team; and have the skills to work effectively in multi-disciplinary teams. 5

1. Possess sufficient knowledge of mathematics, science and program-specific engineering topics; use theoretical and applied knowledge of these areas in complex engineering problems. 4

2. Identify, define, formulate and solve complex engineering problems; choose and apply suitable analysis and modeling methods for this purpose. 3

3. Develop, choose and use modern techniques and tools that are needed for analysis and solution of complex problems faced in engineering applications; possess knowledge of standards used in engineering applications; use information technologies effectively. 5

4. Have the ability to design a complex system, process, instrument or a product under realistic constraints and conditions, with the goal of fulfilling specified needs; apply modern design techniques for this purpose. 2

5. Design and conduct experiments, collect data, analyze and interpret the results to investigate complex engineering problems or program-specific research areas. 4

6. Possess knowledge of business practices such as project management, risk management and change management; awareness on innovation; knowledge of sustainable development. 3

7. Possess knowledge of impact of engineering solutions in a global, economic, environmental, health and societal context; knowledge of contemporary issues; awareness on legal outcomes of engineering solutions; knowledge of behavior according to ethical principles, understanding of professional and ethical responsibility. 2

8. Have the ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. 5

1. Develop knowledge of theories, concepts, and research methods in humanities and social sciences. 5

2. Assess how global, national and regional developments affect society. 4

3. Know how to access and evaluate data from various sources of information. 5

1. Design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs and to solve a computational problem. 3

2. Demonstrate knowledge of discrete mathematics and data structures. 5

3. Demonstrate knowledge of probability and statistics, including applications appropriate to computer science and engineering. 5

1. Formulate and analyze problems in complex manufacturing and service systems by comprehending and applying the basic tools of industrial engineering such as modeling and optimization, stochastics, statistics. 1

2. Design and develop appropriate analytical solution strategies for problems in integrated production and service systems involving human capital, materials, information, equipment, and energy. 3

3. Implement solution strategies on a computer platform for decision-support purposes by employing effective computational and experimental tools. 5

1. Provide constructive analysis of economic phenomena at the national and international level, and interactions between the two. 4

2. Develop an understanding of organizations and institutions in the society as well as their influence on the economy. 4

3. Recognize how incentives shape the behavior of individuals and organizations. 3

4. Identify "economic" problems and propose alternative models and/or design and conduct research to provide viable solutions using theoretical tools and/or quantitative methods. 5

5. Communicate problems and solutions to managerial and policy decision-making units as well as to lay audiences. 3


  Percentage (%)
Assignment 50
Participation 10
Individual Project 30
Presentation 10



Introduction to GIS and Spatial Analysis, Manuel Gimond, available at


R for Geographic Data Science, Stefano De Sabbata, available at

The students should, as soon as possible (preferably during the first two weeks), become familiarized with R by reviewing the following sources: : thorough introduction to R. Watch everything up to "Entering Data" : Introduction to the package "DPLYR" for a data manipulation. : I strongly encourage you to review Manuel Gimond's material on DPLYR.

Alternatively, you can read the first four chapters of De Sabbata's book.