COURSE UNIT TITLE

Offered By

Economics Non-Thesis (Evening)

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Economics Non-Thesis (Evening)

Course Objective

Spatial data science is an evolving field that can be thought of as a collection of concepts
and methods drawn from both statistics/spatial statistics and computer
science/geocomputation. These techniques deal with accessing, transforming,
manipulating, visualizing, exploring and reasoning about data where the locational
component is important (spatial data). The course introduces the types of spatial data
relevant in social science inquiry and reviews a range of methods to explore these data.
We will primarily focus on data gathered for aggregate units, such as census tracts or
counties (e.g., unemployment rates, disease rates by area, crime rates), and will only
briefly consider data measured at spatially located sampling points (such as air quality
monitoring stations and urban sensors) and observations at the point level (e.g.,
locations of crimes, commercial establishments, traffic accidents).
Specific topics covered include the special nature of spatial data, geovisualization and
visual analytics, spatial autocorrelation analysis, cluster detection and regionalization.
An important aspect of the course is to learn and apply open source geospatial software
tools, primarily GeoDa, but also R.

Learning Outcomes of the Course Unit

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Recomended or Required Reading

Anselin, L., & Rey, S. J. (2014). Modern spatial econometrics in practice: A guide to GeoDa, GeoDaSpace and PySAL. GeoDa Press LLC.
GeoDa Workbook - http://geodacenter.github.io/documentation.html
Cathy O Neil and Rachel Schutt (2013). Doing Data Science, Straight Talk from
the Frontline. O Reilly.
Garrett Grolemund and Hadley Wickham (2017). R for Data Science. O Reilly.
W.N. Venables, D.M. Smith and the R Core Team (2019). An Introduction to R.
Notes on R: A Programming Environment for Data Analysis and Graphics Version
3.6.1 (July 2019)
Paul Torfs and Claudia Brauer (2014). A (very) short introduction to R.
Robin Lovelace, James Cheshire, Rachel Oldroyd and others (2015). Introduction
to visualizing spatial data in R
Guy Lansley and James Cheshire (2016). An Introduction to Spatial Data Analysis
and Visualization in R

Planned Learning Activities and Teaching Methods

Assessment Methods

Successful / Unsuccessful

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

firat.gundem@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes