B5138 - SPATIAL DATA ANALYTICS

Academic Year 2025/2026

  • Moduli: Silvia Emili (Modulo 1) Anna Gloria Billè (Modulo 2)
  • Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
  • Campus: Rimini
  • Corso: First cycle degree programme (L) in Economics of Tourism and Cities (cod. 6054)

Learning outcomes

By the end of the course, students will gain basic knowledge about the role of statistical methods for the analysis of economic problems and phenomena, mainly from a spatial perspective. They will learn how to use a statistical software to acquire, manage, and analyze spatial data for various applications, from both a descriptive and modelling point of view. They will develop practical skills to apply in the search of a solution of real-world problems in diverse fields (e.g. urban planning, or environmental science), and will become able to deal with big data and data mining techniques to identify spatial patterns and make informed decisions.

Course contents

MODULE 1

Introduction to basic concepts of spatial phenomena and statistics. The spatial weighting matrix. Global measures of spatial association. Local Indicators of Spatial Association (LISA). Hypothesis testing for global and local spatial association. Linear regression and spatial data. Geostatistics: descriptive measures of spatial dependence, spatial random fields, moments of a spatial random field, variogram, covariogram, kriging.

MODULE 2

Specification and estimation of linear spatial models for crossectional data, spillover effects, marginal effects. Endogeneity of the weighting matrix. Spatial models with regimes. Spatial probit models, estimation and marginal effects for nonlinear models. Hypothesis Testing. Spatial HAC models. Spatial panel data models.

Readings/Bibliography

MODULE 1

Bivand R.S., Pebesma E., Gómez-Rubio V. (2013) Applied Spatial Data Analysis with R. Springer.

MODULE 2

  1. LeSage J. and H.K. Pace, Introduction to Spatial Econometrics, 2009, CRC Press.
  2. Kelejian H. and G. Piras, Spatial Econometrics, 2017, Elsevier
  3. Anselin L., Spatial Regression Analysis in R A Workbook, 2007

Teaching methods

MODULE 1

Lectures and tutorials with the R software in the computer lab.

MODULE 2

Lectures and tutorials with the R software

Assessment methods

The final grade is the average of the grades from the two modules.

MODULE 1

FOR ATTENDING & NOT ATTENDING STUDENTS: Written exam including multiple choices, RStudio coding and output interpretation, questions on all the topics included in the syllabus (Max grade: 33)

The grading system is as follows:

< 18: not sufficient (exam failed)

18-21: sufficient

22-24: satisfactory

25-27: good

28-30: very good

31-33 (30 cum laude): excellent

MODULE 2

Written exam

Teaching tools

MODULE 1

Software: RStudio

MODULE 2

Software: RStudio

Office hours

See the website of Anna Gloria Billè

See the website of Silvia Emili