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  1. Insegnamenti

ECO0016 - SPATIAL DATA LAB

insegnamento
Tipo Insegnamento:
Opzionale
Durata (ore):
30
CFU:
4
SSD:
POLITICA ECONOMICA
Sede:
BRESCIA
Url:
Dettaglio Insegnamento:
Analytics and Data Science for Economics and Management/PERCORSO COMUNE Anno: 2
Anno:
2025
Course Catalogue:
https://permalink.unibs.it/suacds/afcc/2025?corso=...
  • Dati Generali
  • Syllabus
  • Corsi

Dati Generali

Periodo di attività

Secondo Quadrimestre (02/03/2026 - 09/06/2026)

Syllabus

Obiettivi Formativi

a) Knowledge and understanding (KN)
The aim of the laboratory is to provide the knowledge for identifing the main types of spatial data and to properly analyse te latter. During the laboratory the students will learn ad-hoc software such as Geoda, GeoDaSpace and GWR and the main R packages for Geographical Information Science (GIS) and spatial econometrics.
b) Applying knowledge and understanding (AKN)
Students will be able to develop the empirical skills needed to analyse georeferenced data. They will learn to search for data online, to develop their own R scripts, and to use the most common econometric methods to analyse data.
c) Making judgements (MJ)
The students will have developed the ability to understand and critically interpret the main results of their analyses. Furthermore, they will be able to read and understand specialized articles that make use spatial econometric techniques and to make judgements about their results.
d) Communication skills (CS)
The students will be able to communicate using the appropriate lexicon to different interlocutors. They will write and present a team project in class, and they will have to demonstrate to be able to reply to questions made by the teacher and other students.
e) Learning skills (LS)
The students will reach the ability to learn autonomously what techniques have to be used according to the context of analysis an of the type of spatial data they handle. Furthermore, they will learn academic writing, to work in team in a project with a deadline, and to expose in public the results of their research

Prerequisiti

Knowledge of R programming language and of the basic econometric/statistical analysis

Metodi didattici

Students will actively work on a project assigned by the teacher. It is recommended to be equipped with their personal notebook during the lessons.

Verifica Apprendimento

Students' final evaluation of knowledge (KN), applied knowledge (AKN), making judgements (MJ), learning (LS) and communication skills (CS) is based on the construction of a dashboard with the aim of applying the acquired knowledge to a case study related to the field of geomarketing, transports, land use, environment, social science (poverty, migration, economic growth), etc. Students will have to analyse georeferenced data by means of charts, maps and by using spatial econometric tools [(KN), (AKN), (LS)].

Testi

• Francisco Rowe and Dani Arribas-Bel: Spatial Modelling for Data Scientists https://gdsl-ul.github.io/san/
• Michael Dorman: Introduction to Spatial Data Programming with R https://geobgu.xyz/r/index.html
• Robin Lovelace, Jakub Nowosad, Jannes Muenchow: Geocomputation with R https://bookdown.org/robinlovelace/geocompr/
• Taro Mieno: R as GIS for Economists https://tmieno2.github.io/R-as-GIS-for-Economists/

Contenuti

The Laboratory aims to provide the knowledge useful to make students autonomous in the use of geospatial data in the context of the analysis of social and socio-economic phenomena and for business purposes such as geomarketing. In the first part of the course the main types of georeferenced data (vectors, polygons and raster) will be presented, and the main concepts of distance and connectivity in space (matrices of spatial weights) will be defined. Then, in the second part, the focus will be on analysis of the spatial data. In this regard, the aim is to present the main methods for manipulating georeferenced data and for analysing discrete georeferenced data that include the Exploratory Spatial Data Analysis (ESDA), cross-sectional and panel spatial regression models to deal with spatial autocorrelation and regression models capable of managing spatial heterogeneity (Geographically Weighted Reegression, GWR). For the practical implementation of these models, open source software such as R, Geoda, GeoDaSpace, GWR4 will be used.

Lingua Insegnamento

Inglese

Altre informazioni

It is necessary to send an email to book an appointment for office hours.

Corsi

Corsi

Analytics and Data Science for Economics and Management 
Laurea Magistrale
Corso ad esaurimento
2 anni
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