Spatial Data Analysis

Recommended Prerequisites

This course makes the convergence and application of competencies in spatial data management and analysis. A prerequisite is the transversal use of the knowledge acquired in a bachelor’s degree in informatics engineering, computer science or data analysis.

Teaching Methods

The learning process takes place along theoretical, data lab classes, and work developed autonomously.

Materials for the classes include:
- PPT presentations?
- demonstration videos
- real data sets
- geographic information system
- toolkit for geo-data analysis

Learning Outcomes

This course addresses the topic of spatial data analysis with an emphasis on georeferenced data to support decision-making on topics such as urban mobility, land use, security and prevention of terrorism in urban spaces, energy, water and waste management. Theoretical classes provide the concepts and techniques necessary for the course. The practical classes are structured around the Data Lab. The Data Lab provides real datasets that are used in the course Project. The Project involves collecting data from various sources, including georeferenced data. In the first phase, this project aims to develop students' skills in using Geographic Information Systems and toolkits for spatial data analysis. The Project includes the choice of a practical application aimed at developing skills in building decision support systems for urban planning.

Work Placement(s)

No

Syllabus

Concepts
Geographic Information Systems
Positioning
Behaviour Modeling
Land Use, Mobility and Infrastructures

Head Lecturer(s)

Alberto Jorge Lebre Cardoso

Assessment Methods

Assessment
Exam: 30.0%
Project: 70.0%

Bibliography

GIS: A Computing Perspective (3rd Edition)
Matt Duckham, Qian Sun, Michael F. Worboys
CRC Press, 2023

Location-Based Services in Cellular Networks: from GSM to 5G NR (2nd Edition)
Adrián Cardalda García, Stefan Maier, Abhay Phillips
Artech House, 2020

Mobile Positioning and Tracking: From Conventional to Cooperative Techniques
Simone Frattasi and Francescantonio Della Rosa
IEEE Press, 2017

Chao, P., Xu, Y., Hua, W., Zhou, X. (2020). A Survey on Map-Matching Algorithms. In: Borovica-Gajic, R., Qi, J., Wang, W. (eds)
Databases Theory and Applications. ADC 2020. Lecture Notes in Computer Science, vol 12008. Springer

Mohammed Okmi, Lip Yee Por, Tan Fong Ang, and Chin Soon Ku (2023). Mobile Phone Data: A Survey of Techniques, Features, and
Applications. Sensors 23(2):34 DOI:10.3390/s23020908