Urban Analytics


Previous knowledge in data bases.

General characterization





Responsible teacher

Miguel de Castro Simões Ferreira Neto


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English


T.1 - Geographic Information Systems

  1.1 Introduction

  1.2 Geospatial Technology as a Key to Smart Cities

  1.3 Power of Mapping a City

  1.4 How does GIS can work for our city

  1.5 Smart GIS applications for SMART Cities (Case Studies)

      1.5.1 Spatial problems

      1.5.2 Data acquisition

      1.5.3 Data quality

      1.5.4 Modelling and data analysis tools

      1.5.5 Data sharing

T.2 ¿ Information Dashboard Design

2.1 Introduction

2.2 Information Design

2.3 Dashboard Design Challenges

2.4 Dashboard Design Best Practices

2.5 Power BI

   2.5.1 Introducing Power BI

   2.5.2 Getting data

   2.5.3 Building a data model

   2.5.4 Creating reports and dashboards

   2.5.5 Advanced topics: sharing a dashboard; refreshing data; and enterprise integration


On line resources

Teaching method

Evaluation variables:

   a) GIS Project

   b) Dashboard Project

   c) Final Exam

Grading, in both exam seasons, will result from the following evaluation variables weights:

   a) 35%

   b) 35%

   c) 30%

To pass a minimum of 9,5 must be obtained in the final exam.

  • Groups composition defined by the students
  • Maximum number of group members: 2

Evaluation method


Subject matter

Computers labs with project based approach using ESRI Arc GIS (Part I) and Microsoft Power BI (Part II).