City Analytics


This course looks at the importance of analytic capacities as an information management tool. It focuses on the vital role that data processing, analytics and visualization play in the context of smart cities.

With the previous knowledge acquired in the courses of Sustainable Cities, Databases and Geographic Information Systems, this course will cover methodologies, processes and tools supported namely by databases, geographic information systems and open data to deliver urban intelligent solutions.

To reach this objective the course will adopt a project based approach including two distinct sections. One covering geographic information systems to undertake spatial analysis and another one in the field of data management and visualization through the use of dashboards.

General characterization





Responsible teacher


Weekly - Available soon

Total - Available soon

Teaching language

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


Databases and general knowledge in geographic science and information systems.


The Data Warehouse Lifecycle Toolkit, 2008 by Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, Bob Becker; Implementing a Data Warehouse with Microsoft SQL Server 2012 (Microsoft Press Training Kit), 2012 by Dejan Sarka, Matija Lah, Grega Jerkic; Building a Data Warehouse: With Examples in SQL Server, 2007 by Vincent Rainard; 0; 0

Teaching method

The course will adopt a methodology of practical teaching taking advantage of the accomplishment of exercises where the acquired knowledge will be applied.

In the first part of the course, dedicated to the geographic information systems, through the realization of GIS projects to apply the concepts of information geoprocessing in urban context and, in the second part, by buidling reports and dashboards for manipulation and analysis of information in the context of urban analytics with Power BI.

Evaluation 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.

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

Subject matter

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