Urban Analytics
Objectives
Previous knowledge in data bases.
General characterization
Code
400091
Credits
7.5
Responsible teacher
Miguel de Castro Simões Ferreira Neto
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
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
Bibliography
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
English
Subject matter
Computers labs with project based approach using ESRI Arc GIS (Part I) and Microsoft Power BI (Part II).