| Study programme competencies |
|
Code
|
Study programme competences
|
| A7 |
CE4.1 - Understand the ongoing digital transformation processes, becoming familiar with analytical and urban modeling tools to apply them in decision-making processes (reactive and preventive) in urban planning and management, based on analytical information. |
| A8 |
CE4.2 - Plan and recommend intelligent information gathering systems in order to monitor sustainability, quality of life and urban intelligence. |
| B2 |
CB7 - That students know how to apply their acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study. |
| B3 |
CB8 - That students are able to integrate knowledge and face the complexity of making judgments based on incomplete or limited information, including reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments. |
| B5 |
CB10 - That students possess the learning skills that will enable them to continue studying in a manner that will be largely self-directed or autonomous. |
| B8 |
CG3 - To acquire high-level knowledge, tools and resources to meet the research and professional expectations of students and society in the study of urban development, planning and management. |
| C2 |
CT2 - Use the basic tools of information and communication technologies (ICT) necessary for the exercise of their profession and for lifelong learning. |
| C5 |
CT5 - Value the importance of research, innovation and technological development in the socioeconomic and cultural advancement of society. |
| Learning aims |
| Learning outcomes |
Study programme competences |
| Prepare professionals capable of participating in the construction of cities analytics, through the development of innovative solutions for the collection, processing and analysis of city data that promote greater sustainability in its management and governance in parallel with a more active and participatory citizenship. |
AC7
AC8
|
BC2
BC3
BC5
BC8
|
CC2
CC5
|
| Contents |
| Topic |
Sub-topic |
| 1. Introduction to Smart Cities |
Smart cities: context, challenges and opportunities. |
| 2. Introduction to Sensorization |
Sensorization: context, challenges and opportunities. |
3. Exploring data and processing systems for urban environments
|
Exploratory data analysis. Systems for data processing in the urban environment. |
4. Data processing and analysis for decision making
|
Data processing and business intelligence. |
| 5. Applications and examples |
Representative applications and smart city projects. |
| Planning |
| Methodologies / tests |
Competencies |
Ordinary class hours |
Student’s personal work hours |
Total hours |
| Laboratory practice |
A7 A8 B8 B2 B3 B5 C2 |
15 |
51 |
66 |
| Workbook |
A7 B8 B5 C5 |
0 |
29 |
29 |
| Supervised projects |
A8 B8 B2 B3 B5 C2 C5 |
0 |
15 |
15 |
| Seminar |
A7 B8 B5 C5 |
10 |
0 |
10 |
| |
| Personalized attention |
|
5 |
0 |
5 |
| |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
| Methodologies |
|
Methodologies |
Description |
| Laboratory practice |
Carrying out practical activities, such as demonstrations, exercises, experiments and research. |
| Workbook |
Reading of didactic material, viewing of videos and consultation of multimedia material. |
| Supervised projects |
Carrying out work after searching and managing information, writing texts and preparing documents. |
| Seminar |
Intensive study of a topic in a small group with discussion, participation, preparation of documents and conclusions that must be reached by all components of the seminar. |
| Personalized attention |
|
Methodologies
|
| Supervised projects |
| Seminar |
| Laboratory practice |
|
| Description |
| During the laboratory practices, supervised works, and seminars, the students will be able to present questions, doubts, etc. The teacher, responding to her requests, will review concepts, solve new problems or use any activity that he considers appropriate to resolve the issues raised. |
|
| Assessment |
|
Methodologies
|
Competencies |
Description
|
Qualification
|
| Supervised projects |
A8 B8 B2 B3 B5 C2 C5 |
Continuous monitoring of student activity on a proposed topic. In case of impossibility of follow-up, the work will be evaluated by means of the final exam. |
15 |
| Seminar |
A7 B8 B5 C5 |
Continuous monitoring of student participation in the seminar. In case of impossibility of follow-up, the work will be evaluated by means of the final exam. |
15 |
| Laboratory practice |
A7 A8 B8 B2 B3 B5 C2 |
Completion of the proposed practices. |
70 |
| |
|
Assessment comments |
In order to pass the subject, it is a mandatory condition to present contributions in the three methodologies and that the final weighting of the three is equal to or greater than a 5 out of 10. On the second opportunity, the same laboratory practices will be presented and, as it is not possible to continue monitoring the student, 30% of the grade will correspond to the final exam.
|
| Sources of information |
|
Basic
|
Anders Lisdorf (2019). Demystifying Smart Cities: practical perspectives on how cities can leverage the potential of new technologies. Apress / Springer
|
|
|
Complementary
|
Y. Karimi, M.H. Kashani, M. Akbari, E. Mahdipour (2021). Leveraging big data in smart cities: A systematic review (in Journal Concurrency and Computation: Practice and Experience). Wiley
|
|
| Recommendations |
| Subjects that it is recommended to have taken before |
|
| Subjects that are recommended to be taken simultaneously |
|
| Subjects that continue the syllabus |
| IoT and Ambient Intelligence Technologies for Building Smart Cities/630541013 | | Information Systems for Smart Cities/630541014 |
|
|