- Big cities so that there is sufficient literature
- Just choose a topic you’re interested in!
- All topics correspond to the lecture of that particular week and are obligatory to work on. If there was the possibility to choose one or various topics, I would recommend IoT, KBS or Data mining since these topics are not always fully covered in other courses
- I would recommend something that is in your field of interest. The topics need to be in a certain subject, for example the first assignment needs to be applicable to energy neutral cities, the second assignment to super networks – mobility. So you are not completely free, however you are allowed to choose a specific topic in that field
Smart Urban Environments
- General info (Source: Osiris)
- Current issues in urban planning and the need for smart solutions (health, social, mobility, energy)
- The need of integrated land-use and transport planning and creating benefits by synchronizing networks
- Applications and potential of integrating ICT in urban infrastructure and personal information systems
- Techniques and applications of data mining to extract information from big data
- Techniques and applications of knowledge-based systems for urban planning
- Other courses recommended by students
- Urban Research Methods
- Built Environment and Smart Mobility
- Smart Cities
- Survey outcomes (0: low, 5: high)
- Additional information
- Different aspects of the smart city
- SPSS which is beneficial
- Analytic researching, knowledge based systems, SPESS, Decision tables creation, data mining and the use of rhaspberry pi
- Learned to work with sensors and sensory data, also using hivemq. Furthermore it focused a lot on scientific research and writing.
- Decision table; BBN network
- Writing reports, new methods like KBS
- A lot!! Every topic was new
- I learned that there are very different practical approaches to one and the same problem.
- A lot of different ones
- BBN and SPSS.
- Literature review, writing, making models (e.g. decision table)
- Reviewing policies, designing policies, working with statistical data in SPSS, developing decision tables, using Raspberri Pi (interesting!)
- BBN approach was new. Furthermore, you learn nothing new.
- literature research, basic programming, data analysis, academic writing
- Understanding policies, super networks, spss, and bbns
- Academic writing skills, ICT knowledge, Python, SPSS, GeNie Academic
- The basics of spss, phyton and bayesion belief networks. Furthermore, literature research
- Metadata
Disclaimer: The following data has been collected by SERVICE among students that followed the Smart Urban Environments course in 2020/2021. Based on this feedback or other causes, it is possible that the course will have a different set up in the future. Keep this in mind when you use these data for selecting your courses.
| Quartile: | 2 |
| Time Slot: | D |
| Course Type: | Specialization Elective |
| Code: | 7ZW5M0 |
| Responsible Lecturer: | dr.ir. A.D.A.M. Kemperman |
| ECTS: | 5 |
| Exams: | No |
| Required courses: | None |
| Course description: | Cities are booming and constitute the heart of economic and cultural developments. At the same time, threats of the quality of living environments ask for smart solutions in areas such as mobility, health and energy. In this course, new perspectives offered by emerging technologies and research are addressed. The course considers current issues in urban development (smart cities, healthy cities, smart grids) and links these issues to new approaches in urban analysis and decision support (big data).
The course consists of a series of lectures. Each lecture addresses a particular topic and is accompanied by a practical and assignments where the students apply the theory to a case. The following topics are addressed:
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| Useful preliminary courses: | None |
| Useful follow-up courses: |
| Time input: | You need to be logged in to view this content |
| Difficulty: | You need to be logged in to view this content |
| Fun to do: | You need to be logged in to view this content |
| Interesting: | You need to be logged in to view this content |
| Application to practice: | You need to be logged in to view this content |
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| What did people like (in year 2020-2021): | You need to be logged in to view this content |
| What can be improved (in year 2020-2021): | You need to be logged in to view this content |
| Applied skills / methods: | |
| Project course: | Yes |
| Chosen topics: | A policy evaluation of Dutch energy performance certificates (energie labels) – assignment 1 Traffic solution for rotterdam Electric vehicle policies Park and ride in a super network Smart mobility, Nitrogen emissions First assignment: policy for adoption of EVs SDE+ in the first assignment about smart energy systems Biking policy in Eindhoven, neighborhood level analysis healthy cities energy, supernetworks/mobility, healthy cities, IoT, KBS, Data mining A topic we could pick within a larger topic Low Emission Zones, Policy Bundles Offshore-wind energy, flexible work-hours and the effects on mobility, how to decrease the car-use to education facilities |
| Recommended topics: | Big cities so that there is sufficient literature Just choose a topic you’re interested in! All topics correspond to the lecture of that particular week and are obligatory to work on. If there was the possibility to choose one or various topics, I would recommend IoT, KBS or Data mining since these topics are not always fully covered in other courses I would recommend something that is in your field of interest. The topics need to be in a certain subject, for example the first assignment needs to be applicable to energy neutral cities, the second assignment to super networks – mobility. So you are not completely free, however you are allowed to choose a specific topic in that field |
| Data source: | Own survey |
| Applied method: | Questionnaire |
| Response rate: | 24% |
| Sample size: | 20 |
| Academic year: | 2020-2021 |