The Smart City approach requires a paradigm shift with regard to
information management. The challenge is, to inter
weave long-term and short-term data into a coherent model, providing tangible information to public administration/
politics, interdisciplinary working experts and last but not least to the citizens. Although being even more complex due
to incoherent data and more/ more diverse stakeholders, the change process could be compared to the development of
BIM in the construction sector, where so far segmented planning and operating activities are requested to be integrated in one joint database / approach. E-Government and E-Participation are additional fields of application demanding not only new ICT-solutions but different organizational procedures. Always to be considered: Data security and privacy.
Learning Unit 4.1 Smart Data Components
|Smart Data is the basis of a Smart City. It
needs data for almost all processes in a Smart City. The
following lectures of the module are based on this basic
lecture.The students learn:
• what data is needed for and what role it plays in the Smart City
• what types of data exist and how they can be generated and collected
• how data can be processed
• the benefits of data and how it is used
• which platforms exist and how they work technically
• how GIS, City Information Model and digital platforms and services work together
Learning Unit 4.2 Geographic Information Systems
A core element of Smart City Management is smart data
management. Different approaches are practiced, such as
FIWARE in Santander, Spain. As almost all Smart City data
management implementations strongly rely on spatial data
management using GIS, these technologies are considered to be
the basis of Smart Information Modeling.
Learning Unit 4.3 City Information Model
|Cmart Information modeling is based on formal information
modeling languages such as XML, UML and SYSML to define
conceptual information and complex system models. Therefore in
depth knowledge on these formal modelling languages is
essential to be able to understand standardized data models
(OGC, BSI, ISO, etc,) . These are relevant for Smart Cities as
they are specified using these modelling languages. Methods and
tools to implement information models, specified in UML in a
spatial database, populate the data model with data and query
the data with GIS
On successful completion of this module, students will be able to:
• analyze a given task in the context of a smart city such as flood management and develop a conceptual data model to develop a data driven solution to solve the given problem.
• synthesize and implement data models combining spatial and non-spatial data tasks with respect to different application domains on the conceptual, logical, and physical level.
• analyze spatial and non-spatial data using SQL.
• Select standardized data models for a given problem
• Develop a concept to integrate existing data into the data model
Learning Unit 4.4 Digital Platforms & Services
In a smart city there are digital platforms and services
through which users can book services and use information.
The data collected and processed in advance are then used to
output data that can be used by the residents.