Smart Information Modelling

Module 4: Smart Information Modelling

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.
On successful completion of this module, students will be able to:
• explain the fundamental concepts of GIS (raster vs. vector representation, layers and objects, dimensions, topology, classification of GIS)
• apply workflows for secondary data acquisition and data analysis in order to receive consistent spatial data.
• design and arrange complex analysis workflow for spatial data for solving real world problems.
• apply cartographic grammar to present analysis results e.g. on thematic maps
• Use a Geographic Information System to manage large spatial data sets using a spatial database

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.
The students learn:
• how collected data can be used
• which platforms exist for municipalities, how these work and which services and applications result from them
• the benefits of platforms and data analysis for municipalities and citizens
• how data privacy is secured what methods of data protection are available
• how the municipality itself can be digitised and what services are available as a result