Why is this Important?
There is a wide range of assets in urban environments: public spaces, buildings, concentrations of demography, utilities, history, movement of people and objects and so on. These assets carry significant value for all those interacting with them but the nature and manifestation of the value depends on the context and the use case.
The nature of value associated with the same asset depends on the nature of the interaction: occupancy for a realtor, history for a tourist, compliance for the Code Enforcement employee and so on – all built around the same digital twin of the host city.
Following proper authorization, head mounted displays or handheld devices can unlock the value contained in an asset, guiding and enhancing the interactions of a user with a city and its inhabitants.
This research topic also includes architecting a multipurpose Digital Twin of a set of assets, associating them with various layers of value and examining modalities of consumption from a human factors perspective.
Stakeholders
Elected and professional urban leaders: Mayors, CIOs and CISOs of urban infrastructure, Independent Software Vendors, geospatial infrastructure providers, points of interest publishers
Possible Methodologies
This research will require scanning and associating data with urban assets and developing methodologies to test different use cases and scenarios. User satisfaction and productivity studies in field trials will contribute to development of best practices for specific industries.
Research Program
This topic is a good fit with most topics focused on Smart Cities and long-range outdoor positioning.
Miscellaneous Notes
The UN has published reports about urbanization and the challenges it raises for those managing urban data.
Keywords
Public asset management, public services, scanning, smart cities, urbanization, intelligent buildings, urban growth, town and country planning, urban planning, digital twin, smart cities, street lighting
Research Agenda Categories
Technology, Business
Expected Impact Timeframe
Long
Related Publications
Using the words in this topic description and Natural Language Processing analysis of publications in the AREA FindAR database, the references below have the highest number of matches with this topic:
More publications can be explored using the AREA FindAR research tool.
Author
Peter Orban
Last Published (yyyy-mm-dd)
2021-08-31