The City of Tomorrow from… the Data of Today

Abstract

In urban planning, a common unit of measure for housing density is the number of households per hectare. However, the actual size of the physical space occupied by a household, i.e., a dwelling, is seldom considered, neither in 2D nor in 3D. This article proposes a methodology to estimate the average size of a dwelling in existing urban areas from available open data, and to use it as one of the design parameters for new urban-development projects. The proposed unit of measure, called “living space”, includes outdoor and indoor spaces. The idea is to quantitatively analyze the city of today to help design the city of tomorrow. First, the “typical”-dwelling size and a series of Key Performance Indicators are computed for all neighborhoods from a semantic 3D city model and other spatial and non-spatial datasets. A limited number of neighborhoods is selected based on their similarities with the envisioned development plan. The size of the living space of the selected neighborhoods is successively used as a design parameter to support the computer-assisted generation of several design proposals. Each proposal can be exported, shared, and visualized online. As a test case, a to-be-planned neighborhood in Amsterdam, called “Sloterdijk One”, has been chosen.

Volume Comparison of Automatically Reconstructed Multi-Lod Building Models for Urban Planning Applications

“…In the context of urban planning, this paper takes inspiration and continues the work recently published by Agugiaro et al (2020), in which an new approach was proposed to support the computerassisted urban planning and design process by exploiting a semantic 3D city model. Additionally, a prototypic software tool was also presented and described.…”

“…The nDSM model was generated by gathering heterogeneous, spatial, and non-spatial datasets that were then harmonised and integrated. The complete list of used dataset as well as details regarding the data integration process can be found in Agugiaro et al (2020). Here only the most important characteristics will be mentioned.…”

Urban Data Dynamics: A Systematic Benchmarking Framework to Integrate Crowdsourcing and Smart Cities’ Standardization

“…The authors of Reference [4], conducting a systematic review on SC data analytics, found that the design, development and maintenance of smart services requires the exploitation of data from various urban data sources. Urban data constitutes a valuable asset of cities as its exploitation provides insights into their operation and performance, which are necessary for decision-making and urban planning [31], and as aptly pointed out by [43], "the city of tomorrow is designed using the data of today". In this context, several conventional cities that strive to transform into smart and resilient ones have recognized the important role of urban data and developed infrastructure for their collection and utilization [44].…”

Inception of Harmonising Data Silos and Urban Simulation Tools Using 3d City Models for Sustainable Management of the Urban Food Water and Energy Resources

“…Different use cases have shown the usefulness of CityGML globally. For example, its use in neighbourhood planning (Agugiaro et al, 2020), disaster mapping (Schulte and Coors, 2009), energy demand simulation (Padsala et al, 2020), noise mapping (Czerwinski et al, 2007), the Helsinki 3D+ Digital Twins applications (Airaksinen, 2019) and many other such urban modelling related use cases. CityGML, being an application-independent data model, does not contain application-specific classes and attributes.…”