Current urbanization trends have led to higher population densities and increasing complexity in cities, especially in central areas. Local governments are now faced with the challenge of how to manage growth while maintaining urban efficiency and quality of living. This challenge has led to the emergence of the concept of the “smart city”, which weds information and communication technologies (ICTs) with city infrastructure to improve the monitoring, prediction and comprehension of city dynamics. Michael Carnevale of OCAD University explains the iCity project in the Education Spotlight column.
The idea of a modern-day smart city not only encompasses technological innovation such as the use of big data, but it also emphasizes institutional and human factors, encouraging entrepreneurship and creativity, and enhancing cooperation between citizens and government institutions. Since population growth and rapid urbanization are taking place all around the world, the movement for building smart cities has become international. In Toronto, this has led to iCity, a $4.9-million initiative meant to develop and apply advanced data analysis and visualization capabilities to help improve urban transportation system performance and build design-efficient, sustainable cities.
iCity is a collaborative, five-year research project led by Dr. Eric J. Miller of the University of Toronto Transportation Research Institute. It includes researchers from OCADU and the University of Waterloo, as well as public and private organizations like Esri Canada, Waterfront Toronto, and others. As Dr. Brent Hall, director of Education & Research at Esri Canada explains, “This project illustrates a productive collaboration between academia and the private geospatial business sector where the strengths of each can be interwoven to achieve project outputs. Our collaboration, especially, with Professor Sara Diamond and her colleagues at OCAD University in this first phase of the work has been especially rewarding.”
Why 3D Visualization?
One research theme within the broader iCity project is to develop a high-quality 3D representation of the Waterfront Toronto region so that it can be used by multiple stakeholders – government, various industries and the public – as an information visualization tool for city data analytics, development proposal visualization, urban factors research, and ultimately, for informing the City’s policy decision-making.
High-quality 3D representation of the Waterfront Toronto region as part of iCity initiative.
Advanced 3D city models like the one being developed for the Waterfront precinct in Toronto are also being developed for countries like Singapore, using well-established international standards for sharing and cataloguing information (e.g., use of CityGML and Building Information Model (BIM) Level-of-Detail (LOD) specifications).
Dr. Sara Diamond, president and vice-chancellor of OCADU, points out, “Cities can benefit greatly from 3D modeling because these models stimulate understanding and imagination making abstract ideas concrete. 3D models support planning at all levels of decision-making, and scenario development that looks at implications through modeling data (e.g. density, economic data, traffic, transit, complete streets). Another important benefit is that 3D models are also strong pitch documents to gather project commitment and funding - they help us imagine the future.”
Using Geovisualization Tools
We used Esri’s CityEngine software in conjunction with ArcGIS Pro to develop the 3D representation. CityEngine is a procedural modelling software tool that can use traditional vector features such as point, line and shape data, and procedurally generate them into complex geometries including buildings with textured facades and roofs that can be modified by changing feature parameter values.
A Collaborative Approach
We deployed data and tools from multiple sources to bring the best in technology to create the iCity Toronto Waterfront 3D model.
External data sources that provided the basic building blocks for features were transformed into procedurally generated models. Ground information such as street and transit networks, building footprints, tree locations and bike paths were obtained from the Toronto Open Data Web Portal and imported into CityEngine to generate the 3D textured models. Other data sources included Esri Canada’s Community Map of Canada, as well as personal visits to sites at the waterfront to verify characteristics of the local urban environment.
By using ArcGIS Pro as an intermediate point in the process we are able to modify, crop, analyze and reconfigure source data in CityEngine. A key dataset found on the City of Toronto’s open data portal is the City’s3D building massing models.
Close-up view of Queens Quay West towards Spadina Ave intersection. Note the Martin Goodman bike trail and the adjacent streetcar tracks.
With the help of David Wasserman’s Complete Street Ruleset for CityEngine, various 2D GIS feature sets like street networks composed of lines and nodes were transformed into 3D representations. Use of these rules helps to generate procedurally the 3D geometries and surface textures that define different scenarios for street use, the integration of multi-modal transit and transport, and general inner-city livability. In addition, satellite imagery was used from Esri’s Living Atlas of the World as a reference for manual inputs of street parameters (e.g., street width, number of lanes, bike lanes, etc.) to generate the 3D representation of CityEngine street models.
Waterfront Toronto’s street networks are highly irregular, requiring customization of procedural rules and solutions. For example, the Gardiner Expressway - an elevated roadway that required a custom rule to generate bridge piers that could be repositioned to account for the Lakeshore Boulevard running underneath. Other examples include custom rules for the streetcar as well as the train track network that passes through Union Station, as well as the underground tunnel connecting Billy Bishop Airport on Toronto Island to the Waterfront mainland. Creating a 3D model for this tunnel required specific rules for building the tunnel, escalators and other internal furnishings.
Gardiner Expressway upheld by piers to prevent interference with Lake Shore Blvd West street network.
Procedural generation of urban landscape from terrain raster image only (top left), to incorporating point and line network (top right), to polygons and shape geometries (bottom left), to final fully textured model (bottom right).
Although the current Waterfront model for Toronto does not use BIM content beyond LOD 1 to 2 at this stage, the 3D model is detailed enough to be used to redesign sections of the city within a contextualized environment that can help predict traffic and human spatial behaviours, analyze multiple forms of data, and engage the public in democratic processes.
Some applications of this model can include comparing and contrasting different physical development scenarios within the broader context, while also being able to conduct shadow and line-of-sight analyses to understand how sunlight will fall on the urban landscape. It is also possible to model how the occupants of a densely populated, large building in the downtown core might respond in an emergency, and how this might affect local traffic. The model can also be made available to city residents for review in a modern web browser, where they can provide user-generated content and respond directly to local developments or proposed changes in the city.
This project is still a work in progress as part of the longer-term iCity initiative, but a demonstrable and highly useful model of the study area is completed. With the 3D map fundamentals developed, updating or redesigning aspects of the entire region has significantly become easier.
Street modeling conforming to real-world terrain using City Engine.
Thus far, the street networks that have been modelled and textured include bridge networks and boulevards, 3D textured buildings, sidewalk geometries, streetcar and train tracks, and details including green areas. The next steps include mapping and adjusting the entire model to conform to the real-world terrain incorporating land elevation data, importing and modelling further vertical urban assets, and focusing on specific regions of interest.
The CityEngine project will be exported to the web as an Esri web scene, where users can access this visualization project online on a well-rendered platform designed for communicating further urban plans.
The project has used advanced 3D city and data modelling geodesign techniques to allow visualization and evaluation of design solutions to issues related to environmental performance, transportation and utility networks, the impact of construction and maintenance, and other factors that affect the growth and quality of life in cities. Geodesign methods are inherently collaborative, as they can communicate science- and value-based design solutions to multiple stakeholders simultaneously, while also allowing the general public to participate by submitting user-generated content.
“We look forward to putting the model to work with iCity researchers at the University of Toronto to explore complete street ruleset modifications and their application, as well as integrating the model with demographic, transit use and planning scenario-based data to explore future renditions of the form and function of Toronto’s waterfront.” says Dr. Hall.
As our increasingly globalized world begins to face the effects of population growth, climate change and lack of resources, smart cities around the world can benefit from employing and sharing geodesign methods and insights to work towards a smarter, more connected and more sustainable future.