The intention of this collaborative research effort is to create an accurate digital 3D model of the City of Toronto waterfront that can be used by city planners to visualize factors relevant to the city when making high-level decisions regarding infrastructure and policy. This project is a collaboration between OCAD University and Esri Canada Limited, a software company that specializes in geographical information systems (GIS). EC has offered the use of their proprietary software CityEngine, which optimizes the process of creating realistic 3D city and geographical models.
The year 2017 marks the 375th anniversary of the foundation of the city of Montreal. The Laboratory of Remote Sensing, Department of Geography, Montreal University, and Esri Canada want to take part in the celebrations by offering to the Montreal community an interactive tool illustrating the historical evolution of the city of Montreal. To develop such a tool the establishment of an historical GIS is needed.
In 2006, the Government of Ontario introduced “Places to Grow: Growth Plan for the Greater Golden Horseshoe” to control urban sprawl, protect farmland and green spaces, create complete communities and revitalize downtowns and urban centres. Key specifications include minimum intensification targets, such as requiring 40% of new residential development to occur in existing built-up areas. A recently published report by Allen and Campsie (2013) found that many municipalities across the Toronto region are not planning to adhere to the mandated intensifications targets.
This project is focused on exploring volunteered geographic information (VGI), a special classification of widely available and pervasive data, which is commonly afflicted by issues degrading its overall quality. These issues make the applicability of VGI for a variety of projects in government, private, and scientific sectors questionable. We hope to design and implement several software programs that will be easily adoptable to current and future research programs in alleviating these concerns. Using VGI, we will identify and diminish uncertainty in commonly used datasets and sources.