This research project supports the development of cycling planning tools within an online transportation planning platform called the Transportation GeoXchange (TGX). Research focusses on the development and integration of Level of Traffic Stress (LTS) network analysis in case studies using the TGX. LTS networks are routable graphs with impedance functions that characterize the subjective level of stress associated with traversing each street segment and intersection.
This proposal details an approach for evaluating a planned project led by SII called Participatory Cities: a new inclusive, system-based approach to stimulating and supporting dense networks of practical ‘participation culture’ in cities around the world. With proof of concept developed and tested in London, UK, by the Participatory City Foundation, the model will now be implemented in Montreal and Halifax, as well as the community at the centre of this proposal, Regent Park in Toronto.
This project will be studying the relationship of Green Anacondas with their surroundings by describing the ways Anacondas use their habitats and how these behaviours change throughout the year. We will be using a technology called “radio telemetry” to follow Anacondas in order to observe them and collect information. Drone and Satellite imagery will be used to describe the Anacondas territories and habitats. Anacondas are important “top” predators of rainforest ecosystems and are also valuable in conservation land management through eco-tourism.
Marine species are threatened by a growing number of human activities occurring in our seas and oceans. Understanding the consequences of pollution, for instance, requires combining information relative to the distribution of pollutants in marine environments with information relative to the spatial distribution of marine species. Our project aims at creating a framework for the assessment of marine species exposure to pollutants such as noise, light and toxic chemical compounds. The project consists of two phases: framework design and testing.
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.
Join a thriving innovation ecosystem. Subscribe now