Collaborations
The Linkable Open Data Environment (LODE) compiles data made open by their sources. It is developed and enriched through collaborative projects with external partners, involving data or code development as well as open data analysis. LODE products are independent of other Statistics Canada products.
This page lists completed and ongoing collaborations and provides links to collaborative outputs.
Microsoft
In 2018, Statistics Canada entered into a collaborative agreement with the Bing Maps team at Microsoft. Microsoft used a preliminary version of the Open Database of Buildings, as training data for their satellite imagery processing algorithms to extract building footprints for all of Canada. This resulted in a parallel release of open databases of building footprints that provide virtually complete coverage for Canada.
OpenAddresses
OpenAddresses is a not-for-profit that has compiled and standardized over 500 million addresses across the world. Statistics Canada entered into a collaborative agreement with OpenAddresses for work on an Open Database of Addresses for Canada.
Academic collaborations
The LODE databases can be used for analytic projects in academic and research settings. The Data Exploration and Integration Lab collaborates on applied analytic projects and more such collaborations are expected.
University of British Columbia, Master of Data Science – Capstone project 2020
Title: Modeling and Visualization of the COVID-19 Outbreak in Ontario
Using the Open Database of Healthcare Facilities and Proximity Measures Database, Ngan Lyle, Sofia Bahmutsky, Kaitlyn Hobbs and Shreeram Murali statistically modeled and visualized both COVID-19 outbreaks in long-term care facilities and incidences in public health units in Ontario. Scripts and reports are available in the Centre for Special Business Projects GitHub repository, along with an interactive visualization.
Fleming College, GIS program – Capstone project 2019
Title: Developing open data statistics with the Open Database of Buildings
Using the Open Database of Buildings, Sarah Gilmour, Vraj Patel, Stephanie Tang , and Zachary Bist, computed measures of building density and proximity for several Canadian cities, and developed an interactive webmap – Open Database of Buildings Statistics Viewer – to visualize these outputs.
University of British Columbia, Master of Data Science – Capstone project 2019
Title: Data Analytics with the Open Database of Buildings
Using the Open Database of Buildings, Jiachen Wei, Rui Li, and Debangsha Sarkar developed a Python code for building footprint data analytics for several base geographies, as well as a clustering analysis to identify different types of neighbourhoods. Program codes are available on the project's Github repository.
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