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.

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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