Tentative: Open geo-spatial data and tools: Linking urban and rural analysis with localized SDGs - Statistics Canada Workshop

What:
Workshop
When:
10:00 AM, Monday 26 Apr 2021 EDT (2 hours)
Discussion:
0
Tickets for this workshop must be claimed in advance.

Tentative Workshop

Workshop organized by

Centre for Special Business Project – Statistics Canada

Centre for Entrepreneurship, SMEs, Regions and Cities – OECD

Data Innovation and Capacity Branch, Statistics Division, Department of Economic and Social Affairs, United Nations

Over the last few years, the volume of open geospatial data has grown steadily and has provided new opportunity for geographically granular analysis, including on rural and urban areas. Open geospatial data are also increasingly accessible through global platforms (from open satellite imagery to mapping applications like OpenStreetMap, to collaborative applications such as GitHub), which cross national boundaries and provide new opportunities for international collaborative analysis in an open environment.

This workshop aims at bringing into a Canadian context the perspectives of two international organizations, the OECD and the UN, that are working with open geospatial data for analysing socio-economic and well-being conditions in cities, urban and rural areas as well as for assessing their path towards the Sustainable Development Goals (SDGs). In so doing, this workshop provides new opportunities for collaboration and knowledge sharing, on the use of open geospatial data.

By bringing together these ongoing strands of works, this workshop will enable a dialogue to address a set of forward-looking questions on open geospatial data for local analytics and SDG indicators: What types of open geospatial data sources are most promising to advance the measurement agenda for rural-urban analysis and localized SDGs? Are there methodological approaches that facilitate comparisons between Canada’s context and that of other peer countries? How can we facilitate working in the open for scalability, replicability and transparency of methods?