The United Nations (UN) have an agenda to achieve global sustainable development by 2030. To measure the success of this agenda, the statistical and mapping communities need to collaborate, but this collaboration comes with its challenges, all of which are being brought to the table at this year’s EFGS (European Forum for Geography and Statistics) conference.

As this is one of the 5 hottest topics affecting the statistics and mapping communities, you should read on to find out more…

The UN-GGIM

In recognition of the importance of making authoritative geospatial data available globally, a committee of experts was formed called the UN Committee of Experts on Global Geospatial Information Management (UN-GGIM). The UN-GGIM’s vision is “To make accurate, authoritative, reliable geospatial information readily available to support national, regional and global development.” One of the topics being discussed by the UN-GGIM is the 2030 Agenda for Sustainable Development.

What is the 2030 Agenda for Sustainable Development?

The 2030 Agenda for Sustainable Development is a list of goals agreed upon by world leaders to help achieve global sustainable development by 2030. The 17 goals, known as sustainable development goals (SDGs), were established in 2015 and include ending poverty, combating climate change, achieving food security and fighting gender inequality.

What are targets and indicators?

Each goal has a number of targets and indicators attached to it. In total, there are 169 targets and 232 indicators. Targets outline the specific actions needed for the goal to be achieved. Indicators are the variables used to monitor progress, for example:

Goal 9 Build resilient infrastructure, promote inclusive and sustainable industrialisation and foster innovation
Target 9.1 Develop quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all
Indicator 9.1.1 Proportion of the rural population who live within 2 km of an all-season road
Indicator 9.1.2 Passenger and freight volumes, by mode of transport

The SDGs are not legally binding, however governments are expected to take ownership for their achievement. One of the ways that governments are expected to take ownership is by developing their own set of national indicators.

The challenge for statisticians & geospatial experts

It has been estimated that roughly one-third of the SDG indicators have a spatial dimension. Some can be produced directly (e.g. percentage of forest area relative to total land area) while others can be obtained by combining geospatial and statistical data (e.g. Ratio of land consumption rate to population growth rate).

The challenge faced by statistical and mapping bodies is to identify the data sources and work out the methodology for producing statistics for indicators that have a spatial component. This will involve strong collaboration among geospatial experts across the public sector at national level.

The next steps

In addition to identifying specific geospatial inputs for certain indicators, the statistical and mapping agencies can also work together towards the establishment of data visualisation tools as part of an overall national monitoring platform for the SDGs.

The EFGS conference will hope to address this topic and other SDG related issues.

Join the conversation

If your work relates to the Sustainable Development Goals, we invite you to register your attendance to the EFGS conference or submit a paper. We welcome papers on various topics including (but not limited to):

  • Refining a select group of national indicators
  • Data interoperability between the statistics and geospatial industries
  • Semantic interoperability
  • Governance and policy
  • Making SDG-related datasets more widely available
  • Establishing common work principles
  • Avoiding duplication of efforts
  • Making use of existing standards in order to avoid building standards from scratch
  • Data ownership and data ‘openess’
  • Pilot projects
  • Developing a glossary to encourage a common vocabulary
  • Data sharing platforms