Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1551–1558, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1551-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1551–1558, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1551-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  05 Jun 2019

05 Jun 2019

MONITORING SDG 9 WITH GLOBAL OPEN DATA AND OPEN SOFTWARE – A CASE STUDY FROM RURAL TANZANIA

C. M. Ilie1,2, M. A. Brovelli3, and S. Coetzee4 C. M. Ilie et al.
  • 1Technical University of Civil Engineering of Bucharest, Bucharest, Romania
  • 2Terrasigna, Bucharest, Romania
  • 3Department of Civil and Environmental Engineering, Politecnico di Milano, Italy
  • 4Centre for Geoinformation Science, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, South Africa

Keywords: crowdsourcing, open geospatial data, open source software, rural accessibility, sustainable development goals (SDG), Tanzania

Abstract. The 17 goals adopted by the United Nations (UN) are aimed at achieving a better and more sustainable future for all. For each goal, a set of indicators has been defined. The indicators measure progress towards achieving the respective SDG. For the majority of these indicators, geospatial information is needed to evaluate the current state of the indicator. While geospatial information is largely available in developed countries, this is not the case in many developing countries of the world. Furthermore, skills and capacity for calculating indicator values are also limited in many developing countries. To address these shortcomings, the third challenge of the 2018 UN OSGeo Committee Educational Challenges called for the development of training material for using open source software together with freely available high resolution global geospatial datasets in support of monitoring SDG progress. The resulting training material provides a step-by-step guide for calculating the state of SDG indicator 9.1.1, Proportion of the rural population who live within 2km of an all-season road, using open software and open data with global coverage. Through the development of this training material, we showed that anyone can monitor progress towards achieving SDG indicator 9.1.1 for their specific part of the world. Because open source software and open data were used, the indicator calculation is cost effective and completely sustainable.