## 4 Discussion
### Open Data and Tools Filling Gaps of a Tier II Indicator  
The action oriented outcome of the GHSL framework architecture that was demonstrated by our results is that indicator 11.3.1 was estimated for the year 0 of the 2030 Development Agenda (2015) against 25 years of spatial and demographic change in urban centers. In practice, using open and free data, it could be possible to raise SDG 11.3.1 from its Tier II status using the GHSL approach to fill the present data gap. With respect to UNDESA metadata, the GHSL operationalization of the SDG 11.3.1 estimation has been performed with two key premises. Firstly, the adoption of the _Degree of Urbamisation_ people based \"Global Harmonized Definition of Cities and Settlements\" allowed for the delineation of the spatial units of analysis (the extent of urban centers in the epoch 2015). Secondly, LCR and PGR were defined, respectively, as the change in the built-up areas (from GHS-BUILT derived from Landsat) and in population (from GHS-POP) between epochs in these spatial units. This is a pragmatic approach to implement the conceptual definition of \"land consumption\" intended as \"the expansion of built-up area which can be directly measured\" (SDG 11.3.1 metadata, p.2). This application was made possible by the GHSL principles and key characteristics:  
* global geographical coverage;
* multi-temporal (diachronic) information;
* demographic and spatial (built-up areas) information;
* open and free data; and,  
Figure 4: Regional comparison of built-up areas per capita. * capacity to adapt to user requirements and hierarchical abstraction concepts. In particular, the GHS-SMOD allowed for the identification of urban centers in a spatially consistent way (meeting the requirement of the stakeholders that are involved in the global commitment to develop a \"harmonized definition of cities and settlements\"--the European Union, the Organisation for Economic Co-operation and Development, the World Bank, and UN-Habitat), and to derive their population and built-up areas extent in a harmonized manner across epochs and areas of the world. This approach excludes forms of sampling, while it captures urban centers of any size greater than 50,000 inhabitants in 2015. Sampling methods to reduce the complexity of \"real world big data scenarios\" to limit the population of the dataset (the number of cities to ease information handling and production) may introduce aberrations and reveal patterns that are caused by the set of assumptions of the sampling methodology. Within the GHSL framework, the user is empowered to set its own user requirements (i.e., demographic criteria to define urban centers), replicate analyses, and information production. Further to the above motives, the GHSL framework offers a suite of free tools, especially the MASADA (Massive Spatial Automatic Data Analytics), DUG (Degree of Urbanisation Grid), and LUE (Land Use Efficiency) tools. The MASADA Tool is developed to support the production of local and regional settlement layers by automatic classification of satellite imagery (both high and very high-resolution data). Using as a training set, land cover information or a coarse settlement map (to incorporate in the workflow radiometric, textural, and morphological features), a supervised classification of remotely sensed data (through SML classifier) extracts built-up area information. The tool is primarily devoted to the extraction of built-up areas with a preset of workflows for SPOT-5, SPOT-6/7, RapidEye, and CBERS-4, yet it can be applied to other imagery sources (i.e., GeoEye-1, WorldView-2/3, Pleiades, and Quickbird) to derive other land cover maps--provided that the appropriate training set is given in input. The second tool supporting the SDG estimate is dedicated to the delineation of urban areas, as defined in the _Degree of Urbanisation_ definition [49]. Urban centers, urban clusters, and rural areas can be delineated using the DUG tool.