## 2 Materials and Methods
### Case Study Selection  
In the following section, three cases are used to show how major challenges can be solved which would allow unlocking the potential of EO data (Figure 3). First, to solve data aggregation, one solution is the use of regular area grids. The use of a gridded mapping system allows data producers to provide consistent data with improved spatial resolution and near-continuous coverage (Section 3.1). Grid-based maps show a continuous distribution across a defined (usually rectangular) area, providing an efficient solution to the problem caused by geographically arbitrary units such as administrative areas, in which averages may hide poor areas. Grids can be up-scaled to larger geographic units such as administrative boundaries. Hence, grids offer a consistent and standardised approach to spatial data collection, analysis or visualisation. Furthermore, considering the growing availability of machine learning for the analysis of satellite images (e.g., for more timely information), the discrete nature of grid-based datasets opens up new possibilities. The urgency of providing consistent base data to support rapid yet informed decision-making during challenging situations, such as COVID-19, has been stressed by [42]. The development and role of gridded mapping systems are highlighted in two cases (one national and one continental) to highlight development stages and reflect on data aggregation and mapping ethics. Second, most studies on mapping slums work with VHR commercial images, leading to very high data costs (Section 3.2). However, there is an insufficient critical analysis on the cost-benefit relation between information needs and spatial resolution (towards low-cost mapping systems). Therefore, the second case  
Figure 2: Mapping ethics and privacies in the context of slum mapping.  
illustrates the role of low-cost data for the basic mapping of slums versus more detailed slum area characterisation. The case emphasises methodological innovations required to address the challenges in selecting appropriate EO data, as well as the development of suitable methods to inform end-users and decision-making supporting the creation of public policies. This case focuses on one city (Nairobi, Kenya) to show the potential of different EO data at different scales of analysis to support local data needs. The third case (Section 3.3) looks into the complexity of measuring the intra-urban variability of socio-economic conditions, departing from the mapping focus on slums by understanding urban multi-deprivation poverty as a continuous phenomenon and asking for mapping approaches that do not point to slums as stigmatised areas. This case uses a rich dataset that combines EO data with socio-economic data to model deprivation at the city scale for the case of Bangalore, India. The combination of cases will provide the way forward towards unlocking the sociodemographic knowledge with EO data and addressing major urban data gaps.  
Therefore, the following section highlights innovations towards solving these three challenges ((i) data aggregation (gridded mapping), (ii) development of low-cost mapping systems, and (iii) intra-urban variability of socio-economic conditions. Furthermore, the cases also deal with privacy and data ethics. These challenges were identified based on a scoping literature review carried out for the period 2017-2020 (including some preprints/versions that were published in 2021). The cases show how innovations in EO can contribute to policy-relevant information, with the aim to provide stakeholders (ranging from the local to national levels) with the urgently required information for evidence-based policymaking.