## 2 Materials and Methods
### Ethics for Urban Poverty Mapping  
The case studies (Section 3) show how privacy can be optimally addressed. When implementing machine learning methodologies and selecting a framework to use EO as input for mapping slums, \"Mapping Ethics\" should entail an assessment of five components: access, choice, value, power, and barriers (Figure 2). Firstly, the level of access to EO data and technologies affects the capacity of those mapping and those being mapped to interact with, challenge or agree on the representations made by EO technologies. This capacity depends on whether the activity of mapping is inclusive or exclusive. However, this capacity also depends on whether those being mapped have a choice in being mapped or not, as well as the technical and socio-political barriers that may exist. The choice to be mapped reflects the existence or lack of a dialogue between those in urban deprived areas, researchers, municipal authorities and international NGOs. This dialogue should increase awareness among those being mapped about the value and impacts of the data used to design EO products, a dialogue that transitions those being mapped from data subjects to potential data citizens [31]. However, this dialogue should also feature the choice of those being mapped on how they would like to be mapped. For instance, the youth in Brazil, \"have developed an aversion to any location-disclosure behaviour, including tagging, to protect their personal safety\" [32] due to gang presence in favelas. There is, therefore, a need to pay attention to international as well as local data disclosure and privacy protection concerns [33; 34] which influences what kinds of visualisations and platforms are used to represent the information on those living in urban deprived areas.  
More so, this dialogue on how and whether to be mapped can be impeded by technical and socio-political barriers and the representational power of EO products. In its technical use, representational power points to the ability of computational neural networks to assign labels to specific instances of defined classes (in this case, boundaries of \"slum\"/\"non-slum\"). However, the socio-political dimension of representational power, relates to the impact of these designations on the lives of those being mapped. EO technologies and products do not operate in a scientific vacuum; they can affect how well the communities of those being mapped are seen at various levels (e.g., by researchers, municipal authorities and international organisations); and the decisions made at these levels can lead to improving not only the technical representation in terms of accuracy [15; 25; 36; 37; 38] but also socio-political representation such as recognition of rights [39; 40]. However, the technical, as well as socio-political dimensions of representational power, can be impeded by a number of barriers, (i) time taken to set up EO technologies and platforms, (ii) cost of acquiring EO data and technologies, (iii) skills to understand and manipulate EO data, (iv) interest from State authorities in using EO data and products [41], and (v) trust between those being mapped and those doing the mapping ([34], p. 3). These components frame the potential as well as challenges in setting up a \"Mapping Ethics\" in the context of urban poverty mapping. In light of data privacy and Mapping Ethics, the development of a data ecosystem needs a careful design which considers: the protection of vulnerable communities from being further stigmatised; the dialogue with communities in terms of awareness and access to data; and local understanding of which data should not be shared in such a system.