## 1 Introduction  
The variations of urban socio-economic conditions, in particular, the proliferation and expansion of slums, are a global concern [1]. The world's slum population is estimated to reach 3 billion people by 2030 [2]. However, these statistics come with many inherent uncertainties, for instance, due to large data gaps, dated statistics, or difficulties accessingareas for field verification [3]. Hence, the systematic quantification of socio-economic conditions (and slums) requires methods to capture their spatial patterns in a consistent manner to support pro-poor policies, upgrading programmes and SDG 11 monitoring. Therefore, an increasing number of research activities aim to develop robust tools and techniques for collecting information on socio-economic conditions, and to fill gaps in publicly available datasets [4]. It is important to clarify that slums and informal settlements are not synonymous. In many settings, slums refer to urban areas without essential services and adequate living conditions. Informal settlements are those settlements developed outside legal regulations. Typically, slums are also informal settlements, but this may not always be the case; it is possible to find informal settlements with wealthier conditions, and slum conditions are observable by a field survey, while informality is corroborated by administrative records. Although slum conditions are visible, their exact locations are often missing in official maps; additionally, local slum definitions might differ, and thus, boundaries are often fuzzy. In general, it is necessary to consider multiple dimensions to characterise slum conditions. The approach that is commonly used to monitor SDG 11 relies on aggregated household assets data from censuses and surveys [5], and produces imprecise estimates of people living in slums, informal settlements, and other deprived urban areas. Census data are routinely collected in many countries, nevertheless, they are costly and require huge logistics to visit each and every single household in the country, making it difficult to routinely acquire data (commonly every 10 years). The available data are aggregated to relatively small areas to protect individual privacy. However, even if the information is available every 10 years, the rapid socio-economic and physical changes on the ground, especially in slums, make data often outdated at the moment they are published. Furthermore, the degree of data aggregation can also introduce a bias, also referred to as the modifiable areal unit problem [6]. Other limitations of this approach are that poor households do not necessarily congregate in deprived areas, and household poverty (e.g., unimproved structure, crowding, or inadequate water and sanitation) represent different phenomena than area-level deprivation (e.g., lack of infrastructure, pollution, social exclusion, or limited access to basic services) [7,8]. Furthermore, the recent COVID-19 pandemic has caused additional challenges for collecting information on the ground, urgently increasing the use of alternative data sources to capture socio-economic characteristics. National Statistical Organizations (NSOs) are being challenged by serious disruptions to their usual work. A survey launched in May 2020 (for details, see Appendix A) shows the heavy impact of COVID-19 on NSOs (work carried out by the aegis of the United Nations Statistical Division (UNSD), the World Bank's Development Data Group and the UN Regional Commissions) [9]. The questionnaire was sent to 218 NSOs, from which 122 responses were received. The survey revealed that 65% of NSO offices are fully or partially closed. Face-to-face data collection has been impacted 96% of responding offices, with 69% stopping it completely [9]. Fieldwork had to be postponed or stopped. Meanwhile, for all censuses that were planned to be carried out during 2020, preparatory activities were impacted in 55% of cases. For example, the Brazilian census (planned in 2020) has not been conducted, which will dramatically impact SDG monitoring.