## 3 Cases Studies: Unlocking the Sociodemographic Knowledge with EO-Methods
### Gridded Systems for Collecting Sociodemographic Data and the Role of Data Cubes  
To link EO data with the existing statistical data, data cubes as well as data ecosystems, there are innovative solutions. In the context of data sciences for EO, and for lessening the difficulty of comparing, merging and sharing data that comes from sources with different geographies, the use of grids [43] has emerged as a good option. A grid is a  
Figure 3: Framework towards unlocking the sociodemographic knowledge with EO data. set of continuous geo-spatial units--cells--which have the same geometry as well as a homogeneous distribution of a thematic content (population, housing, vegetation, slope of the terrain, etcetera) (Figure 4). The purpose of Mexico's grid is to provide accurate training data to their Data Cube as it provides a standard unit throughout time (instead of the varied statistical units that are traditionally used to report the data), in order to take advantage of its descriptive and predictive capacities (Figure 5). Thus, Mexico's grid was designed under geometric and cartographic specifications of the Mexican Geospatial Data Cube. This allows summarising the socio-demography, the economic and geographical data of the country at the grid cell level. For a particular grid, it was necessary to proportionately distribute 2.3 million city blocks to the grid cells, consisting of more than 112 million people, a little more than 28 million inhabited private households and 4.3 million economic establishments (INEGI, 2010), as well as several geographic layers, to solve the urban-rural dichotomy through several density indicators as well as service availability inside the households. Figure 4: Two examples of gridded data, regular squared grid (**left**) and hexagonal grid (**right**). Figure 5: Gridded map of the population density and employment of Mexico City, 2015 (Source: INEGI). Similarly, the Integrated Deprived Area Mapping System (IDEAMAPS) is also currently implementing a gridded mapping approach in the form of a data ecosystem that combines data to understand urban deprivation (the first pilot of the system is available [[https://ideamapsnetwork.org/](https://ideamapsnetwork.org/)]([https://ideamapsnetwork.org/](https://ideamapsnetwork.org/)) (accessed on 20 August 2021). IDEAMAPS was conceived in 2019 to produce routine, accurate maps of urban deprivation in Low-and-Middle-Income Countries (LMICs) by integrating the strengths of existing, silo-ed \"slum\" mapping approaches [11]. The IDEAMAPS Network officially launched in 2020 with funding from a UK Research and Innovation grant [11]. The use of EO and other spatial data in machine learning models is well suited to map area-level phenomena; however, models need to be combined with community-based data to be validated and to be locally relevant [18]. Therefore, the IDEAMAPS approach combines citizen-generated, EO data, census, survey, social media, web-scraped, and other data to produce a common, dynamic, accurate map of deprived urban areas in support of evidence-based policy-making and local planning and upgrading so that all cities can become equitable, healthy, and prosperous. The underlying IDEAMAPS data ecosystem facilitates fair exchanges of data among stakeholders and evolves improved accuracy over time (Figure 6). While a plethora of open datasets exists, they are dispersed, aggregated, inconsistent, or require GIS skills to access and preprocess (e.g., to clean data inconsistencies), and IDEAMAPS aims to remove these barriers. Neighbourhood-specific data are vital to community leaders for advocacy, gaining legal tenure, development, and crisis response. In IDEAMAPS, government stakeholders validate models in exchange for neighbourhood-level data summaries and deprivation maps classified into slum/non-slum areas (or multiple levels of deprivation) for SDG 11 monitoring, urban planning, budget planning, and other decision-making.