Characterised and geo-localised poverty: mapping deprived urban areas through a multidimensional perspective.

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Submission Summary
Cities are complex and incrementally developing systems, but current urban planning and data collection systems follow rigid parameters. Parameterising developments seems essential to encompass a comprehensive multidimensional understanding. The pace and incremental urban growth pose daunting challenges, particularly in low- and middle-income countries, such as deprived urban areas (DUA), reflecting the concentration of poverty. When communities are unmapped, governments can easily ignore them (Gevaert et al., 2019). Consequently, they miss out on vital resources that they should be able to access. A consistent and timely mapping is important not only to identify the most DUA or to design renewal strategies including supplying basic services, but also to carry out spatial analysis, e.g., overlaying maps of DUA from datasets such as socio-economic, environmental, morphological indicators in different scales to support upgrading programs (Snel and Henninger, 2002). There is no agreement on the methodology to conceptualise, quantify or to map deprived areas (Thomson et al., 2020). Most methods stay at a simplification of slum versus non-slum areas and at best provide spatial information on their boundaries. We observe a fundamental lack of a conceptual framework that allows combining household-based measures of slums (e.g. expressed as the slum definition of UN-Habitat) and area level-based measurements of deprivation. In this contribution, we present the IDEAMAPS-framework responding to this gap. The IDEAMAPS-framework has been developed based on a literature review and stakeholder workshops in several African cities. However, most frameworks are based on employing census data, thus household data and lack an appropriate understanding of area-level aspects of deprivation. Such area-level deprivations relate to additional burdens communities face when living in areas that are prone to hazards, unsafe or stigmatised. Upgrading programs would strongly benefit from combining both understandings (i.e., household and area level). Existing programs drawn up with general objectives aligned with the Sustainable Development Goals (SDGs). However, they typically lack a conceptual framework with measurable indicators to tackle urban deprivation. On the one hand, there exists a large body of literature on household level deprivation studies, such as the UK deprivation indices (Dymond-Green, 2020). On the other hand, Earth Observation (EO) and geo-spatial modelling studies have stressed the importance of area level mapping of deprivation (Taubenböck et al., 2018). However, no fully combined conceptualisation exists that can be supported with open data in an increasingly geo-spatial world. A framework was developed based on a review of existing literature that used a systematic coding of employed indicators, data sources, and scales of analysis. The analysis of the large body of literature was conceptualised in collaboration with local stakeholders (in the form of workshops) to develop domains of deprivation and indicator groups that allow their operationalisation. The framework was designed to improve data availability, quality, consistency, timeliness and disaggregation. The IDEAMAPS-framework emphasised the need for an area-level conceptualisation to encompass deprivation levels within a broader scope. The framework is divided in three levels: household level, within area level and area-connect level. IDEAMAPS-framework allows us to understand the spatial patterns of deprivation as well as urban changes in its spatial dimension, e.g., where and how changes took place and how these physical changes relate to the economy, welfare or health outcomes. It provides the flexibility to evaluate and describe the city beyond the restrictions inherent in census data (e.g., low temporal frequency). Thus the IDEAMAPS-framework supports the provision of systematic spatial knowledge on deprivation combining open geo-data and EO data. Responding also to the demand of the UN for frequently updated data for the SDGs (UN 2015).
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3: Smartness and development. Al-Souq: innovating for performance and management
PhD student
University of Navarra
University of Warwick
University of Twente
University of Lagos
University of Southampton

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