The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Building Forward Better: Inclusive Livelihood Support in Nairobi’s Informal Settlements

For the large population living in Nairobi’s informal settlements, the long-term effects of Covid-19 pose a threat to livelihoods, health, and wellbeing. For those working in the informal sector, who are the lifeblood of the city, livelihoods have been severely supressed by Covid-19 restrictions such as curfews, pushing many into further poverty. This article draws on community data, meetings, and authors’ observations as community organisers, to explore the challenges posed by existing government responses from a community development perspective. We found that poor accountability structures and targeted income support only for the ‘most vulnerable’ exacerbates tensions, mistrust, and insecurity among already vulnerable communities. We draw on a rapid desk review of existing literature to argue that community-led enumeration to validate entitlement claims, improved accountability for distribution, and widening income support is required to build solidarity and improve the future resilience of these communities.Irish Ai

Particulate matter pollution in an informal settlement in Nairobi : using citizen science to make the invisible visible

We used a citizen science approach to explore personal exposure to air pollution of selected informal settlement dwellers in Nairobi, Kenya. This paper presents the methods used, with the aim of informing others who wish to conduct similar work in the future, and some results, including policy impact. We used three interlinked methods: 1) a personal mobile exposure monitoring campaign in which individual workers used Dylos monitors to measure variations in their exposure to fine particulate matter (PM2.5) within the settlement over the course of a day, 2) a questionnaire conducted before and after the monitoring campaign to assess any changes in knowledge or attitude in the wider community, and 3) two workshops, which facilitated the citizen science approach and brought together members of the community, local policy makers and researchers. The three elements of the study provided the local community, policymakers and scientists with new insights into the challenges air pollution poses for human health in such settlements, and opportunities for exploring how to monitor, mitigate and avoid these pollutants using a citizen science approach. We found significant differences in PM2.5 exposure between individual workers that could be partially explained by spatial differences in concentration that we identified within the settlement. Residents of the informal settlement identified a number of sources that might explain these differences in concentration, although many residents perceived air quality to be good both indoors and outdoors. The workshops raised awareness of the issue of air pollution and brought together affected community members and local and national policy makers to discuss air pollution issues in Nairobi's informal settlements. As a result, a new knowledge exchange network, the Kenya Air Quality Network, of policy-makers, researchers and community members was formed with the aim to facilitate the improvement of air quality across Kenya

Need for an integrated deprived area "slum" mapping system (IDEAMAPS) in low-and middle-income countries (LMICS)

Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The first approach, classifying "slum households" in census and survey data, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human (visual) interpretation and machine classification of air or spaceborne imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of public services. We summarize common areas of understanding, and present a set of requirements and a framework to produce routine, accurate maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making across Low-and Middle-Income Countries (LMICs). We suggest that machine learning models be extended to incorporate social area-level covariates and regular contributions of up-to-date and context-relevant field-based classification of deprived urban areas

"Like we don't have enough on our hands already!": the story of the Kenyan slum youth federation

ABSTRACT Slum dweller federations, like many other social movements, cater for the youth in their constituencies. This is critical to their relevance as agents of change and contributes to the sustainability of the movements. However, the youth formations are not merely scaled-down versions of the movements and often grapple with a set of dynamics unique to that transitory period in life. This story is a case study of the youth federation that is aligned to Kenya's slum dwellers federation

An Integrated Deprived Area Mapping “System”

info:eu-repo/semantics/nonPublishe

Critical Commentary: Need for an Integrated Deprived Area “Slum” Mapping System (IDeAMapS) in LMICs

Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely silo-ed, and each fall short of producing accurate, timely, comparable maps that reflect local contexts. The first approach, classifying “slum households” in census and survey data and aggregating to administrative areas, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human interpretation and machine classification of satellite, aerial, or drone imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of basic public services. The latter, machine classification of imagery, can be automated and extended to incorporate new and multiple sources of data. This diverse collection of authors represent experts from these four approaches to neighborhood deprivation mapping. We summarize common areas of understanding, and present a set of requirements to produce maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making