Die informellen Siedlungen in Nairobi stellen ein komplexes sozioökonomisches Problem dar, das visuell nicht erkennbar ist. Die Geovisualisierung bietet eine praktische und kostengünstige Lösung, um diese methodische Lücke zu schließen.
Technically diverse approaches and toolsets are currently employed for mapping and interpreting the spatial analysis of informal settlements in Nairobi.
These fragmented and inconsistent methodologies used to map Nairobi’s Informal Settlements hamper the success of government programmes such as the Kenya Slum Upgrading Programme (KENSUP) and the Kenya Informal Settlements Improvement Project (KISIP).
The current workflow is poorly integrated and lacks clear visualisation regarding the growth of informal settlements and their relationship to gaps in basic service provision and the increased vulnerability of these settlements to climate change impacts such as flooding or health risks.
Geo-visualisation tools such as ArcGIS StoryMaps and 3D Flood Simulations offer a transformative alternative. These tools integrate spatial and temporal data to create interactive, intuitive maps that enhance monitoring, foster cross-sectoral coordination, and support informed decision-making.
The attainment of Sustainable Development Goal 11 - ‘Sustainable Cities and Communities’- is high on Kenya’s development priorities. SDG 11 provides the international standard that Kenya’s Vision 2030 translates into national objectives, which the Nairobi County Integrated Development Plan (CIDP) then operationalises through specific local projects such as the Kenya Informal Settlements Improvement Project (KISIP). However, the uncontrolled proliferation of informal settlements in Nairobi is undermining these ambitions, particularly the attainment of SDG targets 11.1 (safe and affordable housing), 11.2 (affordable and sustainable transport systems), and 11.6 (reduce the environmental impacts of cities). Despite decades of policy and interventions to upgrade the mapping of the informal settlements, these informal settlements continue to grow at an alarming rate, both in Nairobi and across the country. This expansion is being driven by systemic gaps in land governance, fragmented mapping methodologies and policy implementation, and a lack of coordinated urban planning strategies.
Urban planners and decision-makers in Nairobi face a significant challenge: Current data collection and analysis methods on informal settlements are disjointed across institutions, resulting in inconsistent, outdated, and sometimes unreliable datasets. The lack of an integrated geospatial workflow – from field data collection to analysis and visualisation – limits real-time insight into informal settlements' dynamics. Furthermore, access to Very High Resolution (VHR) satellite or drone imagery remains limited due to cost barriers and technical constraints, leaving planners dependent on low-resolution or outdated imagery. This gap compromises the accuracy of spatial analysis and the effectiveness of evidence-based planning under programmes such as the Kenya Slum Upgrading Programme (KENSUP) and the Kenya Informal Settlements Improvement Project (KISIP).
Against this background, this study aims to evaluate the existing methodological gaps in Nairobi’s urban data systems and propose a geo-visualisation-based workflow that integrates real-time mapping, analysis, and visualisation. By doing so, it seeks to enhance decision-making, improve monitoring of the expansion of informal settlements, and promote spatially informed urban governance.
The origins of informal settlements in Nairobi lie in colonial-era urban policies, which enforced racial segregation and excluded Africans from formal urban residence. This legacy of spatial inequality established the foundation for informal urban development (White, Silberman, & Anderson, 1948; Abbott, 2002; United Nations Human Settlement Programme, 2003). During the post-independence period (1960s-1970s), the Kenyan government assumed economic growth would naturally eliminate informal settlements, a view which neglected issues of urban poverty (Abbott, 2002; Arimah, 2010; Wamuchiru, 2014). When this approach failed, the state adopted informal settlements’ clearance and forced mass evictions, which displaced residents and fueled new informal settlements. Meanwhile, corrupt officials were bought out by investors/speculators, diverting the advantages of informal settlement clearances away from the poor (Abbott, 2002; United Nations Human Settlement Programme, 2003).
The 1980s and 1990s marked a shift to in-situ upgrading and site-and-service schemes (as seen in the neighbourhoods of Dandora and Umoja), intended to provide basic infrastructure and tenure (Huchzermeyer, 2008). Yet corruption, speculative land acquisition, and the displacement of low-income residents undermined these programmes (Mayo & Gross, 1987; Pugh, 1994; Otiso, 2003; Muraya, 2006; Huchzermeyer, 2008; Bagheri, 2012). In the 2000s, pro-poor initiatives like KENSUP were introduced, targeting millions of urban poor. However, their top-down implementation resulted in middle-class gentrification of settlements such as Kibera, which were meant for low-income dwellers (Wamuchiru, 2014; Mkoli R. M., 2020). The 2010 Constitution recognised housing as a human right, leading to the Kenya Informal Settlement Improvement Project (KISIP) – a more participatory framework (Ministry of Lands, 2023; OpenEdition Journals, 2021).
Despite some evolution in policy, over 60% of Nairobi’s population still lives in informal settlements (UN-Habitat, 2021). This figure is rising. For example, the population of the Kibera informal settlement grew from 199,339 in 2019 to 549,322 in 2025 (an annual growth rate of 18.3%). Similarly, in Mathare the population grew from 114,329 in 2019 to 239,000 in 2025 (an annual growth rate of 13%). These settlements continue to embody Nairobi’s urban wicked problem, and illustrate the persistent gap between policy intent and spatial realities.
The use of Geographical Information Systems (GIS) in Kenya dates back to the early 1990s, roughly a decade after civilian applications emerged globally. By 1992, several institutions including the United Nations Environment Programme (UNEP), the International Centre of Insect Physiology and Ecology (ICIPE), the Kenya Rangeland Ecological Monitoring Unit (KREMU) (now the Directorate of Resource Surveys and Remote Sensing (DRSRS)), International Livestock Research Institute (ILRI), the Centre for International Forestry Research and World Agroforestry (ICRAF), and the country’s national museums had adopted GIS technology for environmental and spatial analysis. One of the earliest attempts to map informal settlements was the project ‘Nairobi's Informal Settlements: An Inventory’, developed using aerial photography from 1993. While this initiative provided data on settlement size, location, and density, it lacked ground-level detail and community participation, offering only a static, time-bound snapshot.
Between 1990 and 2010, urban planners increasingly adopted remote sensing imagery to detect spatial changes in informal settlements, such as in Kawangware and Mathare. A landmark initiative – the Map Kibera Project (2009) – revolutionised mapping by involving community members using handheld Global Positioning System (GPS) devices and OpenStreetMap (OSM) tools to create the first open-source map, a participatory digital map of an informal settlement. Following this, Participatory GIS (PGIS) approaches gained traction, emphasising community involvement in mapping urban growth and social infrastructure. Examples include household enumeration in Kisumu (Macoloo & Owusu, 2010), ‘Map Mathare’ (Watkins & Kanyinga, 2013), and the ‘Scenerii Planning Tool' used in Bondeni, Nakuru. However, most of these efforts were pilot projects with limited continuity and lacked integration with government databases.
By the early 2010s, the Government of Kenya sought to formalise geospatial planning through legislation. Nevertheless, the County Government Act (2012) and the Physical and Land Use Planning Act (2019) mandated counties to establish GIS-based planning databases. Despite these provisions, mapping of informal settlements remained underdeveloped and was often conducted independently by donor agencies, NGOs, or research institutions without a unified data schema or consistent methodology. A notable example is KISIP, which mapped 143 informal settlements across Nairobi using GIS. Data was collected using handheld Global Positioning Systems (GPS) and overlaid on satellite images. The exercise was very generic, however, with no clear data structure or schema; Only locational and land-use maps were produced. This was due to the provisions in the relevant legislation not being specifically tailored for mapping informal settlements.
In 2025, KISIP mapped 209 informal settlements using a multi-tool workflow. Data was collected via ODK Central, coded in Excel-based survey forms, processed using QGIS (via the ODK Connector plugin), and further analysed in ArcGIS Desktop. This method, though comprehensive, was inherently fragmented – requiring manual data transfers across platforms. It utilised ESRI basemaps, which are often outdated, and relied on open-source tools not optimised for large-scale, real-time analysis. As such, it did not constitute an integrated system. Instead, as is often the case with such methods, inefficiencies and fragmentation across the data workflow increased data handling errors and impeded continuous visualisation of settlement growth. Moreover, the absence of an integrated schema or centralised platform meant data could not be easily updated or shared among agencies, leading to redundancy and policy misalignment.
These limitations underscore the urgent need for a centralised, interoperable geo-visualisation platform— one capable of unifying data collection, analysis, and visualisation within a single interface. Such a platform should allow for seamless, end-to-end workflows, ensuring real-time monitoring and dynamic visualisation of spatial data. Geo-visualisation tools, such as ArcGIS StoryMaps and 3D flood simulation models, present viable solutions to these challenges. By merging technical precision with interactive visualisation, they transform static spatial data into actionable intelligence. The following section demonstrates, through a Mathare case study, how these tools can enhance urban policy decision-making and disaster risk management in Nairobi.
Geo-visualisation combines advanced GIS technology, remote sensing, and interactive web-based visualisation to enable users to interact with real-world spatial phenomena. It transforms raw geospatial data into dynamic visual narratives such as StoryMaps, 3D models, and interactive web maps – that enhance comprehension and communication of complex urban issues such as the proliferation of informal settlements in Nairobi. To demonstrate the practical value of geo-visualisation tools in policy-oriented urban analysis, this study employs Mathare informal settlement as the case study area. Located approximately seven kilometres northwest of Nairobi Central Business District, Mathare represents the city’s oldest and most densely populated informal settlements. It exhibits high rates of population growth (13% per year), precarious housing, and recurrent flooding – making it an ideal site for analysing spatiotemporal changes and demonstrating the practical value of geo-visualisation tools in policy-oriented urban analysis. Figure 1 shows the location of Mathare in Nairobi.
The first tool utilised is ArcGIS StoryMaps, which integrates maps, narratives, and multimedia into an interactive online storytelling environment. The geo-visualisation process begins with spatial data collection and analysis in ArcGIS Desktop and ArcGIS Pro, where datasets are prepared, styled, and symbolised for web publication. The prepared maps are then published to ArcGIS Online, where they are combined with descriptive text, photographs, charts, and satellite imagery to create an interactive StoryMap. Unlike the current fragmented tools (ODK Central, Excel, ArcGIS, QGIS), and conventional GIS reports that require technical expertise to interpret, StoryMaps transforms geospatial data into an accessible, narrative-driven experience that enables policymakers, planners, and citizens to explore and understand settlement dynamics intuitively. Additionally, unlike QGIS, ArcGIS StoryMaps offers documentation in the form of training resources and technical support.
High-resolution Planet Imagery (acquired through the Planet Research and Education account) was used to capture temporal changes in Mathare informal settlement between 2019 and 2025. The swipe template in Planet Stories was applied to allow side-by-side comparison of land cover imagery from both years, providing a visual representation of settlement expansion. The complete StoryMap, integrating imagery, thematic maps, and analysis is accessible here. Figure 2 demonstrates the swiping.
Quantitative change detection was performed to assess Land Use and Land Cover (LULC) transitions. The analysis categorised land into four classes: Built-up, Bare-land, Water, and Vegetation. The percentage change between 2019 and 2025 was computed to show the degree of the built-up area expansion and the corresponding reduction in vegetation cover. The results are presented in Figure 3, revealing the continuing densification of the settlement core and loss of green buffer zones along the Mathare River corridor.
The second tool used is the Africa GeoPortal, a free, cloud-based spatial platform powered by Esri. The GeoPortal provides African researchers and planners with access to curated geospatial data, analytical tools, and visualisation resources. Unlike open-source GIS applications such as QGIS, which are primarily desktop-based, Africa GeoPortal supports interactive, web-based analysis and 3D visualisation – making it suitable for public communication and participatory planning. For this study, the 3D Flood Simulation tool in ArcGIS Pro was employed to model flood scenarios for Mathare based on recorded rainfall and drainage data of April 26, 2024. The simulation modelled stream overflow and surface runoff, generating a water depth layer, which was exported as a NetCDF file and uploaded to Africa GeoPortal.
A time-enabled 3D web scene was created using building footprint data from the ArcGIS Living Atlas of the World. The model was then configured using the 3D Viewer template, enabling users to interactively explore flood extents and potential impacts on residential structures and access roads. This interactive 3D environment enables decision-makers to visualise the spatial impact of floods in real time, supporting early warning systems and proactive urban planning. As illustrated in Figure 4, the simulation demonstrates how geo-visualisation tools can translate complex hydrological data into policy-relevant visual insights. It can be accessed here.
To address the socio-economic problems posed by the rapid expansion of informal settlements in Nairobi, the County Government has several opportunities:
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Dr. Mildred Ambani-Songoro is a Lecturer at Kenyatta University, Kenya, and a Registered and Practising Physical Planner. Dr. Ambani-Songoro was awarded a SMUS-APRI Urban Africa Postdoctoral Research Scholarship in 2025, and as part of the scholarship, undertook a three-month research stay with KLAB, a research and teaching unit at the Institute for Urban & Regional Planning (ISR) based at the Technische Universität (TU Berlin).
The Global Center of Spatial Methods for Urban Sustainability (SMUS) is funded by the German Academic Exchange Service (DAAD) with funds from the German Federal Ministry for Economic Cooperation and Development (BMZ).
