On the Impact of Urban Planning of Informal Settlements in Sub-Saharan Africa By Johan Mottelson

On the Impact of Urban Planning of Informal Settlements in Sub-Saharan Africa

Johan Mottelson Pending publication

Abstract

In Sub-Saharan Africa, the urban majorities reside in informal settlements. Lack of knowledge on the spatial and socioeconomic impact of urban planning on informal settlements compromises the basis for policy and decision making. This study compares the urban form of three centrally located unplanned informal settlements with three adjacent planned informal settlements in Maputo, Mozambique in order to assess the long-term impact of urban planning on the urban form of informal settlements. The study includes field interviews on household conditions in each of the six cases in order to assess the relationship between the structure of the urban fabric and socio-economic conditions in informal settlements. The study found higher built densities, higher public space ratio, higher average street width, and a higher proportion of tenants in all three planned areas compared to the three unplanned areas. Accordingly, the study suggests that urban planning has a significant long-term impact on the urban form of informal settlements despite the lack of state control of the urban development. The study argues that planned urban areas are seen as more desirable which offsets the market value of land in these areas and leads to shifts in the socio-economic conditions of the residents. The study argues that this dynamic partially accounts for the higher proportion of tenants and the higher built densities found in the planned case study areas. The study argues that smaller plot sizes and more simple plot geometries may further explain the higher built densities in the planned case study areas. The study found urban form variations within the three planned areas which are suggested local governance practices account for. The study argues that urban management capacities should be strengthened in the local neighbourhood administrative systems in order to counter issues with street encroachment and thereby enhance the feasibility of future infrastructure investments.

Keywords: urban form, urban morphology, urban planning, informal settlements, Sub-Saharan Africa, Maputo, Mozambique

Introduction

Most of the ongoing extensive urban growth in East Africa occurs beyond the limits of state control and regulatory systems (UN-Habitat, 2020). The urban majority in the region resides in informal settlements typified by construction without formal permits and lack of formal land titles (UNHabitat, 2020; OECD, 2008). The proliferation of informal urban development will likely continue in the coming decades due to high rates of urbanisation, high fertility rates, poor urban majorities, and limited state capacity to administer the urban growth (Jenkins & Mottelson, 2020). Lack of knowledge on the formation and development of informal settlements limits the capacity to develop effective policy and urban planning in Sub-Saharan Africa (UN-Habitat, 2013). However, limited studies have investigated the urban form of informal settlements despite that spatial factors and urban form indicators such as inadequate access conditions, inefficient use of space, high level of building coverage, and limited public space are linked to common urban deficiencies in informal settlements (Mottelson & Venerandi, 2020). Inadequate urban planning compromises sustainable development in many urban centres in sub-Saharan Africa (Hove et al., 2013). However, the long-term spatial impact of urban planning in informal settlements remains understudied. This study provides a comparative analysis of the urban form of adjacent planned and unplanned informal settlements in Maputo, Mozambique in order to enhance the knowledge on the long-term spatial impact of urban planning on informal settlements and thereby improve the basis for assessing the impact of urban planning in contexts characterized by informal urban development.

Urban Morphology

Urban morphology is the study of the form of human settlements with the aim of describing their spatial structures and understanding the process of their development (Moudon, 1997). Muratori (1959) argues that the structure of a city is understood historically focusing on the diachronic processes of transformations through the ‘operative history’. Furthermore, Muratori introduces a classification of building typology and adjacent open spaces as the basis of understanding of the development of the city (Moudon, 1994). Conzen (1960) defines a town plan as a topographical arrangement of street-systems comprised of streets, street-blocks comprised of plots, and block-plans comprised of buildings. In addition, Conzen introduces a methodology for town-plan analysis focusing on the development of the formative processes of the pattern of streets, the pattern of building forms, and pattern of land use in order to gain an understanding of the socio-economic formative conditions of the development of the city. Hillier and Hanson (1984) use mathematical representation and analysis of street networks, which was later developed into a variety of algorithms and software applications for the analysis of urban form, such as Space Syntax (Hillier, 2007). Multiple methods for street network configuration analysis have emerged, such as node degree centrality, closeness, and betweenness (Porta et al., 2010). Node centrality degree measures the number of nodes that each node is connected to (the number of streets leading into each intersection) and measures the involvement of the node in the network (Opsahl et al., 2010). Closeness (integration in Space Syntax) is a measure of the number of transitions required from one street to reach all other streets in the network using the shortest paths (Vaughan, 2007). Betweenness is a measure of the number of times a node (street intersection) acts as a bridge along the shortest path between two other nodes (Porta et al., 2006). Correlation between street network configuration and human behaviour, such as frequency of crime, the intensity of commercial activities, and movement of people have been documented through the use of these applications (Scoppa & Peponis, 2015; Kim & Hipp, 2019; Hajrasouliha & Yin, 2015). However, configurational analysis does not account for other morphological aspects such as built-mass density thus limiting the scope of such methods

(Ratti, 2004). A number of studies have documented the correlation between urban density measures and energy consumption, mobility, and costs of infrastructure provision thus underpinning the importance of such measures (Álvarez et al., 2014; Horner, 2013; Trepci et al., 2020). Other studies have documented correlation between urban tree canopy cover and urban microclimate, education levels, and median household income in urban areas (Elmes et al., 2017; Heynen & Lindsey, 2003; Schwarz et al., 2015). Gehl (2011) documents that residents of low-rise high-density areas are acquainted with more of their neighbors than residents of tower blocks thus suggesting a correlation between building typologies and the social conditions of urban areas. Rådberg (1996) introduces a quantitative multi indicator classification of urban types using land coverage, Floor Area Ratio (FAR), and building heights as indicators of urban form. Similarly, the Space Matrix treats density as a multivariable phenomenon, in a three-dimensional diagram, with the three fundamental indicators: intensity (FSI), compactness (GSI), and network density (N) on each axis (Berghauser Pont & Haupt, 2009). The increased availability of morphological data and computational power have enabled more complex quantitative forms of analysis and the emergence of urban morphometrics using systematic measurement of multiple configurational and urban density measures as morphological indicators (Dibble et al., 2019; Fleischmann, 2019). Studies using such methods have found a correlation between urban form and public health, energy consumption, and liveability of neighbourhoods (Marshall et al., 2014; Ratti et al., 2005; Venerandi, 2017). However, limited studies have investigated the urban morphology of informal settlements, particularly in the context of Sub-Saharan Africa.

Hillier et al (2000) examine informal settlements in Santiago, Chile and suggest that spatial and locational factors drive the development and consolidation of informal settlements. The study suggests that edge oriented commercial activity and consequent inclusion in the wider local economy is determined by the levels of spatial integration within the surrounding urban fabric and is significant for consolidation of the built form of informal settlements. However, Hillier et al primarily focus on features of the street network, thus missing further aspects of the built environment. Smith & Jenkins (2015) examine the development of informal settlements in Huambo, Angola and suggest that the urban growth entails decreasing household sizes, plot subdivision, increasing provision of rental accommodation, and increasing density of land occupation by buildings. Mottelson and Venerandi (2020), examine the urban form of centrally located informal settlements in major cities in East Africa and find that these are characterized by diverse urban form features. Mottelson (2020b) examines urban densification in peri-urban Maputo (2007-2017) and finds increasing built densities and decreasing population densities in the centrally located settlements over the ten-year period. Dovey et al. (2020) trace the development of street networks and buildings in multiple informal settlements, demonstrating the range of morphologies characterizing these areas. They analyse building types, plots, blocks, streets and lanes in order to highlight how habitable land, affordable housing and public space is produced in different contexts. Jenkins (2013) identifies four types of informal settlements in Maputo, Mozambique. Namely, 1) Planned formal, 2) Unplanned informal, 3) Planned informal, 4) unplanned formal. The planned informal settlements include areas which have been planned by the local government, international agencies or by locally commissioned planners but are not recognized by the municipality as formally planned areas. Construction in these areas is thus extra-legal, as construction permits can only be formally issued in areas with approved urban plans and these urban areas have thus developed under limited formal governance of the state. Accordingly, these areas are informal settlements despite the planned structure of the urban fabrics. However, no studies have investigated the impact of urban planning on the development of the urban form of informal settlements. Accordingly, this lack of knowledge limits the capacity for impact assessment of urban planning projects and the basis for making policy decisions in this context.

In order to reduce this knowledge gap, this study seeks to answer the following questions: what is the impact of urban planning on the development of the urban form of informal settlements? what is the impact of urban planning on the development of socio-economic conditions in informal settlements? This study analyses the urban form and household condition of three case study areas covering planned and unplanned adjacent informal settlements in order to answer these research questions. The study is based on analysis of high-resolution maps created with unmanned aerial vehicles (drones), handheld GPS-devices (smartphones) and photogrammetry software. The study includes field interviews on household conditions with residents of the surveyed areas in order to examine links between urban form and socio-economic conditions of informal settlements. Thereby, the study seeks to increase the knowledge of the spatial impact of urban planning in order to improve the basis for decision making.

Methodology

Three centrally located informal urban areas in Maputo were selected for the study. The areas are located in KaMaxakeni (District 3) in the Maxaquene (A, B, and D) and Polana Caniço A neighbourhoods. The selection criteria for the case study areas were based on the urban structure, the time of establishment, and the proximity to the city centre. Generally, the cases were selected based on overall similarity aside from the urban fabric and local administration conditions. More specifically, the case study areas include unplanned and planned adjacent informal settlements in different administrative areas characterized by similar distances to the city centre, similar times of establishment, and similar socio-economic conditions. Areas with adjacent planned and unplanned informal settlements were selected in order to provide a basis for comparison of the two types of urban fabric. Urban density is linked to transport costs to the city centre (Bertaud, 2018). Accordingly, adjacent cases were selected in order to reduce the influence of location factors for the development of the urban form of the case study areas. Informal settlements densify over

time (Mottelson, 2020b). Accordingly, cases established within a similar time frame were selected for the study in order to reduce the influence of temporal factors on the development of the urban form of the case study areas. Three cases were selected in order to examine differences between different administrative areas with similar location conditions. The urban areas were demarcated based on major roads forming the exterior boundaries. These roads were not included in the data in order to avoid distorting the results. High-resolution orthophotos were produced by mapping the three case study areas using an unmanned aerial vehicle (drone) for systematic collection of high resolution photometric aerial data. The photometric data (aerial photos) were subsequently processed using Pix4D photogrammetry software to generate high-resolution geo-referenced orthophotos, Digital Surface Models (DSM) and digital 3d models of each settlement. Such methods have previously been used by Kurczynski et al (2016). The public space of each settlement was mapped by walking along the boundaries of every block with a handheld GPSdevice (smartphone) while using the SW maps application for tracing the movement. The GPS traces were exported in KMZ format and the orthophotos were exported in geo TIFF format. These were placed and automatically scaled in QGIS and exported in DXF format. The DXF file was opened in AutoCAD and layers for buildings, blocks and street network were created. All building boundaries were traced manually based on the orthophotos. Multi-storey buildings were identified using the DSMs and the 3d models in Pix4D and separate layers were created for each floor. The perimeter of each storey was traced manually and placed on the corresponding layer. The street network was traced manually based on the GPS trace and blocks were subsequently traced based on the orthophoto and street network. The total area of all floors, blocks and the entire case study area were extracted from the properties bar by using the region and union commands. The number of blocks was extracted from the properties bar. The total length of the street network, block perimeter and the building perimeters were extracted using the Total Length (TL) plugin. The total public space area was calculated by extracting the total blocks area from the total case study area. Floor area ratio (FAR), public space ratio (PSR), ground space index (GSI), average block size (ABS), surface area to volume ratio (SAV), urban tree canopy cover (UTC), and average street width (ASW) were calculated based on these data. The urban form indicators and methods of computation are described below.

● Floor area ratio (FAR): the total floor area divided by the total sample area. The metric highlights the density of the built environment (Berghauser Pont & Haupt, 2009). FAR is usually calculated based on the total private space rather than the total area. However, in order to avoid distorting the FAR results by variations in the proportion of public space, the FAR was instead calculated using the total sample area.

● Public space ratio (PSR): the total public space divided by the total sample area. The metric highlights the level of public space (Oliver-Solà et al., 2011).

● Ground space index (GSI) also known as building coverage: the total building footprint divided by the total sample area. The metric highlights the level of open space (Berghauser Pont & Haupt, 2009). GSI is usually calculated based on the total private space rather than the total area. However, in order to avoid distorting the GSI results by variations in the proportion of public space, the GSI was instead calculated using the total area.

● Average block size (ABS): the total private area divided by the number of blocks. A block consists of private space surrounded by public space. The metric highlights the scale of the urban fabric (Berghauser Pont & Haupt, 2009).

● Surface area to volume ratio (SAV): the total building surface area divided by the volume of all buildings. The surface area was calculated by summing the perimeter length of all buildings multiplied by the height of each floor (three meters) and added with the total building footprint area multiplied by two. The volume was calculated by summing the total area of each floor multiplied by the height of each floor (three meters). As the height of the buildings occurs on both sides of the fraction, the imprecise threemeter floor height assessment is largely negated.

The metric highlights the compactness of the built environment (Ratti et al., 2005).

● Average street width (ASW): the total area of the public space divided by the total length of the street network. The metric highlights the level of access conditions (Mottelson, 2020a).

● Urban tree canopy cover (UTC): the total ground area covered by the crowns of trees divided by the total sample area. The metric highlights the density of urban trees (Nowak et al., 1996).

The field interviews were carried out alongside the physical surveys. Sixty residents (ten of each sub-area) were interviewed based on random selection, in different parts of the sample areas. This entailed a fairly even distribution of gender and relative socioeconomic diversity among the interviewees within each context. The interviews inquired regarding household access to water and sanitation, household size, dwelling size, whether the respondent was a tenant or landholder, and the monthly cost of rent. As such, the questions included indicators of livelihood as well as three of five of the UN defined indicators of slums (overcrowding and inadequate access to water and sanitation) (UN-Habitat, 2003). The spatial surveys and the field interviews were carried out in June and July 2019.

Context

Mozambique is located in South-East Africa and is one of the poorest countries in the world (Jenkins, 2000a). Maputo is the capital of the country and the city is characterized by a formal urban centre surrounded by informal settlements to the West and North and the Maputo Bay to the East and South. The urban centre was established in 1781 by Portuguese colonialists, while the majority of the informal urban areas were established after independence in 1975. The urban development after independence was characterized by a large influx of rural population due to the ongoing civil war causing large numbers of refugees along with the abolishment of previous restrictions on the movement of people (Jenkins, 2000b). Maputo consists of 7 administrative districts. District 1 covers the formal city centre, districts 2-5 cover the informal urban areas to the North and West of the centre, while districts 6-7 cover the island of Inhaca and Katembe south of the bay, which was connected to the city centre via a new bridge in 2018. However, the continuous urban fabric of the greater Maputo metropolitan area includes the neighbouring Matola Municipality as well as Marracuene and Boane districts North and West of the Municipality of Maputo (Jenkins & Mottelson, 2020). The city is characterized by widespread continuous informal land-use indicating that the authorities exercise limited control of the land markets and informal settlements are de facto allowed to proliferate despite the extra-legal status (Mottelson, 2020a). Each of the districts has a district administration office (administração do distrito) facilitating contact between the neighbourhoods and the municipal administration. Each district is subdivided into a number of neighbourhoods (bairros) with a neighbourhood administration office (secretário de bairro). The neighbourhoods are subdivided into a number of blocks (quarteirão) typically 35-100 housing units, each of which has an appointed block leader (chefe de quarteirão) (Barros et al., 2014). Building permits can only be formally issued by the municipality on formally owned land in areas with approved urban plans. Nevertheless, informal permits are issued by the local administration (district and neighbourhood), without legal basis and with no involvement of the higher levels of municipal administration. The local neighbourhood administrations thus carry out some form of governance of the development of the neighbourhoods despite the lack of formal mandate to do so (Andersen et al., 2015a, 2015b). However, as such practices are not established on the basis of the law, it is plausible that these practices differ between neighbourhoods with possible impact on the development of the urban form. The three case study areas are located in District 3 which were established during the influx of population subsequent to independence. The three planned case study areas are outcomes of the Urbanization Project of Maxaquene and Polana Caniço (1977–1979) where the population was relocated in order to implement the structural readjustment and consequent planned urban fabrics seen today (Pinsky, 1982). An overview of Maputo along with the location of the three case study areas are presented in Figure 1, an overview of the three case study areas is presented in Figure 2, and aerial photos of planned and unplanned sample areas are presented in Figure 3.

Road Municipal Boundary Sea, estuary & river Case study area

N

5 10 km

Figure 1: Overview of Maputo

Private area Single-storey building Two-storey building Trees Unplanned area Planned area

200 m 400 m

Figure 2: Overview of case study areas

N

Figure 3: Aerial views of planned and unplanned case study areas.

FAR PSR UTC ASW (m) SAV ABS (m2)

Maxaquene A, Planned 0,51 0,11 0,0593 3,00 2,00 2365 Maxaquene A, Unplanned 0,47 0,07 0,0521 1,98 1,78 2887

Maxaquene D, Planned 0,46 0,10 0,0971 3,17 1,88 3289 Maxaquene B, Unplanned 0,43 0,08 0,1334 2,42 1,77 3245 Polana Caniço A, Planned 0,48 0,11 0,1299 2,99 2,05 1598

Polana Caniço A, Unplanned 0,38 0,09 0,1909 2,89 1,79 3250

Planned areas average 0,49 0,11 0,0954 3,05 1,98 2417 Unplanned areas average 0,42 0,08 0,1255 2,43 1,78 3127

Table 1: Urban form data

Tenants (%) Water (%) Electricity (%) Maxaquene A, Planned 20 80 80

Maxaquene A, Unplanned 0 90 90

Maxaquene D, Planned 20 100 100 Maxaquene B, Unplanned 0 80 100 Sanitation (%) Dwelling size (rooms)

Household size (people) 100 4,2 5,7 100 4,4 6,3 90 4,2 6,8 100 5,1 7 People per room

1,36 1,43 1,62 1,37

Polana Caniço A, Planned 30 90 90 90 3,9 4,9 1,26

Polana Caniço A, Unplanned 10 100 100 100 4,5 6,5 1,44

Planned areas average 23,33 90,00 90,00 93,33 4,10 5,80 1,41 Unplanned areas average 3,33 90,00 96,67 100,00 4,67 6,60 1,42

Table 2: Field interview data

Results

The six indicators of urban form computed for each settlement under examination are summarised in table 1. The data show higher levels of built density (FAR), more compact urban form (SAV), higher levels of public space (PSR), and larger average street width (ASW) in all three planned urban areas compared to the respective three adjacent unplanned urban areas. On average the planned areas have a 14% higher FAR, 27% higher PSR, 20% higher ASW, and 10% higher SAV compared to the unplanned areas. The average block size (ABS) was 23% larger in unplanned areas. However, in one case (Maxaquene B and D) the ABS was almost identical in the adjacent planned and unplanned areas. The urban tree canopy cover (UTC) was on average 23% higher in unplanned areas. However, in one case (Maxaquene A) the UTC was slightly higher in the planned area.

The results of the field interviews are summarised in table 2. The data show higher proportions of tenants in all three planned areas compared to the neighbouring unplanned areas. The data show identical levels of household access to water in the planned and unplanned areas. The data show slightly lower levels of access to sanitation and electricity as well as slightly smaller dwelling size and household size in planned areas compared to unplanned areas. However, all the tenant respondents resided in a single room or two-room dwellings with an average dwelling size of 1,5 rooms and an average household size of 2,4 people. Furthermore, tenant households show lower levels of access to sanitation and electricity.

Discussion

This section discusses the results of the study, the wider theoretical implications of the findings, and possible policy responses to these. The study found significant variations in urban form and household conditions between planned and unplanned case study areas. All three planned areas feature higher built density, more compact urban form, higher proportions of public space, higher average street width, smaller average dwelling size, smaller average household size, and a higher proportion of tenants compared to the adjacent respective unplanned areas. Furthermore, on average the urban tree coverage, the levels of household access to electricity and sanitation, and average block size were lower in the planned areas compared to the unplanned

areas. Accordingly, the study suggests that urban planning has a long-term impact on both urban form and household conditions in contexts with limited state governance of the urban development.

Regarding the differences between the planned and unplanned areas seen in household conditions, perhaps the most significant finding is the higher proportion of tenants found in all three planned areas. While the homeowner interview responses were largely consistent across planned and unplanned areas, the tenant households showed lower average levels of access to water and sanitation, smaller dwellings, smaller households, and more people per room. Accordingly, the differences in household conditions across the planned and unplanned samples areas are likely largely attributed to the higher proportion of tenants seen in the planned areas. In general, the indicators of basic livelihood were high across all sample areas, i.e. low proportion of households with overcrowding, lack of electricity, and inadequate access to water and sanitation. However, it is likely that more detailed inquiries into indicators of household conditions such as education level, income level, car ownership and quality of housing would have added more nuance to the socio-economic differences between the planned and unplanned areas. While rental housing provision is relatively limited in the informal settlements in Maputo compared to other major cities in the region, small-scale rental housing in the back yards of individual homeowners is prevalent in the informal settlements across the city (Jenkins, 2013; Mottelson, 2020a). Backyard rental housing provides a secondary income for homeowners and requires investment in the construction of the housing units (Jenkins, 2013). Accordingly, the higher proportion of tenants seen across all the planned areas may be attributed to higher levels of household resources in the planned areas. The urban fabric of the planned sample areas resembles the urban fabric of parts of the formal centre of Maputo which is largely seen as an ideal urban form in the city (Nielsen & Jenkins, 2020). Furthermore, the higher average street width indicates better access conditions for cars in the planned areas compared to the unplanned areas. These factors may constitute amenities increasing the demand for housing in such areas as argued by Smith (1978). Consequently, the increased demand for housing and land in the planned urban areas will likely lead to an increased market value of land in these areas. Accordingly, it is likely that such dynamics lead to small shifts in socio-economic conditions among residents through successive land transactions occurring over time. Accordingly, this study suggests that planned urban areas are generally seen as more desirable in Maputo which likely leads to an increased market value of land in these areas thereby resulting in homeowners with slightly more resources moving into the planned areas compared to the unplanned areas. The study suggests that homeowners in planned areas thereby have more resources for investments in rental housing units and that this mechanism accounts for the higher proportion of tenants in the planned areas compared to the unplanned areas. As residents of informal settlements typically have limited economic resources and limited formal financing options because their properties are extra-legal and thus cannot be used as financial collateral, the investment capacity for construction is largely limited. Households in informal settlements of Maputo typically develop incrementally when the economic resources for home expansion are available and built densification is largely constrained by the household economy (Andersen et al., 2015a, 2015b). If households in planned areas on average have more resources than households in unplanned urban areas, this may in part account for the higher levels of built densities seen in the planned areas compared to the unplanned areas. Accordingly, the study suggests that higher levels of household resources in planned areas partially account for the higher levels of built density seen in the planned areas.

Regarding the differences between the planned and unplanned areas seen in the urban form data, perhaps the most significant findings include the higher levels of built densities, higher levels of public space, and the lower levels of urban tree canopy cover seen in the planned areas compared to the unplanned areas. Urban densification and compact city development are advocated by UN-Habitat and OECD for the beneficial effects on sustainability, resilience, and economic growth as well as cutting the costs of service delivery and infrastructure provision (Organisation for Economic Co-operation and Development, 2012; UNHabitat, 2017). Accordingly, it is arguably significant to understand the drivers of the higher urban densities in the planned areas compared to the unplanned areas. Notably, the planned areas show both higher levels of public space and higher levels of built densities. Accordingly, urban densification does not necessarily compromise the levels of public space and access conditions in informal settlements as is also suggested in previous studies (Mottelson, 2020a; Mottelson & Venerandi, 2020). Higher levels of investment capacity derived from more economic resources among the residents of the planned sample areas may partially explain the higher built densities in the planned sample areas as argued in the previous paragraph. Furthermore, smaller plot sizes and more simple plot geometries are likely the main drivers of the higher urban densities in the planned sample areas aside from the possible higher investment capacity of the homeowners. The study suggests that the plot sizes in unplanned sample areas on average are larger than the approximately 10x15 metres plots in the planned sample areas. Smaller plots generally lead to higher household density and thereby also a likelihood of higher concentration of collective investment capacity. Accordingly, the study suggests that the plots are smaller in the planned areas and that this leads to higher residential density which leads to higher collective investment capacity enabling the development of higher built densities. The study thus argues that smaller plot sizes likely is a key factor explaining the higher built densities seen in the planned areas compared to the unplanned areas. Finally, the simple rectangular plot geometry seen in the planned areas likely increases the land-use efficiency. The standardized corrugated iron sheets (measuring 3600x700mm) used for roof-cladding are widespread in the informal settlements of Maputo. The layout of individual rooms in most dwellings

are adapted for economical optimization of the corrugated iron sheets resulting in simple rectangular geometry and dimensions corresponding to the length of one corrugated iron sheet. The incremental development of dwellings with single-room expansions built according to optimized use of the standardized construction materials have led to the emergence of the ‘Casa Ventoinha’ (fan house) housing typology (Carrilho, 2004). The typology resembles a fan when seen from above with four individual roof surfaces, expressing the interior division of the rooms. The rectangular geometry of the most common housing typology in the informal settlements of Maputo is likely more geometrically compatible with the simple rectangular geometry of the plots in the planned areas than the more complex irregular plot geometries seen in the unplanned areas. This may lead to more efficient use of construction materials and more optimized use of the space in the planned areas as the design of dwellings in the planned areas do not have to correspond to odd angles and irregular plot geometries. Accordingly, it is likely that the simpler rectangular geometry of plots in the planned areas partially accounts for the higher built densities and more compact urban form. The study thus argues that the higher built densities in the planned areas are driven by higher levels of household investment capacity, smaller plot sizes, and simpler plot geometries compared to the unplanned areas. Urban densification leads to improved mobility and more cost-effective investments in infrastructure in the context of Sub-Saharan Africa (Andreasen & Møller-Jensen, 2017). Accordingly, the study suggests that urban planning is important for sustainable development in the region.

All three examined planned urban areas feature higher levels of public space and higher average street width compared to the adjacent examined unplanned urban areas. This is a significant finding as higher levels of public space and higher average street width space both improve the access conditions for service delivery and increase feasibility for implementation of infrastructure (Satterthwaite, 2011). Whitehand (2001) argues that the formation of streetsystems and plot boundaries are essential for the long-term urban development as they typically remain unchanged through successive generations of society. Accordingly, the difference between the planned and unplanned examined areas in terms of levels of public space and higher average street width is likely largely an outcome of the different conditions in which the areas were established. Furthermore, the simple uniform urban fabric of the planned examined areas may be easier for local authorities to maintain as it is likely easier to identify street encroachment in the planned areas compared to the unplanned areas. Consequently, the study suggests that urban planning leads to improved access conditions and higher levels of public space in contexts with limited state governance of the urban development.

The study found a higher average urban tree canopy cover in the unplanned areas. Lack of trees leads to increasing surface urban heat island effect compromising local microclimate (Elmes et al., 2017). Accordingly, the lower level of urban tree canopy cover found in the planned areas may compromise sustainable development. It is not clear what drives this development. Previous studies suggest levels of population density and urban tree canopy cover are not correlated and that higher levels of urban tree canopy cover are linked to higher socioeconomic levels (Heynen & Lindsey, 2003; Schwarz et al., 2015). However, as these studies were conducted in the United States of America these results may not be relevant in this context. Higher levels of built densities, higher levels of public space, and socio-economic factors may account for this development. Perhaps, the increased proportion of the space occupied by buildings in the planned areas partially accounts for the lower levels of urban tree canopy cover in the planned areas. Perhaps, the higher levels of public space partially account for the lower levels of urban tree canopy cover in the planned areas. Perhaps paved ground surface is a symbol of wealth and households with more economic resources cut down trees in order to pave the ground. However, more detailed studies on this subject are needed in order to qualify this discussion.

While the study found higher levels of built density, public space, and average street width in all planned sample areas compared to the adjacent unplanned areas, the study also found some variations in these data across the three planned areas. The study argues that variations in the governance practices in the local neighbourhood administrative systems largely account for these differences. For example, the urban fabric of the planned area in Maxaquene A shows some irregularities which are likely the result of residents appropriating parts of the public space. As some of the local neighbourhood administration governance practice is not based on the formal regulation it is likely that such practices vary between the local neighbourhood administration offices. More specifically, some local administrations may be more tolerant of street encroachment than others. Furthermore, as Jenkins (2000a) suggests that bribes for provision of informal authorization in the local administration occur, perhaps some local administration may be more susceptible to bribery which may partially explain variations in public space of planned areas. Accordingly, the study suggests that variations in public space across the three planned case study areas are largely attributed to variations in local governance practices. This finding underpins the importance of the local administrative systems which to a large extent administer the urban development of the city.

The study suggests that urban planning has a significant impact on the development of the urban form of informal settlements as higher levels of public space and built densities were found in the planned sample areas compared to the adjacent unplanned sample areas. This may have significant policy implications. Previous studies have shown that the cost of structural readjustment of the urban fabric of informal settlements is much more expensive than ‘greenfield’ developments (Lamson-Hall et al., 2019). Accordingly, the study underscores the significance of the implementation of planned urban structures before the urban form consolidates. Furthermore, the study suggests that urban planning may lead to increased feasibility and cost-effectiveness of implementation infrastructure due to

the higher level of public space and higher average street width. Additionally, urban planning may have positive effects on mobility due to the higher built densities and likely higher residential densities in the planned areas. Consequently, the study suggests urban planning has a significant long-term impact even in contexts with limited institutional capacity to enforce urban regulation and thus underpins the significance of allocation of public funds for the provision of spatial planning. Moreover, the study found lower levels of urban tree canopy cover in the planned sample areas which likely leads to increased surface urban heat islands. Accordingly, strategies to increase urban tree canopy cover in planned informal settlements should be considered a policy priority. Finally, as the local neighbourhood administrations to a large extent administer the urban development of the informal settlements in Maputo, these may be provided capacity building and additional resources in order to limit issues with street encroachment and improve the basis for issuing construction permits. Accordingly, the study argues that the capacities for urban governance in the local neighbourhood administrations should be strengthened and additional resources should be allocated for these to invest in locally decided priorities.

Conclusion

The study investigated the impact of urban planning on the development of the urban form of informal settlements through comparison of planned and unplanned sample areas in peri-urban Maputo. The study found significant variations between planned and unplanned case study areas in both urban form and household conditions. All three planned areas feature higher built density, more compact urban form, higher proportions of public space, higher average street width, smaller average dwelling size, smaller average household size, and a higher proportion of tenants compared to the adjacent respective unplanned areas. Furthermore, on average the urban tree coverage, the levels of household access to electricity and sanitation, and average block size were lower in the planned areas compared to the unplanned areas. Accordingly, the study suggests that urban planning has a long term impact on both urban form and household conditions in contexts with limited state governance of the urban development. The study suggests that planned urban areas are generally seen as more desirable in Maputo which likely offsets the market value of land in these areas thereby resulting in homeowners with slightly more resources moving into the planned areas compared to the unplanned areas. The study suggests that this mechanism accounts for the higher proportion of tenants in the planned areas compared to the unplanned areas as homeowners in planned areas thereby have more resources for investments in rental housing units. Accordingly, the study suggests that higher levels of household resources in planned areas partially account for the higher levels of built density seen in the planned areas. The study argues that smaller average plot sizes and simpler plot geometries further contribute to the higher urban densities in the planned sample areas. The study found higher levels of public space and average street width in planned areas which the study argues is a direct outcome of urban planning. The study found lower levels of urban tree canopy cover in the planned areas. The study suggests that this may be an outcome of higher levels of built densities, higher levels of public space or more household resources. The study argues that urban planning leads to improved mobility and increased feasibility of investments in infrastructure. Accordingly, the study argues that urban planning should be given increased policy priority, particularly in peripheral areas where the urban form is not consolidated yet. The study argues that a strategy for increasing the urban tree canopy cover in informal settlements should be developed in order to improve the urban microclimate, increase biodiversity, and improve rain-water infiltration. Finally, the study argues that the local neighbourhood administration should be provided capacity building in urban governance and more economic resources for investments in locally determined priorities.

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