The spatial heterogeneity of urban green space distribution and configuration in Lilongwe City, Malawi

Urban green spaces provide several benefits related to the quality of urban life. The existence and spatial arrangement of these spaces within neighbourhoods and functional land uses have significant implications for the well-being of urban dwellers. Previous studies on green spaces in urban areas of Malawi have focused on a broader and macro-level perspective, offering insightful information on general trends in different cities. However, there is a significant research shortage in localised understanding, which requires carrying out micro-level assessments concentrating on land use zones and neighbourhoods within these cities. In this study, we used remote sensing data and landscape metrics to understand the distribution and configuration of urban green spaces in the city’s neighbourhoods and functional land uses and their relationship with urban form. The study revealed that 20% of neighbourhoods fail to meet the WHO-recommended standard of 9 m2 of green space per person, with a predominant concentration of these undersupplied areas in high-density and quasi-residential zones. In addition, 56.2% of Lilongwe City’s total green area was contained under functional land uses. Particularly, high-rise residential, medium-density residential, low-density residential, quasi-residential, high-rise flat area, commercial class, high-rise commercial, heavy industry, light industry, and government land use zones contained 17.3%, 12.0%, 22.2%, 12.0%, 4.1%, 6.4%, 6.1%, 5.0%, 1.6%, and 13.3% of the total green spaces in functional land uses, respectively. Importantly, this research found significant correlations between urban form metrics, namely building coverage, building density, building perimeter area ratio, road density, and the distribution and configuration of urban green spaces. This necessitates an integrated approach to urban planning and design, emphasising the importance of balancing development with green space preservation.

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Introduction
Urbanisation is the key characteristic of the twenty-first century, as people migrate to cities worldwide in search of better living conditions and employment opportunities [1].However, there are several consequences of rapid urbanisation, including increased environmental degradation, increased socioeconomic disparity, and the loss of natural areas that are essential to the well-being of urban residents [1,2].As cities expand and change, the design and distribution of urban green areas become more key factors that impact the quality of life within them [3,4].
Urban green space equity is becoming increasingly crucial to the sustainable development of cities as they grow.Urban green spaces can be anything from parks and gardens to greenways.Urban green spaces offer multiple benefits to city dwellers, such as improved air quality [5], recreational opportunities [6], ecological balance [7], aesthetic appeal [8], educational opportunities [9], and enhanced psychological well-being [10].To maintain harmonious coexistence between built and natural environments, urban planners and designers nowadays are interested in considering not only the functional requirements of urban green areas but also aesthetic and ecological factors.
Numerous studies have linked the variability of urban green spaces across different land use zones and neighbourhoods to several factors.For instance, urbanisation, population density, and land value play pivotal roles in the availability of urban green space in various locations [11][12][13].In addition, the distribution of green areas within different zones is affected by environmental, governance, regulatory policies, and zoning restrictions [14][15][16].Socioeconomic factors, including race and income disparities, can also influence the availability and quality of green spaces [17][18][19][20].Since the distribution of green space in urban areas tends to be uneven due to numerous factors, mapping the current state of green space is necessary for urban planners to create sustainable cities.
The previous research on green space and land use/land cover in Malawian cities and towns has mostly provided a macro-level perspective [21][22][23][24], offering insightful information on general trends in different cities.However, there is a significant research shortage in localised understanding, which requires carrying out micro-level assessments concentrating on land use zones or neighbourhoods within these cities.Such a localised approach is essential because it recognises that the dynamics of green spaces can differ significantly within a city's boundaries, depending on a variety of variables such as historical development, changing land use regulations, community-driven initiatives, and social-economic factors [11,16,17,19,25].This knowledge gap underscores the need for studies that delve deeper into the specific settings of neighbourhoods or communities, providing a deeper comprehension of how green spaces are distributed in different areas within the cities.
Therefore, the current study assesses the distribution and composition of urban green spaces in the city's neighbourhoods and functional land uses in Lilongwe, Malawi.According to the city's urban structure plan, the functional land uses included in the study were commercial, industrial, government, and residential areas.The residential areas were further classified into high-density, medium-density, low-density, quasi-residential, and high-rise residential areas.The specific objectives of the study were to analyse (1) the distribution of urban green spaces in the city's neighbourhoods and functional land uses; (2) the configuration of urban green spaces in functional land uses; and (3) the relationship between urban form and green space distribution and configuration.A thorough methodology that incorporates information from spatial analytical techniques, remote sensing images, and geographic information systems (GIS) was adopted.The findings of this study may have an impact on urban planning strategies and policies, as well as aid in the creation of more efficient plans for the distribution and management of green spaces.The study also aims to broaden our knowledge of the spatial variability in urban green space and how it impacts the design of sustainable urban environments.However, the city is experiencing rapid urbanisation compared to other cities in Malawi, which is estimated at 4% annually [28].Just like many other cities, Lilongwe is experiencing many

Data
In this study, we used a 10-metre-resolution Sentinel Level-2A imagery acquired on September which was based on census tract data.There is no universally agreed definition of a neighbourhood and most studies have used census boundaries [29].Population census data for the neighbourhoods was extracted from the 2018 Malawi Population and Housing Census report [26].In this study, we adapted the four main functional land use categories from the city's master plan.Land primarily dedicated to housing, such as single-family homes, apartment complexes, and residential neighbourhoods.

Commercial
Encompasses areas where businesses and retail activities take place, such as small shops, shopping malls, restaurants, banks, and office buildings.Industrial (Heavy/Large Scale Industry and Light Industry) Areas where manufacturing, warehousing, and other industrial activities occur.

Government
Government land use includes government office buildings where various government functions and public services take place and includes state residences.

Normalized Difference Vegetation Index
We used the normalised difference vegetation index (NDVI) to identify green spaces in the study area.NDVI is one of the most used indicators of the presence of vegetation [20,[30][31][32][33].The NDVI index has a range of -1 to +1.The negative NDVI index indicates non-green areas, such as deserts, water, rivers, and built-up areas, whereas the positive value denotes green areas [34] an increased NDVI index value suggests more vegetation on the ground.The NDVI value can also be used to assess the condition of plants and vegetation [35].This is because healthy plants will have higher NDVI values than stressed plants.Equation ( 1) was used to calculate the NDVI: Where, NIR [Band 8] and RED [Band 4] denote reflectance in the near-infrared and red bands, respectively, of the Sentinel-2A imaging product.

Per Capita Green Space (PCGS) and Urban Green Space Index (UGSI)
To understand green space allocation for every inhabitant of a neighbourhood, we calculated per capita green space (PCGS).PCGS is widely used to assess the quality of urban environments and their impact on residents' well-being [11,24,33,36,37].Equation (2) was used to calculate PCGS: Where PCGSi denotes per capita green space in neighbourhood i, Gi denotes total green space coverage in neighbourhood i and PNi denote the total population of neighbourhood i.We used population census data for neighbourhoods for the year 2018 [26].
Furthermore, an Urban Green Space Index (UGSI) was adopted to measure the quantity of Urban Green Spaces (UGS) in each neighbourhood.The index denotes the availability of UGS as a percentage and provides a standardised way of comparing UGS in different neighbourhoods [33].
The UGSI is computed as follows: UGSI for i th neighbourhood can be expressed as in equation ( 3): Where, Gi = UGS in the neighbourhood i, Ai = area of the i th neighbourhood (where i = 1 to n).
Whereas total UGS (expressed as a percentage) in a particular neighbourhood is calculated as in equation ( 4):

Urban green space landscape metrics
Guided by previous studies [38][39][40], the composition and configuration of urban green spaces were assessed using the following seven landscape metrics: number of patches, largest patch index (LPI), mean Euclidean nearest neighbour distance (ENN_MEAN), mean shape index (SHAPE_MN), number of patches (NP), and patch density (PD).We used FRAGSTATS 4.2 software to calculate the selected metrics of green spaces at the class level.A detailed description of the metrics is provided in Table 2.

Urban form metrics
Several studies have shown significant impacts of urban form on the spatial distribution and configuration of UGS.The relationship between urban form and green spaces has implications for the overall sustainability, liveability, and well-being of a city.Weighted density, density gradient slope, density gradient intercept, compactness, and street connectivity urban form metrics were used to understand the impact of urban form on green space accessibility in 462 metropolitan areas globally [41].To understand the association between urban morphology and green spaces, building coverage ratio, building perimeter, number of buildings, road coverage ratio, road intersections, and road length ratio metrics were employed [42].Similarly, address density, building density, and household density as urban form indicators were correlated with biodiversity potential and ecosystem performance indicators in five UK cities [43].The perimeter-area ratio (PARA), road density (RD), and compound terrain complexity index were used to evaluate the impact of urban form on UGS structure [44].In Sheffield, road length, building density, and building areas, among other factors, as predictors of the extent and quality of green spaces [30].Therefore, four urban form metrics were employed in the study, namely: building coverage (BC), road network density (RD), mean perimeter area ratio (PARA_MN) of buildings and building density (BD).The calculation of the urban form metrics was done in ArcGIS 10.6 software.
Urban planners and designers use building coverage (BC) as one of the indicators to quantify the total area of land occupied by buildings.The amount of land covered by buildings gives information about how intensively an area is developed and how much of the land area is undeveloped.Lower building coverage enhances the preservation of open spaces, parks, and natural areas.Using Microsoft building footprint data, BC was calculated using the equation ( 5): Where BC, BA, and TA are building coverage, the total area covered by all buildings and the total area occupied by a particular functional land use category, respectively.
The road network density (RD), which is the ratio of an area's total road network's length to its land area, was determined using freely available road network data accessed from the OpenStreetMap (OSM) website (www.openstreetmap.org).The RD is calculated as in the equation (6).
Where RD is the road density, L is the total length of roads in particular land use, and A is the total land area of a particular land use.
The mean perimeter-area ratio (PARA_MN) of buildings is one of the urban form indicators used to understand the efficiency and compactness of urban development.A lower PARA_MN value indicates the compactness of buildings in an area.Compact urban forms are frequently linked to effective land use, lower infrastructure costs, and encourage walkability.PARA for each building is calculated using equation (7): Where PARA is the perimeter-area ratio of each building, Pbuilding is the perimeter of each building, and Abuilding is the total area covered by the building.The mean perimeter-area ratio (PARA_MN) of buildings within a particular functional land use area was computed using equation ( 8): Where n is the total number of buildings in a particular functional land use category.
In addition, building density (BD), which is used to measure the concentration of buildings in a particular area was adopted as one of the indicators of urban form.The BD has an impact on the distribution and composition of urban greeneries.Land for green spaces may be scarce in highdensity areas, while low-density areas may provide spaces for greeneries.However, low-density environments may encourage urban sprawl.BD is calculated using equation (9) below.It is then expressed as the number of buildings per given area: Where BD is the building density, NB is the number of buildings in each area, and A is the total land area.

Statistical Analysis
To find an association between urban green space metrics and urban form metrics, bivariate Pearson correlation analysis was performed in IBM SPSS 20.0, and the relevant tables were prepared.The bivariate Pearson correlation indicates the statistical significance, degree, and direction (increasing or decreasing) of a linear relationship between two continuous variables.A correlation coefficient of 1 indicates a positive correlation between the variables; a coefficient of -1 indicates a negative correlation; and a correlation coefficient of 0 indicates no association [45].

Results
Urban green space per capita distribution in the neighbourhoods in

Lilongwe city
The analysis of the UGS per capita distribution showed significant variations in the availability of green space in the Lilongwe City neighbourhoods (Table 3).

Urban green space index (UGSI) of different areas/neighbourhoods in Lilongwe City
A further analysis of the distribution of UGS in the neighbourhoods indicated different percentages of coverage (Table 4 and Fig 2).The results show that 43.9% of neighbourhoods have less than 10% of their area covered by green space, 33.3% have between 10% and 20%, 14.0% have between 21 and 30%, and 8.8% have between 30% and 33%.

Urban green space distribution and population density
We used Pearson's correlation analysis to unravel the relationship between the distribution of UGS among the city's various neighbourhoods and population density.The study's findings revealed a weak negative association between urban green space and population density (coefficient of -0.419).Areas with high population densities had low PCGS, as also seen in Fig 3 .Additionally, there was a strong positive association between the Urban Green Space Index (UGSI) and per capita UGS of 0.726.Population density and urban green space per capita had a negative correlation (correlation = -0.367).This means that an increase in population density results in a reduction in urban green spaces.

Lilongwe city
The study has shown variations in the distribution of urban green spaces in functional land uses in Lilongwe City (Table 5).The study revealed that 59.7% of Lilongwe City's total green area was contained under functional land uses.In contrast to other functional land uses, the largest proportion of green spaces are found in residential neighbourhoods.In addition, 67.6% of UGS were found in residential zones.Particularly, 17.3%, 12.0%, 22.2%, 12.0%, and 4.1% of UGS were found in high-rise flat areas, medium-density residential areas, low-density residential areas, quasi-residential areas, and low-density residential zones, respectively.Similarly, green spaces covered 12.5%, 6.6%, and 13.3% of the area in commercial, industrial, and government land uses, respectively.

Landscape metrics analysis for the UGS for four land use classes
Spatial patterns, compositions, and configurations varied in the nine land use classes according to the landscape metric analysis results (Table 6).The number of green space patches (NP) found within each functional land use varied significantly, with the highest number of patches being recorded in the residential land use zones (10,226 patches).This indicates that NP in residential areas made up 71.6% of all patches in the study area, with high-density residential areas having the most patches and low-density residential areas having the fewest patches.As regards the patch density (PD), high-rise commercial areas had the greatest PD (133.8),followed by medium-density residential areas (PD of 110.3), and high-rise flat areas had the lowest PD (43.6).In terms of the largest patch index (LPI), low-density residential land use areas had the highest index of 9.81 and the quasi-residential class was the land use category with the smallest patch size, with a value of 0.35.A higher LPI rating for low-density residential denotes the presence of larger and betterconnected green spaces in the landscape.Lower LPI values, on the other hand, imply that the green space is more fragmented, with smaller patches dispersed over the landscape.9.81 and the quasiresidential class was the land use category with the smallest patch size, with a value of 0.35.A higher LPI rating for low-density residential denotes the presence of larger and better-connected green spaces in the landscape.Lower LPI values, on the other hand, imply that the green space is more fragmented, with smaller patches dispersed over the landscape.
In addition, the mean shape index (SHAPE_MN), which is one of the indicators of spatial configuration, showed the high complexity of the shapes of urban green spaces in all the functional land uses.The low-density residential areas had greeneries that were more complex and irregular in shape (SHAPE_MN = 1.32) than other functional land use categories.While the lowest green space shape complexities were observed in the quasi-residential areas (SHAPE_MN = 1.17).

Relationship between landscape metrics of urban green spaces and urban form metrics
Urban green space metrics showed some significant relationships with urban form metrics (Table 8).For instance, RD strongly negatively correlated with SHAPE_MN (-0.663*).BD strongly positively correlated with NP (0.714*), SHAPE_MN (0.681*) and ENN_MN (0.651*).This suggests that as building density increases, the number of patches (NP) in green spaces tends to increase as well.In addition, in areas with higher building density, the green spaces are farther apart from each other on average.MN_PARA had a strong negative correlation with PD (-0.785**), implying that areas with buildings that have a higher mean perimeter area ratio (indicating more irregularly shaped buildings or less open space) have lower patch density in terms of green spaces.

Discussions Urban green space distribution in Lilongwe City
Lilongwe City is one of the cities with abundant greenery.Despite having abundant greenery, there are disparities in the distribution of greenery among the neighbourhoods and functional land uses.
The study has revealed that 20% of the neighbourhoods do not meet the minimum WHO recommended value of 9 m 2 of green space per individual [46].This means that the available green spaces make little contribution to positive living in these neighbourhoods.It is worth noting that most of these neighbourhoods are in high-density and quasi-residential areas.These neighbourhoods contain about 50% of the total population of the city.This shows how serious the disparities are in the distribution of UGS in the city.
In addition, the study revealed that population density was negatively correlated with urban green space.Numerous factors, like a shortage of adequate land in densely populated neighbourhoods or encroachment on areas allocated for greenery, may contribute to this [27].The quasi-residential areas are the informal settlements, and some of these areas have encroached on environmentally sensitive areas with little space to provide them with green spaces.These results are consistent with those of Bille et al. [11], who investigated how population density affects patterns in urban green space around the world.They found a rapid decline in UGS coverage in areas with high population densities.Other authors have also observed the opposite effect of population on urban green spaces [47,48].
In Lilongwe City, most of the people who live in these undersupplied areas (quasi and high-density residential neighbourhoods) are low-incoming residents.In Lilongwe City, 73% of the residential land share is made up of informal settlements and low-income neighbourhoods [49].Disparities in green space distributions were also found in Southeast Asia [50], South Africa [19], Guangzhou, China [20], and Brisbane, Adelaide, Sydney, Perth, and Melbourne [51], with low availability of greeneries found in low-income neighbourhoods.Similarly, the poorest neighbourhoods were observed to have limited access to green and blue spaces in Hong Kong [52].
Urban dwellers who are deprived of the many advantages of UGS may be concerned about uneven distribution and a lack of UGS.The results highlight the difficulties and need for sustainable urban planning and development to ensure equity in the distribution and accessibility of UGS to all city dwellers.Lack of access to green spaces in densely populated areas can exacerbate alreadyexisting health and environmental injustice disparities.

Urban green composition and configuration
As regards to composition of UGS in functional land uses, the study has revealed that about 59.7% of UGS in Lilongwe City is found in functional land uses.Out of this, 67.6% of UGS were found in residential zones.A high proportion of UGS in the residential areas was due to their relatively large areas.In functional land uses, residential usage accounts for around 77% of the total area.
In addition, the mean shape index (SHAPE_MN), which is one of the indicators of spatial configuration, showed the high complexity of the shapes of urban green spaces in all the functional land uses.The low-density residential areas had greeneries that were more complex and irregular in shape than other functional land use categories.While the lowest green space shape complexities were observed in the quasi-residential areas.The complexity of green spaces in low-density residential areas may be attributed to the abundance of greenery in these areas, hence providing a variety of shapes.Studies have shown that more complex and irregularly shaped green spaces may provide higher aesthetic, recreational, ecological and health benefits since they interact well with the surrounding area [53][54][55].This implies that people who live in the low-density residential areas are more advantaged than those in the informal/quasi-residential areas in Lilongwe City.
Additionally, the study revealed that across all functional land uses, the largest patch index values were comparatively low.Most of the functional land uses have an LPI of less than 3.0.Again, densely populated areas showed the lowest LPI levels, which may be attributed to more structures and people habits in these places leading to fragmentation.The low LPI number also highlights the possibility of disparities in how different land use groups have access to green spaces.

The association between urban green space and urban form metrics and implications for urban planning
The study revealed significant correlations between urban green space and urban form.For instance, BD is strongly positively correlated with NP, SHAPE_MN, and ENN_MN.This suggests that as building density increases, the number of patches (NP) of green spaces tends to increase as well.In addition, in areas with higher building density, the green spaces are farther apart from each other, with their shapes becoming more complex.The severity of fragmentation increases with the number of green space patches [56].High dispersion and isolation of UGS patches may be associated with the increase in building density.This agrees with the finding by Yeh et al. [57], who found that the distances between green spaces in highly urbanised areas were higher than those in the less urbanised areas in Taipei Metropolitan, Taiwan.
BC strongly positively correlated with LPI.This implies that as the areas within the city become more urbanised and built-up (increased building coverage), larger and more connected green spaces may be present.Similarly, the building perimeter area ratio (MN_PARA), which is a measure of the complexity of the built-up area, showed a significant negative relationship with PD of green space.We found that areas with compact building forms were less fragmented.This could be due to urban planning interventions that prioritise larger green areas within the central areas of the city [27].Lilongwe City was built on the garden city concept and has abundant large patches of green space, such as nature sanctuaries, botanic gardens, and golf courses within the central part of the city [27].Contrary to the findings by Huang et al. [44] and Bereitschaft & Debbage [38], who found that cities with complex urban areas (higher PARA value) had high fragmentation of UGS, Compact building forms like high-rise buildings and mixed-use developments may improve UGS efficiency since they use land more effectively and free up more space for green spaces [58].
This is accomplished through checking urban sprawl and maximising land usage, which increases the overall green space per capita in denser neighbourhoods.However, compact building forms may result in higher population densities, which may lead to the loss of existing greenery to pave the way for buildings and infrastructure to accommodate the huge populations.Compact building forms can cause UGS fragmentation.
The relationship between the distribution and availability of UGS and urban form and design can be two-way.Urban green space is significantly correlated with urban form, which suggests that urban form is crucial to understanding the distribution and configuration of UGS.Urban development plans should incorporate green spaces and consider their significance.Smart Growth Strategies that prioritize compact and mixed-use development can help with this [58,59].These strategies try to preserve or improve green spaces while increasing road density, building density and coverage.To achieve liveability and sustainability of neighbourhoods, the city should take a balance that integrates green spaces into the urban fabric.The purpose of the present research was to establish relationships between UGS and urban form.To better understand the nature of the association as well as account for confounding factors, future research should consider establishing a cause-and-effect relationship between urban form and UGS.
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challenges including those caused by rapid urbanisation.Environmental deterioration, pollution, deforestation, uncontrolled development on ecologically sensitive and weak regulatory frameworks are among other challenges facing the city.

Fig 1
Fig 1 Location and areas of the Lilongwe city (Malawi) as well as the green spaces within it.

Fig 3
Fig 3 Relationship between PCGS and population density: (a) Population density map of Lilongwe City according to Malawi population and housing census [26], (b) Per Capita Green Space distribution of Lilongwe city Euclidean nearest neighbour distance (ENN_MN) indicated that quasiresidential areas had the highest value of 45.54; similarly, quasi-residential and high-rise flat residential areas indicated high values among the other functional land use types in the study area.This indicates that green spaces in quasi-residential areas are more dispersed or farther apart compared to other land uses.Low clustering or concentration of green areas in the quasi-residential land use zones is indicated by the high value of ENN_MN.On the other hand, low-density residential areas had the lowest ENN_MN values of 26.53.

Figure 2
Figure 2 5, 2022 (Product ID: L2A_T36LXK_A028718_20220905T080208).A satellite image covering the entire Lilongwe city was obtained from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).To minimise haze and acquire cloud-free satellite images of the study area, the image was acquired during the dry season.The image was then projected onto the 1984 World Geodetic System, the Universal Transverse Mercator (UTM) Zone 36S.The Sentinel Level-2A products are already subjected to atmospheric and geometric corrections.The acquired satellite images were clipped to extract study areas using the Lilongwe city boundary shapefile layer obtained from the Lilongwe City Council.We used Microsoft building footprint data, which was downloaded from https://www.microsoft.com/en-us/maps/building-footprints.The city's neighbourhood's boundary shapefile was obtained from the National Statistical Office (NSO),

Table 1 :
Descriptions of the functional land use categories used in the study

Table 2 :
A detailed description of the landscape metrics

Table 3 :
Urban green space per capita distribution in the neighbourhoods/areas in Lilongwe City (Note: Two areas were not included due to the lack of population data)

Table 4 :
Distribution of UGSI in Lilongwe city at area/neighbourhood level Urban Green Spaces Area Numbers (arranged in ascending order based on the percentage of green space)

Table 5 :
Distribution of urban green spaces in functional land uses in Lilongwe City

Table 6 :
Composition and configuration of UGS in functional land use in Lilongwe City

Table 7 :
Correlation between urban green space and urban form metrics The analysis of the mean Euclidean nearest neighbour distance (ENN_MN) indicated that quasiresidential areas had the highest value of 45.54; similarly, quasi-residential and high-rise flat residential areas are more dispersed or farther apart compared to other land uses.The high value of ENN_MN indicates that green areas are sparsely clustered or concentrated within the quasiresidential land use zones.On the other hand, low-density residential areas had the lowest ENN_MN values.The composition and configuration of urban green spaces in functional land uses highlight the challenges and need for sustainable urban planning and development to ensure equity in the distribution and accessibility of UGS to all city dwellers.Urban dwellers who are deprived of the many advantages of UGS may be concerned about uneven distribution and a lack of UGS.The lack of access to green spaces in densely populated areas can exacerbate already-existing health and environmental injustice disparities.