Nhlanhla May

Mapping Human Activities and Resource Flows in Informal Settlements

Introduction
On the first of November 2016 I was tasked with creating a map for an informal settlement as part of the urban Modelling and Metabolism Assessment Research Team (uMAMA). The informal settlement, called eNkanini, is located on the south east periphery of Stellenbosch, Western Cape. It was formed in 2006 with just over 47 families (Van Breda, 2011), and according to data from November 2016, eNkanini has over 2800 iron corrugated households and about 200 basic infrastructure services and socio-economic activities, such as municipal toilets and taps, tuck shops, fast food shops and small farms. The purpose of this map was to collect data and analytics about land use, human activity and infrastructure types that influences the flow of resources in informal communities. This was vital for understanding the dynamics of eNkanini and building stronger knowledge forms about differing resource demands and flows in formal and informal settlements, since the predominant data collection and analysis is about formal settlementsi.

1) satellite image of eNkanini:
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At first glance, eNkanini has a dysfunctional and chaotic semblance like many unratified informal settlements in South Africa. This may not be the experienced reality of residents, , but could be my personal perception about the area - the repercussion of partiality and preconceived ideas I have about informal settlements. For this reason, when I first entered eNkanini, I had no utilitarian expectation of the community or about the settlement itself. However, this changed as I spent more time within the community. Without ignoring the ills within eNkanini, I started to see functional and pragmatic aspects of the settlement, rather than dysfunction based on personal biases.

Community Experiences
Data collection in informal settlements is a challenge and a laborious process. Firstly, informal settlements have narrow, winding pathways and shacks are concentrated in constricted latitudes. Secondly, informal settlements can be socially unstable due to cohesion privation with the broader society outside of their space. For this reason, you are more likely to face resistance in undertaking any data collection within the community, particularly if the community is not participating in the project. Thirdly, politics are a concern especially with projects akin to mapping. This is because the community associates maps with possible infrastructure development, whereas an academic may use maps mainly for data visualisation. This political predicament is acerbated by years of empty promises to deliver services to the community from political leaders once they have been voted in - hence I avoided wearing any shirt that had a colour which might have resembled any major political party in South Africa.
Besides creating a map, this process was valuable to me for getting a glimpse of the challenges faced by South African local municipalities. That is, providing basic services (water, electricity and shelter) to a dynamic population that is constantly moving for better living conditions. One major challenge is that there is no guaranteed revenue for providing and maintaining such services in informal contexts because they are not necessarily recognised as part of the urban landscape (Lemaire, 2015). This constant movement for better living conditions in the end produces more informal settlements, which, according to Huchzermeyer (2008), are key performance indicators of a national government’s ability to mitigate poverty and urgency to better the lives of the poor. This is because informal settlements can be used as a proxy for a community’s risk of floods, fire and other threats to health. This motivated to me the importance of providing full accounts of social and economic activities and access to basic services through spatial specific data.


Technical Considerations
Geographic Information Systems (GIS) has become crucial for meticulous decision making in modern societies at local, sectional and national level, due to the ability to spatially plan with accuracy. GIS data analysis and storage requires time and skill to produce accurate analysis and quality data visualisation through maps. I used handheld e-trex Garmin GPS for data collection. It had its own challenges due to the 95% location accuracy it has, which is about 5 metres in relative distance. Inherently, this imposed a small uncertainty on the data. Moreover, the clustering of different land-use activities within constricted spaces in informal dense areas contributes to this inaccuracy. Therefore, it became difficult to depict accurate and clear location points of every shack, infrastructure and land use activity occurring within the settlement. As a final product, we ended up with a mesh of location points overlapping on each other. Below is an example of how the distance accuracy of the GPS affected visualisation of data and made the initial state of location points unclear.

2) Map showing overlapping way points due to GPS accuracy:
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The most repetitive processes needed before visualisation was possible was cleaning the data and organising it into relational tables with associated attributes. This began by firstly examining how raw data might have deviated from reality and creating erroneous data relations. This was necessary in understanding how uncertainty arises and propagates through geo-data life cycle. Therefore, in this case I focused on the accuracy of the GPS and how it might affect data quality, accuracy and redundancy. Through this process, I was prompted to develop data quality strategies that considered the context and the type of data (geo-data, qualitative and quantitative), how it was stored, data flow from one format to another, and data-sharing within a group of people, requiring a database management system.
Data modelling and entity relation diagrams:

A) Data-base modelling is essential in ensuring databases become user efficient and serve the purpose they are built for. Data modelling begins from data collection to data cleaning and storage especially for complex data-bases. Below, it is a diagram that depicts data modelling processes towards creating a functional relational data-base for eNkanini.

B) Entity Relationship modelling is a top-down analysis technique which shows entities and the relationships that links them i.e. how each piece of data relates to the other as shown below. It begins with an entity relationship diagram that has characteristics that uniquely identifies an entity occurrence, in this case classification, attribute and location. Finally a table was created that allows quantitative analysis through Structured Query Languages (SQL).

Conclusion

Mapping eNkanini could have been done using satellite imagery, raster analysis and digitising main settlement boundaries without having to walk into the community. However, this has limited benefits with regards to data visualisation and analytics because satellite imagery does not provide societal experiences and further insights on self-attained infrastructure and services. From the conceptualisation of the research project, we understood the benefits of entering the community and how holistic spatial data has become a significant part of co-creating knowledge and understanding societal issues.
Furthermore, rich GIS data adds immense value to decision making with advanced analytics capabilities. Spatial analysis can be used to query large and complex data sets to understand behaviours, identify hotspots and predict future outcomes – making it easier for analysts to uncover actionable insights that will help shape communal sustainable growth strategies.

The maps produced through this process will be shared in future vignettes.

Notes

i See Smit S, Musango JK, Brent AC, Kovavic Z (2017). Conceptualising Slum in an urban African context. Cities. 62:107-119 http://dx.doi.org/10.1016/j.cities.2016.12.018 for a discussion of informal settlement types.


Differential African Resource Consumption

As part of an inquiry into the resource implications of rapid African urbanisation, we present 25 maps which depict differential population size, urban proportion and consumption of materials and energy, as well as carbon emissions for 53 of 54 African nations. Such maps are necessary tools for shifting discussion away from ‘a singular Africa,’ particularly in the context of urbanisation and resource requirements. What follows is a (by-no-means exhaustive) discussion of what we observe from these maps.

Africa has a population of almost 1.2 billion people, 30% of which is concentrated in five countries: the Democratic Republic of the Congo (64 million), Ethiopia (89 million), Egypt (79 million) Nigeria (164 million) and South Africa (51 million). Countries that have the highest proportion of urban dwellers, such as Algeria, Djibouti, Gabon, Gambia, Libya and Republic of the Congo, have smaller overall populations. The overall population of the country shows some correlation to the level of aggregate resource consumption, while the level of urbanisation shows direct links to the level of per capita resource consumption. This is partly due to our expectation that urbanisation typically promotes diversification and strengthening of economies, processes which require more resources. Whether urbanisation in Africa is driven by industrialisation or demographic shifts is a debate for another place – though it should be cognisant that each country will differ.

Africa Resources Currie May Consumption Metabolism uMAMA
click map for quality version

Comparing the aggregate consumption level of these countries is useful for global comparisons of consumption, as well as to understand which countries are most responsible for global environmental issues. Comparing per capita consumption may be a better for comparing the resource consumption as it relates to a country’s economy or the quality of life of its population.



Aggregate Consumption

When looking at energy, Northern Africa, Nigeria and South Africa show the highest consumption of fossil fuels and electricity, resulting in high carbon emissions. Due to investment in cheap, non-renewable electricity, Algeria, Egypt, Libya, Nigeria and South Africa show the largest emissions of carbon dioxide. In this way, as much as the global North must accept responsibility for climate change and historical atmospheric pollution, Africa too has its own continental reprobates. These high energy consumers have notably small proportions of renewable electricity generation compared to Middle and East African countries, who are utilizing available hydro-electric and geothermal resources. Egypt and Nigera may be exceptions: they produce a large amount of renewable electricity relative to other countries, but it still makes up only a small portion of their overall consumption. For countries with predominantly renewable electricity generation, their overall level of energy consumption tends to remain low - it will be important to continue to promote renewables here, perhaps through technology leapfrogging, instead of a transition to fossil fuel energy infrastructures. The lowest aggregate energy consumers are Saharan countries as well as Central African Republic, Namibia, Uganda, and Zambia.

Material consumption follows a similar pattern in which Northern countries, Nigeria and South Africa are the highest aggregate consumers of most materials except biomass. The highest biomass consumers are Ethiopia, Nigeria and Sudan, the highest construction material consumer is Egypt and the largest fossil fuel consumer is South Africa. Algeria, Angola, Botswana, Egypt, Mauritania, Egypt and South Africa show high consumption of Industrial Minerals and Ore. This may be a function of data availability, as it is unclear whether these countries show high consumption because they have large extractive industries or because they have strong industrial presence. It is likely a mix of both. The lack of infrastructure and institutions to process, refine and manage these resources represents a challenge described as Africa’s Resource curse, in which extractive economies remain entrenched and unable to diversify.

The levels of aggregate water consumption are somewhat curious as some of the highest consumers are notably water-scarce countries. This may be either due to water-scarce countries having more precise measurements of water consumption (as tracking scarce resources more prevalent, or a reflection of how warm, arid climates, prevalent in Northern and Southern Africa affect both household and industrial water needs.



Per Capita Consumption

South Africa, Egypt, Libya, and Algeria still dominate the consumption of Fossil Fuels, Electricity, Construction Materials, and Water even on per capita basis. They are still the biggest emitters of Carbon Dioxide. Nigeria is an exception as, even though it is a high aggregate consumer of resources, its share of its resources is diluted among a large population. Similarly, though Botswana and Namibia show low aggregate resource consumption, their share of resources is distributed over a small population, leading to quite high per capita consumption of resources.

If we equate resource consumption to quality of life, we might suggest that those living in Nigeria may enjoy a lower quality of life than those in Botswana. However, this does not necessarily take into account economic inequality in which most of the resources may be enjoyed by only a portion of the population, while the rest struggle to gain access to basic services. In this way, inequality measures would be important considerations for further investigation. This also highlights a challenge for promoting resource efficiency in much of the continent – the priority for national governments should be to provide resources those lacking basic services; however, it should be done in resource efficient manners to avoid lock-in to unsustainable infrastructure systems.

Per capita resource comparisons may also be useful for speculating about countries’ degree of progress along the socio-metabolic transition. This is the shift from agrarian economies, which primarily rely on biomass for construction and energy, to industrial economies which rely on fossil fuels for energy, make use of more industrial materials, and may make use of more energy intensive construction materials. Speculating thus, Northern and Southern African countries are farther along the socio-metabolic transition, with Kenya, Tanzania, Gabon, Angola, Ghana, Cote d’Ivoire, Nigeria and Senegal also in transition. Saharan and Middle African Countries show the least progress along the socio-metabolic transition. These are overall speculations and is by no means a rigid categorisation, as pockets of industry, affluence and high quality of life will be present in all African countries.

Data from 2010.