Using nightlights data to map rural electrification in South Africa: observations from a quantitative methods graduate

FullSizeRenderIn this blog Camille Corti-Georgiou, summer intern at the UK Data Service at the University of Essex, offers her own reflections on the workshop, ‘Household energy use in the Agincourt area’, held in July 2017 at the Wits Rural Facility in north eastern South Africa. Camille has just completed her first degree in Political Science and International Relations and is about to embark on a Masters of Enterprise at the University of Manchester Alliance Business School.

Preface

I attended the ‘Household energy use in the Agincourt area’ workshop, organised by Professors Martin WittenbergDataFirst at Cape Town University) and Dr. Mark Collinson (Senior Researcher: MRC/Wits Rural Public Health and Health Transitions Research Unit), as a student of quantitative methods, with a fairly limited understanding of both longitudinal and ‘big data’, and the methods employed by the team at WITS Rural. Yet, what stood out to me, as a recurring theme throughout the two days, was the potential scope of the work being carried out in Agincourt. While the workshop consisted of multiple presentations and talks, looking at various aspects of the study, in this post, I want to pay specific attention to the presentation given by Professor Martin Wittenberg  on the use of nightlights data in the Agincourt area.

The Agincourt HDSS

The Agincourt Health and Socio-Demographic Surveillance System (HDSS) is, by all accounts, a significant research undertaking, yielding a powerful database for studying many aspects of rural life in a developing region. The principal goal of the HDSS is to offer a more enhanced understanding of the dynamics of health, population and social transitions in northeast South Africa. With twenty years of data having been collected since the initial baseline survey was conducted, the research unit in Bushbuckridge now holds a rich and substantial set of data with policy-influencing significance. Moreover, I was fortunate to see the exemplary framework they have developed for which future research may be undertaken.

WITS_rural_site

Wits Rural Research Facility, Bushbuckridge, South Africa

Throwing Light on Rural Development

For developed regions, the use of satellite data in mapping urbanisation has been widely tested and validated. Measuring the electrification of rural areas, using the same method, however, is a much newer phenomenon. At Wits, the team were interested in whether the satellites would pick up the temporal patterns of rural electrification in the Agincourt area, to allow for analyses that could be corroborated by the data collected on the ground.

Takwanisa Machemedze from Datafirst advanced the actual technique of linking satellite nightlights (as shown in Figure 1) and local data, finding a definitive correlation between the two.

Nightlights

Figure 1. NASA satellite view of Earth at night, compiled from 400 satellite images

In explaining this technique, Martin repeatedly referred to the sum of lights (SOL), the sum of all pixel values for a particular region. While in developed regions a SOL measure will generally suffice, in rural regions it can be problematic. Instead of using the average brightness across the pixel for an area, Takwanise broke up the pixels and matched them to the shape of the boundary, before adding them up. While this approach resulted in some loss of brightness, it gave the most accurate indication of electrification in the area.

Previous analyses conducted on the data by Datafirst concluded stable progress in electrification and revealed a steady, upward trend. What was most interesting, however, were the periods of decline and deviation from this trend, most notably the huge electricity dip across the whole of South Africa in 2008. As a result of the Eskom collapse, stemming from the capacity of electricity failing to meet the demands of the growing economy, a state of electrical emergency was declared. The massive load shedding and electrical rations that followed were indicated by the satellite data. Even in the wake of the collapse, the levels failed to regain, due to the massive tariff increases that followed. At this point it was noted, that one of the drawbacks of using satellite data to measure electrification is that it only detects external light. And, as was duly noted by one of the attendees of the workshop, it could very well have been that electrification was still occurring in the area, but, people were only opting to use internal lights and thus their households would not have been detected.

Data from the ground

Moving on from this, the second half of Martin’s presentation shifted focus onto electrification in the villages and homes of Agincourt. Expanding on the work of Hargreaves, villages in the area were sorted into four distinct categories as shown in Figure 2:

Village type Electricity in 2000 (Y/N)
Central communities Yes
Established communities Yes
Undeveloped villages No
Refugee settlements No

Figure 2. Village typology used by HDSS

Non-domestic lighting, such as light from train stations, police stations and supermarkets, was also picked up by the satellite and thus, it was crucial to conduct analyses on the ground to distinguish between sources of non-domestic and domestic lighting. The data collected for Agincourt was compared against data for Kruger and Nelspruit using a Difference in Difference (DID) approach to look at the take up of lighting. With regards to the rates of electrification in each site at the beginning of the analysis in 1992, levels in Kruger and Agincourt were considerably lower than in Nelspruit. However, data showed Agincourt had a significant tendency to get brighter compared to both. In 1992, Agincourt displayed almost total darkness, lighting up incrementally to 2007 before dulling in the midst of the Eskom collapse of 2008.

The nightlights data additionally showed major bright spots in the Agincourt site. On the surface, it appeared the spots could be attributed to three villages. Yet, when looking closer, one could identify two developed villages, one undeveloped village, a taxi rank, a super market and a train station. The nightlight data was thus spurious, giving the impression the third village had gone through the process of electrification when in reality remained relatively undeveloped. Martin informed us of the caveats of using satellite data and once again reiterated the importance of having researches on site. Moreover, coupling the nightlight data with the village typologies, presented more specific findings, such as 200 new village connections increasing the brightness of the area by 1.7 units.

CONCLUDING REMARKS

For me, the workshop delivered a fantastic insight in to the use and application of nightlights data. The analyses conducted on satellite imagery for the last two decades has not only truthfully captured the electrification of the Agincourt area, but displays the differences between the developed and undeveloped areas of the site. Even from a non-technical perspective, the gravity of the research being done is evident and it is clear the potential exists to do so much more.

The full published paper can be read, untitled, Throwing light on rural development: using nightlight data to map rural electrification in South Africa by Takwanisa Machemedze, Taryn Dinkelman, Mark Collinson, Wayne Twine and Martin Wittenberg.

About LouiseCorti

I'm Louise
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