On the potential of Google Street View for environmental waste quantification in urban Africa: An assessment of bias in spatial coverage

ABSTRACT

Mismanaged domestic waste threatens ecosystem health, with substantial increases predicted from developing country cities if current consumption and waste service collection trends continue. Google Street View (GSV) imagery has been used to quantify urban environmental waste in high-income countries. GSV availability is increasing elsewhere, but its coverage is variable. This study aims to evaluate bias in spatiotemporal GSV coverage relative to environmental waste in two case study cities. An environmental survey measured environmental waste in Greater Accra, Ghana and Kisumu, Kenya via 95 and 81 transects, respectively. Six summary metrics of environmental waste were calculated and compared for transects with full, partial, and no GSV coverage via multi-level regression. Multi-level regression indicated no significant differences in scattered waste density for transects with versus without GSV coverage. However, both cities had significantly lower waste burning densities along transects with GSV coverage (4.3 versus 24.2 burning sites/Ha in Kisumu; 1.7 versus 13.6 sites/Ha for Greater Accra) compared to those without Street View density of large waste piles was significantly lower in Kisumu transects with Street View coverage (1.4 versus 11.5 sites/Ha). Because of partial imagery coverage, GSV imagery analysis is likely to under-estimate waste indicators such as waste burning density. Future studies using GSV to quantify waste indicators in African cities should therefore correct for coverage bias.

KEYWORDS:

• Africa
• mapping
• neighbourhood analysis
• slum
• municipal waste management

Public Interest Statement

When mismanaged waste enters the environment, it threatens ecosystems and public health. This issue could soon escalate in developing country cities, given current trends. In future, Google Street View (GSV) imagery could help monitor mismanaged waste, as US researchers have already successfully counted street litter visible on GSV. However, GSV in sub-Saharan Africa mostly covers main roads, with little coverage of minor roads and slums. These minor roads are often inaccessible for waste collection vehicles or home to communities who cannot afford waste services. By missing such areas, GSV could potentially underestimate mismanaged waste. To investigate this, our paper compares indicators such as waste burning and street litter between areas with and without GSV. Via a field survey in Greater Accra (Ghana) and Kisumu (Kenya), we found more waste burning in areas without GSV. This highlights the need to correct any future waste estimates from GSV for bias.

Highlights

• We evaluated bias in spatial-temporal coverage of Google Street View (GSV)

• A transect survey recorded solid waste indicators in two African cities

• Multi-level models showed much lower waste burning for transects with GSV imagery

• GSV may under-estimate waste densities in some cities because of partial coverage

• Studies should assess and adjust waste metrics from GSV for spatial coverage bias

Acknowledgements

This research was funded through a UKRI Collective Fund award via the Global Challenges Research Fund (ref: ES/T008121/1). The support of the UK Economic and Social Research Council (ESRC) is gratefully acknowledged. The study sponsor had no involvement in the study design collection analysis and interpretation of data, writing of the report and decision to submit the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

For the Greater Accra and Kisumu analysis, the environmental transect data are openly available as:

Umar, F., Amponsah, M., Damkjaer, S. Dzodzomenyo, M., Okotto, L.G., Okotto-Okotto, J., Oigo, J., Shaw, P., Wright, J, and Väisänen, H., Wanza, P. (2023). Environmental Transects Surveys of Mismanaged Waste in Off-Grid Neighbourhoods of Kisumu, Kenya, and Greater Accra, Ghana, 2021 [Data Collection]. Colchester, Essex: UK Data Service. https://dx.doi.org/10.5255/UKDA-SN-856145.

OpenStreetMap data are openly available from multiple sources, including Planet: https://planet.openstreetmap.org/.

For the analysis of dry season bias, the UN Habitat Global State of Metropoles Database 2020 is openly available via https://urbanpolicyplatform.org/global-state-of-metropolis/. The World Bank country classification is openly available here: https://datatopics.worldbank.org/world-development-indicators/the-world-by-income-and-region.html. CHIRPS rainfall data are openly available here: https://www.chc.ucsb.edu/data/chirps. Country boundaries are openly available from: https://www.naturalearthdata.com/about/terms-of-use/.

Additional information

Funding

The work was supported by the Economic and Social Research Council [ES/T008121/1].

Notes on contributors

Farouk Umar

Farouk Umar School of Geography and Environmental Science, University of Southampton, Southampton, UK