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 con tinue. 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.