You can read the story at DW.
This section lists all resources used.
| Interviewees | Affiliation |
|---|---|
| Charles Ndika Akong | World Health Organizations |
| Anthony Nyong | African Development Bank |
| Christian Benimana | African Design Center |
| David Dodman | International Institute for Environment and Development |
| Mpho Matsipa | University of Witwatersrand |
| Maimunah Mohd Sharif | UN Habitat |
| Frank Kapangala | Institute for Environment and Development Sustainability |
| Shuaib Lwasa | Makerere University |
| Najib Bateganya | Kampala City Council Authority |
| Precious Akanonu | Centre for the Study of the Economies of Africa |
| Doreen Adengo | Adengo Architecture |
| Phoebe Shikuku | International Red Cross and Red Crescent Federation |
| Helmy Abouleish | SEKEM |
| Nathalie Jean-Baptiste | CityLab Dar es Salaam |
| Ebenezer Amankwaa | United Nations University |
| Mats Eriksson | Stockholm International Water Institute |
| Dolapo Ayokunle Fasawe | Lagos State Environmental Protection Agency |
| Martin Manuhwa | Federation of African Engineering Organizations |
| Hani Sewilam | American University of Cairo |
| Gugu Nonjinge | Afrobarometer |
| Archimedes Muzenda | The African Urban Institute |
| Eloise Marais | University of Leicester |
| Chukwumerije Okereke | Alex Ekwueme Federal University |
| Ibrahim Togola | Mali Folkecenter |
| Ronald Lwakatare | Dar Rapid Transit Agency |
| Chris Kost | Institute for Transportation and Development Policy |
This is a selection of studies, reports and data sources that were useful in the research.
Afrobarometer: Climate Awareness
UN: Urban Population Projections
Africapolis: Urban Agglomerations
Environmental Research Letters: (Moran et al., 2018)
World Resources Institute Cities: (Du et al., 2019)
Lagos State Adaptation Strategy
Canadian Centre for Climate Modelling and Analysis
Journal of Building Engineering: (Rincon et al., 2019)
Uganda National Environment Act 2019
Sustainability: (Aryampa et al., 2019)
Kampala City Council Authority: Waste Management PPP
Journal of the Air and Waste Management Association: (Kinobe et al., 2015)
World Bank Group: Green Urban Development
Global Green Growth Institute: Kampala Municipal Solid Waste
UN Habitat: Climate Change Assessment Kampala
Egypt and the Levant: (Finkelstein et al., 2017)
Earth's Future: (Coffel et al., 2019)
Geological Society of America: (Stanley and Clemente, 2017)
Transparency International: Egyptian Military
Journal of Cleaner Production: (Wahba et al., 2018)
Center for Development Research: (Feye et al., 2014)
University of Twente: (Maliwa, 2019)
African Development Bank: Environmental Impact Assessment
Global Labour Institute: Nairobi BRT
International Growth Center: Ghana BRT
This section contains references to all the datasets used and - where applicable - outlines as to how analysis was conducted.
Data for climate change vulnerability was provided by Verisk Maplecroft, a British research and consultancy company, that regularly releases a self-generated "Climate Change Risk Vulnerability Index". For data licensing reasons, DW was allowed to use and visualize the data, however we are not allowed to publish them, which is why the raw data for this is not included in this repository.
The data source for population growth is the United Nations. They regularly publish a "Data Booklet (on) the World's Cities". For this analysis we scraped the data out of the 2018 pdf version into a csv-file. We calculated the growth rate between the two years
(value_2030 - value_2018) / value_2018 * 100 and matched it with the climate change vulnerability data via VLOOKUP.
We included only cities with more than 1 million inhabitants and with an assigned climate vulnerability index.
We used the Linux command-line tools ncks and ncdump, which are part of the netcdf-bin package on Debian and netcdf on Homebrew. Install this in the normal way.
We downloaded modelled daily maximum near-surface air temperatures (variable name tasmax) from the Canadian Centre for Climate Modelling and Analysis.
The model data we are using is the CanRCM4-AFR-44 data. This is a regional climate model for the African region at a 50km grid resolution. Because this is a large-scale model, the granularity of the estimates should not be overstated, either on a temporal or spatial dimension.
We use the RCP4.5 run, based on a central emissions pathway.
We will average the "daily" figures over five years to provide broad-brush illustrative temperature examples for the Lagos area.
The "present day" temperatures are based on the 2016-2020 file.
-
Download data as .nc file.
-
Run
ncksto extract the grid square we want:
ncks -v tasmax -d rlon,3.3841 -d rlat,6.4550 tasmax_AFR-44_CCCma-CanESM2_rcp45_r1i1p1_CCCma-CanRCM4_r2_day_20260101-20301231.nc tasmax_lagos_2016_2020.nc
- Run ncdump to turn the nc file into a text file.
ncdump tasmax_lagos_2016_2020.nc > tasmax_lagos_2016_2020.txt
As there is no off-the-shelf way to turn the text file into a CSV, simply copy and paste the daily values into a spreadsheet.
We then calculate the mean average over 5 years for each calendar day of the year.
We repeat the steps above for 2046-2050 data.
We download the Near-Surface Specific Humidity (huss) and Surface Air Pressure (ps) variables for the same time periods. These will allow us to calculate the Relative Humidity, which can be combined with the temperature to indicate a notional Heat Index ("feels like") temperature.
The daily maximum temperatures and maximum humidity will not necessarily coincide, meaning our estimates could be on the high side. However, as a broad indication of possible humidity effects it is sufficiently rigorous.
The formula for Relative Humidity was sourced from the Earth Science Stack Exchange.
We then apply a formula from the US National Oceanic and Atmospheric Administration to calculate the Heat Index temperature according to the Rothfusz regression.
The final output data used in the visualisation can be found here.
The figures for waste production in Kampala in 2011 compared to 2017 originate from a joint study by Western Syndey University and Makerere University.
The traffic situation was derived from the "Dar es Salaam Transport Policy and System Development Master Plan" produced by Tanzanian authorities with help of the Japan International Cooperation Agency (JICA) in 2008. Although from 2008, this document comprises the most recent and comprehensive data on the traffic situation in Dar es Salaam and is guiding offical transportation policy developments up to 2030, according to a 2017 World Bank Report.
For our analysis we chose to derive the traffic situation from Figure 5.2.51 (p.34) highlighting the morning peak hours, given that greater economic damage occurs if people get late to work due to traffic jams than if people get late home. The study differentiates between seven different travel speed categories. For our story, we included the category where the average travel speed in the morning peak hours is between 0 and 10 km/h. Additionally we grouped the category 10-20 and 20-30 km/h into a second category displayed.
The status of planned/existing bus lines were taken from a brochure by the Dar Rapid Transit Agency (DART). The pdf file includes a map outlining where bus lines are supposed to operate and in what phases they are supposed to be built. Information on what phases are already implemented was taken from the official Bus Rapid Transit (BRT) website, that states so far only phase one is listed as operating.