This data set comprises a market survey of foods and beverages and their packaging as sold at a sample of retail outlets in urban Greater Accra, Ghana and Kisumu, Kenya. It also comprises responses to a short shopper questionnaire and all data were collected in areas classified as slums. The data set was collected with the aim of quantifying packaging waste associated with food and beverage purchases. It also aimed to develop and evaluate a low-cost method for identifying plastic resin types in resource-poor settings lacking laboratory facilities. At each sampled retail outlet, observations were made of vendor or consumer packaging behaviours when food or beverage purchases took place. Short interviews were conducted with shoppers. Where foods or beverages were wrapped in plastics, packaging samples were collected and further characterised in a basic laboratory. Alongside recording labelling and other packaging characteristics, the laboratory protocol also completed a simple observation checklist (e.g. testing if packaging floated; how it behaved on cutting), so as to infer the packaging's plastic resin type. The data set should thus be of interest to researchers wishing to understand waste generation and packaging in urban Africa.

According to WHO/UNICEF, whilst 91.8% of urban households in Sub-Saharan Africa (SSA) had access to piped or protected groundwater sources in 2015, only 46.2% had safely managed water available when needed. Vendors provide a key role in supplying urban off-grid populations, with consumption of bottled or bagged water (sachets, water sold in 500ml plastic bags) growing in SSA. Whilst several studies show bottles and bags are usually free from faecal contamination, given that many off-grid urban populations lack solid waste disposal services, when people drink such water, there can be problems disposing of the plastic bags and bottles afterwards. This project aims to deliver evidence on the different ways that people sell water to off-grid populations and what this means for plastic waste management. We plan to do this in Ghana, where most urban household now drink bagged water, and by way of contrast, Kenya, where the government has banned plastic bags. In this way, we want to widen access to safe water and waste management services among urban off-grid populations, by supporting water-sellers and waste collectors to fill the gaps in municipal services. Both countries (and many others elsewhere) already have nationwide household surveys that collect data on the food and goods people consume and the services they have. However, as yet, these surveys have not been connected to the problem of waste management. We plan to visit marketplaces, buying foods and then recording packaging and organic waste. By combining this information with the household survey data, we can work out how much domestic waste like plastics gets collected and how much is discarded or burned, ultimately entering the atmosphere or oceans. In Ghana, we will also survey informal waste collectors in urban Greater Accra. We want to find out how much these small businesses support waste collection and recycling across this urban region (particularly plastic from bagged water), so we can help government identify gaps in waste collection coverage. We also believe highlighting the important role of small waste collectors could lead to greater business support for such collectors. We will also evaluate whether community education campaigns to encourage domestic waste recycling reduce the amount of waste and plastic observed in the local environment. Such campaigns are currently pursued by several local charities with support from the Plastic Waste Management Project. In Kenya, where water is usually sold in jerrycans rather than bagged, the jerrycan water often gets contaminated. We plan to find out whether this jerrycan water is safer under an arrangement known as delegated management. This involves a water utility passing on management of the piped network to a local business in slum areas, so as to reduce vandalism of pipes and bring water closer to slum-dwellers. We will compare water quality in areas with and without this arrangement to see if it makes the water sold safer. We also plan to bring water-sellers and consumers together to find and test ways of reducing contamination of water between a jerry-can being filled and water being drunk at home. Rather than imposing a solution, we want to work together with vendors and consumers on this issue, but there are for example containers designed to keep water cleaner that we could explore. Through these activities, we thus plan to develop evidence on different strategies for water-sellers to deliver safer water to people lacking piped connections, whilst managing plastic waste at the same time. In Ghana, this involves trying to increase recycling and waste collection for bagged water, which is relatively safe. In Kenya, this involves trying to reduce...

The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

There are so many problems confronting most contemporary cities in the recent time particularly among the less developed countries around the world. These problems have been recognized to be the product of lack of urban planning by the authority in-charge as well as individual members of the society. However, the negative relationship between urban population and urban development has been identified using different methodologies. The prime objective is to apply the technique of Remote Sensing and GIS technology to examine the trend, pattern, the relationship between sprawl and population as well as the socio-economic implications of urban sprawl in Ibadan. However, the population is estimated to increase by 68.5% between year 2000 and 2020 (2,207,829 – 3,223,429) while the corresponding projected land consumption is also expected to rise by 58.5% (52,220.3 – 89, 192.3 ha) which implies that both would have doubled but the population is likely to double itself much faster than the land mass. Similarly, there was a significant change in the land use of land cover between 1986 and 2000 and a good example was the farmland which had decreased by 67.9% between this periods. The implication of this growth on the socioeconomic well being of the population is that urban development would have encroached on the urban fringe where urban and periurban agriculture is being practiced leading to acute shortage of fresh food supply to the urban populace, while similarly the sprawl is likely to result in slums development.

South Africa ZA: Population Living in Slums: % of Urban Population data was reported at 23.000 % in 2014. This stayed constant from the previous number of 23.000 % for 2009. South Africa ZA: Population Living in Slums: % of Urban Population data is updated yearly, averaging 28.700 % from Dec 1990 (Median) to 2014, with 7 observations. The data reached an all-time high of 46.200 % in 1990 and a record low of 23.000 % in 2014. South Africa ZA: Population Living in Slums: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Population and Urbanization Statistics. Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, and durability of housing.; ; UN HABITAT, retrieved from the United Nation's Millennium Development Goals database. Data are available at : http://mdgs.un.org/; Weighted Average;

The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

Mozambique MZ: Population Living in Slums: % of Urban Population data was reported at 80.300 % in 2014. This records a decrease from the previous number of 80.500 % for 2009. Mozambique MZ: Population Living in Slums: % of Urban Population data is updated yearly, averaging 79.500 % from Dec 1990 (Median) to 2014, with 7 observations. The data reached an all-time high of 80.500 % in 2009 and a record low of 75.600 % in 1990. Mozambique MZ: Population Living in Slums: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mozambique – Table MZ.World Bank: Population and Urbanization Statistics. Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, and durability of housing.; ; UN HABITAT, retrieved from the United Nation's Millennium Development Goals database. Data are available at : http://mdgs.un.org/; Weighted average;

Myanmar MM: Population Living in Slums: % of Urban Population data was reported at 41.000 % in 2014. This records a decrease from the previous number of 45.600 % for 2005. Myanmar MM: Population Living in Slums: % of Urban Population data is updated yearly, averaging 43.300 % from Dec 2005 (Median) to 2014, with 2 observations. The data reached an all-time high of 45.600 % in 2005 and a record low of 41.000 % in 2014. Myanmar MM: Population Living in Slums: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Myanmar – Table MM.World Bank.WDI: Population and Urbanization Statistics. Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, and durability of housing.; ; UN HABITAT, retrieved from the United Nation's Millennium Development Goals database. Data are available at : http://mdgs.un.org/; Weighted average;