Dataset Information

Population distribution, settlement patterns and accessibility across Africa in 2010.

ABSTRACT: The spatial distribution of populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure population concentration and accessibility. Here we outline approaches to developing a new high resolution population distribution dataset for Africa and analyse rural accessibility to population centers. Contemporary population count data were combined with detailed satellite-derived settlement extents to map population distributions across Africa at a finer spatial resolution than ever before. Substantial heterogeneity in settlement patterns, population concentration and spatial accessibility to major population centres is exhibited across the continent. In Africa, 90% of the population is concentrated in less than 21% of the land surface and the average per-person travel time to settlements of more than 50,000 inhabitants is around 3.5 hours, with Central and East Africa displaying the longest average travel times. The analyses highlight large inequities in access, the isolation of many rural populations and the challenges that exist between countries and regions in providing access to services. The datasets presented are freely available as part of the AfriPop project, providing an evidence base for guiding strategic decisions.

SUBMITTER: Linard C

PROVIDER: S-EPMC3283664 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Population distribution, settlement patterns and accessibility across Africa in 2010.

Linard Catherine C Gilbert Marius M Snow Robert W RW Noor Abdisalan M AM Tatem Andrew J AJ

PloS one 20120221 2

The spatial distribution of populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure population concentration and access ...[more]

PMID: 22363717

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Project description:BackgroundThe efficient allocation of financial resources for malaria control and the optimal distribution of appropriate interventions require accurate information on the geographic distribution of malaria risk and of the human populations it affects. Low population densities in rural areas and high population densities in urban areas can influence malaria transmission substantially. Here, the Malaria Atlas Project (MAP) global database of Plasmodium falciparum parasite rate (PfPR) surveys, medical intelligence and contemporary population surfaces are utilized to explore these relationships and other issues involved in combining malaria risk maps with those of human population distribution in order to define populations at risk more accurately.MethodsFirst, an existing population surface was examined to determine if it was sufficiently detailed to be used reliably as a mask to identify areas of very low and very high population density as malaria free regions. Second, the potential of international travel and health guidelines (ITHGs) for identifying malaria free cities was examined. Third, the differences in PfPR values between surveys conducted in author-defined rural and urban areas were examined. Fourth, the ability of various global urban extent maps to reliably discriminate these author-based classifications of urban and rural in the PfPR database was investigated. Finally, the urban map that most accurately replicated the author-based classifications was analysed to examine the effects of urban classifications on PfPR values across the entire MAP database.ResultsMasks of zero population density excluded many non-zero PfPR surveys, indicating that the population surface was not detailed enough to define areas of zero transmission resulting from low population densities. In contrast, the ITHGs enabled the identification and mapping of 53 malaria free urban areas within endemic countries. Comparison of PfPR survey results showed significant differences between author-defined 'urban' and 'rural' designations in Africa, but not for the remainder of the malaria endemic world. The Global Rural Urban Mapping Project (GRUMP) urban extent mask proved most accurate for mapping these author-defined rural and urban locations, and further sub-divisions of urban extents into urban and peri-urban classes enabled the effects of high population densities on malaria transmission to be mapped and quantified.ConclusionThe availability of detailed, contemporary census and urban extent data for the construction of coherent and accurate global spatial population databases is often poor. These known sources of uncertainty in population surfaces and urban maps have the potential to be incorporated into future malaria burden estimates. Currently, insufficient spatial information exists globally to identify areas accurately where population density is low enough to impact upon transmission. Medical intelligence does however exist to reliably identify malaria free cities. Moreover, in Africa, urban areas that have a significant effect on malaria transmission can be mapped.

Project description:ObjectivesSpecies Distribution Models (SDMs) are used to produce predictions of potential Leguminosae diversity in West Central Africa. Those predictions are evaluated subsequently using expert opinion. The established methodology of combining all SDMs is refined to assess species diversity within five defined vegetation types. Potential species diversity is thus predicted for each vegetation type respectively. The primary aim of the new methodology is to define, in more detail, areas of species richness for conservation planning.MethodologyUsing Maxent, SDMs based on a suite of 14 environmental predictors were generated for 185 West Central African Leguminosae species, each categorised according to one of five vegetation types: Afromontane, coastal, non-flooded forest, open formations, or riverine forest. The relative contribution of each environmental variable was compared between different vegetation types using a nonparametric Kruskal-Wallis analysis followed by a post-hoc Kruskal-Wallis Paired Comparison contrast. Legume species diversity patterns were explored initially using the typical method of stacking all SDMs. Subsequently, five different ensemble models were generated by partitioning SDMs according to vegetation category. Ecological modelers worked with legume specialists to improve data integrity and integrate expert opinion in the interpretation of individual species models and potential species richness predictions for different vegetation types.Results/conclusionsOf the 14 environmental predictors used, five showed no difference in their relative contribution to the different vegetation models. Of the nine discriminating variables, the majority were related to temperature variation. The set of variables that played a major role in the Afromontane species diversity model differed significantly from the sets of variables of greatest relative important in other vegetation categories. The traditional approach of stacking all SDMs indicated overall centers of diversity in the region but the maps indicating potential species richness by vegetation type offered more detailed information on which conservation efforts can be focused.

Project description:BackgroundChagas disease is one of the most important neglected tropical diseases in the Americas. Vectorborne transmission of Chagas disease has been historically rare in urban settings. However, in marginal communities near the city of Arequipa, Peru, urban transmission cycles have become established. We examined the history of migration and settlement patterns in these communities, and their connections to Chagas disease transmission.Methodology/principal findingsThis was a qualitative study that employed focus group discussions and in-depth interviews. Five focus groups and 50 in-depth interviews were carried out with 94 community members from three shantytowns and two traditional towns near Arequipa, Peru. Focus groups utilized participatory methodologies to explore the community's mobility patterns and the historical and current presence of triatomine vectors. In-depth interviews based on event history calendars explored participants' migration patterns and experience with Chagas disease and vectors. Focus group data were analyzed using participatory analysis methodologies, and interview data were coded and analyzed using a grounded theory approach. Entomologic data were provided by an ongoing vector control campaign. We found that migrants to shantytowns in Arequipa were unlikely to have brought triatomines to the city upon arrival. Frequent seasonal moves, however, took shantytown residents to valleys surrounding Arequipa where vectors are prevalent. In addition, the pattern of settlement of shantytowns and the practice of raising domestic animals by residents creates a favorable environment for vector proliferation and dispersal. Finally, we uncovered a phenomenon of population loss and replacement by low-income migrants in one traditional town, which created the human settlement pattern of a new shantytown within this traditional community.Conclusions/significanceThe pattern of human migration is therefore an important underlying determinant of Chagas disease risk in and around Arequipa. Frequent seasonal migration by residents of peri-urban shantytowns provides a path of entry of vectors into these communities. Changing demographic dynamics of traditional towns are also leading to favorable conditions for Chagas disease transmission. Control programs must include surveillance for infestation in communities assumed to be free of vectors.

Dataset Information

Population distribution, settlement patterns and accessibility across Africa in 2010.

Publications

Population distribution, settlement patterns and accessibility across Africa in 2010.

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