[show abstract][hide abstract] ABSTRACT: The potential for altered ecosystems and extreme weather events in the context of climate change has raised questions concerning the role that migration plays in either increasing or reducing risks to society. Using modeled data on net migration over three decades from 1970 to 2000, we identify sensitive ecosystems and regions at high risk of climate hazards that have seen high levels of net in-migration and out-migration over the time period. This paper provides a literature review on migration related to ecosystems, briefly describes the methodology used to develop the estimates of net migration, then uses those data to describe the patterns of net migration for various ecosystems and high risk regions. The study finds that negative net migration generally occurs over large areas, reflecting its largely rural character, whereas areas of positive net migration are typically smaller, reflecting its largely urban character. The countries with largest population such as China and India tend to drive global results for all the ecosystems found in those countries. Results suggest that from 1970 to 2000, migrants in developing countries have tended to move out of marginal dryland and mountain ecosystems and out of drought-prone areas, and have moved towards coastal ecosystems and areas that are prone to floods and cyclones. For North America results are reversed for dryland and mountain ecosystems, which saw large net influxes of population in the period of record. Uncertainties and potential sources of error in these estimates are addressed.
Environmental Research Letters 11/2012; · 3.58 Impact Factor
[show abstract][hide abstract] ABSTRACT: The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models. Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites. In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward. KEYWORDS: Population, epidemiology, demography, disease mapping.
Population Health Metrics 05/2012; 10(1):8. · 2.11 Impact Factor
[show abstract][hide abstract] ABSTRACT: Evaluating the total numbers of people at risk from infectious disease in the world requires not just tabular population data, but data that are spatially explicit and global in extent at a moderate resolution. This review describes the basic methods for constructing estimates of global population distribution with attention to recent advances in improving both spatial and temporal resolution. To evaluate the optimal resolution for the study of disease, the native resolution of the data inputs as well as that of the resulting outputs are discussed. Assumptions used to produce different population data sets are also described, with their implications for the study of infectious disease. Lastly, the application of these population data sets in studies to assess disease distribution and health impacts is reviewed. The data described in this review are distributed in the accompanying DVD.
Advances in Parasitology 02/2006; 62:119-56. · 3.78 Impact Factor
[show abstract][hide abstract] ABSTRACT: A database has been developed containing downscaled socio-economic scenarios of future population and GDP at country level and on a geo-referenced gridscale. It builds on the recent Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES), but has been created independently of that report. The SRES scenarios are derived from projected data on economic, demographic, technological and land-use changes for the 21st century in a highly aggregated form consisting of four world regions. Since analysts often need socio-economic data at higher spatial resolutions that are consistent with GCM climate scenarios, we undertook linear downscaling to 2100 of population and GDP to the country level of the aggregated SRES socio-economic data for four scenario families: A1, A2, B1, B2. Using these country-level data, we also generated geo-spatial grids at 1/4° resolution (∼30 km at the equator) for population “density” (people/unit land area) and for GDP “density” (GDP/unit land area) for two time slices, 1990 and 2025. This paper provides background information for the databases, including discussion of the data sources, downscaling methodology, data omissions, discrepancies with the SRES report, problems encountered, and areas needing further work.
[show abstract][hide abstract] ABSTRACT: This paper describes the development of a global georeferenced population database, including urban extents, aimed to quantify the spatial distribution of human population and the extent of urbanized land area. Project outputs include: i) database of human settlements of at least 5,000 persons (points), ii) urban extents (polygons) derived from satellite imagery and additional geographic data sources, and iii) urban-rural surface (grid) at a nominal resolution of 1 km.