Webster, R., Oliver, M.A., Muir, K.R. and Mann, J.R. (1994). Kriging the local risk of a rare disease from a register of diagnoses. Geographical Analysis 26, 168–185. Woodcock, C.E. and Strahler, A.H. (1987). The factor of scale in remote- sensing. Remote Sensing of Environment 21, 311–332. Zhan, Q., Molenaar, M. and Lucieer, A. (2002). Pixel unmixing at the sub- pixel scale based on land cover class probabilities: application to urban areas. In: Uncertainty in Remote Sensing and GIS (G.M. Foody and P.M. Atkinson, eds), pp. 59–76. Wiley: Chichester. P.M. ATKINSON AND A.J. GRAHAM118 Determining Global Population Distribution: Methods, Applications and Data D.L. Balk 1 , U. Deichmann 2 , G. Yetman 1 , F. Pozzi 1,3 , S.I. Hay 4,5 and A. Nelson 6,7 1 Center for International Earth Science Information Network (CIESIN), Columbia University, PO Box 1000, Palisades, NY 10964, USA 2 Development Research Group, World Bank, 1818 H Street, Washington, DC 20433, USA 3 Universita ` Cattolica del Sacro Cuore, sede di Brescia, via dei Musei 41, 25121, Brescia, Italy 4 TALA Research Group, Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK 5 Malaria Public Health & Epidemiology Group, Centre for Geographic Medicine, KEMRI, PO Box 43640, 00100 Nairobi, Kenya 6 School of Geography, University of Leeds, Woodhouse Lane, West Yorkshire, Leeds LS2 9JT, UK 7 JRC (Joint Research Centre of the European Commission), Global Environment Monitoring Unit, TP 440, Via Enrico Fermi 1, I-21020 Ispra (VA), Italy Abstract 120 1. Introduction . . 120 1.1. Rendering Popul ation on a Global Grid . 121 1.2. Institutional Stewardship 122 2. Data 124 2.1. Gridded Popul ation of the World 127 2.2. Global Rural Urban Mapping Project . . . 128 2.3. Accessibility Modeling 130 2.4. Highly Modeled Surfaces . . . 132 3. Methodology . 133 3.1. Adjusting Population Estimates to Target Years . . 133 3.2. Limitations of the Ancillary Data 136 ADVANCES IN PARASITOLOGY VOL 62 ISSN: 0065-308X $35.00 DOI: 10.1016/S0065-308X(05)62004-0 Copyright r 2006 Elsevier Ltd. All rights of reproduction in any form reserved Table 1 Comparison of GPW versions and related databases Data set GPW Accessibility model GRUMP v1 GPW v1 GPW v2 GPW v3 GPW 2015 Publication year 1995 2000 2004 2004 2004 2004 Years of estimation 1994 1990, 1995 1990, 1995, 2000 2015 1960–2000 1990, 1995, 2000 Number of input units 19 000 127 000 376 500 376 500 Varies by continent c. 1 000 000 Modeled inputs None None None None Infrastructure, urban areas Urban areas Spatial extent Global Global Global Global Africa, Asia, Latin America Global Authors Tobler et al. CIESIN, IFPRI, and WRI CIESIN & CIAT CIESIN, FAO, and CIAT Deichmann; WRI; CIAT, UNEP and CIESIN CIESIN, IFPRI, World Bank, and CIAT Gridded surface resolution a 5 0 2.5 0 2.5 0 2.5 0 2.5 0 30 00 Population density  Population counts  Land area  Population-weighted admin. units   Urban extent mask  Settlement points (xls, csv, shp formats)  Note: A dot indicates the data set is publicly available. 0 and 00 represent arc-minutes and arc-seconds, respectively. a Gridded surfaces are available in these formats: 00, bil, ascii. DETERMINING GLOBAL POPULATION DISTRIBUTION 125 Plate 4.3 Process by which GRUMP population surface is constructed, illus- trated for southern Ghana. Panel 1 shows inputs side by side with their population counts. Panel 1A is identical to the inputs to GPW, panel 1B shows the additional urban areas used in GRUMP. In panel 2, the inputs are merged, first illustrated as an overlay of the urban footprints over the administrative polygons in panel 2A, and the final grid, in panel 2B (with administrative and urban) boundaries overlaid (density/ square km). Defining the Global Spatial Limits of Malaria Transmission in 2005 C.A. Guerra 1 , R.W. Snow 2,3 and S.I. Hay 1,2 1 TALA Research Group, Tinbergen Building, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK 2 Malaria Public Health & Epidemiology Group, Centre for Geographic Medicine, KEMRI/Wellcome Trust Research Laboratories, PO Box 43640, 00100 Nairobi, Kenya 3 Centre for Tropical Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK Abstract 157 1. Introduction . . 158 2. The Distribution of Malaria Risk from Travel Guidelines 159 3. The Biological Limits of Transmissio n 161 3.1. Altitudinal Mask . . 161 3.2. Climate Suitability Mask 167 3.3. Population Density Mask . . . 167 4. Distinguishing P. falciparum and P. vivax risk. 169 5. Regional Analysis 169 6. Discussion. . . 173 7. Conclusions. . 174 Acknowledgements . . . 175 References . . 175 ABSTRACT There is no accurate contemporary global map of the distribution of malaria. We show how guidelines formulated to advise travellers on appropriate chemoprophylaxis for areas of reported Plasmodium falciparum and Plasmodium vivax malaria risk can be used to generate ADVANCES IN PARASITOLOGY VOL 62 ISSN: 0065-308X $35.00 DOI: 10.1016/S0065-308X(05)62005-2 Copyright r 2006 Elsevier Ltd. All rights of reproduction in any form reserved . Commission), Global Environment Monitoring Unit, TP 44 0, Via Enrico Fermi 1, I-21020 Ispra (VA), Italy Abstract 120 1. Introduction . . 120 1.1. Rendering Popul ation on a Global Grid . 121 1.2. Institutional. Adjusting Population Estimates to Target Years . . 133 3.2. Limitations of the Ancillary Data 136 ADVANCES IN PARASITOLOGY VOL 62 ISSN: 006 5-3 08X $35.00 DOI: 10.1016/S006 5-3 08X(05)6200 4- 0 Copyright. 1995 2000 20 04 20 04 20 04 20 04 Years of estimation 19 94 1990, 1995 1990, 1995, 2000 2015 1960–2000 1990, 1995, 2000 Number of input units 19 000 127 000 376 500 376 500 Varies by continent c. 1