Population And Settlement Geography Pdf WorksheetsPopulation Distribution, Settlement Patterns and Accessibility across Africa in 2. Catherine Linard,1,2. Marius Gilbert,2,3. Robert W. Snow,4,5. Abdisalan M. Noor,4 and Andrew J. Tatem. 6,7,8,*Guy J- P. Schumann, Editor. Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom. Biological Control and Spatial Ecology, Université Libre de Bruxelles, Brussels, Belgium. Population And Settlement Geography Pdf BooksWww.ocr.org.uk/gcse2012 GCSE 2012 Geography B Specification J385 Version 'FCSVBSZ 201 Geography. N.B. While population geography focuses on the impacts of population on spatial structures and processes, geodemography analyzes the effects of space on demographic structures and processes. However, the boundary between. Population Geography Essential Questions: 1. What is population geography vs. demography? 2. How (and why) do populations grow? 3. What demographic features are particularly relevant in the study of population? 4. How do we. Settlement patterns, population distribution and accessibility. A variety of measures were calculated based on the population distribution dataset in order to characterize the settlement patterns, the population. Fonds National de la Recherche Scientifique, Brussels, Belgium. Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI - University of Oxford - Wellcome Trust Research Programme, Nairobi, Kenya. Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom. Department of Geography, University of Florida, Gainesville, Florida, United States of America. Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America. Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America. University of Bristol, United Kingdom. Conceived and designed the experiments: CL MG RWS AJT. You can download individual PDF posters by right clicking on the images below or scroll down to download the whole set as one file. Q. Find these ten states on a map of Mexico [printable map of Mexico in pdf format]. Do the states with the most people also have the largest land areas? Objective: To describe the factors influencing the density and distribution of population Study the map to the right hand side carefully. It shows an alternative view of the world based on population distribution. The more. In the 1700's, expansion of tobacco farming into the Piedmont required more and more laborers. Virginia imported slaves, but also expanded its cultural mix by encouraging settlement by groups of white Protestants from. Popular Posts. Line 12 of Mexico City’s metro (subway) reopens; The geography of drug trafficking in Mexico; Mexico is a major net importer of “virtual” water; Mexico’s sixteenth century Geographic Accounts: the. Settlement geography is a branch of geography that investigates the earth's surface's part settled by humans. According to the United Nations' Vancouver Declaration on Human Settlements (1976), 'human settlements means the. Performed the experiments: CL. Analyzed the data: CL AJT. Contributed reagents/materials/analysis tools: CL RWS AMN AJT. Wrote the paper: CL AJT. Received 2. 01. 1 Oct 1. Accepted 2. 01. 2 Jan 1. Copyright Linard et al. This is an open- access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article has been cited by other articles in PMC. 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, 9. 0% of the population is concentrated in less than 2. 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 Afri. Pop project, providing an evidence base for guiding strategic decisions. Introduction. Geography plays a significant role in the development process [1]. Three spatial features influence the economic development of a region: the density (e. Improving access to people and markets is a key driver for development and plays an important role in poverty reduction. Development among rural populations depends on access to markets for buying and selling goods, to water and fuel, and to various social and economic services such as education, healthcare or banking and credit [2]–[4]. The lack of a reliable transport system forces rural populations to spend a significant amount of time in travelling to meet basic needs and increases the transport costs incurred to access these services [2]–[4]. These factors often mean that isolation is seen as the main contributor to poverty according to the poor people themselves [4]. The proximity of a major settlement provides business for isolated populations, and connectivity with international and regional markets creates economic opportunities. For example, agricultural productivity is highly correlated to the proximity to urban markets in sub- Saharan Africa [5]. Accessing populations efficiently is also of key importance in public health, for delivering equitable and complete healthcare [6], for planning vaccine campaigns or distributing resources. Improving the accessibility of remote populations is an important priority for many of the Millennium Development Goal (MDG) targets, such as those focussed on eradicating extreme poverty, achieving universal primary education and developing a global partnership for development [4]. The measurement of accessibility of populations and settlements is therefore of importance in measuring progress towards achieving these goals. Where transport infrastructures and access patterns are heterogeneous in space, population distributions and accessibility between populations should be estimated at levels of spatial detail that are similar or finer than the scales of this heterogeneity. Building development strategies and providing policy- relevant guidance therefore requires a detailed and accurate evidence base documenting the spatial distribution of populations, settlements and their interconnectivity. Existing spatial demographic datasets for the low income world, where access remains a substantial barrier to development, are generally based upon outdated and low resolution data, however [7]. Previous studies have developed accessibility indices based on household surveys [4] or more objective GIS- based approaches [8]. However, the lack of sufficiently detailed population and settlement distribution data, especially in Africa, impacts the availability and reliability of spatially- detailed accessibility information. Accurate and spatially detailed population distribution data can make accessibility measures considerably more valuable than can be obtained with aggregated population data. National census population data can be represented as continuous gridded population distribution datasets through the use of spatial interpolation algorithms. Differing projects and modelling techniques for the spatial reallocation of populations within census units have been developed in an attempt to overcome the difficulties caused by input census data of varying resolutions and provide standard global spatial datasets of population distribution. The most widely used of these are the Gridded Population of the World (GPW) [9], [1. Global Rural Urban Mapping Project (GRUMP) [1. Land. Scan [1. 2], and the United Nations Environment Programme (UNEP) Global Population Databases [1. For a comparison of these datasets, see Tatem et al. While these represent valuable resources for a range of applications that require gridded population data, their lack of spatial detail and information on rural settlements make them unsuitable for thorough analyses of accessibility and connectivity. Moreover, the age and low spatial resolution of much of the input census data, shown to be an important factor in the accuracy of population mapping [1. Here we develop and apply population- land cover relationship- based methods [1. Africa at a finer spatial resolution than ever before undertaken. Based on these datasets, we analyse spatial population and settlement patterns, as well as how they are connected by existing transport networks. Given the impact that isolation has on poverty of rural populations and on the economies and health of nations, such spatial demographic datasets are a valuable resource for both research and operational applications in the development of low income countries. Materials and Methods. Population distribution modelling. The methods used here to model population distribution in Africa are adapted from previous work undertaken for East Africa [1. The methods were modified for ease of replication and to facilitate the incorporation of new data. Full details on population distribution modelling methods are presented in Text S1. Tables summarizing country- level input data are available on the Afri. Pop website: www. Recent work showed that Glob. Cover was the global land cover dataset that, combined with detailed settlement extents, produced the most accurate population distribution data in an African context [1. The Glob. Cover dataset was modified to accommodate the more detailed settlement extents obtained from satellite imagery and geolocated points. The Glob. Cover dataset was first resampled to 1. The more detailed settlement extents were then overlaid onto the ‘urban class deprived’ land cover map and land covers beneath were replaced to produce a refined land cover map focussed on detailed and precise mapping of human settlements. Human population census data, official population size estimates and corresponding administrative unit boundaries at the highest level available from the most recent available censuses were acquired for each African country. High resolution census data were available for three countries in Africa: Ghana, Swaziland and Kenya. Kenya data were also available at enumeration area level (finer than level 5) for 5. Kenyan districts. Also obtained was a population density map of Namibia at 1 km spatial resolution (for details on the Namibia density map, see description on www. A table summarizing the spatial resolution, year and source of all data used is available on www. The modelling method distinguishes urban and rural populations in the redistribution of populations. Major settlements have population numbers already derived and validated and this makes up 3. African population. The remaining 6. 2% rural population was redistributed using land cover- based weightings. The refined land cover data and fine resolution population data from Ghana, Kenya, Namibia and Swaziland were used to define per land cover class population densities (i.
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