6,130 research outputs found
Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data
No full textHigh resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin Americ
Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa
Full text linkThe dataset contains input and output files for the following paper:
Yann Forget, Michal Shimoni, Marius Gilbert and Catherine Linard. "Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa". In Press. 2018.
The source code used to produce the output files is available on Github.</p
Mobile phone data for urban climate change adaptation: reviewing applications, opportunities and key challenges
Full text linkClimate change places cities at increasing risk and poses a serious challenge for adaptation. As a response, novel sources of data combined with data-driven logics and advanced spatial modelling techniques have the potential for transformative change in the role of information in urban planning. However, little practical guidance exists on the potential opportunities offered by mobile phone data for enhancing adaptive capacities in urban areas. Building upon a review of spatial studies mobilizing mobile phone data, this paper explores the opportunities offered by such digital information for providing spatially-explicit assessments of urban vulnerability, and shows the ways these can help developing more dynamic strategies and tools for urban planning and disaster risk management. Finally, building upon the limitations of mobile phone data analysis, it discusses the key urban governance challenges that need to be addressed for supporting the emergence of transformative change in current planning frameworks.</p
Hamilton, Catherine Jane [pseud. Retlaw Spring] (1841–1935), author and journalist
Full text linkHamilton, Catherine Jane [pseud. Retlaw Spring] (1841-1935), author and journalist, was born on 25 January 1841 at Kilmersdon, Somerset, where she was baptized on 12 April 1841, the younger of two daughters of Richard Hamilton (1805?-1859), vicar of Kilmersdon, and his wife Charlotte, née Cooper (1809-1882), the fifth daughter of William Cooper, of Queens County, Ireland. She was of Irish heritage on both sides. Her father belonged to a military family with roots in Strabane (county Tyrone) - his father, John Hamilton, and her father’s four older brothers were all officers in the Fifth Foot – and was a graduate of Trinity College Dublin. He had been a bright scholar with an aptitude for languages, and as a preacher was praised for his powerful sermons and his ability to bring the Bible to life for his parishioners
Modelling spatial patterns of urban growth in Africa
No full textAbstractThe population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5â10Â km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1Â km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.info:eu-repo/semantics/publishe
Dakar population estimates at 100x100m spatial resolution - grid layer - Dasymetric mapping
No full textThis dataset contains the a raster layer with the population estimates obtained using a dasymetric mapping procedure (top-down approach). For a detailed description of the methodology, please refer to the following paper:
Grippa, Taïs, Catherine Linard, Moritz Lennert, Stefanos Georganos, Nicholus Mboga, Sabine Vanhuysse, Assane Gadiaga, and Eléonore Wolff. 2019. “Improving Urban Population Distribution Models with Very-High Resolution Satellite Information.” Data 4 (1): 13. https://doi.org/10.3390/data4010013.
Funding and aknowledgement:
This dataset was produced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be) and REACT (http://react.ulb.be), funded by the Belgian Federal Science Policy Office (BELSPO).
The authors gratefully thanks the \href{http://assess-sn.org/}{ASSESS project}, funded by the \href{https://www.ares-ac.be}{ARES-CDD}, that provided the access to the census data.</p
Large-scale spatial population databases in infectious disease research
Full text linkinfo:eu-repo/semantics/publishe
Assessing the use of global land cover data for guiding large area population distribution modelling
Full text linkGridded population distribution data are finding increasing use in a wide range of fields, including resource allocation, disease burden estimation and climate change impact assessment. Land cover information can be used in combination with detailed settlement extents to redistribute aggregated census counts to improve the accuracy of national-scale gridded population data. In East Africa, such analyses have been done using regional land cover data, thus restricting application of the approach to this region. If gridded population data are to be improved across Africa, an alternative, consistent and comparable source of land cover data is required. Here these analyses were repeated for Kenya using four continent-wide land cover datasets combined with detailed settlement extents and accuracies were assessed against detailed census data. The aim was to identify the large area land cover dataset that, combined with detailed settlement extents, produce the most accurate population distribution data. The effectiveness of the population distribution modelling procedures in the absence of high resolution census data was evaluated, as was the extrapolation ability of population densities between different regions. Results showed that the use of the GlobCover dataset refined with detailed settlement extents provided significantly more accurate gridded population data compared to the use of refined AVHRR-derived, MODIS-derived and GLC2000 land cover datasets. This study supports the hypothesis that land cover information is important for improving population distribution model accuracies, particularly in countries where only coarse resolution census data are available. Obtaining high resolution census data must however remain the priority. With its higher spatial resolution and its more recent data acquisition, the GlobCover dataset was found as the most valuable resource to use in combination with detailed settlement extents for the production of gridded population datasets across large areas. © 2010 The Author(s).SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Publisher Correction: Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus (Nature Microbiology, (2019), 4, 5, (854-863), 10.1038/s41564-019-0376-y)
No full textIn the version of this Article originally published, the affiliation for author Catherine Linard was incorrectly stated as ‘6Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK’. The correct affiliation is ‘9Spatial Epidemiology Lab (SpELL), Universite Libre de Bruxelles, Brussels, Belgium’. The affiliation for author Hongjie Yu was also incorrectly stated as ‘11Department of Statistics, Harvard University, Cambridge, MA, USA’. The correct affiliation is ‘15School of Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China’. This has now been amended in all versions of the Article.</p
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