scholarly journals Implications for Tracking SDG Indicator Metrics with Gridded Population Data

2021 ◽  
Vol 13 (13) ◽  
pp. 7329
Author(s):  
Cascade Tuholske ◽  
Andrea E. Gaughan ◽  
Alessandro Sorichetta ◽  
Alex de Sherbinin ◽  
Agathe Bucherie ◽  
...  
Keyword(s):  
Case Studies ◽  
Census Data ◽  
Flash Flood ◽  
Spatial Scales ◽  
Population Data ◽  
End Users ◽  
Population Estimates ◽  
Middle Income ◽  
Hazard Exposure ◽  
Flood Susceptibility

Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products.

scholarly journals How Does Administrative Data Compare with Census Data for the Measurement of Internal Migration? The Case of Northern Ireland, 2001–2011

2021 ◽  
Author(s):  
Brian Foley ◽  
Tony Champion ◽  
Ian Shuttleworth
Keyword(s):  
Administrative Data ◽  
Internal Migration ◽  
Census Data ◽  
Local Population ◽  
Population Data ◽  
Population Estimates ◽  
Demographic Groups ◽  
Small Areas ◽  
Intercensal Period ◽  
Statistical Agencies

AbstractThe paper compares and contrasts internal migration measured by healthcard-based administrative data with census figures. This is useful because the collection of population data, its processing, and its dissemination by statistical agencies is becoming more reliant on administrative data. Statistical agencies already use healthcard data to make migration estimates and are increasingly confident about local population estimates from administrative sources. This analysis goes further than this work as it assesses how far healthcard data can produce reliable data products of the kind to which academics are accustomed. It does this by examining migration events versus transitions over a full intercensal period; population flows into and out of small areas; and the extent to which it produces microdata on migration equivalent to that in the census. It is shown that for most demographic groups and places healthcard data is an adequate substitute for census-based migration counts, the exceptions being for student households and younger people. However, census-like information is still needed to provide covariates for analysis and this will still be required whatever the future of the traditional census.

Censuses of penguin, blue-eyed shag, and southern giant petrel populations in the Antarctic Peninsula region, 1994–2000

Polar Record ◽  
2000 ◽  
Vol 36 (199) ◽  
pp. 323-334 ◽  
Author(s):  
Ron Naveen ◽  
Steven C. Forrest ◽  
Rosemary G. Dagit ◽  
Louise K. Blight ◽  
Wayne Z. Trivelpiece ◽  
...  
Keyword(s):  
Antarctic Peninsula ◽  
Census Data ◽  
Population Data ◽  
Gentoo Penguin ◽  
Population Estimates ◽  
Giant Petrel ◽  
Southern Giant Petrel ◽  
The Antarctic ◽  
First Time ◽  
Pygoscelid Penguins

AbstractThis paper presents new census data and population estimates for penguins, blue-eyed shags, and southern giant petrels from 26 sites in the Antarctic Peninsula, collected by the Antarctic Site Inventory from 1994 to 2000. For nine sites, population data or estimates are published for the first time. The newly discovered gentoo penguin population of 215 nests at Herofna Island (63°24'S, 54°36'W) represents the easternmost location where this species has been found breeding in the Peninsula. All three pygoscelid penguins — gentoo, Adelie, and chinstrap — were found breeding at Gourdin Island (63° 12'S, 57° 18'W), the fourth known site where these species nest contiguously in the Peninsula. During the period, significant declines in nesting populations of blue-eyed shag were documented at three northwestern Peninsula locations.

scholarly journals Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya

2021 ◽  
Author(s):  
Dana R. Thomson ◽  
Andrea E. Gaughan ◽  
Forrest R. Stevens ◽  
Gregory Yetman ◽  
Peter Elias ◽  
...  
Keyword(s):  
Spatial Data ◽  
Urban Areas ◽  
Population Data ◽  
Population Estimates ◽  
Middle Income ◽  
Slum Dwellers ◽  
Port Harcourt ◽  
Modelling Assumptions ◽  
Population Counts ◽  
Promising Source

Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data in small grid squares (e.g., 100x100m) derived from demographic and spatial data are a promising source of current population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. The efficacy of using gridded population data in slum areas remains a question mark especially in the context of UN SDG indicator development. In this study, we use field-referenced boundaries and population counts from Slum Dwellers International (SDI) in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) to assess the accuracy of nine gridded population datasets in slums. We also use a modelled map of all slums in Lagos to assess use of gridded population dataset for SDG11.1.1 (percent of population living in deprived areas). We found that all gridded population estimates vastly under-estimated population counts in populous slums, and the calculation of SDG11.1.1 in Lagos was impossibly low; gridded population datasets estimated that just 1-3% of the Lagos population lived in slums, compared to 56% using the UN-Habitat approach. We outline specific steps that might be taken to improve each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG11.1.1, we are optimistic that some datasets could be following updates to their modelling approaches.

scholarly journals Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya

Urban Science ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 48
Author(s):  
Dana R. Thomson ◽  
Andrea E. Gaughan ◽  
Forrest R. Stevens ◽  
Gregory Yetman ◽  
Peter Elias ◽  
...  
Keyword(s):  
Spatial Data ◽  
Urban Areas ◽  
Population Data ◽  
Population Estimates ◽  
Middle Income ◽  
Slum Dwellers ◽  
Port Harcourt ◽  
Modelling Assumptions ◽  
Population Counts ◽  
Promising Source

Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. In this study, we compared field-referenced boundaries and population counts from Slum Dwellers International in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) with nine gridded population datasets to assess their statistical accuracy in slums. We found that all gridded population estimates vastly underestimated population in slums (RMSE: 4958 to 14,422, Bias: −2853 to −7638), with the most accurate dataset (HRSL) estimating just 39 per cent of slum residents. Using a modelled map of all slums in Lagos to compare gridded population datasets in terms of SDG 11.1.1 (percent of population living in deprived areas), all gridded population datasets estimated this indicator at just 1–3 per cent compared to 56 per cent using UN-Habitat’s approach. We outline steps that might improve that accuracy of each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG 11.1.1, we are optimistic that some could be used in the future following updates to their modelling approaches.

scholarly journals Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia

2021 ◽  
Author(s):  
Dana R. Thomson ◽  
Forrest R. Stevens ◽  
Robert Chen ◽  
Gregory Yetman ◽  
Alessandro Sorichetta ◽  
...  
Keyword(s):  
Rural Areas ◽  
Local Development ◽  
Grid Cell ◽  
Population Data ◽  
Training Data ◽  
Population Estimates ◽  
Top Down ◽  
Middle Income ◽  
True Value ◽  
Deprived Areas

People living in slums and other deprived areas in low- and middle-income country (LMIC) cities are under-represented in censuses, and subsequently in "top-down" gridded population estimates. Modelled gridded population data are a unique source of disaggregated population information to calculate local development indicators such as the Sustainable Development Goals (SDGs). This study evaluates if, and how, WorldPop-Global (WPG) -Unconstrained and -Constrained “top-down” datasets might be improved in a simulated realistic LMIC urban population by incorporating slum profile population counts into model training. We found that the WPG-Unconstrained model with or without slum training data grossly underestimated population in urban deprived areas while grossly overestimating population in rural areas. SDG 11.1.1, the percent of population living in slums, for example, was estimated to be 20% or less compared to a "true" value of 29.5%. The WPG-Constrained model, which included building auxiliary datasets, far more accurately estimated the population in all grid cells (including rural areas), and the inclusion of slum training data further improved estimates such that SDG 11.1.1 was estimated at 27.1% and 27.0%, respectively. Inclusion of building metrics and slum training data in “top-down” gridded population models can substantially improve grid cell-level accuracy in both urban and rural areas.

Rapid flash flood impact assessments at different spatial scales

2021 ◽  
Author(s):  
Josias Ritter ◽  
Marc Berenguer ◽  
Shinju Park ◽  
Daniel Sempere-Torres
Keyword(s):  
Flash Flood ◽  
Spatial Scales ◽  
End Users ◽  
Specific Information ◽  
Civil Protection ◽  
Emergency Responders ◽  
Flood Impacts ◽  
Spatial Domains ◽  
Flood Response ◽  
Flood Impact

<p>In September 2019, a weather phenomenon known in Spain as “DANA” brought rainfall accumulations of up to 452 mm in 48 h to the south-eastern part of Spain, triggering numerous flash floods and a severe fluvial flood in the Segura river. As a consequence, seven people died, over 5000 were evacuated, and the economic losses exceeded 2.2 billion Euros.</p><p>During such devastating events, early warning systems (EWSs) are a key element for the effective mitigation of impacts. They provide emergency responders (e.g. civil protection authorities) with essential information for the coordination of the flood response.</p><p>In Europe, emergency responders co-operate on different spatial scales: National and regional civil protection authorities collaborate in monitoring and applying specific actions, such as evacuations, road closures, or the installation of mobile flood barriers. For this task, they require location-specific information in high spatiotemporal resolution. At a larger scale, the Emergency Response Coordination Centre of the European Union (ERCC) monitors the entire continent for upcoming emergencies and supports the regional and national authorities with information and resources. Such international actors prefer order-of-magnitude statements over large spatial domains to make informed decisions. The different requirements of end-users operating at different spatial scales need to be taken into account for the development of EWSs.</p><p>Traditionally, flood EWSs are designed to predict the hazard component of the flood (e.g. in terms of river discharge). In recent years, however, a number of methods were developed that automatically translate the flood hazard into the corresponding socio-economic impacts (e.g. the number of people affected). Such impact-based EWSs enhance the decision support for the emergency responders and thus facilitate an effective flood response.</p><p>In this work, we analyse the DANA event of 2019 from the perspective of impact-based early warning. We present, validate, and compare rapid flash flood impact assessments from the following two methods:</p><p>Firstly, the ReAFFIRM method (Ritter et al., 2020) generating quantitative flash flood impact estimates in high resolution to support decisions at local and regional scales. Secondly, a newly developed method (named ReAFFINE) that qualitatively estimates flash flood impacts with pan-European coverage, as decision support for end-users operating over large spatial domains.</p><p>Simulation results for the DANA event show that the flash flood impact assessments from the pan-European method (ReAFFINE) correspond well to reported impacts and to the results from the regional method (ReAFFIRM) while providing more context-specific information for end-users operating at the international level.</p><p> </p><p>References:</p><p>Ritter, J., Berenguer, M., Corral, C., Park, S., Sempere-Torres, D., 2020. ReAFFIRM: Real-time Assessment of Flash Flood Impacts – a Regional high-resolution Method. Environ. Int. 136, 105375. https://doi.org/10.1016/j.envint.2019.105375</p>

scholarly journals Spatially disaggregated population estimates in the absence of national population and housing census data

2018 ◽  
Vol 115 (14) ◽  
pp. 3529-3537 ◽  
Author(s):  
N. A. Wardrop ◽  
W. C. Jochem ◽  
T. J. Bird ◽  
H. R. Chamberlain ◽  
D. Clarke ◽  
...  
Keyword(s):  
New Technologies ◽  
Census Data ◽  
Spatial Scales ◽  
Demographic Data ◽  
Cost Effective ◽  
Population Estimates ◽  
National Population ◽  
Population Distributions ◽  
Sustainable Implementation ◽  
Scale Population

Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. Here we review past and ongoing work aimed at producing spatially disaggregated local-scale population estimates, and discuss how new technologies are now enabling robust and cost-effective solutions. Recent advances in the availability of detailed satellite imagery, geopositioning tools for field surveys, statistical methods, and computational power are enabling the development and application of approaches that can estimate population distributions at fine spatial scales across entire countries in the absence of census data. We outline the potential of such approaches as well as their limitations, emphasizing the political and operational hurdles for acceptance and sustainable implementation of new approaches, and the continued importance of traditional sources of national statistical data.

scholarly journals Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Babak Khavari ◽  
Alexandros Korkovelos ◽  
Andreas Sahlberg ◽  
Mark Howells ◽  
Francesco Fuso Nerini
Keyword(s):  
World Bank ◽  
Population Distribution ◽  
Population Data ◽  
Sub Saharan Africa ◽  
Demographic And Health Surveys ◽  
Middle Income ◽  
Disease Response ◽  
The World Bank ◽  
The World ◽  
Urban Rural

AbstractHuman settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response.

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