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Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.
Corrine W. Ruktanonchai; Shengjie Lai; Chigozie E. Utazi; Alex D. Cunningham; Patrycja Koper; Grant E. Rogers; Nick W. Ruktanonchai; Adam Sadilek; Dorothea Woods; Andrew J. Tatem; Jessica E. Steele; Alessandro Sorichetta. Practical geospatial and sociodemographic predictors of human mobility. Scientific Reports 2021, 11, 1 -14.
AMA StyleCorrine W. Ruktanonchai, Shengjie Lai, Chigozie E. Utazi, Alex D. Cunningham, Patrycja Koper, Grant E. Rogers, Nick W. Ruktanonchai, Adam Sadilek, Dorothea Woods, Andrew J. Tatem, Jessica E. Steele, Alessandro Sorichetta. Practical geospatial and sociodemographic predictors of human mobility. Scientific Reports. 2021; 11 (1):1-14.
Chicago/Turabian StyleCorrine W. Ruktanonchai; Shengjie Lai; Chigozie E. Utazi; Alex D. Cunningham; Patrycja Koper; Grant E. Rogers; Nick W. Ruktanonchai; Adam Sadilek; Dorothea Woods; Andrew J. Tatem; Jessica E. Steele; Alessandro Sorichetta. 2021. "Practical geospatial and sociodemographic predictors of human mobility." Scientific Reports 11, no. 1: 1-14.
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.
Cascade Tuholske; Andrea Gaughan; Alessandro Sorichetta; Alex de Sherbinin; Agathe Bucherie; Carolynne Hultquist; Forrest Stevens; Andrew Kruczkiewicz; Charles Huyck; Greg Yetman. Implications for Tracking SDG Indicator Metrics with Gridded Population Data. Sustainability 2021, 13, 7329 .
AMA StyleCascade Tuholske, Andrea Gaughan, Alessandro Sorichetta, Alex de Sherbinin, Agathe Bucherie, Carolynne Hultquist, Forrest Stevens, Andrew Kruczkiewicz, Charles Huyck, Greg Yetman. Implications for Tracking SDG Indicator Metrics with Gridded Population Data. Sustainability. 2021; 13 (13):7329.
Chicago/Turabian StyleCascade Tuholske; Andrea Gaughan; Alessandro Sorichetta; Alex de Sherbinin; Agathe Bucherie; Carolynne Hultquist; Forrest Stevens; Andrew Kruczkiewicz; Charles Huyck; Greg Yetman. 2021. "Implications for Tracking SDG Indicator Metrics with Gridded Population Data." Sustainability 13, no. 13: 7329.
M. Santini; A. Taramelli; A. Sorichetta. Author correction: ASPHAA: A GIS‐based algorithm to calculate cell area on a latitude–longitude (geographic) regular grid. Transactions in GIS 2021, 25, 1646 -1647.
AMA StyleM. Santini, A. Taramelli, A. Sorichetta. Author correction: ASPHAA: A GIS‐based algorithm to calculate cell area on a latitude–longitude (geographic) regular grid. Transactions in GIS. 2021; 25 (3):1646-1647.
Chicago/Turabian StyleM. Santini; A. Taramelli; A. Sorichetta. 2021. "Author correction: ASPHAA: A GIS‐based algorithm to calculate cell area on a latitude–longitude (geographic) regular grid." Transactions in GIS 25, no. 3: 1646-1647.
Summary Background Understanding subnational variation in age-specific fertility rates (ASFRs) and total fertility rates (TFRs), and geographical clustering of high fertility and its determinants in low-income and middle-income countries, is increasingly needed for geographical targeting and prioritising of policy. We aimed to identify variation in fertility rates, to describe patterns of key selected fertility determinants in areas of high fertility. Methods We did a subnational analysis of ASFRs and TFRs from the most recent publicly available and nationally representative cross-sectional Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016 for 70 low-income, lower-middle-income, and upper-middle-income countries, across 932 administrative units. We assessed the degree of global spatial autocorrelation by using Moran's I statistic and did a spatial cluster analysis using the Getis-Ord Gi* local statistic to examine the geographical clustering of fertility and key selected fertility determinants. Descriptive analysis was used to investigate the distribution of ASFRs and of selected determinants in each cluster. Findings TFR varied from below replacement (2·1 children per women) in 36 of the 932 subnational regions (mainly located in India, Myanmar, Colombia, and Armenia), to rates of 8 and higher in 14 subnational regions, located in sub-Saharan Africa and Afghanistan. Areas with high-fertility clusters were mostly associated with areas of low prevalence of women with secondary or higher education, low use of contraception, and high unmet needs for family planning, although exceptions existed. Interpretation Substantial within-country variation in the distribution of fertility rates highlights the need for tailored programmes and strategies in high-fertility cluster areas to increase the use of contraception and access to secondary education, and to reduce unmet need for family planning. Funding Wellcome Trust, the UK Foreign, Commonwealth and Development Office, and the Bill & Melinda Gates Foundation.
Carla Pezzulo; Kristine Nilsen; Alessandra Carioli; Natalia Tejedor-Garavito; Sophie E Hanspal; Theodor Hilber; William H M James; Corrine W Ruktanonchai; Victor Alegana; Alessandro Sorichetta; Adelle S Wigley; Graeme M Hornby; Zoe Matthews; Andrew J Tatem. Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010–16: a subnational analysis of cross-sectional surveys. The Lancet Global Health 2021, 9, e802 -e812.
AMA StyleCarla Pezzulo, Kristine Nilsen, Alessandra Carioli, Natalia Tejedor-Garavito, Sophie E Hanspal, Theodor Hilber, William H M James, Corrine W Ruktanonchai, Victor Alegana, Alessandro Sorichetta, Adelle S Wigley, Graeme M Hornby, Zoe Matthews, Andrew J Tatem. Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010–16: a subnational analysis of cross-sectional surveys. The Lancet Global Health. 2021; 9 (6):e802-e812.
Chicago/Turabian StyleCarla Pezzulo; Kristine Nilsen; Alessandra Carioli; Natalia Tejedor-Garavito; Sophie E Hanspal; Theodor Hilber; William H M James; Corrine W Ruktanonchai; Victor Alegana; Alessandro Sorichetta; Adelle S Wigley; Graeme M Hornby; Zoe Matthews; Andrew J Tatem. 2021. "Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010–16: a subnational analysis of cross-sectional surveys." The Lancet Global Health 9, no. 6: e802-e812.
The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa—the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10 m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets.
Daniela Palacios-Lopez; Felix Bachofer; Thomas Esch; Mattia Marconcini; Kytt MacManus; Alessandro Sorichetta; Julian Zeidler; Stefan Dech; Andrew Tatem; Peter Reinartz. High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent. Remote Sensing 2021, 13, 1142 .
AMA StyleDaniela Palacios-Lopez, Felix Bachofer, Thomas Esch, Mattia Marconcini, Kytt MacManus, Alessandro Sorichetta, Julian Zeidler, Stefan Dech, Andrew Tatem, Peter Reinartz. High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent. Remote Sensing. 2021; 13 (6):1142.
Chicago/Turabian StyleDaniela Palacios-Lopez; Felix Bachofer; Thomas Esch; Mattia Marconcini; Kytt MacManus; Alessandro Sorichetta; Julian Zeidler; Stefan Dech; Andrew Tatem; Peter Reinartz. 2021. "High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent." Remote Sensing 13, no. 6: 1142.
Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time-series of past extents and urban growth/expansion models have not had parallel advances at high-spatial resolution. Here our goal was to present a globally applicable predictive modelling framework, as informed by a short, preceding time-series of built-settlement extents, capable of producing annual, near-future built-settlement extents. To do so, we integrated a random forest, dasymetric redistribution, and autoregressive temporal models with open and globally available subnational data, estimates of built-settlement population, and environmental covariates. Using this approach, we trained the model on a 11 year time-series (2000–2010) of European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover “Urban Areas” class and predicted annual, 100m resolution, binary settlement extents five years beyond the last observations (2011–2015) within varying environmental, urban morphological, and data quality contexts. We found that our model framework performed consistently across all sampled countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and the withheld validation settlement extents. When comparing manually delineated building footprints of small settlements to the modelled extents, we saw that the modelling framework had a 12 percent increase in accuracy compared to withheld validation settlement extents. However, how this framework performs when using different input definitions of “urban” or settlement remains unknown. While this model framework is predictive and not explanatory in nature, it shows that globally available “off-the-shelf” datasets and relative changes in subnational population can be sufficient for accurate prediction of future settlement expansion. Further, this framework shows promise for predicting near-future settlement extents and provides a foundation for forecasts further into the future.
Jeremiah J. Nieves; Maksym Bondarenko; Alessandro Sorichetta; Jessica E. Steele; David Kerr; Alessandra Carioli; Forrest R. Stevens; Andrea E. Gaughan; Andrew J. Tatem. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sensing 2020, 12, 1545 .
AMA StyleJeremiah J. Nieves, Maksym Bondarenko, Alessandro Sorichetta, Jessica E. Steele, David Kerr, Alessandra Carioli, Forrest R. Stevens, Andrea E. Gaughan, Andrew J. Tatem. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sensing. 2020; 12 (10):1545.
Chicago/Turabian StyleJeremiah J. Nieves; Maksym Bondarenko; Alessandro Sorichetta; Jessica E. Steele; David Kerr; Alessandra Carioli; Forrest R. Stevens; Andrea E. Gaughan; Andrew J. Tatem. 2020. "Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations." Remote Sensing 12, no. 10: 1545.
Human mobility, both short and long term, are important considerations in the study of numerous systems. Economic and technological advances have led to a more interconnected global community, further increasing the need for considerations of human mobility. While data on human mobility are better recorded in many developed countries, availability of such data remains limited in many low- and middle-income countries around the world, particularly at the fine temporal and spatial scales required by many applications. In this study, we used 5-year census-based internal migration microdata for 32 departments in Colombia (i.e., Admin-1 level) to develop a novel spatial interaction modeling approach for estimating migration, at a finer spatial scale, among the 1,122 municipalities in the country (i.e., Admin-2 level). Our modeling approach addresses a significant lack of migration data at administrative unit levels finer than those at which migration data are typically recorded. Due to the widespread availability of census-based migration microdata at the Admin-1 level, our modeling approach opens up for the possibilities of modeling migration patterns at Admin-2 and Admin-3 levels across many other countries where such data are currently lacking.
Amir S. Siraj; Alessandro Sorichetta; Guido España; Andrew J. Tatem; T. Alex Perkins. Modeling human migration across spatial scales in Colombia. PLOS ONE 2020, 15, e0232702 .
AMA StyleAmir S. Siraj, Alessandro Sorichetta, Guido España, Andrew J. Tatem, T. Alex Perkins. Modeling human migration across spatial scales in Colombia. PLOS ONE. 2020; 15 (5):e0232702.
Chicago/Turabian StyleAmir S. Siraj; Alessandro Sorichetta; Guido España; Andrew J. Tatem; T. Alex Perkins. 2020. "Modeling human migration across spatial scales in Colombia." PLOS ONE 15, no. 5: e0232702.
Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet’s limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code to enable users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate these data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems.
Sebastian Dunnett; Alessandro Sorichetta; Gail Taylor; Felix Eigenbrod. Harmonised global datasets of wind and solar farm locations and power. Scientific Data 2020, 7, 1 -12.
AMA StyleSebastian Dunnett, Alessandro Sorichetta, Gail Taylor, Felix Eigenbrod. Harmonised global datasets of wind and solar farm locations and power. Scientific Data. 2020; 7 (1):1-12.
Chicago/Turabian StyleSebastian Dunnett; Alessandro Sorichetta; Gail Taylor; Felix Eigenbrod. 2020. "Harmonised global datasets of wind and solar farm locations and power." Scientific Data 7, no. 1: 1-12.
Mapping urban features/human built-settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban features/human built-settlement datasets have become available, issues still exist in remotely-sensed imagery due to spatial and temporal coverage, adverse atmospheric conditions, and expenses involved in producing such datasets. Remotely-sensed annual time-series of urban/built-settlement extents therefore do not yet exist and cover more than specific local areas or city-based regions. Moreover, while a few high-resolution global datasets of urban/built-settlement extents exist for key years, the observed date often deviates many years from the assigned one. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we describe an interpolative and flexible modelling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modelling with open source subnational data to produce annual 100 m × 100 m resolution binary built-settlement datasets in four test countries located in varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85 and 99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to built-settlement in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban features/built-settlement datasets derived from remotely-sensed imagery, provides a base upon which to create urban future/built-settlement extent projections, and enables further exploration of the relationships between urban/built-settlement area and population dynamics.
Jj Nieves; A Sorichetta; C Linard; Je Steele; Fr Stevens; Ae Gaughan; A Carioli; Dj Clarke; T Esch; Aj Tatem. Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night. Computers, Environment and Urban Systems 2020, 80 .
AMA StyleJj Nieves, A Sorichetta, C Linard, Je Steele, Fr Stevens, Ae Gaughan, A Carioli, Dj Clarke, T Esch, Aj Tatem. Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night. Computers, Environment and Urban Systems. 2020; 80 ():.
Chicago/Turabian StyleJj Nieves; A Sorichetta; C Linard; Je Steele; Fr Stevens; Ae Gaughan; A Carioli; Dj Clarke; T Esch; Aj Tatem. 2020. "Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night." Computers, Environment and Urban Systems 80, no. : .
The rapid economic growth, the exodus from rural to urban areas, and the associated extreme urban development that occurred in China in the decade of the 2000s have severely impacted the environment in Beijing, its vicinity, and beyond. This article presents an innovative approach for assessing mega-urban changes and their impact on the environment based on the use of decadal QuikSCAT (QSCAT) satellite data, acquired globally by the SeaWinds scatterometer over that period. The Dense Sampling Method (DSM) is applied to QSCAT data to obtain reliable annual infrastructure-based urban observations at a posting of ~1 km. The DSM-QSCAT data, along with different DSM-based change indices, were used to delineate the extent of the Beijing infrastructure-based urban area in each year between 2000 and 2009, and assess its development over time, enabling a physical quantification of its urbanization which reflects the implementation of various development policies during the same time period. Eventually, as a proxy for the impact of Beijing urbanization on the environment, the decadal trend of its infrastructure-based urbanization is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) column densities as observed from the Global Ozone Monitoring Experiment (GOME) instrument aboard the second European Remote Sensing satellite (ERS-2) between 2000 and 2002, and from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY aboard of the ESA’s ENVIronmental SATellite (SCIAMACHY /ENVISAT) between 2003 and 2009. Results reveal a threefold increase of the yearly tropospheric NO2 column density within the Beijing infrastructure-based urban area extent in 2009, which had quadrupled since 2000.
Alessandro Sorichetta; Son V. Nghiem; Marco Masetti; Catherine Linard; Andreas Richter. Transformative Urban Changes of Beijing in the Decade of the 2000s. Remote Sensing 2020, 12, 652 .
AMA StyleAlessandro Sorichetta, Son V. Nghiem, Marco Masetti, Catherine Linard, Andreas Richter. Transformative Urban Changes of Beijing in the Decade of the 2000s. Remote Sensing. 2020; 12 (4):652.
Chicago/Turabian StyleAlessandro Sorichetta; Son V. Nghiem; Marco Masetti; Catherine Linard; Andreas Richter. 2020. "Transformative Urban Changes of Beijing in the Decade of the 2000s." Remote Sensing 12, no. 4: 652.
Advances in the availability of multitemporal and global built-/human-settlements datasets as derived from Remote Sensing (RS) can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time-series of past extents and urban growth/expansion models have not had parallel advances at high-spatial resolution. We present a flexible modelling framework for producing annual built-settlement extents in the near future past last observed extents as provided by RS-based data. Using a random forest and autoregressive temporal models with short time-series of built-settlement extents and subnational level data, we predict annual 100m resolution binary settlement extents five years beyond the last observations. We applied this framework within varying contexts and predicted annual extents from 2010 to 2015. We found that our model framework preformed consistently across all sample countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and validation extents. When comparing building footprints of small settlements to forecast extents, we saw that the modelling framework had a 12 percent increase in ground-truth accuracy. This framework shows promise for predicting near-future settlement extents, and provides a foundation for forecasts further into the future.
Jeremiah J. Nieves; Maksym Bondarenko; Alessandro Sorichetta; Jessica E. Steele; David Kerr; Alessandra Carioli; Forrest R. Stevens; Andrea E. Gaughan; Andrew J. Tatem. Annual Projections of Future Built-Settlement Expansion Using Relative Changes in Projected Small Area Population and Short Time-Series of Built-Extents. 2019, 1 .
AMA StyleJeremiah J. Nieves, Maksym Bondarenko, Alessandro Sorichetta, Jessica E. Steele, David Kerr, Alessandra Carioli, Forrest R. Stevens, Andrea E. Gaughan, Andrew J. Tatem. Annual Projections of Future Built-Settlement Expansion Using Relative Changes in Projected Small Area Population and Short Time-Series of Built-Extents. . 2019; ():1.
Chicago/Turabian StyleJeremiah J. Nieves; Maksym Bondarenko; Alessandro Sorichetta; Jessica E. Steele; David Kerr; Alessandra Carioli; Forrest R. Stevens; Andrea E. Gaughan; Andrew J. Tatem. 2019. "Annual Projections of Future Built-Settlement Expansion Using Relative Changes in Projected Small Area Population and Short Time-Series of Built-Extents." , no. : 1.
In the production of gridded population maps, remotely sensed, human settlement datasets rank among the most important geographical factors to estimate population densities and distributions at regional and global scales. Within this context, the German Aerospace Centre (DLR) has developed a new suite of global layers, which accurately describe the built-up environment and its characteristics at high spatial resolution: (i) the World Settlement Footprint 2015 layer (WSF-2015), a binary settlement mask; and (ii) the experimental World Settlement Footprint Density 2015 layer (WSF-2015-Density), representing the percentage of impervious surface. This research systematically compares the effectiveness of both layers for producing population distribution maps through a dasymetric mapping approach in nine low-, middle-, and highly urbanised countries. Results indicate that the WSF-2015-Density layer can produce population distribution maps with higher qualitative and quantitative accuracies in comparison to the already established binary approach, especially in those countries where a good percentage of building structures have been identified within the rural areas. Moreover, our results suggest that population distribution accuracies could substantially improve through the dynamic preselection of the input layers and the correct parameterisation of the Settlement Size Complexity (SSC) index.
Daniela Palacios-Lopez; Felix Bachofer; Thomas Esch; Wieke Heldens; Andreas Hirner; Mattia Marconcini; Alessandro Sorichetta; Julian Zeidler; Claudia Kuenzer; Stefan Dech; Andrew J. Tatem; Peter Reinartz. New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products. Sustainability 2019, 11, 6056 .
AMA StyleDaniela Palacios-Lopez, Felix Bachofer, Thomas Esch, Wieke Heldens, Andreas Hirner, Mattia Marconcini, Alessandro Sorichetta, Julian Zeidler, Claudia Kuenzer, Stefan Dech, Andrew J. Tatem, Peter Reinartz. New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products. Sustainability. 2019; 11 (21):6056.
Chicago/Turabian StyleDaniela Palacios-Lopez; Felix Bachofer; Thomas Esch; Wieke Heldens; Andreas Hirner; Mattia Marconcini; Alessandro Sorichetta; Julian Zeidler; Claudia Kuenzer; Stefan Dech; Andrew J. Tatem; Peter Reinartz. 2019. "New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products." Sustainability 11, no. 21: 6056.
Forrest R. Stevens; Andrea E. Gaughan; Jeremiah J. Nieves; Adam King; Alessandro Sorichetta; Catherine Linard; Andrew J. Tatem. Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South. International Journal of Digital Earth 2019, 13, 78 -100.
AMA StyleForrest R. Stevens, Andrea E. Gaughan, Jeremiah J. Nieves, Adam King, Alessandro Sorichetta, Catherine Linard, Andrew J. Tatem. Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South. International Journal of Digital Earth. 2019; 13 (1):78-100.
Chicago/Turabian StyleForrest R. Stevens; Andrea E. Gaughan; Jeremiah J. Nieves; Adam King; Alessandro Sorichetta; Catherine Linard; Andrew J. Tatem. 2019. "Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South." International Journal of Digital Earth 13, no. 1: 78-100.
Population data represent an essential component in studies focusing on human–nature interrelationships, disaster risk assessment and environmental health. Several recent efforts have produced global- and continental-extent gridded population data which are becoming increasingly popular among various research communities. However, these data products, which are of very different characteristics and based on different modeling assumptions, have never been systematically reviewed and compared, which may impede their appropriate use. This article fills this gap and presents, compares and discusses a set of large-scale (global and continental) gridded datasets representing population counts or densities. It focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses. Written by the data producers and members of the user community, through the lens of the “fitness for use” concept, the aim of this paper is to provide potential data users with the knowledge base needed to make informed decisions about the appropriateness of the data products available in relation to the target application and for critical analysis.
Stefan Leyk; Andrea E. Gaughan; Susana B. Adamo; Alex de Sherbinin; Deborah Balk; Sergio Freire; Amy Rose; Forrest R. Stevens; Brian Blankespoor; Charlie Frye; Joshua Comenetz; Alessandro Sorichetta; Kytt MacManus; Linda Pistolesi; Marc Levy; Andrew J. Tatem; Martino Pesaresi. The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use. Earth System Science Data 2019, 11, 1385 -1409.
AMA StyleStefan Leyk, Andrea E. Gaughan, Susana B. Adamo, Alex de Sherbinin, Deborah Balk, Sergio Freire, Amy Rose, Forrest R. Stevens, Brian Blankespoor, Charlie Frye, Joshua Comenetz, Alessandro Sorichetta, Kytt MacManus, Linda Pistolesi, Marc Levy, Andrew J. Tatem, Martino Pesaresi. The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use. Earth System Science Data. 2019; 11 (3):1385-1409.
Chicago/Turabian StyleStefan Leyk; Andrea E. Gaughan; Susana B. Adamo; Alex de Sherbinin; Deborah Balk; Sergio Freire; Amy Rose; Forrest R. Stevens; Brian Blankespoor; Charlie Frye; Joshua Comenetz; Alessandro Sorichetta; Kytt MacManus; Linda Pistolesi; Marc Levy; Andrew J. Tatem; Martino Pesaresi. 2019. "The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use." Earth System Science Data 11, no. 3: 1385-1409.
Tracking spatiotemporal changes in GHG emissions is key to successful implementation of the United Nations Framework Convention on Climate Change (UNFCCC). And while emission inventories often provide a robust tool to track emission trends at the country level, subnational emission estimates are often not reported or reports vary in robustness as the estimates are often dependent on the spatial modeling approach and ancillary data used to disaggregate the emission inventories. Assessing the errors and uncertainties of the subnational emission estimates is fundamentally challenging due to the lack of physical measurements at the subnational level. To begin addressing the current performance of modeled gridded CO2 emissions, this study compares two common proxies used to disaggregate CO2 emission estimates. We use a known gridded CO2 model based on satellite-observed nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO2, ODIAC) and a gridded population dataset driven by a set of ancillary geospatial data. We examine the association at multiple spatial scales of these two datasets for three countries in Southeast Asia: Vietnam, Cambodia and Laos and characterize the spatiotemporal similarities and differences for 2000, 2005, and 2010. We specifically highlight areas of potential uncertainty in the ODIAC model, which relies on the single use of NTL data for disaggregation of the non-point emissions estimates. Results show, over time, how a NTL-based emissions disaggregation tends to concentrate CO2 estimates in different ways than population-based estimates at the subnational level. We discuss important considerations in the disconnect between the two modeled datasets and argue that the spatial differences between data products can be useful to identify areas affected by the errors and uncertainties associated with the NTL-based downscaling in a region with uneven urbanization rates.
Andrea E Gaughan; Tomohiro Oda; Alessandro Sorichetta; Forrest R Stevens; Maksym Bondarenko; Rostyslav Bun; Laura Krauser; Greg Yetman; Son V Nghiem. Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO 2 emission in Vietnam, Cambodia and Laos. Environmental Research Communications 2019, 1, 091006 -14.
AMA StyleAndrea E Gaughan, Tomohiro Oda, Alessandro Sorichetta, Forrest R Stevens, Maksym Bondarenko, Rostyslav Bun, Laura Krauser, Greg Yetman, Son V Nghiem. Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO 2 emission in Vietnam, Cambodia and Laos. Environmental Research Communications. 2019; 1 (9):091006-14.
Chicago/Turabian StyleAndrea E Gaughan; Tomohiro Oda; Alessandro Sorichetta; Forrest R Stevens; Maksym Bondarenko; Rostyslav Bun; Laura Krauser; Greg Yetman; Son V Nghiem. 2019. "Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO 2 emission in Vietnam, Cambodia and Laos." Environmental Research Communications 1, no. 9: 091006-14.
Population data represent an essential component in studies focusing on human-nature interrelationships, disaster risk assessment and environmental health. Several recent efforts have produced global and continental-extent gridded population data which are becoming increasingly popular among various research communities. However, these data products, which are of very different characteristics and based on different modeling assumptions, have never been systematically reviewed and compared which may impede their appropriate use. This article fills this gap and presents, compares and discusses a set of large-scale (global and continental) gridded datasets representing population counts or densities. It focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses. Written by the data producers and members of the user community, through the lens of the “fitness for use” concept, the aim of this paper is to provide potential data users with the knowledge base needed to make informed decisions about the appropriateness of the data products available in relation to the target application and for critical analysis.
Stefan Leyk; Andrea E. Gaughan; Susana B. Adamo; Alex De Sherbinin; Deborah Balk; Sergio Freire; Amy Rose; Forrest R. Stevens; Brian Blankespoor; Charlie Frye; Joshua Comenetz; Alessandro Sorrichetta; Kytt MacManus; Linda Pistolesi; Marc Levy; Andrew J. Tatem. Allocating people to pixels: A review of large-scale gridded population data products and their fitness for use. 2019, 2019, 1 -30.
AMA StyleStefan Leyk, Andrea E. Gaughan, Susana B. Adamo, Alex De Sherbinin, Deborah Balk, Sergio Freire, Amy Rose, Forrest R. Stevens, Brian Blankespoor, Charlie Frye, Joshua Comenetz, Alessandro Sorrichetta, Kytt MacManus, Linda Pistolesi, Marc Levy, Andrew J. Tatem. Allocating people to pixels: A review of large-scale gridded population data products and their fitness for use. . 2019; 2019 ():1-30.
Chicago/Turabian StyleStefan Leyk; Andrea E. Gaughan; Susana B. Adamo; Alex De Sherbinin; Deborah Balk; Sergio Freire; Amy Rose; Forrest R. Stevens; Brian Blankespoor; Charlie Frye; Joshua Comenetz; Alessandro Sorrichetta; Kytt MacManus; Linda Pistolesi; Marc Levy; Andrew J. Tatem. 2019. "Allocating people to pixels: A review of large-scale gridded population data products and their fitness for use." 2019, no. : 1-30.
Multi-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas. In response to these agendas, a method has been developed to assemble and harmonise a unique, open access, archive of geospatial datasets. Datasets are provided as global, annual time series, where pertinent at the timescale of population analyses and where data is available, for use in the construction of population distribution layers. The archive includes sub-national census-based population estimates, matched to a geospatial layer denoting administrative unit boundaries, and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density. Here, we describe these harmonised datasets and their limitations, along with the production workflow. Further, we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics. The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.
Christopher T. Lloyd; Heather Chamberlain; David Kerr; Greg Yetman; Linda Pistolesi; Forrest R. Stevens; Andrea E. Gaughan; Jeremiah J. Nieves; Graeme Hornby; Kytt MacManus; Parmanand Sinha; Maksym Bondarenko; Alessandro Sorichetta; Andrew J. Tatem. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data 2019, 3, 108 -139.
AMA StyleChristopher T. Lloyd, Heather Chamberlain, David Kerr, Greg Yetman, Linda Pistolesi, Forrest R. Stevens, Andrea E. Gaughan, Jeremiah J. Nieves, Graeme Hornby, Kytt MacManus, Parmanand Sinha, Maksym Bondarenko, Alessandro Sorichetta, Andrew J. Tatem. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data. 2019; 3 (2):108-139.
Chicago/Turabian StyleChristopher T. Lloyd; Heather Chamberlain; David Kerr; Greg Yetman; Linda Pistolesi; Forrest R. Stevens; Andrea E. Gaughan; Jeremiah J. Nieves; Graeme Hornby; Kytt MacManus; Parmanand Sinha; Maksym Bondarenko; Alessandro Sorichetta; Andrew J. Tatem. 2019. "Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets." Big Earth Data 3, no. 2: 108-139.
Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date, as well as urban planning, infrastructure development, and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analyzing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modeled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared with censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. The results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.
Shengjie Lai; Elisabeth Zu Erbach-Schoenberg; Carla Pezzulo; Nick W. Ruktanonchai; Alessandro Sorichetta; Jessica Steele; Tracey Li; Claire A. Dooley; Andrew J. Tatem. Exploring the use of mobile phone data for national migration statistics. Palgrave Communications 2019, 5, 1 -10.
AMA StyleShengjie Lai, Elisabeth Zu Erbach-Schoenberg, Carla Pezzulo, Nick W. Ruktanonchai, Alessandro Sorichetta, Jessica Steele, Tracey Li, Claire A. Dooley, Andrew J. Tatem. Exploring the use of mobile phone data for national migration statistics. Palgrave Communications. 2019; 5 (1):1-10.
Chicago/Turabian StyleShengjie Lai; Elisabeth Zu Erbach-Schoenberg; Carla Pezzulo; Nick W. Ruktanonchai; Alessandro Sorichetta; Jessica Steele; Tracey Li; Claire A. Dooley; Andrew J. Tatem. 2019. "Exploring the use of mobile phone data for national migration statistics." Palgrave Communications 5, no. 1: 1-10.
Gridded human population data provide a spatial denominator to identify populations at risk, quantify burdens, and inform our understanding of human-environment systems. When modeling gridded population, the information used for training the model may differ in spatial resolution than what is produced by the model prediction. This case arises when approaching population modeling from a top-down, dasymetric approach in which one redistributes coarse administrative unit level population data (i.e., source unit) to a finer scale (i.e., target unit). However, often overlooked are issues associated with the differing variance across the scale, spatial autocorrelation and bias in sampling techniques. In this study, we examine the effects of intentionally biasing our sampling from the source to target scale within the context of a weighted, dasymetric mapping approach. The weighted component is based on a Random Forest estimator, which is a non-parametric ensemble-based prediction model. We investigate issues of autocorrelation and heterogeneity in the training data using 18 different types of samples to show the variations in training, census-level (i.e., source) and output, grid-level (i.e., target) predictions. We compare results to simple random sampling and geographically stratified random sampling. Results indicate that the Random Forest model is sensitive to the spatial autocorrelation inherent in the training data, which leads to an increase in the variance of the residuals. Sample training datasets that are at a spatial scale representative of the true population produced the best fitting models. However, the true representative dataset varied in autocorrelation for both scales. More attention is needed with ensemble-based learning and spatially-heterogeneous data as underlying issues of spatial autocorrelation influence results for both the census-level and grid-level estimations.
Parmanand Sinha; Andrea E. Gaughan; Forrest R. Stevens; Jeremiah J. Nieves; Alessandro Sorichetta; Andrew J. Tatem. Assessing the spatial sensitivity of a random forest model: Application in gridded population modeling. Computers, Environment and Urban Systems 2019, 75, 132 -145.
AMA StyleParmanand Sinha, Andrea E. Gaughan, Forrest R. Stevens, Jeremiah J. Nieves, Alessandro Sorichetta, Andrew J. Tatem. Assessing the spatial sensitivity of a random forest model: Application in gridded population modeling. Computers, Environment and Urban Systems. 2019; 75 ():132-145.
Chicago/Turabian StyleParmanand Sinha; Andrea E. Gaughan; Forrest R. Stevens; Jeremiah J. Nieves; Alessandro Sorichetta; Andrew J. Tatem. 2019. "Assessing the spatial sensitivity of a random forest model: Application in gridded population modeling." Computers, Environment and Urban Systems 75, no. : 132-145.
Urban areas are expanding worldwide due to increasing population, standard of living, and migration from rural areas. This study uses satellite and road data to quantify the urbanization of two megacities, New Delhi and Los Angeles, between 2000 and 2009. It then estimates, with a three‐dimension nested global‐through‐urban climate, weather, and air pollution model, GATOR‐GCMOM (Gas, Aerosol, Transport, Radiation, General Circulation, Mesoscale, and Ocean Model), the short‐term atmospheric impacts of such urbanization alone. The simulations account for changes in meteorologically driven natural emissions, but not anthropogenic emissions, between 2000 and 2009. New Delhi's urban extent, defined based on the physical existence of its built structures and the transitional gradient from buildings to rural areas rather than on abrupt administrative borders, increased by ~80%, and Los Angeles', by ~22.5% between 2000 and 2009. New Delhi experienced a larger increase in its urban extent relative to its population during this period than did Los Angeles. In both megacities, urbanization increased surface roughness, increasing shearing stress and vertical turbulent kinetic energy, decreasing near surface and boundary layer wind speed, contributing to higher column pollution levels. Urbanization may also have increased downward solar plus thermal‐infrared radiation fluxes to the ground and consequently upward latent and sensible heat fluxes from the ground to the air, increasing near‐surface air temperatures. As such, urbanization alone may have had notable impacts on both meteorology and air quality.
Mark Z. Jacobson; Son V. Nghiem; Alessandro Sorichetta. Short‐Term Impacts of the Megaurbanizations of New Delhi and Los Angeles Between 2000 and 2009. Journal of Geophysical Research: Atmospheres 2019, 124, 35 -56.
AMA StyleMark Z. Jacobson, Son V. Nghiem, Alessandro Sorichetta. Short‐Term Impacts of the Megaurbanizations of New Delhi and Los Angeles Between 2000 and 2009. Journal of Geophysical Research: Atmospheres. 2019; 124 (1):35-56.
Chicago/Turabian StyleMark Z. Jacobson; Son V. Nghiem; Alessandro Sorichetta. 2019. "Short‐Term Impacts of the Megaurbanizations of New Delhi and Los Angeles Between 2000 and 2009." Journal of Geophysical Research: Atmospheres 124, no. 1: 35-56.