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Article ; Online: Harmonised global datasets of wind and solar farm locations and power

Sebastian Dunnett / Alessandro Sorichetta / Gail Taylor / Felix Eigenbrod

Scientific Data, Vol 7, Iss 1, Pp 1-

2020 Volume 12

Abstract: Measurement(s)geographic location • powerTechnology Type(s)digital curation • computational modeling techniqueFactor Type(s)landscape area • panel area • turbinesSample Characteristic - Environmentatmospheric wind • stellar radiation • power plantSample ... ...

Abstract	Measurement(s)geographic location • powerTechnology Type(s)digital curation • computational modeling techniqueFactor Type(s)landscape area • panel area • turbinesSample Characteristic - Environmentatmospheric wind • stellar radiation • power plantSample Characteristic - LocationEarth (planet) Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12063225
Keywords	Science ; Q
Language	English
Publishing date	2020-04-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Transformative Urban Changes of Beijing in the Decade of the 2000s

Alessandro Sorichetta / Son V. Nghiem / Marco Masetti / Catherine Linard / Andreas Richter

Remote Sensing, Vol 12, Iss 4, p

2020 Volume 652

Abstract: 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 ... ...

Abstract	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 (NO 2 ) 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 NO 2 column density within the Beijing infrastructure-based urban area extent in 2009, which had quadrupled since 2000.
Keywords	dense sampling method ; beijing ; urbanization ; change indices ; tropospheric no 2 columns ; Science ; Q
Subject code	910
Language	English
Publishing date	2020-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Modeling human migration across spatial scales in Colombia.

Amir S Siraj / Alessandro Sorichetta / Guido España / Andrew J Tatem / T Alex Perkins

PLoS ONE, Vol 15, Iss 5, p e

2020 Volume 0232702

Abstract: 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 ... ...

Abstract	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.
Keywords	Medicine ; R ; Science ; Q
Subject code	337
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: High-resolution gridded population datasets for Latin America and the Caribbean using official statistics

Tom McKeen / Maksym Bondarenko / David Kerr / Thomas Esch / Mattia Marconcini / Daniela Palacios-Lopez / Julian Zeidler / R. Catalina Valle / Sabrina Juran / Andrew J. Tatem / Alessandro Sorichetta

Scientific Data, Vol 10, Iss 1, Pp 1-

2023 Volume 17

Abstract: Abstract “Leaving no one behind” is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by ... ...

Abstract	Abstract “Leaving no one behind” is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the ‘top-down’ approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.
Keywords	Science ; Q
Subject code	333
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Author Correction

Emanuele Strano / Matheus P. Viana / Alessandro Sorichetta / Andrew J. Tatem

Scientific Reports, Vol 8, Iss 1, Pp 1-

Mapping road network communities for guiding disease surveillance and control strategies

2018 Volume 2

Abstract: A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper. ...

Abstract	A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2018-07-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mapping road network communities for guiding disease surveillance and control strategies

Emanuele Strano / Matheus P. Viana / Alessandro Sorichetta / Andrew J. Tatem

Scientific Reports, Vol 8, Iss 1, Pp 1-

2018 Volume 9

Abstract: Abstract Human mobility is increasing in its volume, speed and reach, leading to the movement and introduction of pathogens through infected travelers. An understanding of how areas are connected, the strength of these connections and how this translates ...

Abstract	Abstract Human mobility is increasing in its volume, speed and reach, leading to the movement and introduction of pathogens through infected travelers. An understanding of how areas are connected, the strength of these connections and how this translates into disease spread is valuable for planning surveillance and designing control and elimination strategies. While analyses have been undertaken to identify and map connectivity in global air, shipping and migration networks, such analyses have yet to be undertaken on the road networks that carry the vast majority of travellers in low and middle income settings. Here we present methods for identifying road connectivity communities, as well as mapping bridge areas between communities and key linkage routes. We apply these to Africa, and show how many highly-connected communities straddle national borders and when integrating malaria prevalence and population data as an example, the communities change, highlighting regions most strongly connected to areas of high burden. The approaches and results presented provide a flexible tool for supporting the design of disease surveillance and control strategies through mapping areas of high connectivity that form coherent units of intervention and key link routes between communities for targeting surveillance.
Keywords	Medicine ; R ; Science ; Q
Subject code	390
Language	English
Publishing date	2018-03-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Towards an Improved Large-Scale Gridded Population Dataset

Daniela Palacios-Lopez / Thomas Esch / Kytt MacManus / Mattia Marconcini / Alessandro Sorichetta / Greg Yetman / Julian Zeidler / Stefan Dech / Andrew J. Tatem / Peter Reinartz

Remote Sensing, Vol 14, Iss 325, p

A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling

2022 Volume 325

Abstract: Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged ... ...

Abstract	Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D ...
Keywords	large-scale gridded population dataset ; dasymetric modelling ; accuracy assessment ; world settlement Footprint-3D ; random forest classifier ; spatial metrics ; Science ; Q
Subject code	333
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Measuring the contribution of built-settlement data to global population mapping

Jeremiah J. Nieves / Maksym Bondarenko / David Kerr / Nikolas Ves / Greg Yetman / Parmanand Sinha / Donna J. Clarke / Alessandro Sorichetta / Forrest R. Stevens / Andrea E. Gaughan / Andrew J. Tatem

Social Sciences and Humanities Open, Vol 3, Iss 1, Pp 100102- (2021)

2021

Abstract: Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built- ...

Abstract	Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RS-derived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain. These gaps have prompted the interpolation of the built-environment and settlements, but the utility of such interpolated data in further population modelling applications has garnered little research. Thus, our objective was to determine the utility of modelled built-settlement extents in a top-down population modelling application. Here we take modelled global built-settlement extents between 2000 and 2012, created using a spatio-temporal disaggregation of observed settlement growth. We then demonstrate the applied utility of such annually modelled settlement data within the application of annually modelling population, using random forest informed dasymetric disaggregations, across 172 countries and a 13-year period. We demonstrate that the modelled built-settlement data are consistently the 2nd most important covariate in predicting population density, behind annual lights at night, across the globe and across the study period. Further, we demonstrate that this modelled built-settlement data often provides more information than current annually available RS-derived data and last observed built-settlement extents.
Keywords	Urban ; Population ; Growth model ; Built ; Settlement ; Machine learning ; History of scholarship and learning. The humanities ; AZ20-999 ; Social sciences (General) ; H1-99
Subject code	910
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: High-Resolution Gridded Population Datasets

Daniela Palacios-Lopez / Felix Bachofer / Thomas Esch / Mattia Marconcini / Kytt MacManus / Alessandro Sorichetta / Julian Zeidler / Stefan Dech / Andrew J. Tatem / Peter Reinartz

Remote Sensing, Vol 13, Iss 1142, p

Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent

2021 Volume 1142

Abstract: 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 ... ...

Abstract	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 (~10m 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.
Keywords	gridded population distribution mapping ; large-scale population distribution modelling ; World Settlement Footprint ; percent of impervious surface ; accuracy assessment ; dasymetric modelling ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Practical geospatial and sociodemographic predictors of human mobility

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

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 14

Abstract: Abstract 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, ... ...

Abstract	Abstract 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 ...
Keywords	Medicine ; R ; Science ; Q
Subject code	380
Language	English
Publishing date	2021-07-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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