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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.
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.
Dana Thomson; Andrea Gaughan; Forrest Stevens; Gregory Yetman; Peter Elias; Robert Chen. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. Urban Science 2021, 5, 48 .
AMA StyleDana Thomson, Andrea Gaughan, Forrest Stevens, Gregory Yetman, Peter Elias, Robert Chen. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. Urban Science. 2021; 5 (2):48.
Chicago/Turabian StyleDana Thomson; Andrea Gaughan; Forrest Stevens; Gregory Yetman; Peter Elias; Robert Chen. 2021. "Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya." Urban Science 5, no. 2: 48.
Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment provides an opportunity to readily locate, visualize, correlate, and apply remote sensing techniques to the data. Using 26 core billet samples from the San Andreas Fault Observatory at Depth (SAFOD), this study developed GIS-based procedures for: 1. Spatially referenced visualization and storage of various microstructural data from core billets; 2. 3D modeling of billets and thin section positions within each billet, which serve as a digital record after irreversible fragmentation of the physical billets; and 3. Vector feature creation and unsupervised classification of a multi-generation calcite vein network from cathodluminescence (CL) imagery. Building on existing work which is predominantly limited to the 2D space of single thin sections, our results indicate that a GIS can facilitate spatial treatment of data even at centimeter to nanometer scales, but also revealed challenges involving intensive 3D representations and complex matrix transformations required to create geographically translated forms of the within-billet coordinate systems, which are suggested for consideration in future studies.
Elliott Holmes; Andrea Gaughan; Donald Biddle; Forrest Stevens; Jafar Hadizadeh. Geospatial Management and Analysis of Microstructural Data from San Andreas Fault Observatory at Depth (SAFOD) Core Samples. ISPRS International Journal of Geo-Information 2021, 10, 332 .
AMA StyleElliott Holmes, Andrea Gaughan, Donald Biddle, Forrest Stevens, Jafar Hadizadeh. Geospatial Management and Analysis of Microstructural Data from San Andreas Fault Observatory at Depth (SAFOD) Core Samples. ISPRS International Journal of Geo-Information. 2021; 10 (5):332.
Chicago/Turabian StyleElliott Holmes; Andrea Gaughan; Donald Biddle; Forrest Stevens; Jafar Hadizadeh. 2021. "Geospatial Management and Analysis of Microstructural Data from San Andreas Fault Observatory at Depth (SAFOD) Core Samples." ISPRS International Journal of Geo-Information 10, no. 5: 332.
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.
Dana R. Thomson; Andrea E. Gaughan; Forrest R. Stevens; Gregory Yetman; Peter Elias; Robert Chen. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. 2021, 1 .
AMA StyleDana R. Thomson, Andrea E. Gaughan, Forrest R. Stevens, Gregory Yetman, Peter Elias, Robert Chen. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. . 2021; ():1.
Chicago/Turabian StyleDana R. Thomson; Andrea E. Gaughan; Forrest R. Stevens; Gregory Yetman; Peter Elias; Robert Chen. 2021. "Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya." , no. : 1.
Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.
Kyle Woodward; Narcisa Pricope; Forrest Stevens; Andrea Gaughan; Nicholas Kolarik; Michael Drake; Jonathan Salerno; Lin Cassidy; Joel Hartter; Karen Bailey; Henry Luwaya. Modeling Community-Scale Natural Resource Use in aTransboundary Southern African Landscape: IntegratingRemote Sensing and Participatory Mapping. Remote Sensing 2021, 13, 631 .
AMA StyleKyle Woodward, Narcisa Pricope, Forrest Stevens, Andrea Gaughan, Nicholas Kolarik, Michael Drake, Jonathan Salerno, Lin Cassidy, Joel Hartter, Karen Bailey, Henry Luwaya. Modeling Community-Scale Natural Resource Use in aTransboundary Southern African Landscape: IntegratingRemote Sensing and Participatory Mapping. Remote Sensing. 2021; 13 (4):631.
Chicago/Turabian StyleKyle Woodward; Narcisa Pricope; Forrest Stevens; Andrea Gaughan; Nicholas Kolarik; Michael Drake; Jonathan Salerno; Lin Cassidy; Joel Hartter; Karen Bailey; Henry Luwaya. 2021. "Modeling Community-Scale Natural Resource Use in aTransboundary Southern African Landscape: IntegratingRemote Sensing and Participatory Mapping." Remote Sensing 13, no. 4: 631.
Joel Hartter; Lawrence C Hamilton; Mark J Ducey; Angela E Boag; Jonathan D Salerno; Nils D Christoffersen; Paul T Oester; Michael W Palace; Forrest R Stevens. Finding common ground: agreement on increasing wildfire risk crosses political lines. Environmental Research Letters 2020, 15, 065002 .
AMA StyleJoel Hartter, Lawrence C Hamilton, Mark J Ducey, Angela E Boag, Jonathan D Salerno, Nils D Christoffersen, Paul T Oester, Michael W Palace, Forrest R Stevens. Finding common ground: agreement on increasing wildfire risk crosses political lines. Environmental Research Letters. 2020; 15 (6):065002.
Chicago/Turabian StyleJoel Hartter; Lawrence C Hamilton; Mark J Ducey; Angela E Boag; Jonathan D Salerno; Nils D Christoffersen; Paul T Oester; Michael W Palace; Forrest R Stevens. 2020. "Finding common ground: agreement on increasing wildfire risk crosses political lines." Environmental Research Letters 15, no. 6: 065002.
Auriel Fournier; Matthew Boone; Forrest Stevens; Emilio Bruna. refsplitr: Author name disambiguation, author georeferencing, and mapping of coauthorship networks with Web of Science data. Journal of Open Source Software 2020, 5, 2028 .
AMA StyleAuriel Fournier, Matthew Boone, Forrest Stevens, Emilio Bruna. refsplitr: Author name disambiguation, author georeferencing, and mapping of coauthorship networks with Web of Science data. Journal of Open Source Software. 2020; 5 (45):2028.
Chicago/Turabian StyleAuriel Fournier; Matthew Boone; Forrest Stevens; Emilio Bruna. 2020. "refsplitr: Author name disambiguation, author georeferencing, and mapping of coauthorship networks with Web of Science data." Journal of Open Source Software 5, no. 45: 2028.
Conducting research on coupled social-ecological systems (SESs) presents inherent challenges, such as coordination across disparate disciplines or integrating across multiple scales and levels of governance. To overcome these common challenges, we propose that structuring the research design itself according to SES principles provides for integrative execution of SES science. First, starting with pilot work, human and natural science researchers should work as a team to identify and access multi-level entry points (i.e. points of direct engagement) within the system, relative to the spatiotemporal scales under investigation. Second, teams should implement an adaptive process that begins with the proposed research design and uses shared experiences from pilot work to refine protocols prior to subsequent data collection. We provide examples of multi-level and multi-scale entry points, and show that adaptive management of research design through coordinated iteration allows for better research integration and applicable outcomes.
Narcisa Gabriela Pricope; Lin Cassidy; Andrea Elizabeth Gaughan; Jonathan David Salerno; Forrest Robert Stevens; Joel Hartter; Michael Drake; Patricia Mupeta-Muyamwa. Addressing Integration Challenges of Interdisciplinary Research in Social-Ecological Systems. Society & Natural Resources 2019, 33, 418 -431.
AMA StyleNarcisa Gabriela Pricope, Lin Cassidy, Andrea Elizabeth Gaughan, Jonathan David Salerno, Forrest Robert Stevens, Joel Hartter, Michael Drake, Patricia Mupeta-Muyamwa. Addressing Integration Challenges of Interdisciplinary Research in Social-Ecological Systems. Society & Natural Resources. 2019; 33 (3):418-431.
Chicago/Turabian StyleNarcisa Gabriela Pricope; Lin Cassidy; Andrea Elizabeth Gaughan; Jonathan David Salerno; Forrest Robert Stevens; Joel Hartter; Michael Drake; Patricia Mupeta-Muyamwa. 2019. "Addressing Integration Challenges of Interdisciplinary Research in Social-Ecological Systems." Society & Natural Resources 33, no. 3: 418-431.
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.
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.
The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded population maps, little has been done to explore how remotely sensed, built-area datasets might be used to dasymetrically constrain these estimates. This study presents the effectiveness of three different high-resolution built area datasets for producing gridded population estimates through the dasymetric disaggregation of census counts in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. Modeling techniques include a binary dasymetric redistribution, a random forest with a dasymetric component, and a hybrid of the previous two. The relative merits of these approaches and the data are discussed with regards to studying human populations and related spatially explicit phenomena. Results showed that the accuracy of random forest and hybrid models was comparable in five of six countries.
Fennis J. Reed; Andrea E. Gaughan; Forrest R. Stevens; Greg Yetman; Alessandro Sorichetta; Andrew J. Tatem. Gridded Population Maps Informed by Different Built Settlement Products. Data 2018, 3, 33 .
AMA StyleFennis J. Reed, Andrea E. Gaughan, Forrest R. Stevens, Greg Yetman, Alessandro Sorichetta, Andrew J. Tatem. Gridded Population Maps Informed by Different Built Settlement Products. Data. 2018; 3 (3):33.
Chicago/Turabian StyleFennis J. Reed; Andrea E. Gaughan; Forrest R. Stevens; Greg Yetman; Alessandro Sorichetta; Andrew J. Tatem. 2018. "Gridded Population Maps Informed by Different Built Settlement Products." Data 3, no. 3: 33.
Forests in the United States are managed by multiple public and private entities making harmonization of available data and subsequent mapping of management challenging. We mapped four important types of forest management, production, ecological, passive, and preservation, at 250-meter spatial resolution in the Southeastern (SEUS) and Pacific Northwest (PNW) USA. Both ecologically and socio-economically dynamic regions, the SEUS and PNW forests represent, respectively, 22.0% and 10.4% of forests in the coterminous US. We built a random forest classifier using seasonal time-series analysis of 16 years of MODIS 16-day composite Enhanced Vegetation Index, and ancillary data containing forest ownership, roads, US Forest Service wilderness and forestry areas, proportion conifer and proportion riparian. The map accuracies for SEUS are 89% (10-fold cross-validation) and 67% (external validation) and PNW are 91% and 70% respectively with the same validation. The now publicly available forest management maps, probability surfaces for each management class and uncertainty layer for each region can be viewed and analysed in commercial and open-source GIS and remote sensing software.
Matthew Marsik; Caroline G. Staub; William J. Kleindl; Jaclyn M. Hall; Chiung-Shiuan Fu; Di Yang; Forrest R. Stevens; Michael W. Binford. Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest. Scientific Data 2018, 5, 180165 .
AMA StyleMatthew Marsik, Caroline G. Staub, William J. Kleindl, Jaclyn M. Hall, Chiung-Shiuan Fu, Di Yang, Forrest R. Stevens, Michael W. Binford. Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest. Scientific Data. 2018; 5 (1):180165.
Chicago/Turabian StyleMatthew Marsik; Caroline G. Staub; William J. Kleindl; Jaclyn M. Hall; Chiung-Shiuan Fu; Di Yang; Forrest R. Stevens; Michael W. Binford. 2018. "Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest." Scientific Data 5, no. 1: 180165.
Joel Hartter; Lawrence Hamilton; Angela E. Boag; Forrest R. Stevens; Mark J. Ducey; Nils D. Christoffersen; Paul T. Oester; Michael W. Palace. Does it matter if people think climate change is human caused? Climate Services 2018, 10, 53 -62.
AMA StyleJoel Hartter, Lawrence Hamilton, Angela E. Boag, Forrest R. Stevens, Mark J. Ducey, Nils D. Christoffersen, Paul T. Oester, Michael W. Palace. Does it matter if people think climate change is human caused? Climate Services. 2018; 10 ():53-62.
Chicago/Turabian StyleJoel Hartter; Lawrence Hamilton; Angela E. Boag; Forrest R. Stevens; Mark J. Ducey; Nils D. Christoffersen; Paul T. Oester; Michael W. Palace. 2018. "Does it matter if people think climate change is human caused?" Climate Services 10, no. : 53-62.
Angela E. Boag; Joel Hartter; Lawrence C. Hamilton; Nils D. Christoffersen; Forrest R. Stevens; Michael W. Palace; Mark J. Ducey. Climate change beliefs and forest management in eastern Oregon: implications for individual adaptive capacity. Ecology and Society 2018, 23, 1 .
AMA StyleAngela E. Boag, Joel Hartter, Lawrence C. Hamilton, Nils D. Christoffersen, Forrest R. Stevens, Michael W. Palace, Mark J. Ducey. Climate change beliefs and forest management in eastern Oregon: implications for individual adaptive capacity. Ecology and Society. 2018; 23 (4):1.
Chicago/Turabian StyleAngela E. Boag; Joel Hartter; Lawrence C. Hamilton; Nils D. Christoffersen; Forrest R. Stevens; Michael W. Palace; Mark J. Ducey. 2018. "Climate change beliefs and forest management in eastern Oregon: implications for individual adaptive capacity." Ecology and Society 23, no. 4: 1.
Eduardo Gelcer; Clyde W. Fraisse; Lincoln Zotarelli; Forrest R. Stevens; Daniel Perondi; Daniel D. Barreto; Hipólito A. Malia; Carvalho C. Ecole; Verona Montone; Jane Southworth. Influence of El Niño-Southern oscillation (ENSO) on agroclimatic zoning for tomato in Mozambique. Agricultural and Forest Meteorology 2018, 248, 316 -328.
AMA StyleEduardo Gelcer, Clyde W. Fraisse, Lincoln Zotarelli, Forrest R. Stevens, Daniel Perondi, Daniel D. Barreto, Hipólito A. Malia, Carvalho C. Ecole, Verona Montone, Jane Southworth. Influence of El Niño-Southern oscillation (ENSO) on agroclimatic zoning for tomato in Mozambique. Agricultural and Forest Meteorology. 2018; 248 ():316-328.
Chicago/Turabian StyleEduardo Gelcer; Clyde W. Fraisse; Lincoln Zotarelli; Forrest R. Stevens; Daniel Perondi; Daniel D. Barreto; Hipólito A. Malia; Carvalho C. Ecole; Verona Montone; Jane Southworth. 2018. "Influence of El Niño-Southern oscillation (ENSO) on agroclimatic zoning for tomato in Mozambique." Agricultural and Forest Meteorology 248, no. : 316-328.
Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit. Here we make use of recently released multi-temporal high-resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.
Catherine Linard; Caroline W. Kabaria; Marius Gilbert; Andrew J. Tatem; Andrea E. Gaughan; Forrest R. Stevens; Alessandro Sorichetta; Abdisalan M. Noor; Robert W. Snow. Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009. International Journal of Digital Earth 2017, 10, 1017 -1029.
AMA StyleCatherine Linard, Caroline W. Kabaria, Marius Gilbert, Andrew J. Tatem, Andrea E. Gaughan, Forrest R. Stevens, Alessandro Sorichetta, Abdisalan M. Noor, Robert W. Snow. Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009. International Journal of Digital Earth. 2017; 10 (10):1017-1029.
Chicago/Turabian StyleCatherine Linard; Caroline W. Kabaria; Marius Gilbert; Andrew J. Tatem; Andrea E. Gaughan; Forrest R. Stevens; Alessandro Sorichetta; Abdisalan M. Noor; Robert W. Snow. 2017. "Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009." International Journal of Digital Earth 10, no. 10: 1017-1029.
Public opinion can impact the success of natural resource management policies and programs. In this case study, we assess the degree to which demographic and place-based factors are associated with changing public opinions on climate change, wolves, renewable energy, and land development regulations in rural northeast Oregon. Based on cross-sectional telephone survey data collected in 2011 and 2014, our observations suggest declining support for eliminating wolves, increased support for renewable energy, and increasingly favorable views of regulations that limit development in rural landscapes. We find that while demographic change and local events contribute to some of the observed shifts in opinion on wolves, exogenous factors acting at state and national levels likely contribute to shifting opinions on climate change, renewable energy, and land use regulations.
Angela E. Boag; Lawrence Hamilton; Joel Hartter; Forrest Stevens; Michael W. Palace; Mark Ducey. Shifting environmental concern in rural eastern Oregon: the role of demographic and place-based factors. Population and Environment 2016, 38, 207 -216.
AMA StyleAngela E. Boag, Lawrence Hamilton, Joel Hartter, Forrest Stevens, Michael W. Palace, Mark Ducey. Shifting environmental concern in rural eastern Oregon: the role of demographic and place-based factors. Population and Environment. 2016; 38 (2):207-216.
Chicago/Turabian StyleAngela E. Boag; Lawrence Hamilton; Joel Hartter; Forrest Stevens; Michael W. Palace; Mark Ducey. 2016. "Shifting environmental concern in rural eastern Oregon: the role of demographic and place-based factors." Population and Environment 38, no. 2: 207-216.
Wildfire risk in temperate forests has become a nearly intractable problem that can be characterized as a socioecological “pathology”: that is, a set of complex and problematic interactions among social and ecological systems across multiple spatial and temporal scales. Assessments of wildfire risk could benefit from recognizing and accounting for these interactions in terms of socioecological systems, also known as coupled natural and human systems (CNHS). We characterize the primary social and ecological dimensions of the wildfire risk pathology, paying particular attention to the governance system around wildfire risk, and suggest strategies to mitigate the pathology through innovative planning approaches, analytical tools, and policies. We caution that even with a clear understanding of the problem and possible solutions, the system by which human actors govern fire‐prone forests may evolve incrementally in imperfect ways and can be expected to resist change even as we learn better ways to manage CNHS.
A Paige Fischer; Thomas A Spies; Toddi A Steelman; Cassandra Moseley; Bart R Johnson; John D Bailey; Alan A Ager; Patrick Bourgeron; Susan Charnley; Brandon M Collins; Jeffrey D Kline; Jessica E Leahy; Jeremy S Littell; James DA Millington; Max Nielsen‐Pincus; Christine S Olsen; Travis B Paveglio; Christopher I Roos; Michelle M Steen‐Adams; Forrest Stevens; Jelena Vukomanovic; Eric M White; David Mjs Bowman. Wildfire risk as a socioecological pathology. Frontiers in Ecology and the Environment 2016, 14, 276 -284.
AMA StyleA Paige Fischer, Thomas A Spies, Toddi A Steelman, Cassandra Moseley, Bart R Johnson, John D Bailey, Alan A Ager, Patrick Bourgeron, Susan Charnley, Brandon M Collins, Jeffrey D Kline, Jessica E Leahy, Jeremy S Littell, James DA Millington, Max Nielsen‐Pincus, Christine S Olsen, Travis B Paveglio, Christopher I Roos, Michelle M Steen‐Adams, Forrest Stevens, Jelena Vukomanovic, Eric M White, David Mjs Bowman. Wildfire risk as a socioecological pathology. Frontiers in Ecology and the Environment. 2016; 14 (5):276-284.
Chicago/Turabian StyleA Paige Fischer; Thomas A Spies; Toddi A Steelman; Cassandra Moseley; Bart R Johnson; John D Bailey; Alan A Ager; Patrick Bourgeron; Susan Charnley; Brandon M Collins; Jeffrey D Kline; Jessica E Leahy; Jeremy S Littell; James DA Millington; Max Nielsen‐Pincus; Christine S Olsen; Travis B Paveglio; Christopher I Roos; Michelle M Steen‐Adams; Forrest Stevens; Jelena Vukomanovic; Eric M White; David Mjs Bowman. 2016. "Wildfire risk as a socioecological pathology." Frontiers in Ecology and the Environment 14, no. 5: 276-284.
Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo-located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo-located tweets in 1x1 km grid cells over a 2-month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests-based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media-derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.
Nirav N. Patel; Forrest Stevens; Zhuojie Huang; Andrea E. Gaughan; Iqbal Elyazar; Andrew J. Tatem. Improving Large Area Population Mapping Using Geotweet Densities. Transactions in GIS 2016, 21, 317 -331.
AMA StyleNirav N. Patel, Forrest Stevens, Zhuojie Huang, Andrea E. Gaughan, Iqbal Elyazar, Andrew J. Tatem. Improving Large Area Population Mapping Using Geotweet Densities. Transactions in GIS. 2016; 21 (2):317-331.
Chicago/Turabian StyleNirav N. Patel; Forrest Stevens; Zhuojie Huang; Andrea E. Gaughan; Iqbal Elyazar; Andrew J. Tatem. 2016. "Improving Large Area Population Mapping Using Geotweet Densities." Transactions in GIS 21, no. 2: 317-331.
Scientific Data is a new open-access, online-only publication for descriptions of scientifically valuable datasets.
Andrea A.E. Gaughan; Forrest F.R. Stevens; Zhuojie Z. Huang; Jeremiah J.J. Nieves; Alessandro Sorichetta; Shengjie Lai; Xinyue X. Ye; Catherine Linard; Graeme Hornby; Simon I. Hay; Hongjie Yu; Andrew A.J. Tatem. Spatiotemporal patterns of population in mainland China, 1990 to 2010. Scientific Data 2016, 3, 160005 .
AMA StyleAndrea A.E. Gaughan, Forrest F.R. Stevens, Zhuojie Z. Huang, Jeremiah J.J. Nieves, Alessandro Sorichetta, Shengjie Lai, Xinyue X. Ye, Catherine Linard, Graeme Hornby, Simon I. Hay, Hongjie Yu, Andrew A.J. Tatem. Spatiotemporal patterns of population in mainland China, 1990 to 2010. Scientific Data. 2016; 3 (1):160005.
Chicago/Turabian StyleAndrea A.E. Gaughan; Forrest F.R. Stevens; Zhuojie Z. Huang; Jeremiah J.J. Nieves; Alessandro Sorichetta; Shengjie Lai; Xinyue X. Ye; Catherine Linard; Graeme Hornby; Simon I. Hay; Hongjie Yu; Andrew A.J. Tatem. 2016. "Spatiotemporal patterns of population in mainland China, 1990 to 2010." Scientific Data 3, no. 1: 160005.