This page has only limited features, please log in for full access.

Dr. Andrea Gaughan
Department of Geography and Geosciences, University of Louisville, Louisville, Kentucky, USA

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Land Systems
0 Human-environment dynamics
0 Human population mapping
0 Climate variability/change
0 Remote sensing and Geospatial analysis

Fingerprints

Remote sensing and Geospatial analysis
Land Systems

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 30 June 2021 in Sustainability
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (13):7329.

Chicago/Turabian Style

Cascade 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.

Journal article
Published: 20 June 2021 in Urban Science
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (2):48.

Chicago/Turabian Style

Dana 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.

Journal article
Published: 14 May 2021 in ISPRS International Journal of Geo-Information
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (5):332.

Chicago/Turabian Style

Elliott 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.

Journal article
Published: 10 February 2021 in Remote Sensing
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (4):631.

Chicago/Turabian Style

Kyle 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.

Journal article
Published: 07 January 2021 in Social Sciences & Humanities Open
Reads 0
Downloads 0

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.

ACS Style

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. Measuring the contribution of built-settlement data to global population mapping. Social Sciences & Humanities Open 2021, 3, 100102 .

AMA Style

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. Measuring the contribution of built-settlement data to global population mapping. Social Sciences & Humanities Open. 2021; 3 (1):100102.

Chicago/Turabian Style

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. 2021. "Measuring the contribution of built-settlement data to global population mapping." Social Sciences & Humanities Open 3, no. 1: 100102.

Contributed paper
Published: 23 December 2020 in Conservation Science and Practice
Reads 0
Downloads 0

The Kazavango‐Zambezi Transfrontier Conservation Area is home to the largest remaining elephant population in Africa but is also the site of high levels of human‐elephant conflict through crop depredation. Offsetting the costs of coexisting with elephants in this area is critical to incentivizing elephant conservation within community‐based conservation (CBC) areas, and trophy hunting has long been touted as a method for generating revenue for communities from wildlife. However, the idea that sustainable elephant hunting can offset the costs of crop depredation remains largely untested. We combined household survey data, financial records, and elephant population data to compare the potential benefits of sustainable hunting with the costs of crop depredation in a CBC area in northeastern Namibia. We determined that sustainable trophy hunting only returns ~30% of the value of crops lost to the community and cannot alone offset the current costs of coexistence with elephants. As core institutions supporting the practice of conservation, CBC efforts must promote community management capacity to combine multiple wildlife‐based income streams and build partnerships at multiple scales of governance to address the challenges of elephant management.

ACS Style

Michael D. Drake; Jonathan Salerno; Ryan E. Langendorf; Lin Cassidy; Andrea E. Gaughan; Forrest R. Stevens; Narcisa G. Pricope; Joel Hartter. Costs of elephant crop depredation exceed the benefits of trophy hunting in a community‐based conservation area of Namibia. Conservation Science and Practice 2020, 3, 1 .

AMA Style

Michael D. Drake, Jonathan Salerno, Ryan E. Langendorf, Lin Cassidy, Andrea E. Gaughan, Forrest R. Stevens, Narcisa G. Pricope, Joel Hartter. Costs of elephant crop depredation exceed the benefits of trophy hunting in a community‐based conservation area of Namibia. Conservation Science and Practice. 2020; 3 (1):1.

Chicago/Turabian Style

Michael D. Drake; Jonathan Salerno; Ryan E. Langendorf; Lin Cassidy; Andrea E. Gaughan; Forrest R. Stevens; Narcisa G. Pricope; Joel Hartter. 2020. "Costs of elephant crop depredation exceed the benefits of trophy hunting in a community‐based conservation area of Namibia." Conservation Science and Practice 3, no. 1: 1.

Journal article
Published: 12 May 2020 in Remote Sensing
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (10):1545.

Chicago/Turabian Style

Jeremiah 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.

Preprint
Published: 02 December 2019
Reads 0
Downloads 0

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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Jeremiah 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.

Letter
Published: 21 August 2019 in Environmental Research Communications
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (9):091006-14.

Chicago/Turabian Style

Andrea 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.

Journal article
Published: 16 July 2019 in Land
Reads 0
Downloads 0

Understanding how individuals, communities, and populations vary in their vulnerability requires defining and identifying vulnerability with respect to a condition, and then developing robust methods to reliably measure vulnerability. In this study, we illustrate how a conceptual model translated via simulation can guide the real-world implementation of data collection and measurement of a model system. We present a generalizable statistical framework that specifies linkages among interacting social and biophysical components in complex landscapes to examine vulnerability. We use the simulated data to present a case study in which households are vulnerable to conditions of land function, which we define as the provision of goods and services from the surrounding environment. We use an example of a transboundary region of Southern Africa and apply a set of hypothesized, simulated data to illustrate how one might use the framework to assess vulnerability based on empirical data. We define vulnerability as the predisposition of being adversely affected by environmental variation and its impacts on land uses and their outcomes as exposure (E), mediated by sensitivity (S), and mitigated by adaptive capacity (AC). We argue that these are latent, or hidden, characteristics that can be measured through a set of observable indicators. Those indicators and the linkages between latent variables require model specification prior to data collection, critical for applying the type of modeling framework presented. We discuss the strength and directional pathways between land function and vulnerability components, and assess their implications for identifying potential leverage points within the system for the benefit of future policy and management decisions.

ACS Style

Andrea Elizabeth Gaughan; Forrest Robert Stevens; Narcisa Gabriela Pricope; Joel Hartter; Lin Cassidy; Jonathan Salerno. Operationalizing Vulnerability: Land System Dynamics in a Transfrontier Conservation Area. Land 2019, 8, 111 .

AMA Style

Andrea Elizabeth Gaughan, Forrest Robert Stevens, Narcisa Gabriela Pricope, Joel Hartter, Lin Cassidy, Jonathan Salerno. Operationalizing Vulnerability: Land System Dynamics in a Transfrontier Conservation Area. Land. 2019; 8 (7):111.

Chicago/Turabian Style

Andrea Elizabeth Gaughan; Forrest Robert Stevens; Narcisa Gabriela Pricope; Joel Hartter; Lin Cassidy; Jonathan Salerno. 2019. "Operationalizing Vulnerability: Land System Dynamics in a Transfrontier Conservation Area." Land 8, no. 7: 111.

Data descriptor
Published: 04 September 2018 in Data
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (3):33.

Chicago/Turabian Style

Fennis 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.

Dataset
Published: 16 February 2016 in Scientific Data
Reads 0
Downloads 0

Scientific Data is a new open-access, online-only publication for descriptions of scientifically valuable datasets.

ACS Style

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 Style

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 (1):160005.

Chicago/Turabian Style

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. 2016. "Spatiotemporal patterns of population in mainland China, 1990 to 2010." Scientific Data 3, no. 1: 160005.

Original articles
Published: 13 October 2014 in International Journal of Digital Earth
Reads 0
Downloads 0

Interactions between humans, diseases, and the environment take place across a range of temporal and spatial scales, making accurate, contemporary data on human population distributions critical for a variety of disciplines. Methods for disaggregating census data to finer-scale, gridded population density estimates continue to be refined as computational power increases and more detailed census, input, and validation datasets become available. However, the availability of spatially detailed census data still varies widely by country. In this study, we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models. We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts. Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia. Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country. This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.

ACS Style

A.E. Gaughan; Forrest Stevens; C. Linard; N.N. Patel; A.J. Tatem. Exploring nationally and regionally defined models for large area population mapping. International Journal of Digital Earth 2014, 8, 989 -1006.

AMA Style

A.E. Gaughan, Forrest Stevens, C. Linard, N.N. Patel, A.J. Tatem. Exploring nationally and regionally defined models for large area population mapping. International Journal of Digital Earth. 2014; 8 (12):989-1006.

Chicago/Turabian Style

A.E. Gaughan; Forrest Stevens; C. Linard; N.N. Patel; A.J. Tatem. 2014. "Exploring nationally and regionally defined models for large area population mapping." International Journal of Digital Earth 8, no. 12: 989-1006.