@article{nokey,

title = {Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling},

author = {Palacios-Lopez, Daniela and Esch, Thomas and MacManus, Kytt and Marconcini, Mattia and Sorichetta, Alessandro and Yetman, Greg and Zeidler, Julian and Dech, Stefan and Tatem, Andrew J. and and Reinartz, Peter},

doi = {10.3390/rs14020325},

issn = {2072-4292},

year  = {2022},

date = {2022-01-20},

urldate = {2022-01-20},

journal = {Remote Sensing},

volume = {14},

number = {2},

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 dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.},

keywords = {Europe, Population, Random forest},

pubstate = {published},

tppubtype = {article}

}

@article{NIEVES2020101444,

title = {Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night},

author = {Jeremiah J. Nieves and Alessandro Sorichetta and Catherine Linard and Maksym Bondarenko and Jessica E. Steele and Forrest R. Stevens and Andrea E. Gaughan and Alessandra Carioli and Donna J. Clarke and Thomas Esch and Andrew J. Tatem},

url = {https://www.sciencedirect.com/science/article/pii/S019897151930290X},

doi = {https://doi.org/10.1016/j.compenvurbsys.2019.101444},

issn = {0198-9715},

year  = {2020},

date = {2020-01-01},

journal = {Computers, Environment and Urban Systems},

volume = {80},

pages = {101444},

abstract = {Mapping urban features/human built-settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban features/human built-settlement datasets have become available, issues still exist in remotely-sensed imagery due to spatial and temporal coverage, adverse atmospheric conditions, and expenses involved in producing such datasets. Remotely-sensed annual time-series of urban/built-settlement extents therefore do not yet exist and cover more than specific local areas or city-based regions. Moreover, while a few high-resolution global datasets of urban/built-settlement extents exist for key years, the observed date often deviates many years from the assigned one. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we describe an interpolative and flexible modelling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modelling with open source subnational data to produce annual 100 m × 100 m resolution binary built-settlement datasets in four test countries located in varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85 and 99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to built-settlement in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban features/built-settlement datasets derived from remotely-sensed imagery, provides a base upon which to create urban future/built-settlement extent projections, and enables further exploration of the relationships between urban/built-settlement area and population dynamics.},

keywords = {Built-settlements, Dasymetric modelling, Population, Random forest, Spatial growth, Urban features},

pubstate = {published},

tppubtype = {article}

}