Publications
1.
Weber, Eric M.; Seaman, Vincent Y.; Stewart, Robert N.; Bird, Tomas J.; Tatem, Andrew J.; McKee, Jacob J.; Bhaduri, Budhendra L.; Moehl, Jessica J.; Reith, Andrew E.
Census-independent population mapping in northern Nigeria Journal Article
In: Remote Sensing of Environment, vol. 204, pp. 786-798, 2018, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: Demographics, Nigeria, Polio, Population, Settlement mapping
@article{WEBER2018786,
title = {Census-independent population mapping in northern Nigeria},
author = {Eric M. Weber and Vincent Y. Seaman and Robert N. Stewart and Tomas J. Bird and Andrew J. Tatem and Jacob J. McKee and Budhendra L. Bhaduri and Jessica J. Moehl and Andrew E. Reith},
url = {https://www.sciencedirect.com/science/article/pii/S0034425717304364},
doi = {https://doi.org/10.1016/j.rse.2017.09.024},
issn = {0034-4257},
year = {2018},
date = {2018-01-01},
journal = {Remote Sensing of Environment},
volume = {204},
pages = {786-798},
abstract = {Although remote sensing has long been used to aid in the estimation of population, it has usually been in the context of spatial disaggregation of national census data, with the census counts serving both as observational data for specifying models and as constraints on model outputs. Here we present a framework for estimating populations from the bottom up, entirely independently of national census data, a critical need in areas without recent and reliable census data. To make observations of population density, we replace national census data with a microcensus, in which we enumerate population for a sample of small areas within the states of Kano and Kaduna in northern Nigeria. Using supervised texture-based classifiers with very high resolution satellite imagery, we produce a binary map of human settlement at 8-meter resolution across the two states and then a more refined classification consisting of 7 residential types and 1 non-residential type. Using the residential types and a model linking them to the population density observations, we produce population estimates across the two states in a gridded raster format, at approximately 90-meter resolution. We also demonstrate a simulation framework for capturing uncertainty and presenting estimates as prediction intervals for any region of interest of any size and composition within the study region. Used in concert with previously published demographic estimates, our population estimates allowed for predictions of the population under 5 in ten administrative wards that fit strongly with reference data collected during polio vaccination campaigns.},
keywords = {Demographics, Nigeria, Polio, Population, Settlement mapping},
pubstate = {published},
tppubtype = {article}
}
Although remote sensing has long been used to aid in the estimation of population, it has usually been in the context of spatial disaggregation of national census data, with the census counts serving both as observational data for specifying models and as constraints on model outputs. Here we present a framework for estimating populations from the bottom up, entirely independently of national census data, a critical need in areas without recent and reliable census data. To make observations of population density, we replace national census data with a microcensus, in which we enumerate population for a sample of small areas within the states of Kano and Kaduna in northern Nigeria. Using supervised texture-based classifiers with very high resolution satellite imagery, we produce a binary map of human settlement at 8-meter resolution across the two states and then a more refined classification consisting of 7 residential types and 1 non-residential type. Using the residential types and a model linking them to the population density observations, we produce population estimates across the two states in a gridded raster format, at approximately 90-meter resolution. We also demonstrate a simulation framework for capturing uncertainty and presenting estimates as prediction intervals for any region of interest of any size and composition within the study region. Used in concert with previously published demographic estimates, our population estimates allowed for predictions of the population under 5 in ten administrative wards that fit strongly with reference data collected during polio vaccination campaigns.
2.
Patel, Nirav N.; Angiuli, Emanuele; Gamba, Paolo; Gaughan, Andrea; Lisini, Gianni; Stevens, Forrest R.; Tatem, Andrew J.; Trianni, Giovanna
Multitemporal settlement and population mapping from Landsat using Google Earth Engine Journal Article
In: International Journal of Applied Earth Observation and Geoinformation, vol. 35, pp. 199-208, 2015, ISSN: 0303-2434.
Abstract | Links | BibTeX | Tags: Google Earth Engine, Landsat, Multitemporal, Population mapping, Settlement mapping, Spatial demography, Urbanization
@article{PATEL2015199,
title = {Multitemporal settlement and population mapping from Landsat using Google Earth Engine},
author = {Nirav N. Patel and Emanuele Angiuli and Paolo Gamba and Andrea Gaughan and Gianni Lisini and Forrest R. Stevens and Andrew J. Tatem and Giovanna Trianni},
url = {https://www.sciencedirect.com/science/article/pii/S0303243414001998},
doi = {https://doi.org/10.1016/j.jag.2014.09.005},
issn = {0303-2434},
year = {2015},
date = {2015-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {35},
pages = {199-208},
abstract = {As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.},
keywords = {Google Earth Engine, Landsat, Multitemporal, Population mapping, Settlement mapping, Spatial demography, Urbanization},
pubstate = {published},
tppubtype = {article}
}
As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.
3.
Tatem, Andrew J.; Noor, Abdisalan M.; Hay, Simon I.
Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery Journal Article
In: Remote Sensing of Environment, vol. 93, no. 1, pp. 42-52, 2004, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: JERS-1 SAR, Kenya, Landsat TM, Neural network, Population, Public health, Settlement mapping, Texture
@article{TATEM200442,
title = {Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery},
author = {Andrew J. Tatem and Abdisalan M. Noor and Simon I. Hay},
url = {https://www.sciencedirect.com/science/article/pii/S0034425704001944},
doi = {https://doi.org/10.1016/j.rse.2004.06.014},
issn = {0034-4257},
year = {2004},
date = {2004-01-01},
journal = {Remote Sensing of Environment},
volume = {93},
number = {1},
pages = {42-52},
abstract = {This paper presents an appraisal of satellite imagery types and texture measures for identifying and delineating settlements in four Districts of Kenya chosen to represent the variation in human ecology across the country. Landsat Thematic Mapper (TM) and Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) imagery of the four districts were obtained and supervised per-pixel classifications of image combinations tested for their efficacy at settlement delineation. Additional data layers including human population census data, land cover, and locations of medical facilities, villages, schools and market centres were used for training site identification and validation. For each district, the most accurate approach was determined through the best correspondence with known settlement and non-settlement pixels. The resulting settlement maps will be used in combination with census data to produce medium spatial resolution population maps for improved public health planning in Kenya.},
keywords = {JERS-1 SAR, Kenya, Landsat TM, Neural network, Population, Public health, Settlement mapping, Texture},
pubstate = {published},
tppubtype = {article}
}
This paper presents an appraisal of satellite imagery types and texture measures for identifying and delineating settlements in four Districts of Kenya chosen to represent the variation in human ecology across the country. Landsat Thematic Mapper (TM) and Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) imagery of the four districts were obtained and supervised per-pixel classifications of image combinations tested for their efficacy at settlement delineation. Additional data layers including human population census data, land cover, and locations of medical facilities, villages, schools and market centres were used for training site identification and validation. For each district, the most accurate approach was determined through the best correspondence with known settlement and non-settlement pixels. The resulting settlement maps will be used in combination with census data to produce medium spatial resolution population maps for improved public health planning in Kenya.
