Special Issue "Spatial Denominators: Modeling Population and Demographic Distributions"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (30 November 2018).

Special Issue Editors

Dr. Andrea E. Gaughan
E-Mail Website
Guest Editor
Department of Geography and Geosciences, University of Louisville, Louisville, Kentucky, USA
Tel. +1-502-852-2697
Interests: Human-environment dynamics; Land systems; Human population mapping; Climate variability/change; Remote sensing and Geospatial analysis
Special Issues and Collections in MDPI journals
Dr. Catherine Linard
E-Mail Website
Guest Editor
Department of Geography, University of Namur, 5000 Namur, Belgium
Interests: Spatial epidemiology; population distribution modeling; environmental changes; urbanization; remote sensing and geospatial analysis

Special Issue Information

Dear Colleagues,

Fundamental to applications, including health, economic development, humanitarian relief, and changes attributable to land use and climate dynamics, is the spatiotemporal knowledge of the distribution, movement and concentration of human population. While census data is a historical source of population data, the past few decades have seen advancements in the techniques and data sources used for creating gridded population products. These gridded population products provide a spatially-explicit human denominator, from which a multitude of other research and policy initiatives rely, making, not only the accuracy of the population data important, but also a need to understand how that population data was created and appropriate applications for use. Gridded population datasets also provide timely measuring and mapping of residential or ambient population patterns over decades, with different underlying methods informing the relevant application and use of subsequent population data sets for further studies.

This Special Issue aims to publish research on measuring, mapping and modeling the human denominator at small or large spatial scales. In particular, we seek novel ways of treating data and statistically describing associations of measured or estimated population counts with associated covariates, both over time and space. To complement information and details of underlying methods and data construction, authors should also address issues of potential endogeneity, temporal specificity and spatial conformity in further use of the data.

We invite submissions that address the following topics:

  • Construction and method development of gridded population data
    (including ancillary data)
  • Spatial demographic mapping
  • Production of gridded population datasets from various data sources (censuses, surveys, mobile phones, voluntary geographic information)
  • Use of Earth Observation and other geospatial data for informing population distributions
  • Comparison and discussion of population modelling approaches
  • Measuring uncertainty in spatial demographics
  • Gridded population dynamics, modeling over space and time
Dr. Andrea Gaughan
Dr. Catherine Linard
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

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Open AccessArticle
Comparison of Micro-Census Results for Magarya Ward, Wurno Local Government Area of Sokoto State, Nigeria, with Other Sources of Denominator Data
Data 2019, 4(1), 20; https://doi.org/10.3390/data4010020 - 25 Jan 2019
Abstract
Routine immunization coverage in Nigeria is suboptimal. In the northwestern state of Sokoto, an independent population-based survey for 2016 found immunization coverage with the third dose of Pentavalent vaccine to be 3%, whereas administrative coverage in 2016 was reported to be 69%. One [...] Read more.
Routine immunization coverage in Nigeria is suboptimal. In the northwestern state of Sokoto, an independent population-based survey for 2016 found immunization coverage with the third dose of Pentavalent vaccine to be 3%, whereas administrative coverage in 2016 was reported to be 69%. One possibility driving this large discrepancy is that administrative coverage is calculated using an under-estimated target population. Official population projections from the 2006 Census are based on state-specific standard population growth rates. Immunization target population estimates from other sources have not been independently validated. We conducted a micro-census in Magarya ward, Wurno Local Government Area of Sokoto state to obtain an accurate count of the total population living in the ward, and to compare these results with other sources of denominator data. We developed a precise micro-plan using satellite imagery, and used the navigation tool EpiSample v1 in the field to guide teams to each building, without duplications or omissions. The particular characteristics of the selected ward underscore the importance of using standardized shape files to draw precise boundaries for enumeration micro-plans. While the use of this methodology did not resolve the discrepancy between independent and administrative vaccination coverage rates, a simplified application can better define the target population for routine immunization services and estimate the number of children still unprotected from vaccine-preventable diseases. Full article
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Open AccessArticle
Improving Urban Population Distribution Models with Very-High Resolution Satellite Information
Data 2019, 4(1), 13; https://doi.org/10.3390/data4010013 - 16 Jan 2019
Cited by 4
Abstract
Built-up layers derived from medium resolution (MR) satellite information have proven their contribution to dasymetric mapping, but suffer from important limitations when working at the intra-urban level, mainly due to their difficulty in capturing the whole range of variation in terms of built-up [...] Read more.
Built-up layers derived from medium resolution (MR) satellite information have proven their contribution to dasymetric mapping, but suffer from important limitations when working at the intra-urban level, mainly due to their difficulty in capturing the whole range of variation in terms of built-up densities. In this regard, very-high resolution (VHR) remote sensing is known for its ability to better capture small variations in built-up densities and to derive detailed urban land use, which plead in favor of its use when mapping urban populations. In this paper, we compare the added value of various combinations of VHR data sets, compared to a MR one. A top-down dasymetric mapping strategy is applied to reallocate population counts from administrative units into a regular 100 × 100 m grid, according to different weighting layers. These weighting layers are created from MR and/or VHR input data, using simple built-up proportion or reallocation “weights”, obtained from a set of multiple ancillary data used to train a Random Forest regression model. The results reveal that (1) a built-up mask derived from VHR can improve the accuracy of the reallocation by roughly 13%, compared to MR; (2) using VHR land-use information alone results in lower accuracy than using a MR built-up mask; and (3) there is a clear complementarity between VHR land cover and land use. Full article
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Open AccessArticle
Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy
Data 2019, 4(1), 8; https://doi.org/10.3390/data4010008 - 08 Jan 2019
Cited by 2
Abstract
Urban mobility is known to have a relevant impact on work related car accidents especially during commuting. It is characterized by highly dynamic spatial–temporal variability. There are open questions about the size of this phenomenon; its spatial, temporal, and demographic characteristics; and driving [...] Read more.
Urban mobility is known to have a relevant impact on work related car accidents especially during commuting. It is characterized by highly dynamic spatial–temporal variability. There are open questions about the size of this phenomenon; its spatial, temporal, and demographic characteristics; and driving mechanisms. A case study is here presented for the city of Rome, Italy. High-resolution population presence and demographic data, derived from mobile phone traffic, were used. Hourly profiles of a defined mobility factor (NPM) were calculated for a gridded domain during working days and cluster analyzed to obtain mean diurnal NPM mobility patterns. Age distributions of the population were calculated from demographic data to get insight in the type of population involved in mobility, and spatially linked with the mobility patterns. Census data about production units and their employees were related with the classified NPM mobility patterns. Seven different NPM mobility patterns were identified and mapped over the study area. The mobility slightly deviates from the census-based demography (0.15 on average, in a range of 0 to 1). The number of employees per 100 inhabitants was found to be the main driving mechanism of mobility. Finally, contributions of people employed in different economic macrocategories were assigned to each mobility time-pattern. Results provide a deeper knowledge of urban dynamics and their driving mechanisms in Rome. Full article
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Open AccessArticle
Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia
Data 2018, 3(3), 30; https://doi.org/10.3390/data3030030 - 09 Aug 2018
Abstract
Whether evaluating gridded population dataset estimates (e.g., WorldPop, LandScan) or household survey sample designs, a population census linked to residential locations are needed. Geolocated census microdata data, however, are almost never available and are thus best simulated. In this paper, we simulate a [...] Read more.
Whether evaluating gridded population dataset estimates (e.g., WorldPop, LandScan) or household survey sample designs, a population census linked to residential locations are needed. Geolocated census microdata data, however, are almost never available and are thus best simulated. In this paper, we simulate a close-to-reality population of individuals nested in households geolocated to realistic building locations. Using the R simPop package and ArcGIS, multiple realizations of a geolocated synthetic population are derived from the Namibia 2011 census 20% microdata sample, Namibia census enumeration area boundaries, Namibia 2013 Demographic and Health Survey (DHS), and dozens of spatial covariates derived from publicly available datasets. Realistic household latitude-longitude coordinates are manually generated based on public satellite imagery. Simulated households are linked to latitude-longitude coordinates by identifying distinct household types with multivariate k-means analysis and modelling a probability surface for each household type using Random Forest machine learning methods. We simulate five realizations of a synthetic population in Namibia’s Oshikoto region, including demographic, socioeconomic, and outcome characteristics at the level of household, woman, and child. Comparison of variables in the synthetic population were made with 2011 census 20% sample and 2013 DHS data by primary sampling unit/enumeration area. We found that synthetic population variable distributions matched observed observations and followed expected spatial patterns. We outline a novel process to simulate a close-to-reality microdata census geolocated to realistic building locations in a low- or middle-income country setting to support spatial demographic research and survey methodological development while avoiding disclosure risk of individuals. Full article
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Open AccessData Descriptor
Estimating Internal Migration in Contemporary Mexico and its Relevance in Gridded Population Distributions
Data 2019, 4(2), 50; https://doi.org/10.3390/data4020050 - 04 Apr 2019
Abstract
Given downward trends in fertility and mortality, population dynamics –and thus the
estimation of spatially-explicit population dynamics and gridded population and derivative
products– are increasingly sensitive to mobility processes and their changes in spatiality. In this
paper, we present a procedure to produce [...] Read more.
Given downward trends in fertility and mortality, population dynamics –and thus the
estimation of spatially-explicit population dynamics and gridded population and derivative
products– are increasingly sensitive to mobility processes and their changes in spatiality. In this
paper, we present a procedure to produce origin-destination intermunicipal/intercounty and
interstate migration matrices, briefly discussing their use and application in gridded population
products. To illustrate our approach, we produce total and sex-specific matrices with information
from the 2000 and 2010 Mexican Census long-form 10% surveys. We share the code required to
reproduce the extraction of these and for potentially at least another 122 country-periods based on
harmonized publicly-available data from IPUMS International, which allow for the addition of
ancillary social and economic data and individual and household levels, or IPUMS Terra, which
further allow for GIS-based mapping, visualization, and manipulation and for the merging of
important contextual, e.g., environmental, data. Besides discussing the likely limitations of these
measures, using official projections from the Mexican government, we illustrate how
migration/mobility data improve the estimation of spatial/gridded population dynamics. We wrap
up with a call for the collection of more adequate, spatially-explicit data on residential mobility and
migration globally. Full article
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Open AccessData Descriptor
Urbanization in India: Population and Urban Classification Grids for 2011
Data 2019, 4(1), 35; https://doi.org/10.3390/data4010035 - 26 Feb 2019
Cited by 3
Abstract
India is the world’s most populous country, yet also one of the least urban. It has long been known that India’s official estimates of urban percentages conflict with estimates derived from alternative conceptions of urbanization. To date, however, the detailed spatial and settlement [...] Read more.
India is the world’s most populous country, yet also one of the least urban. It has long been known that India’s official estimates of urban percentages conflict with estimates derived from alternative conceptions of urbanization. To date, however, the detailed spatial and settlement boundary data needed to analyze and reconcile these differences have not been available. This paper presents gridded estimates of population at a resolution of 1 km along with two spatial renderings of urban areas—one based on the official tabulations of population and settlement types (i.e., statutory towns, outgrowths, and census towns) and the other on remotely-sensed measures of built-up land derived from the Global Human Settlement Layer. We also cross-classified the census data and the remotely-sensed data to construct a hybrid representation of the continuum of urban settlement. In their spatial detail, these materials go well beyond what has previously been available in the public domain, and thereby provide an empirical basis for comparison among competing conceptual models of urbanization. Full article
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Open AccessData Descriptor
Gridded Population Maps Informed by Different Built Settlement Products
Data 2018, 3(3), 33; https://doi.org/10.3390/data3030033 - 04 Sep 2018
Cited by 8
Abstract
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 [...] Read more.
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. Full article
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