Alessandro Sorichetta

High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020

The Latin America and the Caribbean region is one of the most urbanized regions in the world, with a total population of around 630 million that is expected to increase by 25% by 2050. In this context, detailed and contemporary datasets accurately describing the distribution of residential population in the region are required for measuring the impacts of population growth, monitoring changes, supporting environmental and health applications, and planning interventions. To support these needs, an open access archive of high-resolution gridded population datasets was created through disaggregation of the most recent official population count data available for 28 countries located in the region. These datasets are described here along with the approach and methods used to create and validate them. For each country, population distribution datasets, having a resolution of 3 arc seconds (approximately 100 m at the equator), were produced for the population count year, as well as for 2010, 2015, and 2020. All these products are available both through the WorldPop Project website and the WorldPop Dataverse Repository.

Reliability of groundwater vulnerability maps obtained through statistical methods

Statistical methods are widely used in environmental studies to evaluate natural hazards. Within groundwater vulnerability in particular, statistical methods are used to support decisions about environmental planning and management. The production of vulnerability maps obtained by statistical methods can greatly help decision making. One of the key points in all of these studies is the validation of the model outputs, which is performed through the application of various techniques to analyze the quality and reliability of the final results and to evaluate the model having the best performance. In this study, a groundwater vulnerability assessment to nitrate contamination was performed for the shallow aquifer located in the Province of Milan (Italy). The Weights of Evidence modeling technique was used to generate six model outputs, each one with a different number of input predictive factors. Considering that a vulnerability map is meaningful and useful only if it represents the study area through a limited number of classes with different degrees of vulnerability, the spatial agreement of different reclassified maps has been evaluated through the kappa statistics and a series of validation procedures has been proposed and applied to evaluate the reliability of the reclassified maps. Results show that performance is not directly related to the number of input predictor factors and that is possible to identify, among apparently similar maps, those best representing groundwater vulnerability in the study area. Thus, vulnerability maps generated using statistical modeling techniques have to be carefully handled before they are disseminated. Indeed, the results may appear to be excellent and final maps may perform quite well when, in fact, the depicted spatial distribution of vulnerability is greatly different from the actual one. For this reason, it is necessary to carefully evaluate the obtained results using multiple statistical techniques that are capable of providing quantitative insight into the analysis of the results. This evaluation should be done at least to reduce the questionability of the results and so to limit the number of potential choices.

Influence of threshold value in the use of statistical methods for groundwater vulnerability assessment

Statistical techniques can be used in groundwater pollution problems to determine the relationships among observed contamination (impacted wells representing an occurrence of what has to be predicted), environmental factors that may influence it and the potential contamination sources. Determination of a threshold concentration to discriminate between impacted or non impacted wells represents a key issue in the application of these techniques. In this work the effects on groundwater vulnerability assessment by statistical methods due to the use of different threshold values have been evaluated. The study area (Province of Milan, northern Italy) is about 2000 km(2) and groundwater nitrate concentration is constantly monitored by a net of about 300 wells. Along with different predictor factors three different threshold values of nitrate concentration have been considered to perform the vulnerability assessment of the shallow unconfined aquifer. The likelihood ratio model has been chosen to analyze the spatial distribution of the vulnerable areas. The reliability of the three final vulnerability maps has been tested showing that all maps identify a general positive trend relating mean nitrate concentration in the wells and vulnerability classes the same wells belong to. Then using the kappa coefficient the influence of the different threshold values has been evaluated comparing the spatial distribution of the resulting vulnerability classes in each map. The use of different threshold does not determine different vulnerability assessment if results are analyzed on a broad scale, even if the smaller threshold value gives the poorest performance in terms of reliability. On the contrary, the spatial distribution of a detailed vulnerability assessment is strongly influenced by the selected threshold used to identify the occurrences, suggesting that there is a strong relationship among the number of identified occurrences, the scale of the maps representing the predictor factors and the model efficiency in discriminating different vulnerable areas.

Fine resolution mapping of population age-structures for health and development applications

The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.

High resolution global gridded data for use in population studies

Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end the WorldPop Project has produced an open access archive of 3 and 30 arc-second resolution gridded data. Four tiled raster datasets form the basis of the archive: (i) Viewfinder Panoramas topography clipped to Global ADMinistrative area (GADM) coastlines; (ii) a matching ISO 3166 country identification grid; (iii) country area; (iv) and slope layer. Further layers include transport networks, landcover, nightlights, precipitation, travel time to major cities, and waterways. Datasets and production methodology are here described. The archive can be downloaded both from the WorldPop Dataverse Repository and the WorldPop Project website.

Spatiotemporal patterns of population in mainland China, 1990 to 2010

According to UN forecasts, global population will increase to over 8 billion by 2025, with much of this anticipated population growth expected in urban areas. In China, the scale of urbanization has, and continues to be, unprecedented in terms of magnitude and rate of change. Since the late 1970s, the percentage of Chinese living in urban areas increased from ~18% to over 50%. To quantify these patterns spatially we use time-invariant or temporally-explicit data, including census data for 1990, 2000, and 2010 in an ensemble prediction model. Resulting multi-temporal, gridded population datasets are unique in terms of granularity and extent, providing fine-scale (~100 m) patterns of population distribution for mainland China. For consistency purposes, the Tibet Autonomous Region, Taiwan, and the islands in the South China Sea were excluded. The statistical model and considerations for temporally comparable maps are described, along with the resulting datasets. Final, mainland China population maps for 1990, 2000, and 2010 are freely available as products from the WorldPop Project website and the WorldPop Dataverse Repository.

Ring of impact from the mega‐urbanization of Beijing between 2000 and 2009

The transient climate, soil, and air quality impacts of the rapid urbanization of Beijing between 2000 and 2009 are investigated with three-dimensional computer model simulations. The simulations integrate a new satellite data set for urban extent and a geolocated crowd-sourced data set for road surface area and consider differences only in urban land cover and its physical properties. The simulations account for changes in meteorologically driven natural emissions but do not include changes in anthropogenic emissions resulting from urbanization and road network variations. The astounding urbanization, which quadrupled Beijing urban extent between 2000 and 2009 in terms of physical infrastructure change, created a ring of impact that decreased surface albedo, increased ground and near-surface air temperatures, increased vertical turbulent kinetic energy, and decreased the near-surface relative humidity and wind speed. The meteorological changes alone decreased near-surface particulate matter, nitrogen oxides (NOx), and many other chemicals due to vertical dilution but increased near-surface ozone due to the higher temperature and lower NO. Vertical dilution and wind stagnation increased elevated pollution layers and column aerosol extinction. In sum, the ring of impact around Beijing may have increased urban heating, dried soil, mixed pollutants vertically, aggravated air stagnation, and increased near-surface oxidant pollution even before accounting for changes in anthropogenic emissions.

A Comparison of Data-Driven Groundwater Vulnerability Assessment Methods

Increasing availability of geo-environmental data has promoted the use of statistical methods to assess groundwater vulnerability. Nitrate is a widespread anthropogenic contaminant in groundwater and its occurrence can be used to identify aquifer settings vulnerable to contamination. In this study, multivariate Weights of Evidence (WofE) and Logistic Regression (LR) methods, where the response variable is binary, were used to evaluate the role and importance of a number of explanatory variables associated with nitrate sources and occurrence in groundwater in the Milan District (central part of the Po Plain, Italy). The results of these models have been used to map the spatial variation of groundwater vulnerability to nitrate in the region, and we compare the similarities and differences of their spatial patterns and associated explanatory variables. We modify the standard WofE method used in previous groundwater vulnerability studies to a form analogous to that used in LR; this provides a framework to compare the results of both models and reduces the effect of sampling bias on the results of the standard WofE model. In addition, a nonlinear Generalized Additive Model has been used to extend the LR analysis. Both approaches improved discrimination of the standard WofE and LR models, as measured by the c-statistic. Groundwater vulnerability probability outputs, based on rank-order classification of the respective model results, were similar in spatial patterns and identified similar strong explanatory variables associated with nitrate source (population density as a proxy for sewage systems and septic sources) and nitrate occurrence (groundwater depth).

ASPHAA: A GIS-Based Algorithm to Calculate Cell Area on a Latitude- Longitude (Geographic) Regular Grid

One characteristic of a Geographic Information System (GIS) is that it addresses the necessity to handle a large amount of data at multiple scales. Lands span over an area greater than 15 million km 2 all over the globe and information types are highly variable. In addition, multi-scale analyses involve both spatial and temporal integration of datasets deriving from different sources. The currently worldwide used system of latitude and longitude coordinates could avoid limitations in data use due to biases and approximations. In this article a fast and reliable algorithm implemented in Arc Macro Language (AML) is presented to provide an automatic computation of the surface area of the cells in a regularly spaced longitude-latitude (geographic) grid at different resolutions. The approach is based on the well-known approximation of the spheroidal Earth’s surface to the authalic (i.e. equal-area) sphere. After verifying the algorithm’s strength by comparison with a numerical solution for the reference spheroidal model, specific case studies are introduced to evaluate the differences when switching from geographic to projected coordinate systems. This is done at different resolutions and using different formulations to calculate cell areas. Even if the percentage differences are low, they become relevant when reported in absolute terms (hectares). tgis_1200 351..378

Exploring the high-resolution mapping of gender-disaggregated development indicators

Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.