High-resolution Gridded Population Research Articles

Revisiting the relationships between energy consumption, economic development and urban size: A global perspective using remote sensing data

Existing methods of measuring energy consumption require complex statistics and computing. A real-time and globally applicable approach for comparing energy consumption across different cities is still lacking. Additionally, the nonlinear relationships and varying thresholds of energy consumption in relation to economic activities and urbanization remain unconfirmed. This study aims to fill these gaps by utilizing Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data in 2015 and a top-down approach based on a multiple regression model to examine energy consumption in global cities employing a redefined urban boundary. It also explores the accurate relationship between energy consumption, population density (as a proxy of urbanization), and per capita gross domestic product (GDP) across different regions and urban sizes using generalized additive models and regression models. High-resolution gridded population and GDP datasets covering the entire planet are utilized for this purpose. The study also estimates the development potentiality. The study yields followings outcomes: Firstly, the top 30 cities with the highest per capita energy consumption account for over 0.66% of the total per capita energy consumption of all cities. Secondly, in East Asia (EA) and Southeast Asia (SEA), the per capita energy consumption decreases when per capita GDP reaches $40,000 and $75,000, respectively, while it remains stable in cities located in Western Europe (WE) and North America (NA) as per capita GDP increases. Thirdly, the per capita energy consumption declines with increasing urban population density until reaching 10,000 person/km2, 22,000 person/km2, and 4000 person/km2 in EA, SEA, and NA, respectively. Fourthly, in Central Asia (CA), megacities can save over 100 Mbtu/population when per capita GDP increases by $1000 compared to big cities. This pioneering study provides a comparable investigation of energy consumption at the global city level, exploring its relationship with urbanization and economy by employing a unified calculation standard. It will facilitate long-term energy-saving policies and urban planning strategies.

Comparing the suitability of global gridded population datasets for local landslide risk assessments

AbstractLandslide risk assessments are increasingly crucial for meeting global disaster risk reduction strategies and mobilising knowledge for local governments to protect communities and infrastructure. These assessments are data intensive, requiring large amounts of spatial demographic and natural hazard information. There are a growing number of high-resolution gridded population datasets that have global coverage with significant potential to transform disaster risk modelling, however landslide research has not yet compared the suitability of these gridded datasets for local risk assessments. Combining social vulnerability indicators with high-resolution settlement layer (HRSL) and WorldPop gridded population datasets, as well as a local household survey, we layer landslide susceptibility maps to compare three landslide risk estimates, examining the case of the Municipality of Carigara located in the central Philippines. Using statistical t tests, we compare aggregated community landslide risk for 49 communities. Findings revealed that HRSL data resulted in similar landslide risk at community scales when compared to local surveys, however WorldPop data greatly overestimated risk. Our findings point to a high level of accuracy of HRSL when used as an exposure dataset for local landslide risk studies and recommend avoiding WorldPop for such purposes. This research advances understanding of the suitability of open population datasets for use in landslide risk assessments in resource-constrained communities.

High-resolution gridded population datasets for Latin America and the Caribbean using official statistics

“Leaving no one behind” is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the ‘top-down’ approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.

Historic evolution of population exposure to heatwaves in Xinjiang Uygur Autonomous Region, China

Heatwaves have pronounced impacts on human health and the environment on a global scale. Although the characteristics of heatwaves has been well documented, there still remains a lack of dynamic studies of population exposure to heatwaves (PEH), particularly in the arid regions. In this study, we analyzed the spatio-temporal evolution characteristics of heatwaves and PEH in Xinjiang using the daily maximum temperature (Tmax), relative humidity (RH), and high-resolution gridded population datasets. The results revealed that the heatwaves in Xinjiang occur more continually and intensely from 1961 to 2020. Furthermore, there is substantial spatial heterogeneity of heatwaves with eastern part of the Tarim Basin, Turpan, and Hami been the most prone areas. The PEH in Xinjiang showed an increasing trend with high areas mainly in Kashgar, Aksu, Turpan, and Hotan. The increase in PEH is mainly contributed from population growth, climate change and their interaction. From 2001 to 2020, the climate effect contribution decreased by 8.5%, the contribution rate of population and interaction effects increased by 3.3% and 5.2%, respectively. This work provides a scientific basis for the development of policies to improve the resilience against hazards in arid regions.

Refined Estimation of Potential GDP Exposure in Low-Elevation Coastal Zones (LECZ) of China Based on Multi-Source Data and Random Forest

With climate change and rising sea levels, the residents and assets in low-elevation coastal zones (LECZ) are at increasing risk. The application of high-resolution gridded population datasets in recent years has highlighted the threats faced by people living in LECZ. However, the potential exposure of gross domestic product (GDP) within LECZ remains unknown, due to the absence of refined GDP datasets and corresponding analyzes for coastal regions. The climate-related risks faced by LECZ may still be underestimated. In this study, we estimated the potential exposure of GDP in the LECZ across China by overlying DEM with new gridded GDP datasets generated by random forest models. The results show that 24.02% and 22.7% of China’s total GDP were located in the LECZ in 2010 and 2019, respectively, while the area of the LECZ only accounted for 1.91% of China’s territory. Significant variability appears in the spatial-temporal pattern and the volume of GDP across sectors, which impedes disaster prevention and mitigation efforts within administrative regions. Interannual comparisons reveal a rapid increase in GDP within the LECZ, but a decline in its share of the country. Policy reasons may have driven the slow shift of China’s economy to regions far from the LECZ.

An ensemble method to generate high-resolution gridded population data for China from digital footprint and ancillary geospatial data

Fine-scale population datasets are essential to many health and development applications. Quite a few population estimate approaches have been proposed and multiple gridded population datasets have been produced. However, it is still a challenge to accurately estimate daily and even hourly population dynamics. In this study, we present an ensemble learning approach to tackle the challenge through integrating a digital footprint dataset and multiple geospatial ancillary datasets to estimate population dynamics. More specifically, we used the geographically weighted regression model to integrate two aspatial tree-based learning models and generated preliminary hourly and daily gridded population estimates. We then adjusted the fine-scale population estimates based on the county-level estimates and their nonlinear relationship with the grid-level covariates. After sufficient model training and parameter tuning, we produced a series 0.01-degree gridded population maps (FinePop) of China for 2018, including a nationwide daily-average map and provincial hourly-average maps. The FinePop is more accurate than the WorldPop and LandScan datasets, as suggested by the highest R2 (0.72) obtained from the comparison against township-level population census data. The root mean squared error of the township population density estimates for FinePop, WorldPop, and LandScan are 3162, 3327, and 3423, respectively. The FinePop also shows its advantages in unraveling transportation networks and the diurnal-nocturnal population migration patterns in both small and large cities.

Spatially Explicit Population Projections: The case of Copenhagen, Denmark

Abstract. Cities expand rapidly with international migration significantly contributing to urban growth and urban population change. However, cities miss out on a great opportunity of reclaiming valuable knowledge on future population distribution due to the lack of established tools and methodologies to project where it is more likely for people of specific socio-demographic groups to set up home. The present work suggests that spatially explicit projections can play a significant role as a tool for urban planning and for managing diversity creatively, especially when a combination of social, demographic and topographic data is utilized. Machine learning techniques have demonstrated capabilities to capture relationships among this plethora of urban features to estimate future population distribution. We present a flexible, ML-based methodology for high-resolution gridded population projections by demographic characteristics, and specifically by region of origin, for the capital region of Copenhagen, Denmark, by combining various socio-demographic and topographic input layers.

High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent

The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa—the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10 m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets.

Semi-automatic mapping of pre-census enumeration areas and population sampling frames

Enumeration Areas (EAs) are the operational geographic units for the collection and dissemination of census data and are often used as a national sampling frame for various types of surveys. In many poor or conflict-affected countries, EA demarcations are incomplete, outdated, or missing. Even for countries that are stable and prosperous, creating and updating EAs is one of the most challenging yet essential tasks in the preparation for a national census. Commonly, EAs are created by manually digitising small geographic units on high-resolution satellite imagery or physically walking the boundaries of units, both of which are highly time, cost, and labour intensive. In addition, creating EAs requires considering population and area size within each unit. This is an optimisation problem that can best be solved by a computer. Here, for the first time, we produce a semi-automatic mapping of pre-defined census EAs based on high-resolution gridded population and settlement datasets and using publicly available natural and administrative boundaries. We demonstrate the approach in generating rural EAs for Somalia where such mapping is not existent. In addition, we compare our automated approach against manually digitised EAs created in urban areas of Mogadishu and Hargeysa. Our semi-automatically generated EAs are consistent with standard EAs, including having identifiable boundaries for field teams to follow on the ground, and appropriate sizing and population for coverage by an enumerator. Furthermore, our semi-automated urban EAs have no gaps, in contrast, to manually drawn urban EAs. Our work shows the time, labour and cost-saving value of automated EA delineation and points to the potential for broadly available tools suitable for low-income and data-poor settings but applicable to potentially wider contexts.

Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China

High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. Local estimates of the population allow officials and researchers to make a better local planning (e.g., optimizing public services and facilities). This study used a random forest algorithm, on the basis of remote sensing (i.e., satellite imagery) and social sensing data (i.e., point-of-interest and building footprint), to disaggregate census population data for the five municipal districts of Zhengzhou city, China, onto 100 × 100 m grid cells. We used a statistical tool to detect areas with an abnormal population density; e.g., areas containing many empty houses or houses rented by more people than allowed, and conducted field work to validate our findings. Results showed that some categories of points-of-interest, such as residential communities, parking lots, banks, and government buildings were the most important contributing elements in modeling the spatial distribution of the residential population in Zhengzhou City. The exclusion of areas with an abnormal population density from model training and dasymetric mapping increased the accuracy of population estimates in other areas with a more common population density. We compared our product with three widely used gridded population products: Worldpop, the Gridded Population of the World, and the 1-km Grid Population Dataset of China. The relative accuracy of our modeling approach was higher than that of those three products in the five municipal districts of Zhengzhou. This study demonstrated potential for the combination of remote and social sensing data to more accurately estimate the population density in urban areas, with minimum disturbance from the abnormal population density.

An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh

Traditional choropleth maps, created on the basis of administrative units, often fail to accurately represent population distribution due to the high spatial heterogeneity and the temporal dynamics of the population within the units. Furthermore, updating the data of spatial population statistics is time-consuming and costly, which underlies the relative lack of high-resolution and high-quality population data for implementing or validating population modeling work, in particular in low- and middle-income countries (LMIC). Dasymetric modeling has become an important technique to produce high-resolution gridded population surfaces. In this study, carried out in Dhaka City, Bangladesh, dasymetric mapping was implemented with the assistance of a combination of an object-based image analysis method (for generating ancillary data) and Geographically Weighted Regression (for improving the accuracy of the dasymetric modeling on the basis of building use). Buildings were extracted from WorldView 2 imagery as ancillary data, and a building-based GWR model was selected as the final model to disaggregate population counts from administrative units onto 5 m raster cells. The overall accuracy of the image classification was 77.75%, but the root mean square error (RMSE) of the building-based GWR model for the population disaggregation was significantly less compared to the RMSE values of GWR based land use, Ordinary Least Square based land use and building modeling. Our model has potential to be adapted to other LMIC countries, where high-quality ground-truth population data are lacking. With increasingly available satellite data, the approach developed in this study can facilitate high-resolution population modeling in a complex urban setting, and hence improve the demographic, social, environmental and health research in LMICs.

Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data

Fine-scale population distribution is increasingly becoming a research hotspot owing to its high demand in many applied fields. It is of great significance in urban emergency response, disaster assessment, resource allocation, urban planning, market research, and transportation route design. This study employed land cover, building address, and housing price data, and high-resolution stereo pair remote sensing images to simulate fine-scale urban population distribution. We firstly extracted the residential zones on the basis of land cover and Google Earth data, combined them with building information including address and price. Then, we employed the stereo pair analysis method to obtain the building height on the basis of ZY3-02 high-resolution satellite data and transform the building height into building floors. After that, we built a sophisticated, high spatial resolution model of population density. Finally, we evaluated the accuracy of the model using the survey data from 12 communities in the study area. Results demonstrated that the proposed model for spatial fine-scale urban population products yielded more accurate small-area population estimation relative to high-resolution gridded population surface (HGPS). The approach proposed in this study holds potential to improve the precision and automation of high-resolution population estimation.

Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China.

With sea level predicted to rise and the frequency and intensity of coastal flooding expected to increase due to climate change, high-resolution gridded population datasets have been extensively used to estimate the size of vulnerable populations in low-elevation coastal zones (LECZ). China is the most populous country, and populations in its LECZ grew rapidly due to urbanization and remarkable economic growth in coastal areas. In assessing the potential impacts of coastal hazards, the spatial distribution of population exposure in China’s LECZ should be examined. In this study, we propose a combination of multisource remote sensing images, point-of-interest data, and machine learning methods to improve the performance of population disaggregation in coastal China. The resulting population grid map of coastal China for the reference year 2010, with a spatial resolution of 100 × 100 m, is presented and validated. Then, we analyze the distribution of population in LECZ by overlaying the new gridded population data and LECZ footprints. Results showed that the total population exposed in China’s LECZ in 2010 was 158.2 million (random forest prediction) and 160.6 million (Cubist prediction), which account for 12.17% and 12.36% of the national population, respectively. This study also showed the considerable potential in combining geospatial big data for high-resolution population estimation.

Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets

ABSTRACTMulti-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas. In response to these agendas, a method has been developed to assemble and harmonise a unique, open access, archive of geospatial datasets. Datasets are provided as global, annual time series, where pertinent at the timescale of population analyses and where data is available, for use in the construction of population distribution layers. The archive includes sub-national census-based population estimates, matched to a geospatial layer denoting administrative unit boundaries, and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density. Here, we describe these harmonised datasets and their limitations, along with the production workflow. Further, we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics. The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.

A Multi-City Urban Population Mobility Study Using Mobile Phone Traffic Data

Mobility of population is one of major problems in large metropolitan areas. It affects different social, economic and environmental aspects of a city’s life. Urban mobility is characterised by highly dynamic spatial-temporal variability not detected by census or surveys. Mobile phones allow tracking of the population, giving information about their time–space location. A multi-city population mobility study was carried out in seven main Italian large metropolitan areas for 2 months (1st March to 30th April, 2015), starting from high-resolution gridded population data derived from mobile phone traffic data. Mean daily–nightly variations of population density were calculated during weekdays with increments of population of about 25% on average and peaks up by 200% in hotspot areas. Strong spatial gradients were identified with both accumulation and depletion of population in urban zones of geographical dimensions of 55 and 44%, respectively, of the municipality areas on average. Larger metropolitan areas, such as Rome, Milan, and Turin, show the highest increments of population, while medium-sized cities (e.g., Bari and Palermo) exhibit lower values and weaker gradients of variation of population. By means of a developed mobility factor, three main mobility phenomena (morning and evening commuting, diffuse mobility) occurring during weekdays were identified and characterized.

Dasymetric modeling: A hybrid approach using land cover and tax parcel data for mapping population in Alachua County, Florida

Spatial techniques and fine-scale geographic data may be combined in a variety of innovative ways to serve high-resolution population modeling efforts at local scales, which has been further facilitated by growing computation power and access to open-source spatial data. Previous work has highlighted the importance of a dasymetric approach to produce a parcel-based high-resolution gridded population surface (HGPS). In this study, we investigate the application of land-cover data integrated with the parcel-based HGPS to further improve the accuracy of the HGPS. Consideration is given to twelve combinations made by three land cover strategies (1- no land cover class, 2- five separate classes, and 3- three combined classes) and four property type strategies (1- seven types from an empirical study, 2- eight residential types, 3- seventeen types within Alachua County, and 4- twenty-five types within Florida). Results from different strategies are statistically compared with the most significant combination identified as three combined land-cover classes (heavy vegetation, 0–50% and >50–100% impervious surface) and with seven property types from the empirical study (single family, mobile family, multi-family (≥10 and <10 units), condominiums, mobile homes parks, and homes for the aged). A final data set named the Enhanced HGPS (E-HGPS) is created for Alachua County, Florida, with a distribution of population counts at the scale of individual housing units. This study highlights an innovative approach to incorporating land-cover and parcel data for the purpose of spatial population modeling, and holds potential to broaden the E-HGPS to a state or regional scope.

A fine-scale spatial population distribution on the High-resolution Gridded Population Surface and application in Alachua County, Florida

Geospatial techniques, using Geographic Information Systems and remote sensing data, have become more commonly used with dasymetric modeling of fine-scale demographic data. In this study, we apply a dasymetric approach using the Heuristic Sampling Method for 2010 parcel data to disaggregate population counts from the 2010 U.S. Census into a quadrilateral grid composed of 30 × 30 m cells covering the Alachua County, Florida. The final output, termed the High-resolution Gridded Population Surface (HGPS), is compared to a land cover-based population product (LCPP) and the detail of each product is assessed. Results suggest that the HGPS provides increased spatial heterogeneity and more detail in the boundaries of populated areas over the use of census blocks or land cover lots. For an example of the final output, we use a case study at the Cabot–Koppers Superfund Site to demonstrate the advantages of the HGPS over the LCPP. The HGPS is expected to serve as a more accurate input in various research fields, such as public health, crime analysis, and climate change. The approach outlined provides an improved means of producing spatially-explicit population grids where fine-scale ancillary data, such as parcel data, is available.

An elaborate high resolution emission inventory of primary air pollutants for the Central Plain Urban Agglomeration of China

A high resolution emission inventory of primary air pollutants was developed based on the detailed collected activity data and the latest source-specific emission factors for the year 2010 in the Central Plain Urban Agglomeration (CPUA) region of China. The total emissions of SO2, NOx, PM10, PM2.5, CO, VOCs, and NH3 were estimated to be about 863.7 kt, 1058.2 kt, 1180.4 kt, 753.2 kt, 2854.3 kt, 466.1 kt, and 496.0 kt, respectively. Therein, power plants were demonstrated to be the largest sources for NOx, contributing about 36.1% of total emissions; industrial processes and biomass burning sources were proved to be the two major contributors of PM10, PM2.5 and VOCs emissions, together accounting for about 71.1%, 79.2% and 56.9% of the total emissions respectively. Besides, 18.4% of VOCs emissions can be explained by VOCs product-related sources. Other stationary combustion sources accounted for 57.7% of SO2 and 30.3% of CO emissions, respectively. Livestock and N-fertilizer application sources contributed about 81.0% of NH3 emissions together. Further, the emissions were spatially distributed into grid cells with a resolution of 3 km × 3 km, by using spatial allocation surrogates such as high resolution gridded population density and regional GDP. This inventory will benefit for policymakers and researchers to better understand the current situation of complex air pollution in the CPUA region of China and supply important necessary input for regional air quality modeling and policymaking.

Continental Physiography, Climate, and the Global Distribution of Human Population

Continental Physiography, Climate, and the Global Distribution of Human Population