Population Estimation Methods Research Articles

Sometimes simple is good enough: An analysis of methods for residential building population estimation

AbstractResidential building population data can be useful in a breadth of urban planning, health, transportation, and business applications. Unfortunately, complete datasets of residential building populations are not widely available for use in Canada, and therefore either larger census geographies are used or residential building populations must be estimated. This research explores four different methods of estimating residential building populations, including: an equal allocation method, two measures based on building volume, and a novel method that integrates census data at the dissemination area level to calibrate a population estimation model. This work comprises three parts: 1) a description of these approaches, 2) an evaluation of their validity in a case study in Hamilton, Ontario, and 3) an application of these methods in measuring spatial accessibility to schools. Our results show that most methods yield very similar results, and most provide reasonable estimates of building populations that could be useful for some analytical tasks. However, all methods resulted in instances of error, particularly for the largest population buildings. We conclude that while more complex methods do not significantly outperform simpler methods based on building volume alone, the blend of these methods could yield more accurate population predictions.

ESTIMATION POPULATION SIZE PECCARIES (TAYASSU PECARI), IN THE “RIO NEGRO” FARM - BRASILIAN PANTANAL, DURING 2003 - 2004, BY MEANS CAPTURE AND RECAPTURE MULTIPLE METHOD

The Tayassu pecari are predominantly distributed in conserved biomes and the maintenance of the species is very susceptible to human activity. In this context, it is important the search for methods of population estimation as well as apply robust methods that allow more accurate estimative. Therefore, this work aims to estimate the population of peccaries, on the Rio Negro farm (Aquidauana-MS) through the multiple captures and recapture method during the years 2003 and 2004, considering the assumption that the population is closed. The estimate of the population size was obtained by the computational resampling and three procedures are compared. It was verified the marked difference between males and females for both years. The population size it was estimated in 134 and 192, however, by means the bootstrap method, a notable change in the distribution pattern of estimates was verified, being positively asymmetric and symmetric, for 2003 and 2004 respectively.

Scale effects-aware bottom-up population estimation using weakly supervised learning

ABSTRACT Fine-scale population estimation (FPE) is crucial for urban management. After training, the bottom-up FPE models can be applied independently of census data. However, given the lack of real fine-scale population data, the existing bottom-up methods typically apply models trained on coarse-grained census data to FPE directly, causing estimation bias induced by scale effects. Traffic analysis zones (TAZs) balance geo-semantics and fine granularity, but their potential as population analysis units has not been fully exploited. Hence, we developed a weakly-supervised TAZ-scale bottom-up population estimation method (WSTP). Specifically, to mitigate scale effects, the weakly-supervised training procedure involves fine-scale feature input, model prediction, spatial aggregation, and coarse-scale supervision is designed to ensure that WSTP consistently focuses on FPE throughout the training and prediction phases. To enable weakly-supervised training using census data, we treated TAZs as graph nodes and designed a Spatial Aggregation Layer to aggregate TAZ-scale population predictions into communities. Given the diverse distribution patterns across age groups, we decomposed the FPE task by age groups. The experiments showed that WSTP significantly outperformed the baselines with R2 values of 0.821 and 0.785 at the community and TAZ scales, respectively, indicating that WSTP can mitigate scale effects and produce high-resolution, accurate population data.

Camera traps as a research method for carnivore population estimation: Strength, weaknesses, opportunities and threats, analysis and improvements

Camera traps have been gaining popularity in population estimation studies. Based on 149 scientific journals review we evaluated the strengths, weaknesses and improvements of the camera trap method to better understand its effectiveness for studying population parameters. Camera traps have a strong advantage of being a non-invasive method, requiring minimal labor and because of its ability detect multiple species per sampling effort. However, theft and time-consuming data analyses, poor sensor performance and potential behavioral changes of wildlife due to noise and flashlights, prevent the camera traps from being the optimal population estimation method. The population parameter studied depends strongly on the behavior and biology of the target species, although the most common opportunity for development is all related to sensor performance (better triggering response and higher sensitivity) as well as extreme weather condition resistance.

Dual-environment feature fusion-based method for estimating building-scale population distributions

ABSTRACTInformation on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues. However, the discontinuity and strong spatial heterogeneity of research units at the building scale make it challenging to fuse multi-source geographic data, which causes significant errors in population estimation. To address this problem, this study proposes a method for population estimation at the building scale based on Dual-Environment Feature Fusion (DEFF). The dual environments of buildings were constructed by splitting the physical boundaries and extracting features suitable for the dual-environment scale from multi-source geographic data to describe the complex environmental features of buildings. Meanwhile, Data Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution (DQW-TOPSIS) method was proposed to assign appropriate weights to the features of the external environment for better feature fusion. Finally, a regression model was established using dual-environment features for building-scale population estimation. The experimental areas chosen for this study were Jianghan and Wuchang Districts, both located in Wuhan City, China. The estimated results of the DEFF were compared with those of the ablation experiments, as well as three publicly accessible population datasets, specifically LandScan, WorldPop, and GHS-POP, at the community scale. The evaluation results showed that DEFF had an R2 of approximately 0.8, Mean Absolute Error (MAE) of approximately 1200, Root Mean Square Error (RMSE) of approximately 1700, and both Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) of approximately 26%, indicating an improved performance and verifying the validity of the proposed method for fine-scale population estimation.

Misleading overestimation bias in methods to estimate wolf abundance that use spatial models.

Abstract 1. Population abundance is the main criterion used by agencies to manage and conserve species and it allows adaptive decision-making in response to impacts. Its estimation is particularly important for large mammals, especially carnivores that are notoriously difficult to monitor yet have high ecological and economic importance to humans. Reversal of their historic population decline is also vital to restoring ecosystem health through the ecosystem service of trophic interactions. 2. Because bias and precision (variance) are the independent yardsticks of the quality and reliability of population estimates, we preliminarily assessed new abundance estimators for wolves that non-traditionally use spatial models to estimate the area that an observation represents. Among many biases and problems identified by recent assessments, we identified suspected biases due to obvious violation of the closure assumption in occupancy modeling used to determine the area occupied by territorial pack members. Thus, we chose to simulate the effect of spatial resolution (grid size) on the direction and magnitude of that inherent bias in a recent method called iPOM used in Montana. We also examined the potential bias in their estimate of variance and confidence intervals. 3. We found that even the use of small grid cells (relative to wolf territory size), biased the total area occupied and the number of packs used to calculate abundance. The bias rapidly increases with increasing grid cell size. At the grid cell size used in iPOM for Montana (600 km 2 ) there was a severe overestimation bias of 150% that proliferated through the iPOM submodel structure and resulted in estimated wolf abundance 2.5 times larger than true abundance. 4. This bias alone when combined with a misapplication and underreporting of iPOM's estimate of variance (biased low) results in a precariously misleading situation for decision-makers that threatens wolf populations. Other identified biases that inflate abundance likely make this situation worse but they should be further examined and tested. 5. Due to these biases and the high sensitivity of iPOM's spatial models to estimate area, we suggest that such spatial models should not be used in population estimation methods or such methods, iPOM for example, should be improved and/or restructured with submodels robust to assumption violation thereby reducing bias. Given iPOM's current design, there is no ability to detect change let alone determine wolf population size. 6. We provide a comparative framework for testing and improvement and strongly suggest proper model-assisted sampling designs and hierarchical modeling such as is used in capture-recapture models, especially those that use non-invasive procedures that avoid costly capture and marking. We also recommend collaborative activities that lead to using the best available scientific methods to determine carnivore abundance.

Projecting high resolution population distribution using Local Climate Zones and multi-source big data

High precision and easily updatable data on population estimates are critical for urban planning, disaster risk assessment, and public health campaigns. However, traditional administrative census data often faces limitations in spatial resolution, making it difficult for further applications. In this study, we construct a fast and efficient population estimation model using Local Climate Zones (LCZ) products at a fine spatial scale. The population totals are estimated by LCZ units, based on a robust linkage between built LCZ types and population density. Validated by city- and county-level census data in 21 cities in China, the new model exhibits a very good fit with R2 values of 0.77 and 0.73 respectively, which confirms the effectiveness of LCZ-based population estimates. To our knowledge, this is the first study that directly estimates the population via LCZ maps. In addition, it was proven that population density can be used as a new property for LCZ type definition. As LCZ products are convenient to obtain, this work provides a simple, economical, and reliable population estimation method that can complement the traditional census.

Integrating Species Distribution Models to Estimate the Population Size of Forest Musk Deer (Moschus berezovskii) in the Central Qinling Mountains of Shaanxi

Understanding the population size of animals is crucial for formulating scientific management policies, especially for endangered species. The central area of the Qinling Mountains in Shaanxi is a vital area for forest musk deer, but research is insufficient and estimates of its population size are lacking. In this study, we constructed a species distribution model for the forest musk deer in the central Qinling Mountains in Shaanxi using topography, land-use, and bioclimatic variables alongside forest musk deer occurrence data. The Time-to-Event (TTE) model was employed to estimate the population density of forest musk deer in the selected survey area. By utilizing the suitable habitat area provided by the species distribution model, the population density in the central Qinling Mountains of Shaanxi was estimated by extrapolating from the survey area. Our estimate of the population size of forest musk deer in the central Qinling Mountains of Shaanxi is approximately 2722 ± 788. Similar population estimation methods could be more widely applied, especially in areas with limited survey resources.

Assessing the accuracy of population estimation methods for vulture populations: a case study from the Mudumalai Tiger Reserve, Tamil Nadu, India

Two resident and two local migratory vulture species are reported in the Mudumalai Tiger Reserve of the Nilgiri Biosphere Reserve. The early population estimates from the region are either scanty or crude. Vulture population estimation was performed using three different methods based on their breeding seasons and regional movements between 2019 and 2021. Road transect counts, nest counts, and carcass monitoring counts were used for population assessments. The road transect survey and nest site counting during the breeding season were conducted in all three years, while carcass monitoring was done in 2019 – 2020. For population estimation, three nest site colony counts were conducted for the resident Gyps bengalensis and for Gyps indicus. The other two methods, carcass monitoring and the road transect survey, were used for all four focal vulture species of the Mudumalai Tiger Reserve – Gyps bengalensis, Gyps indicus, Neophron percnopterus and Sarcogyps calvus. Significant variation in vulture population count was observed with different assessment methods.

Computing Invasive Species Population Based on a Generalized Random Walk Process: Application to Blue Crab (Callinectes sapidus)

Animal population abundance is a significant parameter for studies on invasive species that can threaten the ecosystem. Researchers have been developing population estimation methods since the 18th century, in order to evaluate species’ evolution and environmental effects. However, studies on the population density of the invasive species Callinectes sapidus are very limited. The present work, using a simulation model combined with field measurements, examines an innovative methodology for estimating the current population of the invasive species Callinectes sapidus in a shallow Mediterranean coastal lagoon. The methodology presented here builds the first stage of modeling and predicting the evolution of this species’ population in marine environments. The simulation model’s results are validated with an estimation of the total population based on juvenile abundance, and a curvature of the species population estimation based on cage catch is implemented. The simulation experiments presented here show the possibility of a robust prediction for blue crab population estimation.

Better Metrics to Guide Public Health Policy: Lessons Learned From COVID-19 for Data Systems Improvement

As communities adjust COVID-19 policies, objective ‘metrics’ are needed to guide decisions. But metrics are only as good as their inputs. Most COVID-19 data are based on case surveillance. Individuals’ decisions to be tested depend on concerns about exposure and symptoms, as well as the availability of testing. These factors cause drop-offs at every step in the reporting process, so the numbers of actual cases, as well as deaths and hospitalizations, are far higher than reported, varying over time and among population subgroups.Metrics are used to compare among population groups and over time, so consistency in data systems is important. The Centers for Disease Control and Prevention (CDC) must provide leadership to standardize public health, hospital, and other data systems, not just case definitions. Public health emergencies are complex phenomena that cannot be summarized in a single indicator, so CDC should develop a balanced portfolio of metrics that together describe the epidemiologic situation and provide information to guide decision-making. Metrics are intended to inform—not decide—policy decisions that balance epidemiologic benefits and social and economic costs, taking into account the current state of the pandemic. Policymakers should avoid hard triggers and consider trends over weeks to months rather than daily numbers.Going forward, we must complement case counts with population estimation methods such as sampling, excess mortality, and wastewater surveillance. While ‘estimation’ sounds less precise than ‘counting,’ these methods can provide a more comprehensive and accurate assessment of the pandemic’s impact on different populations and as it changes over time.

Relationship between age and method of population estimation and the impact of changes in range-size maps on the IUCN Red List assessments of birds

Summary Population size is one component of several criteria in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species (Red List). For these criteria, the quality of the population estimation can therefore have significant impact on the assessed status. To evaluate population estimate quality, we selected 473 species of land birds from the Americas considered by the Red List to be “Critically Endangered”, “Endangered”, or “Vulnerable” at the end of 2021, of which 414 (88%) had a population size estimate. For these species, we determined the age of the estimate and how the population estimate was derived, grouped into eight categories. For 87 species (18%) the population estimate was derived by sampling a small area and extrapolating to the entire range of the species; for these, the population size estimate depends on the estimate of range size. For the subset of 22 of these with complete data, we compared range size estimates obtained from maps published by IUCN with maps produced using the methods of Huang et al. (2021) to see how range map differences could affect population size estimates and therefore Red List status. Potentially half of these species (11 of 22) could change status using the new maps. More than one-third of the population size estimates (38%, 161 species with a date of population estimate) were made in 2000 or earlier. A majority of the species, 63% (300 of 473 species), do not have population size estimates made using a scientific sampling method, although the majority since 2010 have been made using a sampling method, reflecting an effort by Red List assessors to include more scientific information. We encourage the ornithological community to work to obtain current, high quality population size and range estimates to improve the quality of Red List status assessments.

Evaluation of population estimation methods for wastewater-based epidemiology in a metropolitan city

This study evaluated the effect of population estimation on the calculation of drug biomarker consumption using wastewater-based epidemiology. Population estimates using mobile phone data, census data, and wastewater quality parameters, such as biological oxygen demand (BOD), total nitrogen (TN), and total phosphorus (TP), were evaluated in six different wastewater treatment plant catchment areas of Busan Metropolitan City, South Korea. The population based on mobile phone data was affected by the patterns of non-resident population movements in each area. The population-normalized daily loads (PNDLs) of methamphetamine were compared according to the different population results. The PNDLs using the population based on mobile phone data (PNDLMobile) was 5.87-27.0 mg/d/1000 people. The PNDLMobile values were notably different from the PNDLs using wastewater quality parameters (PNDLWastewater) (PNDLWastewater/PNDLMobile: 51-148 %, mean 93 %, relative standard deviation (RSD) 36 %), indicating the unsuitability of population estimation using BOD, TN, and TP. In areas with a large concentration of workplaces, the PNDLs using census data (PNDLCensus) differed from the PNDLMobile values (PNDLCensus/PNDLMobile: 57-124 %, mean 94 %, RSD 27 %), whereas other areas showed similar values for PNDLCensus and PNDLMobile (PNDLCensus/PNDLMobile: 95-108 %, mean 102 %, RSD 4.2 %). In particular, the total population estimates of the six survey areas using census data were approximately the same as those based on mobile phone data (RSD: 0.8 %), indicating a decrease in the influence of the non-residential active population in the entire metropolitan city.

The Wilson’s Phalarope Phalaropus tricolor population and feeding activity at Lake Titicaca

Abstract A population estimation of Wilson’s Phalaropes was conducted in the Confinada Lagoon of the inner bay of Lake Titicaca in the city of Puno between August and May of 2016/2017, 2017/2018 and 2018/2019. We employed the quadrat method of population estimation with three repetitions using 10×50 mm binoculars between 06:00 and 08:00 h. We recorded population fluctuations in August and September (immigration season) and April and May (emigration season). We counted a maximum population of 112,000 Wilson’s Phalaropes during the immigration season, and then in the emigration season. During their stay of approximately 8 months, the birds feed on water fleas, insects and detritus amounted to 146,496 t. The inner bay of Lake Titicaca is an important aquatic habitat for these migratory birds, which has a positive impact on the contribution to the decontamination of the lake.

A comparison of density estimation methods for monitoring marked and unmarked animal populations

AbstractEffective monitoring of wildlife populations forms the foundation of modern‐day conservation biology. Without reliable estimates of population size, it is not possible to determine population trends, a key requirement in determining species status under international legislation. Carnivores are one of the more difficult taxonomic groups to monitor due to low population densities and elusive behavior. Here, we compare conventional live trapping and two more modern, noninvasive field methods of population estimation: genetic fingerprinting from hair tube sampling and camera trapping for the pine marten (Martes martes). We apply marked spatial capture–recapture (SCR) models to the genetic and live‐trapping data where individuals were identifiable, and unmarked SCR (uSCR), camera‐trap distance sampling (CT‐DS), and random encounter models (REMs) to the camera‐trap data where individual ID was not possible. All five approaches produced plausible and relatively consistent point estimates (0.49–1.20 individuals/km2) despite differences in precision, cost, and effort being apparent. Genetic fingerprinting produced the most precise estimate out of the two approaches for marked animal populations and had the key benefit of being noninvasive but was the most expensive of all the methods. Live trapping produced the highest point estimate while being cheapest, but the most labor intensive and least precise. The camera‐trapping methods for unmarked animal populations were the most time efficient and precise except uSCR with a moderately informative prior (uSCRm), which produced the second least precise density estimate of all the methods compared. The CT‐DS produced the most precise estimate of all the methods, followed by REM and then uSCR with a strongly informative prior (uSCRs). While choice of method of density estimation depends on objectives and funding constraints, as well as the species of interest, we demonstrate the importance of usinga prioriknowledge of target species and consideration of planned statistical analysis to produce appropriate experimental designs with critical consideration required regarding trap spacing and spatial extent. Such considerations broaden the comparability and applicability of these methods and will serve to provide key reference estimates for researchers, wildlife managers, and non‐governmental organizations involved in monitoring wildlife populations.

The impact of loneliness and social isolation on health state utility values: a systematic literature review

BackgroundLoneliness and social isolation are recognised as social problems and denote a significant health burden. The aim of this study was to conduct a systematic literature review to explore the health state utility values (HSUVs) associated with loneliness and/or social isolation.MethodPeer-reviewed journals published in English language that reported both HSUVs along with loneliness and/or social isolation scores were identified through five databases. No restrictions were made relating to the population, study design or utility estimation method used.ResultsIn total, 19 papers were included; 12 included a measure of loneliness, four studies included a measure of social isolation and three studies considered both loneliness and social isolation. All studies focused on individuals with pre-existing health conditions—where the EQ-5D-3L instrument was most frequently used to assess HSUVs. HSUVs ranged from 0.5 to 0.95 in those who reported not being lonely, 0.42 to 0.97 in those who experienced some level of loneliness, 0.3 to 0.87 in those who were socially isolated and 0.63 to 0.94 in those who were not socially isolated.ConclusionThere was significant variation in HSUVs complicated by the presence of co-morbidities, population heterogeneity, variations in methods used to derive utility scores and differences in the measurement of loneliness and/or social isolation. Nevertheless, the lower HSUVs observed should be considered to significantly impact quality of life, though we also note the need for further research to explore the unique impact of loneliness and social isolation on HSUVs that can be used in the future economic evaluations.

Population Studies of Deer (Odocoileus virginianus, Mazama americana) in Southern Yucatán, Mexico

Aims: Estimate the population density of deer in the municipality of Tzucacab, Yucatán in the periods of 2003-2004, 2007-2008 and 2008-2009, determine the use of the habitat by these populations and the sustainability of the deer harvest from the estimated population densities.
 Study Design: A descriptive and vertical free-living deer population study was carried out in southern Yucatan, Mexico over a three-year period.
 Methodology: The map of the municipality of Tuzcacab was zoned in quadrants of 36 km2, completing a total of 36 quadrants; Unrestricted random sampling was applied to select seven quadrants in the period from 2003 to 2004 and 18 in each annual period between 2007 and 2009. Population samplings were carried out by applying three population estimation methods: direct sighting in a linear transect of 5 km in length, count of tracks in transect except period 2003-2004 and faecal pellets group count in plots. The evaluation of the use of habitat was carried out using the Bonferroni intervals, from the data of faecal pellets count. The evaluation of the deer harvest was carried out using the sustainable harvest model.
 Results: The population densities were different in each method, the density by the excreta count was 4.63 ± 2.49 deer / km2 in 2003-2004, 0.294 ± 0.198 deer / km2 in 2007-2008, and in the year 2008-2009 was 0.419 ± 0.0000085 deer / km2. Habitat use in 2007-08 and 2008-2009 was higher in the tropical forest, lower in agriculture and similar to that expected in secondary succession forest (acahual). The values of sustainable harvest, taking as a value the density per count of excreta in the plot because it showed the highest statistical precision, in the period 2003-04 it is sustainable, but in the period from 2007 to 2009 it is not sustainable.
 Conclusion: The population densities of deer (O. virginianus and M. americana) in Tuzcacab by means of the excreta count method, have decreased significantly. The habitat use preference is the tropical forest. The deer harvest in the period from 2007 to 2009 is not sustainable.

A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators

BackgroundHousehold survey data are frequently used to measure reproductive, maternal, newborn, child and adolescent health (RMNCAH) service utilisation in low and middle income countries. However, these surveys are typically only undertaken every 5 years and tend to be representative of larger geographical administrative units. Investments in district health management information systems (DHMIS) have increased the capability of countries to collect continuous information on the provision of RMNCAH services at health facilities. However, reliable and recent data on population distributions and demographics at subnational levels necessary to construct RMNCAH coverage indicators are often missing. One solution is to use spatially disaggregated gridded datasets containing modelled estimates of population counts. Here, we provide an overview of various approaches to the production of gridded demographic datasets and outline their potential and their limitations. Further, we show how gridded population estimates can be used as alternative denominators to produce RMNCAH coverage metrics in combination with data from DHMIS, using childhood vaccination as examples.MethodsWe constructed indicators on the percentage of children one year old for diphtheria, pertussis and tetanus vaccine dose 3 (DTP3) and measles vaccine dose (MCV1) in Zambia and Nigeria at district levels. For the numerators, information on vaccines doses was obtained from each country’s respective DHMIS. For the denominators, the number of children was obtained from 3 different sources including national population projections and aggregated gridded estimates derived using top-down and bottom-up geospatial methods.ResultsIn Zambia, vaccination estimates utilising the bottom-up approach to population estimation substantially reduced the number of districts with > 100% coverage of DTP3 and MCV1 compared to estimates using population projection and the top-down method. In Nigeria, results were mixed with bottom-up estimates having a higher number of districts > 100% and estimates using population projections performing better particularly in the South.ConclusionsGridded demographic data utilising traditional and novel data sources obtained from remote sensing offer new potential in the absence of up to date census information in the estimation of RMNCAH indicators. However, the usefulness of gridded demographic data is dependent on several factors including the availability and detail of input data.

Sewage Systems Surveillance for SARS-CoV-2: Identification of Knowledge Gaps, Emerging Threats, and Future Research Needs.

The etiological agent for novel coronavirus (COVID-19, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), not only affects the human respiratory system, but also the gastrointestinal tract resulting in gastrointestinal manifestations. The high rate of asymptomatic infected individuals has challenged the estimation of infection spread based on patients’ surveillance, and thus alternative approaches such as wastewater-based epidemiology (WBE) have been proposed. Accordingly, the number of publications on this topic has increased substantially. The present systematic review thus aimed at providing state-of-the-knowledge on the occurrence and existing methods for sampling procedures, detection/quantification of SARS-CoV-2 in sewage samples, as well as anticipating challenges and providing future research direction to improve the current scientific knowledge. Articles were collected from three scientific databases. Only studies reporting measurements of virus in stool, urine, and wastewater samples were included. Results showed that improving the scientific community’s understanding in these avenues is essential if we are to develop appropriate policy and management tools to address this pandemic pointing particularly towards WBE as a new paradigm in public health. It was also evident that standardized protocols are needed to ensure reproducibility and comparability of outcomes. Areas that require the most improvements are sampling procedures, concentration/enrichment, detection, and quantification of virus in wastewater, as well as positive controls. Results also showed that selecting the most accurate population estimation method for WBE studies is still a challenge. While the number of people infected in an area could be approximately estimated based on quantities of virus found in wastewater, these estimates should be cross-checked by other sources of information to draw a more comprehensive conclusion. Finally, wastewater surveillance can be useful as an early warning tool, a management tool, and/or a way for investigating vaccination efficacy and spread of new variants.

Population mapping in China with Tencent social user and remote sensing data

Real-time population data are vital for urban planning and resource management for sustainable development. To complement satellite-based population estimation methods, geospatial social media data provide additional opportunities to estimate the distribution of population with high levels of efficacy and accuracy. Thus, this study attempts to assess the performance of various sensing data to disaggregate population data in China; the tested data include Tencent location-based service (LBS) data (about 0.8 billion users), satellite-derived land use/cover data, and nightlight imagery data. With the use of census data for validation, the experimental results show that Tencent LBS data are much better than satellite-derived land use/cover data and nightlight satellite data for mapping the population distribution. The overall mapping accuracy at the city level using Tencent LBS data was 88.9%, whereas the accuracy using land use/cover data was 87.1% and that using nightlight satellite data was 85.5%. The experimental results also indicate that LBS data and remote sensing data could both be well integrated to map the population distribution in China. Thus, a population spatialization model was further developed using all of the tested indicators; this model allowed the overall population estimation accuracy at the city level to reach 90.4%. This model could help determine the population distribution on various spatial scales quickly and efficiently, and the developed tool and the provided population estimates may be vital for the sustainable development of cities and regions for which population data are lacking.