APPLICATION OF REMOTE SENSING TECHNOLOGY TO POPULATION ESTIMATION

This paper attempts to explore a new avenue of urban small-regional population estimation by remote sensing technology, creatively and comprehensively for the first time using a residence count method, area (density) method and model method, incorporating the application experience of American scholars in the light of the state of our country. Firstly, the author proposes theoretical basis for population estimation by remote sensing, on the basis of analysing and evaluating the history and state quo of application of methods of population estimation by remote sensing. Secondly, two original types of mathematical models of population estimation are developed on the basis of remote sensing data, taking Tianjin City as an example. By both of the mathematical models the regional population may be estimated from remote sensing variable values with high accuracy. The number of the independent variables in the latter model is somewhat smaller and the collection of remote sensing data is somewhat easier, but the deviation is a little larger. Finally, some viewpoints on the principled problems about the practical application of remote sensing to population estimation are put forward.

An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

This paper addresses the problem of predicting population density leveraging cellular station data.As wireless communication devices are commonly used,cellular station data has become integral for estimating population figures and studying their movement,thereby implying significant contributions to urban planning.However,existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction.To address this,we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data.The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift.Further,we devise a multi-view enhancement model grounded on the Transformer(MVformer),targeting the improvement of the accuracy of extended time-series population predictions.Comparative experiments,conducted on the above-mentioned population dataset using four alternate Transformer-based models,indicate that our proposedMVformer model enhances prediction accuracy by approximately 30%for both univariate and multivariate time-series prediction assignments.The performance of this model in tasks pertaining to population prediction exhibits commendable results.

Multi-criteria Satisfaction Assessment of the Spatial Distribution of Urban Emergency Shelters Based on High-Precision Population Estimation

This article introduces a framework for the multi-criteria satisfaction assessment of the spatial distribution of urban emergency shelters.A GIS-based analytic hierarchy process approach was utilized to conduct the assessment based on selected criteria layers for daytime and nighttime scenarios,respectively.The layers were generated from high-precision land use data based on highresolution aerial images and census data.Considering the uncertainty in criteria weighting,a spatial sensitivity analysis was undertaken for deriving more accurate results.The feasibility of the framework was tested on a case study in Jing'an District,Shanghai,China.The assessment results show that both at nighttime and during daytime,even if all potentially available shelters are open,the demand in large areas can only be marginally satisfied or not satisfied,especially in the northern,eastern,and central parts of Jing'an District.The quantitative analysis of the satisfaction conditions of the buildings or land parcels and the affected people,especially children and the elderly,shows a low satisfaction level of shelter services in these areas.The satisfaction assessment of emergency shelters can help government decision makers find low satisfaction areas of sheltering services and support further locationallocation optimization of urban emergency shelters.

Supply-Demand Analysis of Urban Emergency Shelters Based on Spatiotemporal Population Estimation

Supply–demand analysis is an important part of the planning of urban emergency shelters.Using Pudong New Area,Shanghai,China as an example,this study estimated daytime and nighttime population of the study area based on fine-scale land use data,census data,statistical yearbook information,and Tencent user-density big data.An exponential function-based,probability density estimation method was used to analyze the spatial supply of and demand for shelters under an earthquake scenario.The results show that even if all potential available shelters are considered,they still cannot satisfy the demand of the existing population for evacuation and sheltering,especially in the northern region of Pudong,under both the daytime and the nighttime scenarios.The proposed method can reveal the spatiotemporal imbalance between shelter supply and demand.We also conducted a preliminary location selection analysis of shelters based on the supply–demand analysis results.The location selection results demonstrate the advantage of the proposed method.It can be applied to identify the areas where the supply of shelters is seriously inadequate,and provide effective decision support for the planning of urban emergency shelters.

A new approach on seismic mortality estimations based on average population density

This study examines a new methodology to predict the final seismic mortality from earthquakes in China. Most studies established the association between mortality estimation and seismic intensity without considering the population density. In China, however, the data are not always available, especially when it comes to the very urgent relief situation in the disaster. And the popu- lation density varies greatly from region to region. This motivates the development of empirical models that use historical death data to provide the path to analyze the death tolls for earthquakes. The present paper employs the average population density to predict the final death tolls in earthquakes using a case-based reasoning model from realistic perspective. To validate the forecasting results, historical data from 18 large-scale earthquakes occurred in China are used to estimate the seismic morality of each case. And a typical earthquake case occurred in the northwest of Sichuan Province is employed to demonstrate the estimation of final death toll. The strength of this paper is that it provides scientific methods with overall forecast errors lower than 20 %, and opens the door for conducting final death forecasts with a qualitative and quantitative approach. Limitations and future research are also analyzed and discussed in the conclusion.

Global population estimate and conservation gap analysis for the Nordmann's Greenshank(Tringa guttifer)

Nordmann's Greenshank(Tringa guttifer)is a globally endangered species that has received little research attention.It is threatened by rapid habitat loss,an incomplete network of protected sites,and lack of long-term data on population dynamics.Citizen science data can be combined with survey data to support population estimation and conservation gap analysis.From 2020 to 2021,Nordmann's Greenshank was surveyed in Tiaozini,Xiaoyangkou,and Dongling on the southern coast of Jiangsu Province,China,and the global population of the species was re-evaluated using the data obtained.We integrated citizen science data from eBird and the China Bird Report from 2000 to 2020 with the survey results to identify important habitats harboring over 1%of its total population,and compared this data with existing protected areas to identify gaps in its global conservation.Our survey found that Tiaozini supported at least 1194 individuals.Consequently,its global population was reestimated to be 1500-2000.Moreover,45 important habitats were identified based on citizen data and survey results.Although 44.4%and 50.0%of the priority sites in the world and China,respectively,are located outside protected areas,the Conservation Effectiveness Index(C)is 68.4%and 71.1%,respectively,showing that the current coverage of protected areas for this part of its range is reasonable.This study presents the most complete and recent population data to date.Tiaozini is the most important migration stopover site for Nordmann's Greenshanks.The species is under threat in terms of breeding,wintering,and stopover sites.Therefore,we suggest improving monitoring,establishing new protected sites to complete the habitat protection network,and improving the effectiveness of existing habitat protection strategies,including further developing high tide roosting sites.

Investigation on the Relationship between Population Density and Satellite Image Features——a Deep Learning Based Approach

Timely and accurate population statistic data plays an important role in many fields.To illustrate the demographic characteristics,population density is a crucial factor in evaluating population data.With a dynamic regional migration in population,it is a challenging job to evaluate population density without a census-based survey.We present the approach to classify satellite images in different magnitudes in population density and execute the comparative experiment to discuss the factors that influence the identification to the images with the deep learning approach.In this paper,we use satellite imagery and community population density data.With convolutional neural networks,we evaluated the performance of CNN on population estimation with satellite images,found the features that are important in population estimation,and then perform the sensitive analysis.

A revised species population estimate for the Bar-headed Goose(Anser indicus)

Background: The Bar-headed Goose(Anser indicus) is a species that relies heavily on the plateau wetlands of Asia and whose population was thought to be declining. Over the past decade, south-central Tibet, one of the most important wintering grounds, supported large numbers of Bar-headed Geese, but the population had not been regularly monitored in this area.Methods: We surveyed wintering Bar-headed Geese along the Yarlung Zangbo, Lhasa and Nyang Qu rivers, the three major river valleys and their tributaries in south-central Tibet in January 2014 and recorded their location, flock size and habitat utilization. Based on these data and the latest wintering counts elsewhere, we revised the population estimate for this species.Results: We recorded more than 67,000 Bar-headed Geese in south-central Tibet during January 2014. By geographic area, the geese were most abundant in the Lhasa River valley(38.5%) and the Nyang Qu River valley(31.0%), and by administrative division in Lhunzhub(27.2%) and Shigatse(26.7%). Bar-headed Geese were most often observed feeding in winter wheat fields and ploughed fields, resting on pastureland and marshes. The approximate number of 67,000 geese recorded in Tibet is more than four times the estimate of 1993 for the same region and exceeds the most recent world population estimate of 52,000–60,000. Based on our work in Tibet and the latest wintering counts available from other areas, we revised the estimated population size of this species to 97,000–118,000.Conclusions: Our result reveals a remarkable increase in the number of Bar-headed Geese wintering in south-central Tibet. This population increase most likely stems from a proliferation of cropland and especially winter wheat fields in south-central Tibet. This habitat improvement may also cause short-stopping of the Bar-headed Goose and thus reduce mortality of the geese that would otherwise undertake a somewhat daunting trans-Himalayan migration.

An Non-parametrical Approach to Estimate Location Parameters under Simple Order

This paper deals with estimating parameters under simple order when samples come from location models. Based on the idea of Hodges and Lehmann estimator (H-L estimator), a new approach to estimate parameters is proposed, which is difference with the classical L1 isotonic regression and L2 isotonic regression. An algorithm to compute estimators is given. Simulations by the Monte-Carlo method is applied to compare the likelihood functions with respect to L1 estimators and weighted isotonic H-L estimators.

THE ESTIMATION OF THE PREVALENCE OF HEAR-ING-IMPAIRMENT FOR THE NOISE-EXPOSED POPULATION IN CHINA BASED ON ISO/DIS 1999.2

Based on the noise survey in China ,the formula (H=H+N-HN/120 ) of ISO/DIS 1999.2 (' Acoustics - Determination of Occupational noise exposure and estimation of noise induced hearing impairment ' , 1985) was applied to the calculation of the hearing threshold level associated with age and noise (HTLAN) of the noise - impaired people . According to the Gausscian distribution , when the noise - exposure level LEX8h was 85 ,90 ,95, 100 dB and the hearing threshold frequency is from 0.5k to 6kHz , the HTLAN of noise - exposed people withdifferent duration of exposure and its relation values to the hearing threshold frequency associated with age (HTLA) were obtained . The ISO /DIS 1999.2 has been proved to be applicable in China .

A new pattern-based method for identifying recent HIV-1 infections from the viral env sequence

The long asymptomatic stage of HIV infection poses a great challenge in identifying recent HIV infections. This is a bottleneck for monitoring HIV epidemic trends and evaluating the effectiveness of national AIDS control programs. Several serological methods were used to address this issue with some success. Because of high false-positive rates in patients with advanced infection or in ART treatment, UNAIDS still hesitates to recommend their use in routine surveillance. We developed a new pattern-based method for measuring intra-patient viral genetic diversity for determination of recent infections and estimation of population incidence. This method is verified by using several datasets (424 subtype B and 77 CRF07_BC samples) with clearly identified HIV-1 infection times. Pattern-based diversities of recent infections are significantly lower than that of chronic ones. With larger window periods varying from 200 to 350 days, a higher accuracy (90% 95%) not affected by advanced disease nor ART treatment could be obtained. The pattern-based genetic method is supplementary to the existing serology-based assays, both of which could be suitable for use in low and high epidemic regions, respectively.