ANALYSIS OF VISIBLE INFRARED IMAGING RADIOMETER SUITE CAPABILITY FOR POPULATION ESTIMATION ON JAVA ISLAND

Population data, despite their significance, are often missing or difficult to access, especially in cities/regencies not belonging to the metropolitan areas or centers of various human activities. This hinders practices that are contingent on their availability. In this study, population estimation was carried out using nighttime light imagery generated by the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. The variable illuminated area was integrated with the population data using linear regression based on an allometric formula so as to produce a regression value, correlation coefficient (r), and coefficient of determination (r2). The average r2 between the illuminated area and the total population was 0.86, indicating a strong correlation between the two variables. Validation using samples of population estimates from three different years yielded an average error of 73% for each city and 7% for the entire study area. The estimation results for the number of residents per city/regency cannot be used as population data due to the high percent error, but for the population on a larger regional scale, in this case, the island of Java, they have a much smaller percent error and can be used as an initial picture of the total population.

Failure to account for behavioral variability overestimates bonobo populations and compromises accuracy in population monitoring

Wildlife population monitoring depends on accurate counts of individual animals or artefacts of behavior (e.g., nests or dung), but also must account for potential biases in the likelihood to encounter these animals or artefacts. In indirect surveying, which depends largely upon artefacts of behavior, likelihood to encounter indirect signs of a species is derived from both artefact production and decay. Although environmental context as well as behavior contribute to artefact abundance, variability in behaviors relevant to artefact abundance is rarely considered in population estimation. Here we demonstrate how ignoring behavioral variability contributes to overestimation of population size of a highly endangered great ape endemic only to the Democratic Republic of the Congo, the bonobo (Pan paniscus). Variability in decay of signs of bonobo presence (i.e., nests) is well documented and linked to environmental determinants. Conversely, a single metric of sign production (i.e., nest construction) is commonly used to estimate bonobo density, assumed to be representative of bonobo nest behavior across all contexts. We estimated nest construction rates from three bonobo groups within the Kokolopori Bonobo Reserve and found that nest construction rates in bonobos to be highly variable across populations as well as seasonal within populations. Failure to account for behavioral variability in nest construction leads to potentially severe degradation in accuracy of bonobo population estimates of abundance, accounting for a likely overestimation of bonobo numbers by 34%, and in the worst cases as high as 80% overestimation. Using bonobo nesting as an example, we demonstrate that failure to account for inter- and intra-population behavioral variation compromises our ability to monitor population change or reliably compare contributors to population decline or persistence. We argue that variation in sign production is but one of several potential ways that behavioral variability can affect conservation monitoring, should be measured across contexts whenever possible, and must be considered in population estimation confidence intervals. With increasing attention to behavioral variability as a potential tool for conservation, conservationists must also account for the impact that behavioral variability across time, space, individuals, and populations can play upon precision and accuracy of wildlife population estimation.

Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan

In the context of rapid urbanization, the refined management of cities is facing higher requirements. In improving urban population management levels and the scientific allocation of resources, fine-scale population data plays an increasingly important role. The current population estimation studies mainly focus on low spatial resolution, such as city-scale and county scale, without considering differences in population distributions within cities. This paper mines and defines the spatial correlations of multi-source data, including urban building data, point of interest (POI) data, census data, and administrative division data. With populations mainly distributed in residential buildings, a population estimation model at a subdistrict scale is established based on building classifications. Composed of spatial information and attribute information, POI data are spaced irregularly. Based on this characteristic, the text classification method, frequency-inverse document frequency (TF-IDF), is applied to obtain functional classifications of buildings. Then we screen out residential buildings, and quantify characteristic variables in subdistricts, including perimeter, area, and total number of floors in residential buildings. To assess the validity of the variables, the random forest method is selected for variable screening and correlation analysis, because this method has clear advantages when dealing with unbalanced data. Under the assumption of linearity, multiple regression analysis is conducted, to obtain a linear model of the number of buildings, their geometric characteristics, and the population in each administrative division. Experiments showed that the urban fine-scale population estimation model established in this study can estimate the population at a subdistrict scale with high accuracy. This method improves the precision and automation of urban population estimation. It allows the accurate estimation of the population at a subdistrict scale, thereby providing important data to support the overall planning of regional energy resource allocation, economic development, social governance, and environmental protection.

POPULATION FORECAST WITH THE DATA PROCESSING GROUP METHOD (GMDH) type NEURAL NETWORK FOR EUROPEAN UNION COUNTRIES

Population is a critically important factor in a country's planning, policy making, and setting its social and economic goals. Population estimation and planning in advance are of great importance for policy makers, since the natural resources, which are the production areas where people can meet their basic needs, are limited and they need to protect the areas they live in in order to continue their lives. In this study, the Group Method of Data Handling (GMDH) type Neural Network (NN) approach was used for the annual population estimation of 27 European Union (EU) countries (Germany, Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Finland, France, Cyprus, Croatia, Netherlands, Ireland, Spain, Sweden, Italy, Latvia, Lithuania, Luxembourg, Hungary, Malta, Poland, Portugal, Romania, Slovenia, Slovak Republic, Greece). The data set was obtained from the World Data Bank and analyzed using data from the years 1960 - 2020. The test performances obtained are generally below 10% of the Root Mean Square Percentage Error (RMSPE). The coefficient of determination (R^2) is above 0.90 and generally around 0.99. In addition, the mean absolute percentage error (MAPE) value is below 10%. According to these values, it is concluded that the model predicts extremely accurately. In addition, the analysis was compared with the 2021 - 2032 forecast values in the World Bank Database. According to the findings and comparison results, it has been concluded that the GMDH type Neural Network is a very good approach for the annual population estimation of 27 EU countries, it has almost exactly the same results with the real values in the past years, therefore it is consistent and successful in its predictions for the future years.

Latent Class Analysis for Estimating an Unknown Population Size – with Application to Censuses

Estimation of the unknown population size using capture-recapture techniques relies on the key assumption that the capture probabilities are homogeneous across individuals in the population. This is usually accomplished via post-stratification by some key covariates believed to influence individual catchability. Another issue that arises in population estimation from data collected from multiple sources is list dependence, where an individual’s catchability on one list is related to that of another list. The earlier models for population estimation heavily relied upon list independence. However, there are methods available that can adjust the population estimates to account for dependence among lists. In this article, we propose the use of latent class analysis through log-linear modelling to estimate the population size in the presence of both heterogeneity and list dependence. The proposed approach is illustrated using data from the 1988 US census dress rehearsal.

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

Background Household 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. Methods We 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. Results In 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. Conclusions Gridded 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.