The provincial trend of population aging in China – based on population expansion forecast formula

China is one of the countries that have entered the stage of population aging. At present, the phenomenon of aging population has become a widespread concern of the whole society. Scientific and accurate prediction of aging population will help relevant departments to formulate specific countermeasures. This paper uses the Yearbook of China’s 1% population sampling survey in 2015 and the data published by the National Bureau of statistics. Based on the basic population prediction formula, the population prediction formula is established by using the index extrapolation method to predict the population aging development trend of provinces and cities in China from 2020 to 2050. The results show that: China’s aging degree will continue to increase, the size of the elderly population will continue to increase, 2020–2030 will be a period of rapid growth of the national population aging, after then the aging ratio will decline. The government should formulate security countermeasures for the elderly from various aspects as soon as possible and actively respond to aging the population.

A Hybrid Population Distribution Prediction Approach Integrating LSTM and CA Models with Micro-Spatiotemporal Granularity: A Case Study of Chongming District, Shanghai

Studying population prediction under micro-spatiotemporal granularity is of great significance for modern and refined urban traffic management and emergency response to disasters. Existing population studies are mostly based on census and statistical yearbook data due to the limitation of data collecting methods. However, with the advent of techniques in this information age, new emerging data sources with fine granularity and large sample sizes have provided rich materials and unique venues for population research. This article presents a new population prediction model with micro-spatiotemporal granularity based on the long short-term memory (LSTM) and cellular automata (CA) models. We aim at designing a hybrid data-driven model with good adaptability and scalability, which can be used in more refined population prediction. We not only try to integrate these two models, aiming to fully mine the spatiotemporal characteristics, but also propose a method that fuses multi-source geographic data. We tested its functionality using the data from Chongming District, Shanghai, China. The results demonstrated that, among all scenarios, the model trained by three consecutive days (ordinary dates), with the granularity of one hour, incorporated with road networks, achieves the best performance (0.905 as the mean absolute error) and generalization capability.

Predicting the Population Growth and Structure of China Based on Grey Fractional-Order Models

Scientific prediction and accurate grasp of the future trend of population change are conducive to the formulation of different population policies at different stages, so as to alleviate the adverse effects of the aging population on society and provide scientific theoretical reference for controlling the population size and making policy. Considering that the population system is affected by many complex factors and the structural relationship among these factors is complex, it can be regarded as a typical dynamic grey system. In this paper, the fractional-order GM (1, 1) model and the fractional-order Verhulst model are established, respectively, based on the statistical data of China's population indices from 2015 to 2019 to forecast the population size and the change trend of population structure of China from 2015 to 2050 in the short-term and medium- to long-term. The forecast results show that China’s population will grow in an inverse S shape from 2015 to 2050, when the total population will reach 1.43 billion. Moreover, during this period, the birth rate and natural growth rate of population will decrease year by year, and the proportion of aging population and the dependency ratio of population will increase year by year. Besides, the problem of aging population is going to become increasingly serious. The application of grey system method to population prediction can mine the complex information contained in the population number series. Meanwhile, the fractional-order accumulation can weaken the randomness of the original data series and reduce the influence of external disturbance factors, so it is a simple and effective population prediction method.

Urban Change Detection and Population Prediction Modeling Using Multitemporal Landsat TM Images

Urban environments belong to the most dynamic system on the earth's surface. Urban areas contain nearly half of the world's population. Understanding the growth and change brought on by urbanization is critical for urban planning, environmental studies, and resource management. This study is an attempt to present a satellite-based approach to modelling urban population growth from multitemporal and multispectral Landsat image data. The focus is placed on two aspects: detection of urban land cover changes and population prediction modeling associated with the urban expansion. The study consists of an experimental set-up to generate the land cover maps and to recognize the vegetation-impervious surface-soil (V-I-S) patterns followed by integrating population census data and remote sensing data at the city planning district level. This is done in conjunction with geographic information systems (GIS) in order to model population growth from 1996 to 2001 in the City of Mississauga, Ontario. The main findings of this research show that a total of 81.6 km² of built-up areas have been added with Mississauga's boundaries between 1985 and 2002. This accounts for 25.5% of the total area of Mississauga at the expense of non-built and water covered areas. The results show an increase of 6.5% in built-up areas in the last three years (1999-2002), which results in an average growth rate of 7 km²/year. The previous 14 years (1985-1999) have shown an increase of 19.0% in development, which equals 4.3 km²/year. The investigation also shows that a linear equation adequately describes the relationship between the population counts and the built-up area, or "C-442" area, of V-I-S components.

Urban Change Detection and Population Prediction Modeling Using Multitemporal Landsat TM Images

Urban environments belong to the most dynamic system on the earth's surface. Urban areas contain nearly half of the world's population. Understanding the growth and change brought on by urbanization is critical for urban planning, environmental studies, and resource management. This study is an attempt to present a satellite-based approach to modelling urban population growth from multitemporal and multispectral Landsat image data. The focus is placed on two aspects: detection of urban land cover changes and population prediction modeling associated with the urban expansion. The study consists of an experimental set-up to generate the land cover maps and to recognize the vegetation-impervious surface-soil (V-I-S) patterns followed by integrating population census data and remote sensing data at the city planning district level. This is done in conjunction with geographic information systems (GIS) in order to model population growth from 1996 to 2001 in the City of Mississauga, Ontario. The main findings of this research show that a total of 81.6 km² of built-up areas have been added with Mississauga's boundaries between 1985 and 2002. This accounts for 25.5% of the total area of Mississauga at the expense of non-built and water covered areas. The results show an increase of 6.5% in built-up areas in the last three years (1999-2002), which results in an average growth rate of 7 km²/year. The previous 14 years (1985-1999) have shown an increase of 19.0% in development, which equals 4.3 km²/year. The investigation also shows that a linear equation adequately describes the relationship between the population counts and the built-up area, or "C-442" area, of V-I-S components.