Urban Population Size Research Articles

Empirical Analysis on the Mechanism of Industrial Park Driving Urban Expansion: A Case Study of Xining City

Taking Xining City as an example, this article analyzes the mechanism by which industrial park construction drives the expansion of urban population size and built-up area, based on a review of the process of industrial park development and urban population growth. It also discusses future urban governance models in light of urban development trends. The research finds: (1) In the process of urban development, industrial park construction is often the initial factor in the cumulative and cyclical development of a city; (2) As the level of development improves and the mode of economic growth changes, the government should timely adjust its strategies, shifting from the expansion of industrial park construction towards structural optimization and quality improvement. The most significant difference from previous research is that this paper emphasizes the importance of government planning. This study can not only demonstrate the general process of industrial parks promoting urban expansion, but more importantly, it explains the fundamental reasons for the transition of urban expansion to adjustment from a mechanism perspective, thereby eliminating the drawbacks of simply predicting urban scale evolution through data models.

Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data, 2010–23

Key population HIV programmes in sub-Saharan Africa require epidemiological information to ensure equitable and universal access to effective services. We aimed to consolidate and harmonise survey data among female sex workers, men who have sex with men, people who inject drugs, and transgender people to estimate key population size, HIV prevalence, and antiretroviral therapy (ART) coverage for countries in mainland sub-Saharan Africa. Key population size estimates, HIV prevalence, and ART coverage data from 39 sub-Saharan Africa countries between 2010 and 2023 were collated from existing databases and verified against source documents. We used Bayesian mixed-effects spatial regression to model urban key population size estimates as a proportion of the gender-matched, year-matched, and area-matched population aged 15-49 years. We modelled subnational key population HIV prevalence and ART coverage with age-matched, gender-matched, year-matched, and province-matched total population estimates as predictors. We extracted 2065 key population size data points, 1183 HIV prevalence data points, and 259 ART coverage data points. Across national urban populations, a median of 1·65% (IQR 1·35-1·91) of adult cisgender women were female sex workers, 0·89% (0·77-0·95) were men who have sex with men, 0·32% (0·31-0·34) were men who injected drugs, and 0·10% (0·06-0·12) were women who were transgender. HIV prevalence among key populations was, on average, four to six times higher than matched total population prevalence, and ART coverage was correlated with, but lower than, the total population ART coverage with wide heterogeneity in relative ART coverage across studies. Across sub-Saharan Africa, key populations were estimated as comprising 1·2% (95% credible interval 0·9-1·6) of the total population aged 15-49 years but 6·1% (4·5-8·2) of people living with HIV. Key populations in sub-Saharan Africa experience higher HIV prevalence and lower ART coverage, underscoring the need for focused prevention and treatment services. In 2024, limited data availability and heterogeneity constrain precise estimates for programming and monitoring trends. Strengthening key population surveys and routine data within national HIV strategic information systems would support more precise estimates. UNAIDS, Bill & Melinda Gates Foundation, and US National Institutes of Health.

URBANIZATION AND SOCIAL CRIME: A STUDY ON SOCIO-ECONOMIC DIMENSIONS OF THE MANIPUR CENTRAL VALLEY

Geography is concerned to provide accurate, orderly, and rational description and interpretation of the earth's surface. The study of population dynamics and its impact on social crime is crucial for understanding regional development and planning. The present study examines the dynamics of population growth, urbanization, and social crime in Manipur's Central Valley. Utilizing, the data from the primary and secondary sources (such: NCRB, Superintendent of Police, Manipur central Valley, and Census of India). The research adopts technique, such as Decadal Growth method, Principal component Analysis. Over a 70-year period from 1951 to 2021, the region witnessed a consistent rise in population, growing from 3,87,523 to 18,77,416. Despite this growth, the pace has decelerated, reflecting a shift from high to moderate growth rates due to factors such as birth rates, mortality rates, and migration patterns. Urban population data from the 2011 Census reveals significant regional disparities, with Imphal West being the most urbanized and Bishnupur the least. Principal Component Analysis (PCA) of social crime data identifies three key patterns: "Anomie Violence," "Marginalized Deviance," and "Economic Strain Deviance," each associated with different regions and types of crime. A comparison of social crime patterns with urban population sizes highlights varying correlations, suggesting complex interactions between demographic changes and crime rates. This analysis underscores the need for targeted urban planning and crime prevention strategies in response to evolving socio-economic dimensions.

Design of Radial Flux PM Synchronous Motor for EV Applications

Abstract: In recent years, with the accelerated process of urbanization, the continuous growth of urban areas and population sizes has posed higher demands on urban earthquake safety. The theory of resilient cities offers a new approach to effectively address urban earthquake safety risks. However, there is still a lack of reasonable methods for assessing and constructing urban seismic resilience both domestically and internationally. Therefore, this paper elucidates the connotation, essential characteristics, components, and evaluation indicators of a seismic-resilient city, summarizing the components of urban seismic resilience as "one fundamental, two guarantees," which include the engineering system, institutional system, and social and economic system. This paper establishes a dual-parameter urban seismic resilience assessment method based on post-earthquake losses and recovery time, proposing a "set goals, assess, improve, reassess" approach for building resilient cities. The proposed method can provide theoretical guidance and practical basis for assessing and enhancing the seismic resilience of individual infrastructures, engineering systems, urban systems, and resilience to other disasters.

Urban landscape affects scaling of transportation carbon emissions across geographic scales

Understanding the carbon dynamics of the transportation sector is necessary to mitigate global climate change. While urban scaling laws have been used to understand the impact of urban population size on carbon efficiency, the instability of these scaling relationships raises additional questions. Here, we examined the scaling of on-road transportation carbon emissions across 378 US metropolitan statistical areas (MSAs) using diverse urban landscape patterns and spatial units, from the MSA level down to 1 km grid cells. Beginning with a baseline scaling model that uses only population size, we expanded the model to include landscape metrics at each spatial scale based on correlation results. We found that: (1) urban landscape characteristics provide insights into carbon mechanisms not fully captured by population size alone, (2) the impact of population size on on-road carbon emissions transitions from linear to sub-linear scaling relationships as the geographic scale of analysis decreases, and (3) clustered urban developments can form carbon-efficient landscapes, while fragmented urban areas tend to be carbon-inefficient. Based on empirical evidence, this research advocates for hierarchical spatial planning and supports the implementation of policy measures aligned with smart growth principles to mitigate carbon pollution.

Characteristics and factors influencing the expansion of urban construction land in China

As a new product of rapid urbanization, the sprawl of urban construction land can objectively reflect urban land use efficiency, which is of great significance to China's new urban construction. This study aimed to summarize the expansion patterns and utilization efficiency of urban construction land in China from the perspectives of the status, speed and trends of expansion, and to uncover the key factors that lead to the differential distribution of the expansion of construction land. It can also provide land management experience for other countries with rapid expansion of construction land. The results show the following. (1) The expansion of China's construction land presents a "point–line–plane" pattern of evolution, forming changing stages of point-like aggregation, linear series and planar spread. (2) China's construction land shows the characteristics of disorderly spread, a low utilization rate and low output efficiency. The speed of expansion presents clear characteristics of being high in the east and low in the west, mostly concentrated in the Yangtze River Delta, Pearl River Delta and the Beijing–Tianjin–Hebei urban agglomeration. Shanghai, Beijing, Shenzhen and Guangzhou have the highest intensity of construction land use. In Shandong Peninsula and eastern coastal areas, the intensity of the construction land use is generally high. In Xinjiang and Xizang, the intensity of construction land use is relatively low. (3) The urban economic level, population size, industrial structure, foreign investment and land policies have significant effects on the spatial distribution of the expansion of construction land.

Various Comparative Studies of Constantly Applied Methods in Assisting the Demography Research of China

Demography research is of great importance due to it can affect various aspects of a country. We mainly focus on using various comparative studies to examine and predict the population quantity, female size, and urban population size of China. We initially compared the effectiveness of several interpolation algorithms to add more samples and then we constructed two more comparative studies of the Malthus model, Logistic model, GM (1, 1) model, BP neural network, and Leslie model to assist the demography research of China. Especially, three novel metaheuristic algorithms were applied to the Logistic model. Compared results verified that metaheuristics can assist the Logistic model to obtain far more accurate parameters and also revealed that using the Logistic model combined with a novel metaheuristic to predict the population quantity and urban population size and the BP neural network to estimate the female size can acquire the most ideal results.

Digital Economy Mechanisms and Tests for Promoting Common Wealth

This paper focuses on the two development strategies of digital economy and common wealth, explores the effect, mechanism and spatial spillover effect of digital economy on common wealth, and further examines the heterogeneity of cities of different sizes and locations. The study finds that the digital economy can increase the level of common prosperity development by 37.1%, and effectively reduce the ratio of urban and rural per capita disposable income, and positively affect the level of common prosperity development in neighboring areas through spatial spillover effects. The mechanism test shows that public management, human capital, R&D and innovation play a significant positive mediating role in the development process of the digital economy affecting the common wealth. The study also confirms that the impact of the digital economy on shared prosperity decreases with the expansion of urban population size, and plays a greater advantage in the location of non-core cities.

Does urban sprawl lead to carbon emission growth? ——Empirical evidence based on the perspective of local land transfer in China

The rapid expansion of urban areas has caused a series of impacts on the ecological environment. Exploring the effects of urban sprawl on carbon emissions and its mechanisms is crucial for realizing sustainable development patterns and low-carbon city planning. This study integrates used nighttime light data and population grid data to construct an Urban Sprawl Index. It employs a spatial Durbin model to analyze the spatial impact of urban sprawl on carbon emissions, examining the mechanisms from the perspective of local land transfers. The research reveals the following findings: (1) Urban sprawl significantly contributes to a positive spatial spillover effect on carbon emissions, leading to increased emission levels in surrounding cities. (2) The overall local impact of urban sprawl on urban areas is not significant. It suppresses carbon emissions in municipal districts but increases them in counties, with these effects counterbalancing each other. (3) Mechanism analysis indicates that different types of land transfers have varied effects on carbon emissions in districts and counties, and the regulation of the floor area ratio regulation strengthens the impact of land transfers. (4) Heterogeneity analysis shows that the size of urban populations significantly reduces the local effects of sprawl on carbon emissions, while environmental regulations strength attenuate the impact of urban sprawl on carbon emissions in municipal districts. The findings of this study provide practical references and policy implications for formulating reasonable urban development and land use plans, facilitating the achievement of peak carbon and carbon neutrality goals.

Evolution of urban land and population system coupling micro–dynamics and macro-stability: Trends and paths

Cities are constantly dynamics, but emerge the stability of urban systems as evidenced by robust urban scaling. Both properties serve as necessary perspectives for understanding the evolution of cities and the complexity of urban systems. However, the evolution trends and mechanisms behind the above relationships, particularly concerning geographic processes, remain to be explored in depth. In this study, the dynamic fluctuations within individual cities and the macro-stability of the evolution of urban systems over the past two decades are systematically demonstrated. Subsequently, associations between urban systems and individual cities are established on the basis of micro- and macro-dynamic mechanisms. The trajectory of individual cities can be divided into scaled–driven growth (SG) and urban excess growth (EG). The former is the result of scaling effects within the system, while the latter is affected by global external effects. Taking as examples urban built-up area and social productions, the evolution pathways that vary from different attributes are analyzed. The results indicate that urban built-up area is more affected by scaling effects whereas social productions is driven more by global external effects. Moreover, we examine the driving forces and mechanism of EG from the aspects of economic development level, technological and economic progress, industrial structure and regional policy through an empirical analysis. This study provides a nuanced understanding of the trends and pathways of urban land–population system from the perspective of complex urban systems and holism to deepen the understanding for general properties of cities and co-evolution for urban population size and land expansion.

Population-land spatial coupling in urban non-core region and environmental pollution: evidence from urbanization in China

Urban non-core region land exploitation and population immigration toward urban non-core areas, two fundamentals of urbanization in China, challenge the environment; however, the pollution effect remains unclear. Using prefecture-level data in China between 2003 and 2020, our study investigates the pollution effect of population-land spatial coupling in the urban non-core region. The empirical results demonstrate that excessive urban non-core region land expansion alone negatively impacts the environment. Meanwhile, outward spatial population distribution in a town, primarily measured by the relative proportion of the urban non-core region population size, directly aggravates environmental pollution on the one hand and indirectly improves the environment by alleviating the adverse effect of land-led urbanization. These findings remain robust under various regional heterogeneity analyses and still stand regardless of the investigated pollutants. The practical implications suggest that breaking through the population-land spatial coupling when the urban core region is small is advisable; meanwhile, efforts in maintaining the coupling, especially for areas with sizeable peripheral land exploitation, should be held.

Racial and economic segregation in Brazil: a nationwide analysis of socioeconomic and socio-spatial inequalities

This article aims to analyze residential segregation by race (racial segregation) and income (economic segregation) in Brazil and explore its relationship with socioeconomic and socio-spatial factors. Residential segregation was assessed using the dissimilarity index based on the 2010 demographic census and considering urban census tracts since segregation is sociologically considered an urban problem. The results for racial segregation showed that it is more evident in cities in the South and Southeast of Brazil and mainly affects the self-declared black population. The approach used to calculate economic segregation involved examining the income level of different low-income groups. Therefore, we consider families that earned between 0 and 1 minimum wage as the group with the greatest social vulnerability. We did not find significant correlations between racial and income segregation indices with aspects such as urbanization (urban population size). Finally, we present the racial segregation indices stratifying families by income thresholds for the 27 Brazilian capitals and conclude that per capita household income is a preponderant factor for the segregation of the poorest, especially in families whose residents self-identify as black.

Population size spatial distribution adjustment at different urban hierarchies in China

ABSTRACTUrban population size dynamic adjustment has been widely studied for a long time; however, the dynamic adjustment at the urban hierarchy level remains unknown. This study utilises China’s urban population data to investigate the dynamic adjustment. Specifically, we utilise methods of the Gini coefficient, linear regression, and coefficients of Spearman, and Moran’s I to explore the dynamic adjustment tendency, adjustment rate and correlation of urban population size distribution of different urban hierarchies and groups of different urban hierarchies. We find that the urban population size spatial distribution is gradually even within the same urban hierarchy. The adjustment rate to be even of Megacity II is the highest, and Megacity I is the bottom. We also find the population size spatial distribution of the whole urban system is gradually uneven. We explore the dynamic process by sorting urban hierarchies into six groups. Furthermore, we find there is a complex correlation between different urban hierarchies and global spatial correlation among China cities. These results are valuable as references for Chinese policy-makers and practitioners and will enable them to adopt sustainable land use and urban population management measures. The study outcomes will also benefit future studies on spatial population size distribution in international communities.

Impact of urbanization size and deforestation environmental degradation on economic growth in Nigeria

The study examined the impact of urbanization size and deforestation environmental degradation on economic growth in Nigeria. Specifically, the study sought to: determine the impact of urban population size environmental degradation on the economic growth in Nigeria and ascertain the impact of deforestation environmental degradation on the economic growth in Nigeria. This study made use of ex post-facto research design. The method of data analysis was Autoregressive distributive Lag model. The data of the study were real gross domestic product (RGDP), total deforestation (FOREST), urban population size (UPS), non-renewable energy consumption carbon emission (NREC), industrial sector carbon emission (ISC), electricity generation carbon emission (EGC) and were sourced from on-line World Bank Data indicators over the period of 1991 to 2022. The empirical results show that: urban population size environmental degradation has 22% negative and insignificant impact on economic growth in short run (Probability value of 0.7660 > 0.05) and deforestation environmental degradation has 49 % positive and insignificant impact on economic growth in short run (Probability value of 0.7660 > 0.05). The study government should provide farmers with farm inputs and extension services to educate rural peasant farmers on latest local techniques for maximum output. Farmers should engage in bush fallowing to allow unfertile land to regenerate after some reasonable number of years before putting the land into use.

The technical approach of using mobile positioning data to support urban population size monitoring

This paper summarizes the methods and approaches of using mobile positioning data to estimate and monitor urban population size. It starts with the necessity of using big data to monitor urban population size in territorial spatial planning. Then it elaborates on the difference between the definition of "population size" reflected by mobile positioning data and the common concept of urban population size, and the necessity of verifying the logic and measurement of sample expansion at four levels. Finally, taking Wuhan city as a case, this paper proposes the technical approach of monitoring the size of urban permanent population through multi-source data verification. The study finds that when it comes to monitoring urban population size, mobile positioning data have the advantage of monitoring short-period changes in population size and spatial distribution, yet special attention must be paid to the three technical links of definition, sample expansion and verification.

Prediction of municipal solid waste generation and analysis of dominant variables in rapidly developing cities based on machine learning - a case study of China.

Prediction of municipal solid waste (MSW) generation plays an essential role in effective waste management. The main objectives of this study were to develop models for accurate prediction of MSW generation (MSWG) and analyze the influence of dominant variables on MSWG. To elevate the model's prediction accuracy, more than 50 municipal variables were considered original variables, which were selected from 12 categories. According to the screening results, the dominant variables are classified into four categories: urban greening, population size and residential density, regional economic development and resident income and expenditure. Among the seven machine learning methods, back propagation (BP) neural network has the best model evaluation effect. The R2 of the BP neural network model of Jiangsu, Zhejiang and Shandong provinces were 0.969, 0.941 and 0.971 respectively. The prediction accuracy of Shandong province (93.8%) was the best, followed by Jiangsu province (92.3%) and Zhejiang province (72.7%). The correlation between dominant variables and the MSWG was mined, suggesting that regional GDP and the total retail sales of consumer goods were the most important dominant variables affecting MSWG. Moreover, the MSWG might not absolutely associate with the population size and residential density. The method used in this study is a practical tool for policymakers on regional/local waste management and MSWG control.

Impact of increased subway station density on air quality in the Yangtze River Delta

This study uses empirical methods to explore the relationship between subway system development and air quality in the Yangtze River Delta. We propose a new variable to measure subway development, ‘subway station density value,’ which can more accurately describe subway development than the traditional ‘line opening’ variable. It eliminates the effects of differences in subway line length, layout, and size of the local subway system. Using this variable, we find that subway development has a significant positive impact on air quality improvement; that is, an increase in the density of subway stations is significantly associated with improved air quality. This finding provides a scientific basis for the relevant authorities to formulate the development and layout of public transport facilities. It strongly responds to previous debates on the relationship between subways and air quality. In addition, we find that urban population size, economic development level, and subway system size play moderating roles in the relationship between subways and air quality. Increasing the availability of subways can significantly improve the air quality in cities with high population sizes and economic development levels. However, as the size of a subway system increases, its effect on air quality improvement may weaken, suggesting that the development of subway systems should not be unthinkingly expanded either. Although our study has valuable findings, we must discuss several limitations further. These include restrictions in the selection of the study population, data availability, and potentially oversimplified criteria for considering the impact of urban differences. Overall, this study not only provides new theoretical and empirical perspectives for understanding and explaining how subway development affects air quality but also indicates the direction for further exploration in future studies.

Assessing Spatiotemporal Characteristics and Driving Factors of Urban Public Buildings Carbon Emissions in China: An Approach Based on LMDI Analysis

Urban public buildings carbon emissions exhibit an upward trend and have a large potential in carbon emission reduction. The analysis of spatiotemporal characteristics and driving factors for urban public buildings carbon emissions is essential in formulating effective policies for carbon reduction, meeting commitments to peak carbon emissions and achieving carbon neutrality. This study takes China’s urban public buildings carbon emissions as the research object, employing methods such as spatial autocorrelation analyses, kernel density estimation analyses, and the LMDI decomposition methods to explore the spatiotemporal characteristics and regional disparities in carbon emissions from 2006 to 2019. Furthermore, it quantifies the contributions of driving factors to the spatiotemporal changes in urban public buildings carbon emissions. The results show the following: (1) Urban public buildings carbon emissions among provinces are consistently increasing, indicating an overall upward trend. The spatial distribution highlights significant regional disparities. (2) The spatial characteristics of urban public buildings carbon emissions were basically stable. The eastern coastal regions demonstrate a high-high cluster, while the western regions exhibit a low-low cluster. The overall cluster evolution showed a decreasing trend from east to west. (3) Per capita urban public building area, economic density, urbanization rate, and population size serve as driving factors for carbon emissions from urban public buildings, while energy efficiency and energy consumption intensity act as inhibitory factors. The findings of this research can assist policymakers in getting a deeper comprehension of urban public buildings carbon emissions and providing a scientific basis to formulate appropriate carbon emission reduction policies.

Assessing the nexus between natural resource consumption and urban sprawl: Empirical evidence from 288 cities in China

With the rapid advancement of urbanization in China and the sharp increase in natural resources consumption, it is crucial to quantitatively study the relationship between land resources utilization and energy consumption. This paper uses the panel data at the city level from 2004 to 2019 to analyze the impact of low-density expansion of land resources on power consumption and its internal mechanism. A significant U-shaped relationship exists between urban sprawl and power consumption, which passes a variety of robustness tests. Moreover, geographical indicators are selected as instrumental variables to deal with potential endogeneity. Heterogeneity analysis shows that there are differences in the impact of urban sprawl on electricity resources for different urban locations, population sizes, sprawl characteristics, and electricity consumption types. Further mechanism analysis shows that urban sprawl affects electricity consumption by affecting variables such as real estate investment and Internet development. The conclusions of this paper bridge the conflicting viewpoints of existing research and provide empirical evidence for the utilization of land resources and the formulation of electricity regulation policies in various regions of China.

Empirical analysis and modeling of the allometric scaling of urban freight systems

Heavy trucks which undertake the majority of freight volume play an important role in urban freight systems. By analyzing heavy truck trip data, we find a superlinear scaling relationship for heavy truck trips and a sublinear scaling relationship for heavy truck numbers relative to urban population size. Although these allometric scaling relationships that widely appear in nature and social systems have been explained by many models, a simple model that can cover a wide range of scaling exponents in these systems is still lacking. Here, we develop a partially mixing city operation model by quantifying the mixability of the urban population to explain why the superlinear and sublinear scaling exponents are in the range of 1 and . This simple model not only helps us understand the mechanism of allometric scaling of urban freight systems, but also provides a new framework for other superlinear and sublinear scaling relationships in cities.