On the logistic modeling and forecasting of evolutionary processes: Application to human population dynamics

2010 ◽  
Vol 77 (5) ◽  
pp. 699-711 ◽  
Author(s):  
L.C.M. Miranda ◽  
C.A.S. Lima
Keyword(s):  
Population Dynamics ◽  
Human Population ◽  
Evolutionary Processes ◽  
Logistic Modeling ◽  
Modeling And Forecasting

scholarly journals Multi-Level Relationships between Satellite-Derived Nighttime Lighting Signals and Social Media–Derived Human Population Dynamics

2018 ◽  
Vol 10 (7) ◽  
pp. 1128 ◽  
Author(s):  
Ting Ma
Keyword(s):  
Social Media ◽  
Population Dynamics ◽  
Big Data ◽  
Human Activities ◽  
Human Population ◽  
City Size ◽  
Human Settlements ◽  
Good Opportunity ◽  
Nighttime Light ◽  
Multi Level

Satellite-based measurements of the artificial nighttime light brightness (NTL) have been extensively used for studying urbanization and socioeconomic dynamics in a temporally consistent and spatially explicit manner. The increasing availability of geo-located big data detailing human population dynamics provides a good opportunity to explore the association between anthropogenic nocturnal luminosity and corresponding human activities, especially at fine time/space scales. In this study, we used Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB)–derived nighttime light images and the gridded number of location requests (NLR) from China’s largest social media platform to investigate the quantitative relationship between nighttime light radiances and human population dynamics across China at four levels: the provincial, city, county, and pixel levels. Our results show that the linear relationship between the NTL and NLR might vary with the observation level and magnitude. The dispersion between the two variables likely increases with the observation scale, especially at the pixel level. The effect of spatial autocorrelation and other socioeconomic factors on the relationship should be taken into account for nighttime light-based measurements of human activities. Furthermore, the bivariate relationship between the NTL and NLR was employed to generate a partition of human settlements based on the combined features of nighttime lights and human population dynamics. Cross-regional comparisons of the partitioned results indicate a diverse co-distribution of the NTL and NLR across various types of human settlements, which could be related to the city size/form and urbanization level. Our findings may provide new insights into the multi-level responses of nighttime light signals to human activity and the potential application of nighttime light data in association with geo-located big data for investigating the spatial patterns of human settlement.

Demographic perspectives on human population dynamics

2002 ◽  
pp. 17-40 ◽  
Author(s):  
Andrew Hinde
Keyword(s):  
Population Dynamics ◽  
Human Population

Chinese Demography: The State of the Field

1990 ◽  
Vol 49 (4) ◽  
pp. 807-834 ◽  
Author(s):  
William Lavely ◽  
James Lee ◽  
Wang Feng
Keyword(s):  
Population Dynamics ◽  
Steady State ◽  
Chinese Population ◽  
Human Population ◽  
The State ◽  
Chinese Society ◽  
Demographic Research ◽  
The Past ◽  
Comparative Population ◽  
State Of The Field

As recently as one decade ago, there was no “field” of Chinese demography. There were virtually no demographers of China and little available data. It is fair to say that China was at once the largest and the least known of any human population.The change has been sudden. New sources of data now place China among the better-documented national populations. Publications on Chinese population have boomed. In consequence, we can now speak of a field of Chinese demography, although it is hardly in a steady “state.” We can only outline the explosion of demographic research that is continually expanding and refining our understanding of Chinese population today and in the past. This outpouring of data and knowledge provides unprecedented opportunities for the study of Chinese society and offers unusual challenges to our understanding of comparative population dynamics.

scholarly journals Human population dynamics revisited with the logistic model: How much can be modeled and predicted?

2004 ◽  
Vol 19 (4) ◽  
pp. 247-285 ◽  
Author(s):  
C. Marchetti ◽  
P. S. Meyer ◽  
J. H. Ausubel
Keyword(s):  
Population Dynamics ◽  
Logistic Model ◽  
Human Population

Human Population Dynamics: Cross-Disciplinary Perspectives

2003 ◽  
Vol 28 (4) ◽  
pp. 586
Author(s):  
Don Kerr ◽  
Helen Macbeth ◽  
Paul Collinson
Keyword(s):  
Population Dynamics ◽  
Human Population

scholarly journals Urban biodiversity and the importance of scale

2020 ◽  
Author(s):  
Kenta Uchida ◽  
Rachel V. Blakey ◽  
Joseph R. Burger ◽  
Daniel S. Cooper ◽  
Chase A. Niesner ◽  
...  
Keyword(s):  
Human Population ◽  
Management Strategies ◽  
Urban Biodiversity ◽  
City Size ◽  
Areal Extent ◽  
Evolutionary Processes ◽  
Linear Forms ◽  
Scaling Relationships ◽  
Socioeconomic Drivers ◽  
Inflection Points

Many ecological and evolutionary processes are affected by urbanization, but cities vary by orders of magnitude in their human population size and areal extent. To quantify and manage urban biodiversity we must understand both how biodiversity scales with city size, and how ecological, evolutionary, and socioeconomic drivers of biodiversity scale with city size. We show how environmental abiotic and biotic drivers as well as human cultural and socioeconomic drivers may act through ecological and evolutionary processes differently at different scales to influence patterns in urban biodiversity. Because relationships likely take linear and non-linear forms, we highlight the need to describe the specific scaling relationships, including deviations and potential inflection points, where different management strategies may successfully conserve urban biodiversity.

scholarly journals CHANGING THE PARADIGM OF MODELING AND FORECASTING SOCIO-ECONOMIC SYSTEMS

2021 ◽  
Author(s):  
Svitlana Kolomiiets ◽  
Ruslan Dinits
Keyword(s):  
Differential Equations ◽  
Nonlinear Differential Equations ◽  
Economic Research ◽  
Economic Systems ◽  
Nonlinear Phenomena ◽  
New Paradigm ◽  
Evolutionary Processes ◽  
Principle Of Superposition ◽  
General Law ◽  
Modeling And Forecasting

Modern socio-economic systems demonstrate instability, chaos, and unpredictability. What methodology should be used to modeling modern socio-economic systems? Constant changes and crises in the development of socio-economic systems require new approaches to the research of these systems. The feature of the approach to the study of socio-economic systems in modern conditions is the conversion from a linear to a nonlinear paradigm. The models of socio-economic systems are systems of nonlinear differential equations. Nonlinear differential equations demonstrate different modes of functioning of complex socio-economic systems. Nonlinear equations can have several qualitatively different solutions. This explains the existence of different ways of evolution of nonlinear socio-economic systems. Nonlinearity is a fundamental position of new paradigm of cognition and development. Nonlinearity is a general law of nature and means, first of all, non-observance of the principle of superposition. The whole cannot be the sum of its parts; the result cannot be the sum of efforts, the quality of the whole is not determined by the sum of the qualities of its parts, the reaction of the system is not proportional to the influence. For nonlinear phenomena, knowledge about the behavior of a part of an object does not yet guarantee correct ideas about the behavior of the object as a whole, and its response to changes in conditions may qualitatively depend on the magnitude of these changes. Non-linearity is the multivariance of the evolutionary paths, the presence of a choice of alternative paths and determining the rate of evolution. Nonlinearity is the irreversibility of evolutionary processes; nonlinear, indirect dependence of evolutionary processes on external influences. The article examines the topical issue of changing the paradigm of modeling and forecasting socio-economic systems. The necessity of transition from linear to nonlinear paradigm in economic research is theoretically substantiated. The features of the application of methods of nonlinear dynamics to the modeling of socio-economic systems are considered. The phenomenon of nonlinearity of socio-economic systems is studied.

Modeling the Impacts of Climate Change, Landuse Change, and Human Population Dynamics on Water Availability and Demands in the Southeastern U.S.

2005 ◽  
Author(s):  
Ge Sun ◽  
Steven G. McNulty ◽  
Erika Cohen ◽  
Jennifer Moore Myers ◽  
David Wear
Keyword(s):  
Climate Change ◽  
Population Dynamics ◽  
Water Availability ◽  
Human Population ◽  
Landuse Change ◽  
Impacts Of Climate Change

A Nonlinear Model for Human Population Dynamics

1981 ◽  
Vol 40 (2) ◽  
pp. 266-278 ◽  
Author(s):  
K. E. Swick
Keyword(s):  
Population Dynamics ◽  
Nonlinear Model ◽  
Human Population
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