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Land Cover Change Using An Energy Transition Paradigm In A Statistical Mechanics Approach

Daniel S. Zachary
Published 2013 · Mathematics

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This paper explores a statistical mechanics approach as a means to better understand specific land cover changes on a continental scale. Integrated assessment models are used to calculate the impact of anthropogenic emissions via the coupling of technoeconomic and earth/atmospheric system models and they have often overlooked or oversimplified the evolution of land cover change. Different time scales and the uncertainties inherent in long term projections of land cover make their coupling to integrated assessment models difficult. The mainstream approach to land cover modelling is rule-based methodology and this necessarily implies that decision mechanisms are often removed from the physical geospatial realities, therefore a number of questions remain: How much of the predictive power of land cover change can be linked to the physical situation as opposed to social and policy realities? Can land cover change be understood using a statistical approach that includes only economic drivers and the availability of resources? In this paper, we use an energy transition paradigm as a means to predict this change. A cost function is applied to developed land covers for urban and agricultural areas. The counting of area is addressed using specific examples of a Polya process involving Maxwell–Boltzmann and Bose–Einstein statistics. We apply an iterative counting method and compare the simulated statistics with fractional land cover data with a multi-national database. An energy level paradigm is used as a basis in a flow model for land cover change. The model is compared with tabulated land cover change in Europe for the period 1990–2000. The model post-predicts changes for each nation. When strong extraneous factors are absent, the model shows promise in reproducing data and can provide a means to test hypothesis for the standard rules-based algorithms.
This paper references
The Gradient Projection Method for Nonlinear Programming. Part I. Linear Constraints
J. Rosen (1960)
Plancks Gesetz und Lichtquantenhypothese
Bose (1924)
Bose-Einstein Dynamics and Adaptive Contracting in the Motion Picture Industry
A. D. Vany (1996)
Cellular automata models of road traffic
S. Maerivoet (2005)
Generalized Maxwell-Boltzmann, Bose-Einstein, Fermi-Dirac and Acharya-Swamy Statistics and the Polya Urn Model
R. K. Niven (2008)
Urban spatial pattern as self-organizing system: An empirical evaluation of firm location decisions in Cleveland–Akron PMSA, Ohio
M. Kumar (2007)
Measurement of the universal gas constant R using a spherical acoustic resonator.
Moldover (1988)
Some Supply and Demand Considerations in Urban Spatial Interaction Models
John R. Roy (1984)
Probability Theory and Mathematical Statistics
I. Bronshteĭn (1987)
Large-scale convex optimization methods for air quality policy assessment
D. Carlson (2004)
A model incorporating diversity in urban allocation problems
John F. Brotchie (1978)
Aggregate and per capita GDP in Europe, 1870–2000: continental, regional and national data with changing boundaries
Stephen N. Broadberry (2012)
On the theory of quantum mechanics
P. Dirac (1926)
Trade-offs between energy cost and health impact in a regional coupled energy–air quality model: the LEAQ model
D Harris Zachary (2011)
D. Zachary (2004)
Some distributions associated with bose-einstein statistics.
Y. Ijiri (1975)
A Growth Process for Zipf's and Yule's City-Size Laws
Michael F. Dacey (1979)
Use of Bose-Einstein statistics in population dynamics models of arthropods
Jerry H. Young (1987)
Analysis of urban development of Haridwar, India, using entropy approach
R. Jha (2008)
An Introduction to Probability Theory and Its Applications
W. Feller (1950)
Elementary Principles in Statistical Mechanics
J. W. Gibbs (1902)
The Rank-Frequency Form of Zipf's Law
B. M. Hill (1974)
Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India)
M. K. Jat (2008)
Adjusting Spatial-Entropy Measures for Scale and Resolution Effects
E. Heikkila (2006)
Urban growth analysis using spatial and temporal data
H. S. Sudhira (2003)
Modeling urban growth using a variable grid cellular automaton
J. Vliet (2009)
Zur Quantelung des idealen einatomigen Gases
E. Fermi (1926)

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