A Novel Method to Model Spatial Distribution of Population
A. B. Jayasinghe1, H. W. A. S. Sathsarana2, R. M. Y. L. Rathnayake3, N. S. Bandara4
1A. B. Jayasinghe, Urban Simulation Lab, Department of Towna & Countrya Planning, University of Moratuwa, Moratuwa, Sri Lanka.
2H. W. A. S. Sathsarana, Urban Simulation Lab, Department of Towna & Countrya Planning, University of Moratuwa, Mora tuwa, Sri Lanka.
3R. M. Y. L. Rathnayake, Urban Simulation Lab, Department of Towna & Countrya Planning, University of Moratuwa, Mora tuwa, Sri Lanka.
4N. S. Bandara, Osaka City University, Japan.
Manuscript received on 09 January 2020 | Revised Manuscript received on 05 February 2020 | Manuscript Published on 20 February 2020 | PP: 34-40 | Volume-9 Issue-3S January 2020 | Retrieval Number: C10070193S20/2020©BEIESP | DOI: 10.35940/ijitee.C1007.0193S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study proposes a simulation approach to model the spatial distribution of population (i.e., population density) in a given regioninto a great deal of detail. The approach is based on two mathematical notions: graph theory and fractal geometry. Accordingly, the approach takesself-similarity of road accessibility into account when simulating the spatial distribution of population. The study has followed three steps; (a) formulatea conceptual framework to model the spatial-distribution of population in a given area (i.e. a city or a region) by employingthe network centrality-based fractal dimension, (b) develop a modeling framework, and (c) calibrate the model utilizing empirical data at for five-selected case study areas: Colombo-Sri Lanka, Hanoi-Vietnam, Kolkata -India, Wuhu-China and Singapore. Network centrality-based fractal dimensions were computed by employing open-data and open-source GIS tools. The study applied threetypes of regression techniques: Robust Regression (RR), Ordinary-Least-Squares Regression (OLSR) and Poisson Regression (PR) for derivingthe most appropriate mathematical model. Then, the study validated the model by testing its prediction accuracy. Results revealed that the developed model to simulate spatial distribution of populationin a given area recorded an accepted level of accuracy (R2 >0.75) and predictability (MdAPE< 10%) on a par with the internationalspatial modeling standards. The proposed approach can be adoptedto simulate the spatial-distribution ofpopulation, particularly as a decision-making aid in the domain of urban & regional planning.
Keywords: Accessibility, Fractal Geometry, Open Source GIS, Population Density, Spatial Analysis, Spatial Planning, Transport Network, Urban Simulation.
Scope of the Article: Encryption Methods and Tools