Quantitative Models for Computing Distance and Directions: A Raster-Based Approach

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Modeling Distance and Direction Relationships (DDR) is a key issue in spatial analysis and spatial reasoning. Various fields such as geology, hydrology, ecology, etc. apply DDR models to help digging out valuable patterns hidden in geoscientific dataset. This paper proposed two quantitative models through a raster-based approach for computing Euclidean distance and cardinal direction relationships, respectively, between a pair of spatial objects in a two-dimensional geographical space. The corresponding algorithms were designed and implemented. This new raster-based modeling can work universally on all types of spatial objects (point, line, polygon, or compound objects) and quantify DDR more accurately due to its sensitivity to object shapes. The usefulness of the modeling was demonstrated by various applications.

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449-454

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July 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] A.U. Frank. Qualitative spatial reasoning about distances and directions in geographic space. Journal of Visual Languages & Computing vol. 3 (1992), pp.343-371.

DOI: 10.1016/1045-926x(92)90007-9

Google Scholar

[2] H. Zhang, G.H. Huang. Assessment of non-point source pollution using a spatial multicriteria analysis approach. Ecological Modelling vol. 222 (2011), pp.313-321.

DOI: 10.1016/j.ecolmodel.2009.12.011

Google Scholar

[3] E. Nemeth, P. Bossew, C. Plutzar. A distance-dependent estimation of foraging ranges of neighbouring bird colonies. Ecological Modelling vol. 182 (2005), pp.67-73.

DOI: 10.1016/j.ecolmodel.2004.07.011

Google Scholar

[4] D. Palomino, L.M. Carrascal. Threshold distances to nearby cities and roads influence the bird community of a mosaic landscape. Biological Conservation vol. 140 (2007), pp.100-109.

DOI: 10.1016/j.biocon.2007.07.029

Google Scholar

[5] R. Shahid, S. Bertazzon, M. Knudtson, W. Ghali. Comparison of distance measures in spatial analytical modeling for health service planning. BMC Health Services Research vol. 9 (2009), pp.1-14.

DOI: 10.1186/1472-6963-9-200

Google Scholar

[6] S. Du, , L. Guo, Q. Wang. A model for describing and composing direction relationships between overlapping and contained regions. Information Sciences vol 178 (2008), pp.2928-2949.

DOI: 10.1016/j.ins.2008.03.009

Google Scholar

[7] H. Yan, Y. Chu, Z. Li, R. Guo. A quantitative description model for direction relationships based on direction groups. Geoinformatica vol 10 (2006), p.177–196.

DOI: 10.1007/s10707-006-7578-1

Google Scholar

[8] Z. Sha, Y. Bai, Y. Xie, L. Zhang. Using a hybrid fuzzy classifier (HFC) to map typical grassland vegetation in Xilin River Basin, Inner Mongolia, China. International Journal of Remote Sensing vol 29 (2008), p.2317 – 2337.

DOI: 10.1080/01431160701408436

Google Scholar