Representation of influence zones in commercial GISs for simulation of directional processes

  1. Muñoz Vicente, M Dolores 1
  2. Moreno García, María N 1
  3. López, Vivian F 1
  1. 1 Department of Computing and Automatic, Universityof Salamanca, Plaza de la Merced, s/n, Salamanca, Spain
Revista:
Logic Journal of the IGPL

ISSN: 1367-0751 1368-9894

Año de publicación: 2020

Volumen: 28

Número: 2

Páginas: 185-196

Tipo: Artículo

DOI: 10.1093/JIGPAL/JZY044 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Logic Journal of the IGPL

Resumen

Influence zone generating is used in different fields: environmental studies, urban areas establishment, road construction and fire propagation. The influence zone is defined as the geometric space of the points that are at a shorter or similar distance to a given object (point, polyline or polygon). The simulation of the phenomena that are subject to constant patterns can be graphically represented by modules included in all commercial geographic information systems (GISs). In this case, the calculated zone is represented as a circle called isotropic buffer. Nevertheless, when we simulate processes as a liquid or a gas expansion, there are directional aspects that need to be considered. Directionality may be graphically represented as an ellipse. In this paper, we perform an analysis of how some of the most used commercially available GISs create influence zones and we highlight the lack of some of them to implement anisotropic or directional influence zones. In addition, we proposed a method to check the anisotropy and the creation of a generator oval that allows to delimit zones of influence that represent phenomena with anisotropic characteristics.

Referencias bibliográficas

  • Bailey T.C. , GatrellA.C. A review of: Interactive spatial data analysis, Longma, 2007.
  • Black, (2004), Proceedings International Health Users Conference
  • Cámara, (2015)
  • Casciola, (2007), Applied Mathematics and Computation, 190, pp. 1050, 10.1016/j.amc.2006.11.128
  • Chou, (1997), Exploring Spatial Analysis in GIS
  • Crampin, (1978), Geophysical Journal International of the Royal Astronomical Society, pp. 467, 10.1111/j.1365-246X.1978.tb03754.x
  • Cressie, (1993), Statistics for Spatial Data
  • Esri, (2015)
  • Figueras, (2010)
  • Getis A. Homogeneity and proximal databases in Spatial analysis and GIS. Taylor & Francis, 1994.
  • Gómez, (2005), Instituto Mejicano del Petróleo (IMP)
  • Grass GIS, (2015)
  • Hutchinson, (2004), Inside Arcview GIS 8.3
  • Mardia, (1999), Directional statistics, 10.1002/9780470316979
  • Martinez
  • Martínez, (1999), Modelos digitales del terreno: Estructuras de datos y aplicaciones en análisis de formas del terreno y en Edafología
  • Mata, (2012), Journal of Mathematical Imaging and Vision, 42, pp. 212, 10.1007/s10851-011-0315-x
  • Molina, (2002), Computer & Graphics, 26, pp. 771, 10.1016/S0097-8493(02)00132-2
  • Molina, (2002), 5th Agile Conference on Geographical Information Science
  • Muñoz, (2013), Expert Systems With Applications, 40, pp. 5011, 10.1016/j.eswa.2013.03.024
  • Oyamburu, (1996), Estudio de la anisotropía sísmica cortical de la península ibérica a partir de la polarización de las ondas S
  • Ronald, (2003), Idrisi Kilimanjaro Guía para SIG y procesamiento de imágenes Clack Labs