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32a Geoinquiets, 20 de febrer de 2014

INTEGRACIÓN DE INTELIGENCIA COLECTIVA Y
ARTIFICIAL EN UN SISTEMA GEOESPACIAL

Barce...
Introducción



Más del 80% de la información que maneja el sector público y
privado es de carácter espacial (De Moya Ama...
Introducción


1ª Ley de la Geografía:



Todo está relacionado entre sí,
pero los cosas mas cercanas están
más
relacion...
Introducción



Generalmente una sola postura cognitiva interviene en el
análisis geoespacial para el soporte a las decis...
Introducción



www.sumedico.com/nota11349.htm

Se
han
diseñado
soluciones inteligentes en
sistemas geoespaciales
en base...
Introducción



Inteligencia Colectiva (IC): “capacidad de los
colectivos humanos para participar en la
cooperación intel...
Introducción

IA

IC
Introducción
Prospectiva con datos geoespaciales para la toma de decisiones

Delphi
method
Objetivos


Diseñar, desarrollar y ensayar un sistema espacial
inteligente para el soporte a las decisiones en
escenarios...
Metodología general
Datos Geoespaciales

SSDG + SIG

Lógica Fuzzy

Grupo de expertos

Patrones
iniciales

Redes neuronales...
Métodología IC
Vector Consensus, (Monguet et al., 2012).

Versiones espaciales de los métodos Delphi y Shang (Di Zio & Pac...
Metodología IC

Búsqueda del patrón inicial a través de las opiniones de expertos (Spatial
Delphi + Vectorconsensus)
Metodología IC + IA
Para el establecimiento del patrón geoespacial inicial a través de un
enfoque Fuzzy aplicado a Spatial...
Metodología IA
Búsqueda de correspondencias con el patrón geoespacial inicial
Metodología IA



Red Neuronal Artificial de Tipo Multicapa: capas en
cascada.



Con aprendizaje tipo Backpropagation, ...
Resultados esperados

Datos y resultados hipotéticos,
utilizados exclusivamente con
fines demostrativos

Patrón geoespacia...
Resultados esperados

Datos y resultados hipotéticos,
utilizados exclusivamente con
fines demostrativos

Correspondencia d...
Principales contribuciones


Que la toma de decisiones se efectúe en
base a las alternativas presentadas por un
grupo de ...
La inteligencia, como las herramientas con filo, mejora
enfrentándolas con otras de mayor dureza.
Mario Sarmiento.
32a Geoinquiets, 20 de febrer de 2014

Juan Daniel Castillo Rosas
juan.daniel.castillo@estudiant.upc.edu
@jdcastillor
Referencias















Barrera Guarín, E., & Escobar, J. E. (2003). Un enfoque fuzzy para la prospectiva D...
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32a Geoinquiets - Integración de inteligencia colectiva y artificial en un sistema geoespacial

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Integración de inteligencia colectiva y artificial en un sistema geoespacial

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32a Geoinquiets - Integración de inteligencia colectiva y artificial en un sistema geoespacial

  1. 1. 32a Geoinquiets, 20 de febrer de 2014 INTEGRACIÓN DE INTELIGENCIA COLECTIVA Y ARTIFICIAL EN UN SISTEMA GEOESPACIAL BarcelonaTech Juan Daniel Castillo Rosas
  2. 2. Introducción  Más del 80% de la información que maneja el sector público y privado es de carácter espacial (De Moya Amaris, 2003; Williams, 1987).  Para justificar la importancia de los SIG y el papel que estos juegan hoy en día, es habitual citar el hecho de que aproximadamente un 70% de la información que manejamos en cualquier tipo de disciplina está georreferenciada (Olaya, 2012).  En la Wikipedia alemana el 78 % de todos los artículos tienen, están directamente o indirectamente relacionados con coordenadas geoespaciales. El 80 % no se puede confirmar, tendría que ser reformulado como un 60 %. (Hahmanna & Burghardta, 2013).
  3. 3. Introducción  1ª Ley de la Geografía:  Todo está relacionado entre sí, pero los cosas mas cercanas están más relacionadas que las distantes, (W. Tobler).  Toda entidad y ocurrencia con existencia en el mundo real; tiene, tuvo o tendrá lugar en algún sitio. Por lo tanto puede ser localizada. Espacio y tiempo son dos conceptos que enmarcan todos los aspectos de la disciplina geográfica, (Goodchild, 2013).
  4. 4. Introducción  Generalmente una sola postura cognitiva interviene en el análisis geoespacial para el soporte a las decisiones.  Las opciones posibles derivan de un único razonamiento especializado.
  5. 5. Introducción  www.sumedico.com/nota11349.htm Se han diseñado soluciones inteligentes en sistemas geoespaciales en base a patrones preestablecidos pero también son alternativas que consideran un solo modo de pensar…
  6. 6. Introducción  Inteligencia Colectiva (IC): “capacidad de los colectivos humanos para participar en la cooperación intelectual con el fin de crear, innovar e inventar. Se puede aplicar a cualquier escala, desde equipos de trabajo a las grandes redes o incluso a nuestra especie completa”, (Lévy, 2010).  Inteligencia Artificial (IA): “ciencia e ingenio de hacer máquinas inteligentes, especialmente programas de cómputo inteligentes”.
  7. 7. Introducción IA IC
  8. 8. Introducción Prospectiva con datos geoespaciales para la toma de decisiones Delphi method
  9. 9. Objetivos  Diseñar, desarrollar y ensayar un sistema espacial inteligente para el soporte a las decisiones en escenarios geográficos complejos, articulando las inteligencias colectiva y artificial.  Obtener patrones geoespaciales actuales y prospectivos.  Establecer patrones geoespaciales actuales y prospectivos a partir de los anteriores.  Comprobar la pertinencia y/o veracidad de los anteriores.  Proponer un prototipo de sistema espacial inteligente para el soporte a las decisiones en sistemas complejos.
  10. 10. Metodología general Datos Geoespaciales SSDG + SIG Lógica Fuzzy Grupo de expertos Patrones iniciales Redes neuronales Spatial Delphi + Vectorconcensus IC Consenso IA Correspondencia de patrones
  11. 11. Métodología IC Vector Consensus, (Monguet et al., 2012). Versiones espaciales de los métodos Delphi y Shang (Di Zio & Pacinelli, 2011; Di Zio & Staniscia, 2013). L’Aquila, Italia 2009
  12. 12. Metodología IC Búsqueda del patrón inicial a través de las opiniones de expertos (Spatial Delphi + Vectorconsensus)
  13. 13. Metodología IC + IA Para el establecimiento del patrón geoespacial inicial a través de un enfoque Fuzzy aplicado a Spatial Delphi y Vectorconsensus. Consenso, intersección de opinión de expertos. (Barrera & Escobar, 2003)
  14. 14. Metodología IA Búsqueda de correspondencias con el patrón geoespacial inicial
  15. 15. Metodología IA  Red Neuronal Artificial de Tipo Multicapa: capas en cascada.  Con aprendizaje tipo Backpropagation, (Werbos, P., 1974) con algoritmo supervisado (Conocemos los datos de entrada y la salida deseada).
  16. 16. Resultados esperados Datos y resultados hipotéticos, utilizados exclusivamente con fines demostrativos Patrón geoespacial inicial derivado del consenso (actual y prospectivo).
  17. 17. Resultados esperados Datos y resultados hipotéticos, utilizados exclusivamente con fines demostrativos Correspondencia de patrones en base al inicial, clasificados y ponderados (actuales y prospectivos).
  18. 18. Principales contribuciones  Que la toma de decisiones se efectúe en base a las alternativas presentadas por un grupo de expertos y no por una sola perspectiva cognitiva.  El modelado de alternativas adicionales a las anteriores, ordenadas por su mayor o menor correspondencia con las presentadas por los expertos como la solución al problema.  Un instrumento inteligente de opinión colectiva en ambiente espacial para el estudio de situaciones complejas, que permitirá obtener modelos geoespaciales actuales y prospectivos.
  19. 19. La inteligencia, como las herramientas con filo, mejora enfrentándolas con otras de mayor dureza. Mario Sarmiento.
  20. 20. 32a Geoinquiets, 20 de febrer de 2014 Juan Daniel Castillo Rosas juan.daniel.castillo@estudiant.upc.edu @jdcastillor
  21. 21. Referencias             Barrera Guarín, E., & Escobar, J. E. (2003). Un enfoque fuzzy para la prospectiva Delphi. Ingeniería y Desarrollo, 14, 1–23. Retrieved from http://www.redalyc.org/articulo.oa?id=85201401 De Moya Amaris, M. E. (2003). Representación y clasificación de datos geoespaciales: comparación entre mapas autoorganizativos de Kohonen y el método Gas Neuronal. Revista Ingeniería e Investigación, 53, 12–22. Di Zio, S., & Pacinelli, A. (2011). Opinion convergence in location: a spatial version of the delphi method. Technological Forecasting and Social Change, 78(9), 1565–1578. doi:10.1016/j.techfore.2010.09.010. Di Zio, S., & Staniscia, B. (2013). A Spatial version of the Shang method. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2013.09.011 Goodchild, M. F. (2013). Prospects for a Space–Time GIS. Annals of the Association of American Geographers, 103(5), 1072–1077. doi:10.1080/00045608.2013.792175. Hahmanna, S., & Burghardta, D. (2013). How much information is geospatially referenced? Networks and cognition. International Journal of Geographical Information Science, 27(6), 1171–1189. doi:10.1080/13658816.2012.743664. Lévy, P. (2010). From social computing to reflexive collective intelligence: the IEML research program. Information Sciences, 180(1), 71–94. doi:10.1016/j.ins.2009.08.001. Monguet, J. M., Gutiérrez, A., Ferruzca, M., Alatriste, Y., Martínez, C., Córdoba, C., … Ramírez, M. (2012). Vector Consensus Model. In Organizational Integration of Enterprise Systems and Resources: Advancements and Applications (pp. 303–317). U.S.A.: Business Science Reference (an imprint of IGI Global). Olaya, V. (2012). Sistemas de información geográfica (p. 877). OsGeo. Retrieved from http://wiki.osgeo.org/wiki/Libro_SIG. Tobler, W. (2004). On the first law of Geography: A Reply. Annals of the Association of American Geographers, 94(2), 304–310. Werbos, P. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences. Williams, R. (1987). Selling a geographical information system to government policy makers. In P. Van Demark (Ed.), Annual Conference of the Urban and Regional Information Systems Association (pp. 150–156). Fort Lauderdale, Florida: URISA.

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