IDEAS home Printed from https://ideas.repec.org/b/spr/sprbok/978-3-540-35730-8.html
   My bibliography  Save this book

Spatial Analysis and GeoComputation

Author

Listed:
  • Manfred M. Fischer

    (Vienna University of Economics and Business Administration)

Abstract

No abstract is available for this item.

Individual chapters are listed in the "Chapters" tab

Suggested Citation

  • Manfred M. Fischer, 2006. "Spatial Analysis and GeoComputation," Springer Books, Springer, number 978-3-540-35730-8, June.
    • Handle: RePEc:spr:sprbok:978-3-540-35730-8
    DOI: 10.1007/3-540-35730-0
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yakubu Aliyu Bununu, 2017. "Integration of Markov chain analysis and similarity-weighted instance-based machine learning algorithm (SimWeight) to simulate urban expansion," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(2), pages 217-237, May.
    2. Paula Simões & M. Lucília Carvalho & Sandra Aleixo & Sérgio Gomes & Isabel Natário, 2017. "A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line," Econometrics, MDPI, vol. 5(2), pages 1-23, June.
    3. Richard Harris & David O’Sullivan & Mark Gahegan & Martin Charlton & Lex Comber & Paul Longley & Chris Brunsdon & Nick Malleson & Alison Heppenstall & Alex Singleton & Daniel Arribas-Bel & Andy Evan, 2017. "More bark than bytes? Reflections on 21+ years of geocomputation," Environment and Planning B, , vol. 44(4), pages 598-617, July.
    4. Juliana Mio de Souza & Paulo Morgado & Eduarda Marques da Costa & Luiz Fernando de Novaes Vianna, 2022. "Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    5. Carlos García-Alonso & Leonor Pérez-Naranjo & Juan Fernández-Caballero, 2014. "Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms," Annals of Operations Research, Springer, vol. 219(1), pages 187-202, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprbok:978-3-540-35730-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.