rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: An
    title: introduction to spatial data science with GeoDa
    part number: Volume 1
    part name: Exploring spatial data
  title info:
    title: Exploring spatial data
  name:
    name part: Anselin, Luc
    name part: 1953-
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: EBSCOhost
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2024
    edition: First edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource ( xxviii, 416 pages) : illustrations.
  table of contents: Chapter 1: Introduction. Chapter 2: Basic Data Operations. Chapter 3: GIS Operations. Chapter 4: Geovisualization. Chapter 5: Statistical Maps. Chapter 6: Maps for Rates. Chapter 7: Univariate and Bivariate Data Exploration. Chapter 8: Multivariate Data Exploration. Chapter 9: Space-Time Exploration. Chapter 10: Contiguity-Based Spatial Weights. Chapter 11: Distance-Based Spatial Weights. Chapter 12: Special Weights Operations. Chapter 13: Spatial Autocorrelation. Chapter 14: Advanced Global Spatial Autocorrelation. Chapter 15: Nonparametric Spatial Autocorrelation. Chapter 16: LISA and Local Moran. Chapter 17: Other Local Spatial Autocorrelation Statistics. Chapter 18: Multivariate Local Spatial Autocorrelation. Chapter 19: LISA for Discrete Variables. Chapter 20: Density-Based Clustering Methods. Chapter 21: Postscript - The Limits of Exploration. Appendices, Bibliography
  target audience: adult
  note: Luc Anselin.
  note: "A Chapman & Hall book."
  note: Includes bibliographical references and index.
  note: Luc Anselin is the Founding Director of the Center for Spatial Data Science at the University of Chicago, where he is also a Stein-Freiler Distinguished Service Professor of Sociology and the College, as well as a member of the Committee on Data Science. He is the creator of the GeoDa software and an active contributor to the PySAL Python open-source software library for spatial analysis. He has written widely on topics dealing with the methodology of spatial data analysis, including his classic 1988 text on Spatial Econometrics. His work has been recognized by many awards, such as his election to the U.S. National Academy of Science and the American Academy of Arts and Science.
  subject:
    topic: Spatial analysis (Statistics)
  subject:
    topic: Spatial analysis (Statistics)
    topic: Data processing
  subject:
    title info:
      title: GeoDa (Computer file)
  subject:
    topic: Analyse spatiale (Statistique)
  subject:
    topic: Analyse spatiale (Statistique)
    topic: Informatique
  subject:
    topic: spatial analysis
  subject:
    topic: SCIENCE / Earth Sciences / Geography
  classification: QA278.2 .A565 2024
  classification: 519.5/3
  identifier: 9781003274919
  identifier: 1003274919
  identifier: 9781032229188
  identifier: 9781040010877
  identifier: 1040010873
  identifier: 9781032229621
  identifier: 9781040010907
  identifier: 1040010903
  identifier: 9781003274919 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: TYFRS
    record creation date: 240409
    record change date: 20240617102716.4
    record identifier: in00005644925
    language of cataloging:
      language term: eng