Difference between revisions of "Lesson10"

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<font size=5>Multivariate Spatial Data Analysis Techniques</font>
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<font size=5>Analytical Regionalisation and Geographical Segmentation</font>
  
 
[[Media:Lesson08.pdf| Lesson slides]]
 
[[Media:Lesson08.pdf| Lesson slides]]

Revision as of 22:56, 1 January 2018

Analytical Regionalisation and Geographical Segmentation

Lesson slides

Multivariate Spatial Data

  • aas
  • aas

Discovery the Spatial Structure from Multivariate Geographically Referenced Attributes

  • Multivariate Principle Component Analysis
  • Locally Weighted PCA
  • Geographically Weighted PCA

Spatially Constrained Clustering Algorithms

  • Basic concept analytical regionalisation
  • Geary's c: Principles and methods

Local Indicators of Spatial Association (LISA)

  • Local Moran's I: Principles and methods
  • Local Geary's c: Principles and methods


Hands-on Session

Hands-on Exercise Week 9: Geospatial Data Analysis of Area Patterns


Daily Readings

Moran I [1]

Global Geary's c [2]

Local Indicators of Spatial Association [3]

Spatial Autocorrelation (45 min)[4]


Resources

R Resources

Core libraries

  • R [5]
  • spdep [6]
  • Applied Spatial Data Analysis with R, Chapter 9: Area Data and Spatial Autocorrelation [7]

Ancillary libraries

  • maptools, tools for reading and handling spatial objects[8]
  • sp, classes and methods for spatial data [9]

OpenGeoDa

OpenGeoDa, a free software package that conducts spatial data analysis, geovisualization, spatial autocorrelation and spatial modeling. OpenGeoDa is the cross-platform, open source version of Legacy GeoDa. While Legacy GeoDa only runs on Windows XP, OpenGeoDa runs on different versions of Windows (including XP, Vista and 7), Mac OS, and Linux.

PySAL

PySAL is an open source cross-platform library of spatial analysis functions written in Python. It is intended to support the development of high level applications for spatial analysis.

  • Homepage [12]
  • Getting Started with PySAL [13]


Reference

Sanghoon Kang, Jinwon Kim, and Sarah Nicholls (2014) "National Tourism Policy and Spatial Patterns of Domestic Tourism in South Korea" Journal of Travel Research, November 2014; vol. 53, 6: pp. 791-804. [14]

Yu, D and Wei (2008) "Spatial data analysis of regional development in Greater Beijing, China, in a GIS environment", Papers in Regional Science, Vol 87, No. 1, pp 97-117. [15]

Celebioglu, F. and Dall'erba, S (2010) "Spatial disparities across the regions of Turkey: an exploratory spatial data analysis", Annals of Regional Science, Vol. 45, No. 2, p. 379-400 [16]

Alasdair Rae (2012) "Spatial patterns of labour market deprivation in Scotland: Concentration, isolation and persistence" Local Economy, August/September 2012; vol. 27, 5-6: pp. 593-609. [17]

Hao Luo and Yang Yang (2013) "Spatial pattern of hotel distribution in China" Tourism and Hospitality Research, January 2013; vol. 13, 1: pp. 3-15. [18]

Elias, M and Rey, S.J. (2011) "Educational Performance and Spatial Convegence in Peru [19]

Zulu et al. (2014) "Analyzing spatial clustering and the spatiotemporal nature and trends of HIV/AIDS prevalence using GIS: the case of Malawi, 1994-2010". BMC Infectious Diseases 2014 14:285. [20]

Discussion

Discussion Lesson 9