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| + | <font size = 5; color="#FFFFFF">IS415 GeoSpatial Analytics for Business Intelligence</font> | ||
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| + | [[Course_information| <font color="#FFFFFF">About</font>]] | ||
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| + | [[Lesson_Plan| <font color="#FFFFFF">Weekly Session</font>]] | ||
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| + | [[GAProject| <font color="#FFFFFF">Geospatial Analytics Project</font>]] | ||
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| + | <font size=5>Exploratory Spatial Data Analysis (ESDA)</font> | ||
[[Media:Lesson08.pdf| Lesson slides]] | [[Media:Lesson08.pdf| Lesson slides]] | ||
| − | == | + | ==Lesson Content== |
| − | + | ===Measuring The Geographic Pattern of Feature Attribute=== | |
| − | + | * Why identify geographic patterns? | |
| − | + | * Basic concepts of Tobler First law of Geography | |
| − | == | + | * Basic concepts of spatial autocorrelation |
| − | * | + | * Types of spatial neighbours |
| − | * | + | * Spatial weights matrix |
| − | * | ||
| − | == | + | ===Measures of Global Spatial Autocorrelation=== |
| − | * | + | * Moran's I: Principles and methods |
* Geary's c: Principles and methods | * Geary's c: Principles and methods | ||
Latest revision as of 15:51, 4 March 2018
IS415 GeoSpatial Analytics for Business Intelligence
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Exploratory Spatial Data Analysis (ESDA)
Lesson Content
Measuring The Geographic Pattern of Feature Attribute
- Why identify geographic patterns?
- Basic concepts of Tobler First law of Geography
- Basic concepts of spatial autocorrelation
- Types of spatial neighbours
- Spatial weights matrix
Measures of Global Spatial Autocorrelation
- Moran's I: Principles and methods
- 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.
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]
