Difference between revisions of "Lesson9"

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<font size=5>Analysing and Visualising Geographically Referenced Attributes:
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<br>Exploratory Spatial Data Analysis (ESDA) Methods</font>
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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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[[Media:Lesson08.pdf| Lesson slides]]
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[[Lesson_Plan| <font color="#FFFFFF">Weekly Session</font>]]
  
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==Analysing and Visualising Area Data==
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[[Assignments| <font color="#FFFFFF">Assignments</font>]]
  
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===Introduction to Geographically Referenced Attributes===
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*  What is area (Lattice) patterns?
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;
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*  Basic concepts of Tobler First law of Geography
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[[GAProject| <font color="#FFFFFF">Geospatial Analytics Project</font>]]
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*  Basic concepts of spatial autocorrelation
 
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*  Types of spatial neighbours
 
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*  Spatial weights matrix
 
  
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===Measures of Global Spatial Autocorrelation===
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*  Moran's I: Principles and methods
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;
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*  Geary's c: Principles and methods
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[[Course_Resources| <font color="#FFFFFF">Course Resources</font>]]
  
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===Local Indicators of Spatial Association (LISA)===
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|  &nbsp;
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*  Local Moran's I:  Principles and methods
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*  Local Geary's c:  Principles and methods
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<br/>
  
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<font size=5>Spatial Point Patterns Analysis and Applications</font>
  
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==Hands-on Session==
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==Lesson Content==
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===Introduction to Spatial Point Patterns===
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*  What is Spatial Point Patterns Analysis?
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*  Analysing spatial point patterns
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*  Modelling spatial relationships
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*  Analysing spatio-temporal data
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*  Geographic Knowledge Discovery
  
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Hands-on Exercise Week 9: Geospatial Data Analysis of Area Patterns
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===Density-based Point Patterns Measures===
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* Quadrat analysis
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* Kernel density estimation
  
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===Distance-based Point Pattern Measures===
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*  Nearest neighbour distance
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*  G function
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*  F function
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*  K function
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*  L function
  
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==Daily Readings==
 
  
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Moran I [http://en.wikipedia.org/wiki/Moran%27s_I]
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== Hands-on Session ==
  
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Global Geary's c [http://en.wikipedia.org/wiki/Geary%27s_C]
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Hands-on Exercise Week 8: Visualising and Analysing Spatial Point Patterns
  
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Local Indicators of Spatial Association [http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.1995.tb00338.x/pdf]
 
  
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Spatial Autocorrelation (45 min)[http://geodacenter.asu.edu/spatial-autocor-1]
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== Weekly Readings ==
  
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Point Pattern Analysis [http://gispopsci.org/point-pattern-analysis/]
  
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==Resources==
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Spatial point pattern analysis and its application in geographical epidemiology [http://www.dpi.inpe.br/cursos/ser301/referencias/gattrel_paper.pdf]
  
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===R Resources===
 
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Core libraries
 
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* R [http://www.r-project.org/]
 
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* spdep [http://cran.r-project.org/web/packages/spdep/index.html]
 
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* Applied Spatial Data Analysis with R, Chapter 9: Area Data and Spatial Autocorrelation [http://www.springerlink.com.libproxy.smu.edu.sg/content/hu846880805k2416/fulltext.pdf]
 
  
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===Ancillary libraries===
 
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* maptools, tools for reading and handling spatial objects[http://cran.r-project.org/web/packages/maptools/index.html]
 
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* sp, classes and methods for spatial data [http://cran.r-project.org/web/packages/sp/index.html]
 
  
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===OpenGeoDa===
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==Spatial Point Pattern Analysis software==
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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. 
 
  
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* Download [https://geodacenter.asu.edu/software/downloads]. 
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R [http://www.r-project.org/]
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* OpenGeoDa Tutorials [https://geodacenter.asu.edu/og_tutorials]
 
  
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===PySAL===
 
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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.
 
  
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* Homepage [http://pysal.readthedocs.org/en/latest/index.html]
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===R Packages===
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* Getting Started with PySAL [http://pysal.readthedocs.org/en/latest/users/tutorials/index.html]
 
  
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spatstat
  
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==Reference==
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* Homepage [http://cran.r-project.org/web/packages/spatstat/index.html]
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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. [http://jtr.sagepub.com.libproxy.smu.edu.sg/content/53/6/791.full.pdf+html]
 
  
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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. [http://onlinelibrary.wiley.com.libproxy.smu.edu.sg/doi/10.1111/j.1435-5957.2007.00148.x/epdf]
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* Analysing spatial point patterns in 'R' [https://research.csiro.au/software/wp-content/uploads/sites/6/2015/02/Rspatialcourse_CMIS_PDF-Standard.pdf]
  
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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 [http://download.springer.com.libproxy.smu.edu.sg/static/pdf/10/art%253A10.1007%252Fs00168-009-0313-8.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs00168-009-0313-8&token2=exp=1457146180~acl=%2Fstatic%2Fpdf%2F10%2Fart%25253A10.1007%25252Fs00168-009-0313-8.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Farticle%252F10.1007%252Fs00168-009-0313-8*~hmac=1017a8af0bdb04731455d5b6f2d8ae4a2838c5c7e7a447536b0d4733b5829813]
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aspace [http://cran.r-project.org/web/packages/aspace/index.html]
  
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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. [http://lec.sagepub.com.libproxy.smu.edu.sg/content/27/5-6/593.full.pdf+html]
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maptools [http://cran.r-project.org/web/packages/maptools/index.html]
  
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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. [http://thr.sagepub.com.libproxy.smu.edu.sg/content/13/1/3.full.pdf+html]  
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sp [http://cran.r-project.org/web/packages/sp/index.html]
  
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Elias, M and Rey, S.J. (2011) "Educational Performance and Spatial Convegence in Peru [http://region-developpement.univ-tln.fr/fr/pdf/R33/Elias.pdf]
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shapefiles [http://cran.r-project.org/web/packages/shapefiles/index.html]
  
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Zulu et al. (2014) "Analyzing spatial clustering and the spatiotemporal nature and trends of HIV/AIDS prevalence using GIS: the
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===Others===
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case of Malawi, 1994-2010". BMC Infectious Diseases 2014 14:285. [http://bmcinfectdis.biomedcentral.com/articles/10.1186/1471-2334-14-285]
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SEXTANTE for QGIS [http://sextantegis.com/]and QGIS-SEXTANTE cookbook [http://qgissextante.blogspot.sg/]
  
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==Discussion==
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SAGA [http://www.saga-gis.org/en/index.html]
  
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[[Talk:Lesson09|Discussion Lesson 9]]
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GRASS [http://grass.osgeo.org/] and QGIS GRASS Cookbook [http://grasswiki.osgeo.org/wiki/QGIS_GRASS_Cookbook]
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SaTScan [http://www.satscan.org/]
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==References==
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===Methods===
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Several Fundamentals in Implementing Spatial Statistics in GIS [http://www.iseis.cuhk.edu.hk/downloads/full_paper/1999-163-174.pdf]
 +
 
 +
Open source, spatial analysis, and activity-travel behaviour research: capabilities of the ''aspace'' package [http://www.springerlink.com.libproxy.smu.edu.sg/content/t379u45k5141n5g5/fulltext.pdf]
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Kernel Density Estimation [http://en.wikipedia.org/wiki/Kernel_density_estimation]
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CrimeStat III-Part II: Spatial Description [http://www.icpsr.umich.edu/CrimeStat/files/CrimeStatChapter.4.pdf]
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CrimeStat III-Part III: Spatial Modeling[http://www.icpsr.umich.edu/CrimeStat/files/CrimeStatChapter.8.pdf]
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===Applications===
 +
 
 +
Mapping Crime: Understanding Hot Spots [https://www.ncjrs.gov/pdffiles1/nij/209393.pdf]
 +
 
 +
Spatial Analysis for Identifying Concentrations of Urban Damage [http://cdn.intechopen.com/pdfs-wm/10995.pdf]
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A Tale of Two Cities: Density Analysis of CBD on Two Midsize Urban Areas in Northeastern Italy[https://www.google.com.sg/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjtjMK14JHLAhWBSI4KHQuXC6IQFggbMAA&url=http%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%252F978-3-540-89930-3_3&usg=AFQjCNGSWz5WOTbyscNRKwiNhyh_4ZT1xg]
 +
 
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Crime Mapping and Spatial Analysis in National Forests [http://fes.forestry.oregonstate.edu/sites/fes.forestry.oregonstate.edu/files/PDFs/WingTynonCrimeMappingJoF.pdf]
 +
 
 +
Street centrality vs. commerce and service locations in cities: a Kernel Density Correlation case study in Bologna, Italy [http://arxiv.org/ftp/physics/papers/0701/0701111.pdf]
 +
 
 +
Spatial Point Pattern Analysis for Targeting Prospective New Customers: Bringing GIS Functionality into Direct Marketing [http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=DD5A25F9CBF04B144264719D4393F3A9?doi=10.1.1.13.5926&rep=rep1&type=pdf]
 +
 
 +
Clusters of firms in an inhomogeneous space: The high-tech industries in Milan [http://www.sciencedirect.com/science/article/pii/S0264999311000150]
 +
 
 +
Methods for visualizing the explosive remnants of war [http://www.sciencedirect.com/science/article/pii/S0143622813001021]
 +
 
 +
== Discussion ==

Latest revision as of 16:52, 4 March 2018

Claraview.png IS415 GeoSpatial Analytics for Business Intelligence

About

Weekly Session

Assignments

Geospatial Analytics Project

Course Resources

 


Spatial Point Patterns Analysis and Applications

Lesson Content

Introduction to Spatial Point Patterns

  • What is Spatial Point Patterns Analysis?
  • Analysing spatial point patterns
  • Modelling spatial relationships
  • Analysing spatio-temporal data
  • Geographic Knowledge Discovery

Density-based Point Patterns Measures

  • Quadrat analysis
  • Kernel density estimation

Distance-based Point Pattern Measures

  • Nearest neighbour distance
  • G function
  • F function
  • K function
  • L function


Hands-on Session

Hands-on Exercise Week 8: Visualising and Analysing Spatial Point Patterns


Weekly Readings

Point Pattern Analysis [1]

Spatial point pattern analysis and its application in geographical epidemiology [2]


Spatial Point Pattern Analysis software

R [3]


R Packages

spatstat

  • Analysing spatial point patterns in 'R' [5]

aspace [6]

maptools [7]

sp [8]

shapefiles [9]

Others

SEXTANTE for QGIS [10]and QGIS-SEXTANTE cookbook [11]

SAGA [12]

GRASS [13] and QGIS GRASS Cookbook [14]

SaTScan [15]


References

Methods

Several Fundamentals in Implementing Spatial Statistics in GIS [16]

Open source, spatial analysis, and activity-travel behaviour research: capabilities of the aspace package [17]

Kernel Density Estimation [18]

CrimeStat III-Part II: Spatial Description [19]

CrimeStat III-Part III: Spatial Modeling[20]


Applications

Mapping Crime: Understanding Hot Spots [21]

Spatial Analysis for Identifying Concentrations of Urban Damage [22]

A Tale of Two Cities: Density Analysis of CBD on Two Midsize Urban Areas in Northeastern Italy[23]

Crime Mapping and Spatial Analysis in National Forests [24]

Street centrality vs. commerce and service locations in cities: a Kernel Density Correlation case study in Bologna, Italy [25]

Spatial Point Pattern Analysis for Targeting Prospective New Customers: Bringing GIS Functionality into Direct Marketing [26]

Clusters of firms in an inhomogeneous space: The high-tech industries in Milan [27]

Methods for visualizing the explosive remnants of war [28]

Discussion

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