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Introduction to spatial analyses and tools
ESTP course on Geographic Information Systems (GIS):
Use of GIS for making statistics in a production environment
Statistics Norway, Oslo, 26th to 30th of March 2012
Attribution (by) Licensees may copy, distribute, display and perform the work and make derivative
works based on it only if they give the author or licensor the credits in the manner specified by these.
Mrs Diana Makarenko-Piirsalu
MSc in Landscape Ecology and Environmental
Protection
Geolytics OÜ
Mere tee 15, Saviranna,
Jõelähtme vald, Harjumaa, ESTONIA
diana.m.piirsalu@gmx.ch
Mob. +372 556 19 636
http://creativecommons.org/licenses/by/3.0/
Topics
• What is spatial analyses?
• What are important
and fundamental issues in spatial
statistics?
• Examples of the spatial analyses types
• gvSIG
incorporated spatial analyses and introduction
SEXTANTE
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What is spatial analyses?
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In statistics, spatial analysis or spatial statistics includes any of the
formal techniques which study entities using their topological, geometric,
or geographic properties.
(Source: http://wiki.gis.com/wiki/index.php/Spatial_analysis)
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The process of examining the locations, attributes, and relationships of
features in spatial data through overlay and other analytical techniques in
order to address a question or gain useful knowledge. Spatial analysis
extracts or creates new information from spatial data. GIS Dictionary
(Source:http://support.esri.com/en/knowledgebase/GISDictionary/term/spatial%20analysis )
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In a very broad sense: answering to the question : „What happens
where? “
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Main steps of analysing reality spatially
Reality
Raw Data
Data
collection
Results
Data Model
Conceptualize
Spatial
analyses
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The aim is to create new knowledge
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Extracting or creating new information from spatial data
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What is important in spatial analyses?
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How geographic phenomena are arranged in the real world?
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We should consider the arrangement of geographic phenomena along discrete –
continuous and abrupt – smooth continua
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Discrete phenomena
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occur at distinct locations with space in between
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Example: individual person in a city . Location can be specified for each person, with
space between individuals
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Continuous phenomena
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occur throughout a geographic region of interest
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Example: elevation, every longitude and latitude position has a value above or below
sea level.
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Discrete and continuous phenomena can also be considered as either abrupt or
smooth.
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Example: Number of votes in local municipalities is abrupt phenomena and
precipitations in a humid region are smooth.
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Continuos- discrete – abrupt – smooth
phenomena
• Considering the distribution of geographic phenomena is important in
selecting proper spatial analyses or appropriate method of
symbolisation in visualising data in thematic mapping
Source: Thematic cartography and geovisualization, T A. Solcum et al, 2009
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Fundamental issues in spatial analyses
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A fundamental concept in geography is that nearby entities often share more
similarities than entities which are far apart.
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This idea is known as „Tobler´s first law of geography„ - everything is related
to everything else, but near things are more related than distant things„. Source:
Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46,
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Spatial auto-correlation – correlation of variables with itself through the space
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Possible causes:
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Simple correlation- whatever is causing an observation in one location also
causes similar observations in nearby locations
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Causality - something at a given location directly influences the
characteristics of nearby locations
Source: http://wiki.gis.com/wiki/index.php/Spatial_analysis
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Fundamental issues in spatial analyses
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Spatial dependency or auto-correlation – correlation of variables with itself
though the space
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Standard statistical techniques assume independence among observations
•
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Standard regression analyses may result in unreliable significance tests.
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Spatial regression models (for example - Geographically weighted
regression - GWR ) capture these relationships and do not suffer from these
weaknesses.
It is also appropriate to view spatial dependency as a source of information
rather than something to be corrected.
Source: http://wiki.gis.com/wiki/index.php/Spatial_analysis
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Spatial autocorrelation statistics
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Measure the strength of spatial autocorrelation
Test the assumption of independence or randomness
Negative
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None
Positive
Classic spatial autocorrelation statistics are:
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Moran´s I - compares the value of the variable at any one location with the variable at all
other locations . The value of Moran´s I lies between 1 and +1. The higher the coeficient the
stronger the aotocorrelation is. A random arrangement of square colors would give Moran's I
a value that is close to 0.
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Geary ´s C – Geary's C is inversely related to Moran´s I, but it is not identical. The value of
Geary's C lies between 0 and 2. 1 means no spatial autocorrelation. Smaller than 1 means
positive spatial autocorrelation
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Moran's I is a measure of global spatial autocorrelation, while Geary's C is more sensitive to
Source:http://en.wikipedia.org
local spatial autocorrelation.
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Fundamental issues in spatial statistics – MAUP
• Modifiable areal unit problem MAUP
• is an issue in the analysis of spatial data arranged in zones, where the
conclusion depends on the particular shape or size of the zones used in
the analysis.
• spatial units are therefore arbitrary or modifiable and contain artifacts
related to the degree of spatial aggregation or the placement of boundaries
• Example : Statistical units as NUTS, LAU etc
• MAUP can cause random variables to appear as if there is a significant
association, when there is not. Multivariate regression parameters are
more sensitive to MAUP than correlation coefficients
• http://wiki.gis.com/wiki/index.php/Modifiable_areal_unit_problem#MAUP_s
ensitivity_analysis
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Fundamental issues in spatial statistics
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Scale
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Spatial and temporal scale are still under the research in spatial
analysis.
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ensuring that the conclusion of the analysis does not depend on any
arbitrary scale.
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Using quantitative metrics which do not depended on the scale at
which they were measured are the solution
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What components of spatial dimensions
can be analysed?
• Geometry
• Topology
• Pattern
• Proximity
• Accesibilty
• Dynamics
Source: GITTA, 2012
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Steps in spatial analyses
Source:http://www.spatialanalysisonline.com/output/
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Examples of spatial analyses types
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One of the GIS power is to cobine spatial data
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Overlay analyses – „What is on above what?“
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Joining and viewing together separate data sets that share all or part of
the same area
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The result of overlay analyses is a new data set that identifies the
spatial relationships
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Overlay analyse tools available in gvSIG:
Clip
Difference
Intersection
Union
Spatial selection
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Examples of spatial analyses types
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Proximity analyses – „What is close to ? „ How far is ..?“
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Proximity analyse tools available in gvSIG
Buffer
Spatial join
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Examples of spatial analyses types
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Network analyses – the spatial analysis of linear (line) features
• analyzing structure (connectivity pattern) of networks
• analyzing movement (flow) over the network system
• Costs (weights) can be analysed
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Network analyse tools available in gvSIG
Service area
Shortest path
Closest facility
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Examples of spatial analyses types
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Interpolation
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Spatial interpolation - estimating
the value of properties at
unsampled sites within the area
covered by existing observations
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can be thought of as the reverse of
the process used to select the few
points from a DEM which
accurately represent the surface
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rationale behind spatial
interpolation Tobler´s first law of
geography
Source:http://www.geog.ubc.ca/courses/klink/gis.notes/ncgia/u40.html#SEC40.2
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Examples of spatial analyses types
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Point pattern analyses
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The spatial pattern of distribution of point featrues
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Valid measure of the distribution are the number of occurances in the pattern
and respective geographic location
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Spatial pattern of all points in the study area
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Examples of spatial analyses types
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Neighbourhood analyses
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analyzes the relationship between an object and similar surrounding
objects in a surface
is based on local or neighborhood characteristics of the data
computes an output grid where the value at each location is a function
of the input cells within a specified neighborhood of the location
computes an output grid where the value at each location is a function
of the input cells within a specified neighborhood of the
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Source of pictures:http://www.esri.com
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Examples of spatial analyses types
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Neighbourhood analyses algorithms in gvSIG can be found from
SEXTANTE – Focal statistics
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SEXTANTE
• http://www.sextantegis.com/
• Developed by Victor Olaya
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Main tools to use SEXTANTE in gvSIG
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Main tools to use SEXTANTE in gvSIG
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THANK YOU!
ESTP course on Geographic Information Systems (GIS):
Use of GIS for making statistics in a production environment
Statistics Norway, Oslo, 26th to 30th of March 2012
Mrs Diana Makarenko-Piirsalu
MSc in Landscape Ecology and Environmental
Protection
Geolytics OÜ
Mere tee 15, Saviranna,
Jõelähtme vald, Harjumaa, ESTONIA
diana.m.piirsalu@gmx.ch
Mob. +372 556 19 636
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