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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 environmentStatistics 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. http://creativecommons.org/licenses/by/3.0/

Mrs Diana Makarenko-PiirsaluMSc in Landscape Ecology and Environmental ProtectionGeolytics OÜMere tee 15, Saviranna, Jõelähtme vald, Harjumaa, ESTONIAdiana.m.piirsalu@gmx.chMob. +372 556 19 636

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)

• 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 )

•  In a very broad sense: answering to the question : „What happens where? “

Main steps of analysing reality spatially

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• The aim is to create new knowledge

• Extracting or creating new information from spatial data

Reality Data ModelResults

Data collection Conceptualize

Spatial analyses

Raw Data

What is important in spatial analyses?

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• How geographic phenomena are arranged in the real world?

• We should consider the arrangement of geographic phenomena along discrete – continuous and abrupt – smooth continua

• Discrete phenomena • occur at distinct locations with space in between• Example: individual person in a city . Location can be specified for each person, with

space between individuals

• Continuous phenomena • occur throughout a geographic region of interest • Example: elevation, every longitude and latitude position has a value above or below

sea level.

• Discrete and continuous phenomena can also be considered as either abrupt or smooth.

• 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

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• 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

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.

• 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,

• Spatial auto-correlation – correlation of variables with itself through the space

• Possible causes:

• Simple correlation- whatever is causing an observation in one location also causes similar observations in nearby locations

• Causality - something at a given location directly influences the characteristics of nearby locations

Source: http://wiki.gis.com/wiki/index.php/Spatial_analysis

Fundamental issues in spatial analyses

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• Spatial dependency or auto-correlation – correlation of variables with itself though the space

• Standard statistical techniques assume independence among observations

• Standard regression analyses may result in unreliable significance tests.

• 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

Spatial autocorrelation statistics

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• Measure the strength of spatial autocorrelation• Test the assumption of independence or randomness

• Classic spatial autocorrelation statistics are:

• 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.

• 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

• Moran's I is a measure of global spatial autocorrelation, while Geary's C is more sensitive to local spatial autocorrelation.

Negative None Positive

Source:http://en.wikipedia.org

Fundamental issues in spatial statistics – MAUP

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• 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_sensitivity_analysis

Fundamental issues in spatial statistics

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• Scale

• Spatial and temporal scale are still under the research in spatial analysis.

• ensuring that the conclusion of the analysis does not depend on any arbitrary scale.

• Using quantitative metrics which do not depended on the scale at which they were measured are the solution

What components of spatial dimensionscan be analysed?

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Source: GITTA, 2012

• Geometry

• Topology

• Pattern

• Proximity

• Accesibilty

• Dynamics

Steps in spatial analyses

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Source:http://www.spatialanalysisonline.com/output/

Examples of spatial analyses types

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• One of the GIS power is to cobine spatial data

• Overlay analyses – „What is on above what?“

• Joining and viewing together separate data sets that share all or part of the same area

• The result of overlay analyses is a new data set that identifies the spatial relationships

Overlay analyse tools available in gvSIG:

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Clip Difference

Intersection UnionSpatial selection

Examples of spatial analyses types

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• Proximity analyses – „What is close to ? „ How far is ..?“

• Proximity analyse tools available in gvSIG

Buffer Spatial join

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

• Network analyse tools available in gvSIG

Service area Shortest path Closest facility

Examples of spatial analyses types

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• Interpolation

• Spatial interpolation - estimating the value of properties at unsampled sites within the area covered by existing observations

• can be thought of as the reverse of the process used to select the few points from a DEM which accurately represent the surface

• rationale behind spatial interpolation Tobler´s first law of geographySource:http://www.geog.ubc.ca/courses/klink/gis.notes/ncgia/u40.html#SEC40.2

Examples of spatial analyses types

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• Point pattern analyses

• The spatial pattern of distribution of point featrues

• Valid measure of the distribution are the number of occurances in the pattern and respective geographic location

• Spatial pattern of all points in the study area

Examples of spatial analyses types

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• Neighbourhood analyses

• 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

Source of pictures:http://www.esri.com

Examples of spatial analyses types

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• Neighbourhood analyses algorithms in gvSIG can be found from SEXTANTE – Focal statistics

SEXTANTE

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• http://www.sextantegis.com/• Developed by Victor Olaya

Main tools to use SEXTANTE in gvSIG

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Main tools to use SEXTANTE in gvSIG

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THANK YOU!

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ESTP course on Geographic Information Systems (GIS): Use of GIS for making statistics in a production environmentStatistics Norway, Oslo, 26th to 30th of March 2012

Mrs Diana Makarenko-PiirsaluMSc in Landscape Ecology and Environmental ProtectionGeolytics OÜMere tee 15, Saviranna, Jõelähtme vald, Harjumaa, ESTONIAdiana.m.piirsalu@gmx.chMob. +372 556 19 636