Presentation on theme: "Introduction to spatial analyses and tools"— Presentation transcript:
1 Introduction to spatial analyses and tools A proposalAgainst the background of the seven hypotheses presented in this document, the European Forum for Geostatistics (EFGS), under a generous grant from EUROSTAT, have from the beginning of this year started work on the GEOSTAT ESSnet project. The project consortium of nine NSI’s is led by Statistics Norway. The need to take action for the integration of geography and statistics should not be limited to the European scale.We therefore propose:The establishment of a Global Forum For Geostatistics (GFGS) to serve as a professional reference group (PRG) for Geostatistics and provide a professional link between UN and the NSI’s in issues related to this field.. This PRG will primarily be concerned with questions related to the integration of geography (GSDI projects) and statistics (GEOSTAT)The initiation of a joint UN spatial data infrastructure (SDI) project for geostatistics. This is the first step towards a GSDI based Global Integrated Information System (GGIISS) and should be developed in close cooperation with the national mapping agency (NMA) -led GSDI and INSPIRE projects.The initiation of the GEOSTAT GSSgrid project to provide a high-resolution harmonised infrastructure for geostatistics liberated from administrative areas and demonstrating its power and utility through a UN project to plot the global population on km grids.Keywords: GSDI (Global Spatial Data Infrastructure), GSS (Global Statistical System), Small area statistics, GIS (Geographical Information System), GGIISS (Global Geographic Integrated Information Systems of Systems)Mrs Diana Makarenko-PiirsaluMSc in Landscape Ecology and Environmental ProtectionGeolytics OÜMere tee 15, Saviranna,Jõelähtme vald, Harjumaa, ESTONIAMobESTP course on Geographic Information Systems (GIS):Use of GIS for making statistics in a production environmentStatistics Norway, Oslo, 26th to 30th of March 2012Attribution (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.
2 Topics What is spatial analyses? What are important and fundamental issues in spatial statistics?Examples of the spatial analyses typesgvSIG incorporated spatial analyses and introduction SEXTANTE
3 What is spatial analyses? In statistics, spatial analysis or spatial statistics includes any of the formal techniques which study entities using their topological, geometric, or geographic properties.(Source: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? “
4 Main steps of analysing reality spatially The aim is to create new knowledgeExtracting or creating new information from spatial dataResultsRealityRaw DataData ModelData collectionConceptualizeSpatial analyses
5 What is important in spatial analyses? How geographic phenomena are arranged in the real world?We should consider the arrangement of geographic phenomena along discrete – continuous and abrupt – smooth continuaDiscrete phenomenaoccur at distinct locations with space in betweenExample: individual person in a city . Location can be specified for each person, with space between individualsContinuous phenomenaoccur throughout a geographic region of interestExample: 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.
6 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 mappingSource: Thematic cartography and geovisualization, T A. Solcum et al, 2009
7 Fundamental issues in spatial analyses 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 spacePossible causes:Simple correlation- whatever is causing an observation in one location also causes similar observations in nearby locationsCausality - something at a given location directly influences the characteristics of nearby locationsSource:
8 Fundamental issues in spatial analyses Spatial dependency or auto-correlation – correlation of variables with itself though the spaceStandard statistical techniques assume independence among observationsStandard 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:
9 Spatial autocorrelation statistics Measure the strength of spatial autocorrelationTest the assumption of independence or randomnessClassic 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 autocorrelationMoran's I is a measure of global spatial autocorrelation, while Geary's C is more sensitive to local spatial autocorrelation.NegativeNonePositiveSource:http://en.wikipedia.org
10 Fundamental issues in spatial statistics – MAUP Modifiable areal unit problem MAUPis 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 boundariesExample : Statistical units as NUTS, LAU etcMAUP 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
11 Fundamental issues in spatial statistics ScaleSpatial 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
12 What components of spatial dimensions can be analysed? GeometryTopologyPatternProximityAccesibiltyDynamicsSource: GITTA, 2012
13 Steps in spatial analyses Source:http://www.spatialanalysisonline.com/output/
14 Examples of spatial analyses types One of the GIS power is to cobine spatial dataOverlay analyses – „What is on above what?“Joining and viewing together separate data sets that share all or part of the same areaThe result of overlay analyses is a new data set that identifies the spatial relationships
15 Overlay analyse tools available in gvSIG: ClipDifferenceIntersectionUnionSpatial selection
16 Examples of spatial analyses types Proximity analyses – „What is close to ? „ How far is ..?“Proximity analyse tools available in gvSIGBufferSpatial join
17 Examples of spatial analyses types Network analyses – the spatial analysis of linear (line) featuresanalyzing structure (connectivity pattern) of networksanalyzing movement (flow) over the network systemCosts (weights) can be analysedNetwork analyse tools available in gvSIGShortest pathClosest facilityService area
18 Examples of spatial analyses types InterpolationSpatial interpolation - estimating the value of properties at unsampled sites within the area covered by existing observationscan be thought of as the reverse of the process used to select the few points from a DEM which accurately represent the surfacerationale behind spatial interpolation Tobler´s first law of geographySource:http://www.geog.ubc.ca/courses/klink/gis.notes/ncgia/u40.html#SEC40.2
19 Examples of spatial analyses types Point pattern analysesThe spatial pattern of distribution of point featruesValid measure of the distribution are the number of occurances in the pattern and respective geographic locationSpatial pattern of all points in the study area
20 Examples of spatial analyses types Neighbourhood analysesanalyzes the relationship between an object and similar surrounding objects in a surfaceis based on local or neighborhood characteristics of the datacomputes an output grid where the value at each location is a function of the input cells within a specified neighborhood of the locationcomputes an output grid where the value at each location is a function of the input cells within a specified neighborhood of theSource of pictures:http://www.esri.com
21 Examples of spatial analyses types Neighbourhood analyses algorithms in gvSIG can be found from SEXTANTE – Focal statistics
25 THANK YOU! ESTP course on Geographic Information Systems (GIS): Mrs Diana Makarenko-PiirsaluMSc in Landscape Ecology and Environmental ProtectionGeolytics OÜMere tee 15, Saviranna,Jõelähtme vald, Harjumaa, ESTONIAMobESTP course on Geographic Information Systems (GIS):Use of GIS for making statistics in a production environmentStatistics Norway, Oslo, 26th to 30th of March 2012
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