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Interfacing Spatial Statistics sofware and GIS: a case study TerraLib: Open source GIS library Data management Functions All of data (spatial + attributes) is in database Spatial statistics, Image Processing, Map Algebra Innovation Based on state-of-the-art techniques Same timing as similar commercial products Web-based co-operative development http://www.terralib.org Operational Vision of TerraLib DBMS TerraLib Geographic Application Spatial Operations API for Spatial Operations Spatial Operations Access Oracle Spatial MySQL Postgre SQL TerraLib MapObjects + ArcSDE + cell spaces + spatio-temporal models TerraLib applications Cadastral Mapping Public Health Indicators of social exclusion in innercity areas Land-use change modelling Spatial statistical tools for epidemiology and health services Social Exclusion Improving urban management of large Brazilian cities Spatio-temporal models of deforestation in Amazonia Emergency action planning Oil refineries and pipelines (Petrobras) TerraLib Structure Java Interface COM Interface OGIS Services C++ Interface Functions kernel Visualization Controls Spatio-Temporal Data Structures File and DBMS Access I/O Drivers External Files DBMS TerraView TerraLib client Connects to DBMS Visualization with ‘views’ and ‘themes’ Images, Grids, vectors and tables Brushing between graphics and maps Analysis Oracle, PostGIS, MySQL and SQLServer Geocoding, ESDA, Clustering (more to come) I/O SHP, MIF, and SPRING vectors TIFF and GEOTIFF rasters Creating a MySQL spatial database in TerraView File...Open Database menu Importing GIS files File...Import data Select the rio_inf_mort.shp (Rio infant mortality data) Use OBJET_ID_1 as the column Use Projection... Lat/Long and Datum SAD69 Databases, layers, views and themes A database has many layers This is a storage concept Visualization is based on views and themes A view is a set of spatial objects from a layer A theme is a way to display these objects Changing the legend of a theme Theme...Change legend Change the legend to display the NEO_RATE Quantiles Five slices Resulting TerraView interface Producing a scatterplot Theme...Graphic Parameters Selection a DISPERSION graphic X – TOTBIRTH Y – NEO_RATE Brushing Operation...TileWindows Shows both graphics and map Brush between the two and the table R – TerraLib integration Strong coupling mechanism R – TerraLib integration Enables R to access a TerraLib database Results in R are incorporated in a GIS database directly Data is analysed in R using packages such as gstat or geoR Results are visualized in TerraView Integration is performed by the aRT API aRT: API R-TerraLib aRT is an R package that provides the integration between the software R and the GIS classes TerraLib. Developed by UFPR, Brazil http://www.est.ufpr.br/aRT/ aRT: API R-TerraLib Classes to manipulate data and functions in TerraLib aRT aRTdb Creation and access to a database aRTlayer Enables connection to the DBMS for database administration Enables manipulation of layers in aRT aRTtheme Enables visualização of themes in TerraView Integration R - TerraLib R graphics TerraView map (Neto et al., 2005) R-TerraLib integration: example 1 Load the geoR and arT packages into R manually geoR is a library for advanced geostatistics (also developed by Paulo Ribeiro, UFPR) Get the geoR and arT packages from the course homepage Expand the zip files and copy them to the directory C:\Program Files\R\rw2011\library R-TerraLib integration: example 1 ## ## loading packages ## require(geoR) require(aRT) ## ## Exemple 1: data for parana state ## data(parana) points(parana, bor=borders) A new type: geodata > class (parana) [1] "geodata" > parana $coords east north [1,] 402.9529 164.52841 [2,] 501.7049 428.77100 [3,] 556.3262 445.27065 [4,] 573.4043 447.04177 [5,] 702.4228 272.29590 [6,] 668.5442 261.67070 [7,] 435.8477 286.54044 [8,] 434.0125 317.90506 [9,] 432.4622 288.37001 A new type: geodata $data [1] [8] [15] [22] [29] [36] [43] [50] [57] [64] [71] 306.09 282.07 237.87 351.73 199.91 212.50 321.69 302.13 344.59 236.25 165.46 200.88 319.12 222.87 277.92 167.00 242.08 208.89 335.41 366.10 228.82 320.31 167.07 244.67 263.10 323.08 182.88 247.79 238.62 330.87 201.84 258.12 232.80 162.77 233.31 236.91 253.32 197.10 187.27 248.76 329.49 218.27 232.17 266.27 163.57 224.46 247.01 315.33 257.50 222.54 193.48 262.81 200.38 248.17 301.10 178.61 206.12 240.58 379.94 205.16 313.60 240.48 365.88 229.40 240.66 244.78 301.54 248.99 304.28 197.09 224.07 269.92 265.56 359.08 235.07 184.59 248.57 A new type: geodata $borders [1,] [2,] [3,] [4,] [5,] [6,] [7,] [8,] [9,] [10,] [11,] [12,] [13,] east 670.2049 663.7187 656.0667 649.9714 642.6346 635.2717 628.7421 622.7559 617.2708 610.4570 604.4217 600.2620 592.6210 north 111.7610 107.0510 105.2420 100.7915 97.9930 101.1545 105.5405 110.7495 116.3970 120.5795 119.6670 121.8980 119.9675 A new type: geodata $loci.paper [,1] [1,] 300.336 [2,] 647.755 [3,] 361.764 [4,] 410.100 [,2] 484.453 317.463 438.781 260.373 R-TerraLib integration: example 1 ## ## connection to MySQL ## > con <- openConn(user=“gilberto") > con Object of class aRTconn DBMS: "MySQL" User: "gilberto" Password: "" Port: 3306 Host: "localhost" Cleaning the database If the database exists, clean it > showDbs(con) [1] "ifgi" "intro" "test" "mysql" "parana" > if(any(showDbs(con)=="parana")) deleteDb(con, "parana", force=T) Deleting database 'parana' ... yes Creating a new database ## creating a new database > pr = createDb(con, "parana") Connecting with database 'parana' ... no Creating database 'parana' ... yes Creating conceptual model of database 'parana' ... yes Loading layer set of database 'parana' ... yes Loading view set of database 'parana' ... yes Creating a layer in the database ## ## Creating a layer to hold the data ## l_data <- createLayer(pr, "data") Building projection to layer 'data' ... yes Creating layer 'data' ... yes Loading points into the database ## loading points > artcoords <- .gr2aRTpoints(parana) # adding points to a layer > addPoints(l_data, artcoords) Converting points to TerraLib format ... yes Adding 143 points to layer 'data' ... yes Reloading tables of layer 'data' ... yes # adding a table to the layer > t_data = createTable(l_data, "t_data",gen=T) Creating static table 't_data' on layer 'data' ... yes Creating link ids ... yes Plotting the data > plot(l_data) Inserting attributes on the GIS table > artdata <- data.frame(.gr2aRTattributes(parana)) > > artdata[1:5,] id data 1 1 306.09 2 2 200.88 3 3 167.07 4 4 162.77 5 5 163.57 > > names(artdata) [1] "id" "data" > createColumn(t_data, "data") Checking for column 'data' in table 't_data' ... no Creating column 'data' in table 't_data' ... yes > updateColumns(t_data, artdata) Converting 2 attributes to TerraLib format ... yes Converting 143 rows to TerraLib format ... yes Updating columns of table 't_dados' ... yes Creating views and themes for TerraView ## criando vistas e temas automaticas para o TV (opcional) > th_data <- createTheme(l_data, “stations", view = "view") Checking for theme ‘stations' in layer 'parana' ... no Creating theme ‘stations' on layer 'dados' ... yes Checking for view 'view' in database 'parana' ... no Creating view 'view' ... yes Inserting view 'view' in database 'parana' ... yes Checking tables of theme ‘stations' ... yes Saving theme ‘stations' ... yes Building collection of theme ‘stations' ... yes > setVisual(tema_dados, visualPoints(pch=22, color="black", size=5)) Creating a layer to store borders > artpols <- .gr2aRTpolygons(parana) > > l_pol<-createLayer(pr, “borders") Building projection to layer ‘borders' ... yes Creating layer ‘borders' ... yes > addPolygons(l_pol, artpols) Converting polygons to TerraLib format ... yes Adding 1 polygons to layer ‘borders' ... yes Reloading tables of layer ‘borders' ... yes > createTable(l_pol, "t_pol") Creating static table 't_pol' on layer ‘borders' ... yes Creating link ids ... yes Object of class aRTtable Table: "t_pol" Type: static Layer: “borders" Rows: 1 Attributes: id: string[16] (key) > plot(l_pol) Creating a view and theme for the polygons > tema_pol<-createTheme(l_pol, “borders", view="view") Checking for theme ‘borders' in layer 'parana' ... no Creating theme ‘borders' on layer ‘borders' ... yes Checking for view 'view' in database 'parana' ... yes Checking tables of theme ‘borders' ... yes Saving theme ‘borders' ... yes Building collection of theme ‘borders' ... yes > setVisual(tema_pol, visualPolygons()) Creating a grid for the interpolated surface > > > > > > > gx <- seq(50,800, by=10) gy <- seq(-100,650, by=10) loc0 <- expand.grid(gx,gy) points(parana, bor=borders) points(loc0, pch=".", col=2) loc1 <- polygrid(loc0, bor=parana$bor) points(loc1, pch="+", col=4) Kriging using geoS: model fitting > # Kriging using geoS > # Step 1 – fitting the variogram > ml <- likfit(parana, trend="1st", ini=c(1000, 100)) --------------------------------------------------likfit: likelihood maximisation using the function optim. likfit: WARNING: This step can be time demanding! --------------------------------------------------likfit: end of numerical maximisation. Kriging using geoS: interpolation > kc <- krige.control(trend.d="1st", trend.l="1st", obj=ml) > kc <- krige.conv(parana, loc=loc0, krige= KC, bor=parana$borders) krige.conv: results will be returned only for prediction locations inside the borders krige.conv: model with mean given by a 1st order polynomial on the coordinates krige.conv: Kriging performed using global neighbourhood > save.image() > > image(kc, col=terrain.colors(15)) Exporting the grid to TerraLib > # preparing object for aRT > georpred <- .prepare.graph.kriging(locations=loc0, borders=parana$borders, values=kc$pred) > artpred <- .gr2aRTraster(georpred) > # creating a layer in TerraLib > l_pred <- new("aRTlayer", pr, layer="pred", create=T) Building projection to layer 'pred' ... yes Creating layer 'pred' ... yes > addRaster(l_pred, artpred) Adding raster data to layer 'pred' ... yes Reloading tables of layer 'pred' ... yes Creating a theme and a view for the grid > th<-createTheme(l_pred, "raster", view="view") Checking for theme 'raster' in layer 'parana' ... no Creating theme 'raster' on layer 'pred' ... yes Checking for view 'view' in database 'parana' ... yes Checking tables of theme 'raster' ... yes Saving theme 'raster' ... yes > > setVisual(th, visualRaster(), mode="r") Plotting the data in R > > > > # plotting the data in R plot(l_pred, col=terrain.colors(15)) plot(l_dados, add=T) plot(l_pol, add=T) Now, let’s see the data in TerraView File...OpenDatabase... Parana database is there! Create Views and Themes (if needed) Create View parana Create themes Borders Data Pred Play with the data! R data in TerraView