{"id":15675,"date":"2018-05-15T03:49:50","date_gmt":"2018-05-15T03:49:50","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=15675"},"modified":"2021-12-05T21:54:29","modified_gmt":"2021-12-05T16:24:29","slug":"stat-spatial-analysis","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/stat-spatial-analysis\/","title":{"rendered":"SAS\/STAT Spatial Analysis – 4 Important Procedures"},"content":{"rendered":"

In our last tutorial, we studied SAS survival analysis Procedure. Here, we will discuss SAS\/STAT Spatial Analysis. We will also learn important procedures used in Spatial Analysis in SAS\/STAT: PROC KRIGE2D, PROC SIM2D, PROC SPP, and PROC VARIOGRAM with Syntax & Examples.<\/p>\n

So, let’s start SAS\/STAT<\/strong> Spatial Analysis.<\/p>\n

What is SAS\/STAT Spatial Analysis?<\/h3>\n

Like other processes, SAS Spatial analysis also turns raw data into useful information. The basic motive behind SAS\/STAT spatial data analysis is to derive useful insights from real-world phenomena such as crimes, natural disasters, mining of ores, vegetation, and so by making use of their location and context.<\/p>\n

You will always find spatial analysts very curious as to why things happen, how they happen, where they do. They are also curious about the effect of those phenomena on nearby regions. A spatial analysis in SAS\/STAT finds its application in various fields such as agriculture, public health, insurance, forestry, crime analysis, environmental monitoring, and, energy.<\/p>\n

Procedures for Spatial Analysis in SAS\/STAT<\/h3>\n

Following procedures use to compute SAS\/STAT spatial analysis of a sample data. Let us explore it.<\/p>\n

a. PROC KRIGE2D<\/h4>\n

SAS PROC KRIGE2D performs spatial prediction of any spatial point reference data. The basic function is kriging, kriging is a procedure that generates estimates of a particular surface by making use of a scattered set of points that have z-values.<\/p>\n

It has a grid statement where we specify the locations of kriging predictions. The output data set has both the kriging predictions and the standard errors associated with the data.<\/p>\n

A Syntax of PROC KRIGE2D-<\/strong><\/p>\n

PROC krige2d DATASET<options>;\r\nCOORDINATES | COORD\u00a0coordinate-variables;\r\nGRID grid-options;\r\nPREDICT | PRED | P predict-options;\r\nMODEL\u00a0model-options;<\/pre>\n
PROC KRIGE2D Example-<\/strong><\/p>\n
ods graphics on;\u00a0\r\nproc krige2d data=sashelp.iris plots=all;\r\n\u00a0\u00a0 coordinates xcoord=sepallength ycoord=sepalwidth;\r\n\u00a0\u00a0 predict var=petallength radius=60;\r\n\u00a0\u00a0 model scale=7.4599 range=30.1111 form=gauss;\r\n\u00a0\u00a0 grid x=0 to 100 by 2.5 y=0 to 100 by 2.5;\u00a0\r\nrun;<\/pre>\n
The PROC PREDICT, KRIGE2D and the MODEL statement is required and you provide the prediction parameters in the\u00a0PREDICT statement.
\nThe coordinates of the variable are specified in the\u00a0COORDINATES\u00a0statement.
\nThe\u00a0MODEL statement contains the parameters that describe your data spatial correlation. Namely, the\u00a0FORM=\u00a0option specifies the model type say Gaussian, exponential etc, based on its mathematical form.
\nThe\u00a0SCALE=\u00a0and\u00a0RANGE=options specify the model scale on which we want the data to be analyzed and range, respectively.
\nThe region of predictions is specified with the\u00a0GRID statement.<\/p>\n
\"SAS\/STAT<\/a>
SAS\/STAT Spatial Analysis –\u00a0PROC KRIGE2D<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Spatial Analysis –\u00a0PROC KRIGE2D<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Spatial Analysis –\u00a0PROC KRIGE2D<\/p><\/div>\n
b. PROC SIM2D<\/h4>\n
The SIM2D procedure in SAS\/STAT spatial analysis also\u00a0produces a spatial simulation but for a Gaussian field. It uses a decomposition technique to also specify mean and covariance structure in two dimensions. The most important feature is that it enables you to specify the covariance and the mean structure by naming the form.<\/p>\n
The locations of the simulation points that are specified in the grid statement are also available with this procedure just like the krige2d procedure.<\/p>\n
A Syntax of PROC SIM2D-<\/strong><\/p>\n
PROC SIM2D DATASET<options>;\r\nSIMULATE simulate-options;\r\nMEAN\u00a0mean-options;\u00a0\r\nCOORDINATES | COORD\u00a0coordinate-variables;\u00a0\r\nGRID grid-options;<\/pre>\n
The\u00a0PROC SIM2D and SIMULATE statement is required.<\/p>\n
PROC SIM2D Example-<\/strong><\/p>\n
title 'finding sugar content in chocolates Using PROC SIM2D for Spatial Simulation';\r\ndata chocolates;\r\n\u00a0\u00a0 input munch kitkat gems @@;\r\n\u00a0\u00a0 datalines;\r\n\u00a0\u00a0\u00a0 0.7\u00a0 59.6\u00a0 34.1\u00a0\u00a0 2.1\u00a0 82.7\u00a0 42.2\u00a0\u00a0 4.7\u00a0 75.1\u00a0 39.5\r\n\u00a0\u00a0\u00a0 4.8\u00a0 52.8\u00a0 34.3\u00a0\u00a0 5.9\u00a0 67.1\u00a0 37.0\u00a0\u00a0 6.0\u00a0 35.7\u00a0 35.9\r\n\u00a0\u00a0\u00a0 6.4\u00a0 33.7\u00a0 36.4\u00a0\u00a0 7.0\u00a0 46.7\u00a0 34.6\u00a0\u00a0 8.2\u00a0 40.1\u00a0 35.4\r\n\u00a0\u00a0 13.3\u00a0\u00a0 0.6\u00a0 44.7\u00a0 13.3\u00a0 68.2\u00a0 37.8\u00a0 13.4\u00a0 31.3\u00a0 37.8\r\n\u00a0\u00a0 17.8\u00a0\u00a0 6.9\u00a0 43.9\u00a0 20.1\u00a0 66.3 \u00a037.7\u00a0 22.7\u00a0 87.6\u00a0 42.8\r\n\u00a0\u00a0 23.0\u00a0 93.9\u00a0 43.6\u00a0 24.3\u00a0 73.0\u00a0 39.3\u00a0 24.8\u00a0 15.1\u00a0 42.3\r\n\u00a0\u00a0 24.8\u00a0 26.3\u00a0 39.7\u00a0 26.4\u00a0 58.0\u00a0 36.9\u00a0 26.9\u00a0 65.0\u00a0 37.8\r\n\u00a0\u00a0 27.7\u00a0 83.3\u00a0 41.8\u00a0 27.9\u00a0 90.8\u00a0 43.3\u00a0 29.1\u00a0 47.9\u00a0 36.7\r\n\u00a0\u00a0 29.5\u00a0 89.4\u00a0 43.0\u00a0 30.1\u00a0\u00a0 6.1\u00a0 43.6\u00a0 30.8\u00a0 12.1\u00a0 42.8\r\n\u00a0\u00a0 32.7 \u00a040.2\u00a0 37.5\u00a0 34.8\u00a0\u00a0 8.1\u00a0 43.3\u00a0 35.3\u00a0 32.0\u00a0 38.8\r\n\u00a0\u00a0 37.0\u00a0 70.3\u00a0 39.2\u00a0 38.2\u00a0 77.9\u00a0 40.7\u00a0 38.9\u00a0 23.3\u00a0 40.5\r\n\u00a0\u00a0 39.4\u00a0 82.5\u00a0 41.4\u00a0 43.0\u00a0\u00a0 4.7\u00a0 43.3\u00a0 43.7\u00a0\u00a0 7.6\u00a0 43.1\r\n\u00a0\u00a0 46.4\u00a0 84.1\u00a0 41.5\u00a0 46.7\u00a0 10.6\u00a0 42.6\u00a0 49.9\u00a0 22.1\u00a0 40.7\r\n\u00a0\u00a0 51.0\u00a0 88.8\u00a0 42.0\u00a0 52.8\u00a0 68.9\u00a0 39.3\u00a0 52.9\u00a0 32.7\u00a0 39.2\r\n\u00a0\u00a0 55.5\u00a0 92.9\u00a0 42.2\u00a0 56.0\u00a0\u00a0 1.6\u00a0 42.7\u00a0 60.6\u00a0 75.2\u00a0 40.1\r\n\u00a0\u00a0 62.1\u00a0 26.6\u00a0 40.1\u00a0 63.0\u00a0 12.7\u00a0 41.8\u00a0 69.0\u00a0 75.6\u00a0 40.1\r\n\u00a0\u00a0 70.5\u00a0 83.7\u00a0 40.9\u00a0 70.9\u00a0 11.0\u00a0 41.7\u00a0 71.5\u00a0 29.5\u00a0 39.8\r\n\u00a0\u00a0 78.1\u00a0 45.5\u00a0 38.7\u00a0 78.2\u00a0\u00a0 9.1\u00a0 41.7\u00a0 78.4\u00a0 20.0\u00a0 40.8\r\n\u00a0\u00a0 80.5\u00a0 55.9\u00a0 38.7\u00a0 81.1\u00a0 51.0\u00a0 38.6\u00a0 83.8\u00a0\u00a0 7.9\u00a0 41.6\r\n\u00a0\u00a0 84.5\u00a0 11.0\u00a0 41.5\u00a0 85.2\u00a0 67.3\u00a0 39.4\u00a0 85.5\u00a0 73.0\u00a0 39.8\r\n\u00a0\u00a0 86.7\u00a0 70.4\u00a0 39.6\u00a0 87.2\u00a0 55.7\u00a0 38.8\u00a0 88.1\u00a0\u00a0 0.0\u00a0 41.6\r\n\u00a0\u00a0 88.4\u00a0 12.1\u00a0 41.3\u00a0 88.4\u00a0 99.6\u00a0 41.2\u00a0 88.8\u00a0 82.9\u00a0 40.5\r\n\u00a0\u00a0 88.9\u00a0\u00a0 6.2\u00a0 41.5\u00a0 90.6\u00a0\u00a0 7.0\u00a0 41.5\u00a0 90.7\u00a0 49.6\u00a0 38.9\r\n\u00a0\u00a0 91.5\u00a0 55.4\u00a0 39.0\u00a0 92.9\u00a0 46.8\u00a0 39.1\u00a0 93.4\u00a0 70.9\u00a0 39.7\r\n\u00a0\u00a0 55.8\u00a0 50.5\u00a0 38.1\u00a0 96.2\u00a0 84.3\u00a0 40.3\u00a0 98.2\u00a0 58.2\u00a0 39.5\r\n;\u00a0\r\nods graphics on;\r\nproc sim2d data=chocolates outsim=sim plot=(observ sim);\r\n\u00a0\u00a0 coordinates xc=munch yc=kitkat;\r\n\u00a0\u00a0 simulate var=gems numreal=5000 seed=79931\r\n\u00a0\u00a0\u00a0\u00a0\u00a0 scale=7.4599 range=30.1111 nugget=1e-8 form=gauss;\r\n\u00a0\u00a0 mean 40.1173;\r\n\u00a0\u00a0 grid x=0 to 100 by 2.5 y=0 to 100 by 2.5;\r\nrun;<\/pre>\n
 <\/p>\n
\"SAS\/STAT<\/a>
Spatial Analysis in SAS\/STAT –\u00a0PROC SIM2D<\/p><\/div>\n
\"SAS<\/a>
SAS PROC SIM2D<\/p><\/div>\n
\"SAS\/STAT<\/a>
Spatial Analysis in SAS\/STAT –\u00a0PROC SIM2D<\/p><\/div>\n
c. PROC SPP<\/h4>\n
The SPP procedure in SAS\/STAT describes the occurrence of observations by using spatial point pattern analysis. The observations are discrete and random, so through analysis, it characterizes the spatial process. Its unique abilities include fitting an inhomogeneous Poisson process, produce non-parametric estimates, compute correlation functions and many more. Apart from this, it also performs marked point pattern analysis.<\/p>\n
A Syntax of PROC SPP-<\/strong><\/p>\n
PROC SPP DATASET<options>;\r\nPROCESS name\u00a0=\u00a0(variables <\/pattern-options>)<\/process-options <distance-function-options>>;\r\nTREND\u00a0name\u00a0=\u00a0FIELD(field-definition\u00a0);<\/pre>\n
The\u00a0PROC SPP and PROCESS statement is required.<\/p>\n
PROC SPP Example-<\/strong><\/p>\n
ods graphics on;\r\nproc spp data=sashelp.IRIS plots=all;\r\n\u00a0\u00a0 process SPECIES = (SEPALLENGTH, SEPALWIDTH \/ EVENT=SPECIES);\r\n\u00a0\u00a0 trend PETALLENGTH = field(SEPALLENGTH,SEPALWIDTH, PETALLENGTH );\r\n\u00a0\u00a0 trend PETALWIDTH = field(SEPALLENGTH,SEPALWIDTH, PETALWIDTH);\r\nrun;<\/pre>\n
The variables PETALLENGTH and PETALWIDTH are both continuous functions because any arbitrary point that is chosen in the study area has a value for both these variables.<\/p>\n
In SAS\/STAT spatial analysis, such CONTINUOUS variables are termed field variables and are associated with a spatial trend. They can be included in the SPP procedure by using the TREND statement.\u00a0In the SPP procedure, you are required to identify the point pattern event identifier separately. This is done by using the\u00a0EVENT= option in the\u00a0PROCESS statement to specify that the variable SPECIES identifies the event.<\/p>\n
\"SAS\/STAT<\/a>
SAS Spatial Analysis –\u00a0PROC SPP<\/p><\/div>\n
\"SAS<\/a>
SAS Spatial Analysis –\u00a0PROC SPP<\/p><\/div>\n
\"SAS<\/a>
SAS Spatial Analysis –\u00a0PROC SPP<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS Spatial Analysis –\u00a0PROC SPP<\/p><\/div>\n
 <\/p>\n
\"SAS\/STAT<\/a>
SAS\/STAT Spatial Analysis<\/p><\/div>\n
d. PROC VARIOGRAM<\/h4>\n
The VARIOGRAM in SAS\/STAT is used\u00a0to describe the spatial covariance structure in spatial point referenced data by computing variogram diagnostics. SAS\u00a0VARIOGRAM procedure has the ability to fit theoretical models so that they can be used for subsequent analysis and can also handle semivariogram and anisotropic models. It makes use of eight semivariogram models such as cubic, Gaussian, exponential, spheric and so on.
\nA Syntax PROC VARIOGRAM-<\/strong><\/p>\n
PROC VARIOGRAM DATASET<options>;\r\nCOMPUTE\u00a0computation-options;\u00a0\r\nCOORDINATES coordinate-variables;<\/pre>\n
The\u00a0PROC VARIOGRAM, COMPUTE, and COORDINATE statements are required.
\nPROC VARIOGRAM Example-<\/strong><\/p>\n
ods graphics on;\u00a0\r\nproc variogram data=sashelp.IRIS plots=all;\r\n\u00a0\u00a0 compute novariogram nhc=20;\r\n\u00a0\u00a0 coordinates xcoord=sepallength ycoord=sepalwidth;\r\n\u00a0\u00a0 var petalwidth;\u00a0\r\nrun;<\/pre>\n
 <\/p>\n
\"SAS\/STAT<\/a>
SAS\/STAT Spatial Analysis –\u00a0PROC VARIOGRAM<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Spatial Analysis –\u00a0PROC VARIOGRAM<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Spatial Analysis –\u00a0PROC VARIOGRAM<\/p><\/div>\n
So, this was all\u00a0about SAS\/STAT Spatial Analysis Tutorial. Hope you like our explanation<\/b>.<\/p>\n
Conclusion<\/h3>\n
Hence, this was all about SAS\/STAT Spatial Analysis and procedures offered by spatial analysis in SAS\/STAT:\u00a0PROC KRIGE2D, PROC SIM2D, PROC SPP, and PROC VARIOGRAM with Examples & Syntax. For any queries, post your doubts in the comments section below.<\/p>\n","protected":false},"excerpt":{"rendered":"
In our last tutorial, we studied SAS survival analysis Procedure. Here, we will discuss SAS\/STAT Spatial Analysis. We will also learn important procedures used in Spatial Analysis in SAS\/STAT: PROC KRIGE2D, PROC SIM2D, PROC...<\/p>\n","protected":false},"author":6,"featured_media":15690,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56],"tags":[173,180,181,186,5071,10106,10107,10112,10135,10136,12038,12257,12317,12365,12366,12721,13178,13179,13180,15350],"class_list":["post-15675","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sas-stat","tag-a-syntax-of-proc-krige2d","tag-a-syntax-of-proc-sim2d","tag-a-syntax-of-proc-spp","tag-a-syntax-proc-variogram","tag-geospatial-data-analysis","tag-proc-sim2d","tag-proc-sim2d-example","tag-proc-spp-example","tag-proc-variogram","tag-proc-variogram-example","tag-sas-geospatial","tag-sas-spatial-data","tag-sas-variogram-procedure","tag-sasstat-spatial-analysis","tag-sasstat-spatial-analysis-procedures","tag-semivariogram-sas","tag-spatial-analysis-in-sasstat","tag-spatial-analysis-in-stat","tag-spatial-join-in-sas","tag-variogram-in-sas"],"yoast_head":"\nSAS\/STAT Spatial Analysis - 4 Important Procedures - DataFlair<\/title>\n<meta name=\"description\" content=\"SAS\/STAT Spatial Analysis- Procedure used in Spatial Analysis in SAS\/STAT-PROC SPP, SAS PROC VARIOGRAM,PROC SIM2D,PROC KRIGE2D with example & syntax,geospatial\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/stat-spatial-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SAS\/STAT Spatial Analysis - 4 Important Procedures - DataFlair\" \/>\n<meta property=\"og:description\" content=\"SAS\/STAT Spatial Analysis- Procedure used in Spatial Analysis in SAS\/STAT-PROC SPP, SAS PROC VARIOGRAM,PROC SIM2D,PROC KRIGE2D with example & syntax,geospatial\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/stat-spatial-analysis\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2018-05-15T03:49:50+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2021-12-05T16:24:29+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Spatial-Analysis-in-SAS-STAT-01.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"9 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"SAS\/STAT Spatial Analysis - 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