ࡱ> t 2w<lmpJRoot EntryRoot Entry uVersionR8Contentsa4Tool0 g  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~Tool1 K3Tool2 Tool3 qTool4 nCTool5  sTool6 Tool7 HeTool8 ?Tool9  7Tool10.=Tool11 $KTool12[Tool13ETool14=Tool15 }Tool16Tool17JTool18xqTool19_Tool20&Tool21XTool22͟Tool232Tool247*Tool253vTool26*Tool27 "ConvertRastertoVectorGridRev042012#Convert Raster Layer To Vector GridThis tool converts a raster map layer into a vector grid of equally-sized polygonal cells and records the value for each cell (i.e., as the "GRID_CODE" variable). 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NlCnId|GERROR 000124: These two pair parameters cannot be zero at the same timeӲ NlCnJd|GERROR 000124: These two pair parameters cannot be zero at the same timeӲ NlCnKd|GERROR 000124: These two pair parameters cannot be zero at the same timeӲ NlCnLd2ERROR 000424: Opposite corner coordinate is neededӲ NlCnMӲ NlCnNӲ NlCnOӲ NlCnP#c8DæiQ CreateFishnetCreate Fishnet8Creates a featureclass of a fishnet of rectangular cells Feature ClassGKcIRData Management Tools5ZqO:S~C:\Program Files (x86)\ArcGIS\Desktop10.0\ArcToolbox\ToolboxesTOOLBOX: Workspace = \\JCAPLAN-PC\C$\Program Files (x86)\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes;Toolbox DataZX|O:TDATABASE~C:\Program Files (x86)\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes1#4*)G9gK ItrOG^aa-Uout_feature_classOutput Feature Class* +DkbVVDEFeatureClass Feature Class Feature Class Data Typex/ WdmE[A۶.erICCMVXe:2.EItrOG^aa-Y origin_coordFishnet Origin Coordinate * +DkbVZGPPointPointPoint Data Typex/ [DAW+OIF)e:2.E\ 0顾DMF7t]* +DkbV^GPPointPointPoint Data 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Features* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ ˮDAW+OIF).erICCMVe:2.EItrOG^aa- join_features Join Features* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ ˲DAW+OIF).erICCMVe:2.E-ItrOG^aa-out_feature_classOutput Feature Class* +DkbVDEFeatureClass Feature Class Feature Class Data Typex/ ˶dmE[A۶e:2.E$ItrOG^aa-join_operationJoin Operation* +DkbVGPStringStringString Data Typex/ ˹DAW+OIF)qfK5Y JOIN_ONE_TO_ONE"JOIN_ONE_TO_MANY34jgJ$  JOIN_ONE_TO_ONE34jgJ$ "JOIN_ONE_TO_MANYe:2.E 34jgJ$  JOIN_ONE_TO_ONE* +DkbVGPStringStringString Data Typex/ DAW+OIF)ItrOG^aa- join_typeKeep All Target Features* +DkbV GPBooleanBooleanBoolean data type Data Typex/ DAW+OIF)qfK5YKEEP_ALLKEEP_COMMONYf^EkIYf^EkIe:2.E Yf^EkI* +DkbV GPBooleanBooleanBoolean data type Data Typex/ DAW+OIF)ItrOG^aa- field_mappingField Map of Join Features* +DkbVGPFieldMappingField MappingsField Mappings Data Typex/ DAW+OIF) join_featurese:2.E -'+*Fњ6=+*Fњ6>XAB/7gd j<7)EaGTR KQ| MS Shell Dlgj<7)EaG?cR KQ| 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You should visually inspect the over and under predictions evident in your regression residuals to see if t "ConvertSpatialWeightsMatrixtoTable(Convert Spatial Weights Matrix to Table >Converts a Spatial Weights Matrix (*.swm) to a database table.Analysis Tools(G..\..\..\..\..\..\Program Files\ArcGIS\Desktop10.0\help\ArcInfoMain.chmR..\..\..\..\..\..\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Scripts\SWM2Table.pyo1?GW:.N_!Input_Spatial_Weights_Matrix_File!Input Spatial Weights Matrix File* +DkbVDEFileFileFile Data Typex/ DING4 L-swm\:A= DEFileo1?GW:.N_ Output_Table Output Table* +DkbVDETableTableTable Data Typex/ dmE[A۶K-5KDETable A0gK-/MZX|O:5uOߟ_|BhM$wv-q:C@H́}+ TRUEConvert Spatial Weights Matrix to Table (Spatial Statistics)ESRI, Inc.380 New York StreetRedlandsCalifornia92373 - 8100info@esri.comUnited States909-793-2853909-793-5953010 Converts a binary spatial weights matrix file (.swm) to a table. 005ChangeCopyDbaseDbfExportFileGdbGeodatabaseImportInfoPersonalSwmTurnArcToolBox Tool Converts a binary spatial weights matrix file (.swm) to a table. ConvertSpatialWeightsMatrixtoTable_stats The full pathname for the spatial weights matrix file (.swm) you want to convert. The full pathname for the spatial weights matrix file (.swm) you want to convert. A full pathname to the table you want to create. A full pathname to the table you want to create. This tool allows you to edit a spatial weights matrix file, if necessary: Create a spatial weights matrix file using the or tool. Convert the resultant spatial weights matrix file to a table using this tool. Edit the table and modify the spatial relationships as desired. Use the tool to convert the modified table back to the binary format. Convert Spatial Weights Matrix to Table Example (Python Window) The following Python Window script demonstrates how to use the Convert Spatial Weights Matrix to Table tool. import arcpy arcpy.env.workspace = "c:/data" arcpy.ConvertSpatialWeightsMatrixtoTable_stats("euclidean6Neighs.swm","euclidean6Neighs.dbf") Convert Spatial Weights Matrix to Table Example (Stand-alone Python script) The following stand-alone Python script demonstrates how to use the Convert Spatial Weights Matrix to Table tool. # Create a Spatial Weights Matrix based on Network Data # Import system modules import arcpy # Set the geoprocessor object property to overwrite existing output arcpy.gp.overwriteOutput = True # Local variables... workspace = r"C:\Data\USCounties\US" try: # Set the current workspace (to avoid having to specify the full path to the feature classes each time) arcpy.env.workspace = workspace # Create Spatial Weights Matrix # Process: Generate Spatial Weights Matrix... swm = arcpy.GenerateSpatialWeightsMatrix_stats("USCounties.shp", "MYID", "euclidean6Neighs.swm", "K_NEAREST_NEIGHBORS", "#", "#", "#", 6) # Dump Spatial Weights to Database Table # Process: Convert Spatial Weights Matrix to Table... dbf = arcpy.ConvertSpatialWeightsMatrixtoTable_stats("euclidean6Neighs.swm", "euclidean6Neighs.dbf") # Now you can edit the spatial weights (add, subtract and alter # neighbors and weights) # Read weights from table back into Spatial Weights Matrix format # Process: Generate Spatial Weights Matrix... swm = arcpy.GenerateSpatialWeightsMatrix_stats("USCounties.shp", "MYID", "euclidean6Neighs.swm", "CONVERT_TABLE", "#", "#", "#", "#", "#", "#", "euclidean6Neighs.dbf") except: # If an error occurred when running the tool, print out the error message. print arcpy.GetMessages() C:\Program Files\ArcGIS\Desktop10.0\Help\gp  import WeightsUtilities as WU class ToolValidator: """Class for validating a tool's parameter values and controlling the behavior of the tool's dialog.""" def __init__(self): """Setup the Geoprocessor and the list of tool parameters.""" import arcgisscripting as ARC self.GP = ARC.create(9.3) self.params = self.GP.getparameterinfo() def initializeParameters(self): """Refine the properties of a tool's parameters. This method is called when the tool is opened.""" return def updateParameters(self): """Modify the values and properties of parameters before internal validation is performed. This method is called whenever a parmater has been changed.""" #### Add Output Field Schema #### addFields = [] swmFile = str(self.params[0].value) if not self.params[0].hasBeenValidated: try: swmInfo = WU.readWeightsHeader(swmFile) f, masterField, spatialRefName, N, rowStandard = swmInfo f.close() newField = self.GP.CreateObject("field") newField.name = masterField newField.type = "LONG" addFields.append(newField) fieldNames = [self.GP.GetIDMessage(84216), self.GP.GetIDMessage(84217)] for ind, field in enumerate(fieldNames): newField = self.GP.CreateObject("field") newField.name = field if ind == 0: newField.type = "LONG" else: newField.type = "DOUBLE" addFields.append(newField) self.params[1].schema.additionalFields = addFields except: #### Problem Reading SWM Header #### self.params[1].schema.additionalFields = [] def updateMessages(self): """Modify the messages created by internal validation for each tool parameter. This method is called after internal validation.""" #### Invalid SWM File #### fields = self.params[1].schema.additionalFields if not len(fields): swmFile = str(self.params[0].value) self.params[0].clearMessage() self.params[0].setIDMessage("ERROR", 977, swmFile) : d alturl="yes" number="activeXLink_an_overview_of_general_settings_134268_0" module="geoprocessing_environments.chm" filename="an_overview_of_general_settings.htm" anchor="LMS_Link1"/> 20080424 07472800 TRUE {A0CECF46-3C7F-4B68-985F-122CEDAD5585} Spatial Autocorrelation (Morans I) (Spatial Statistics) ESRI, Inc. 380 New York Street Redlands California 92373 - 8100 info@esri.com United States 909-793-2853 909-793-5953 010 Measures spatial autocorrelation based on feature locations and attribute values. 005 spatial statistics;analyzing patterns;spatial autocorrelation;Moran's I;clustering;dispersion;attribute similarity ArcToolBox Tool Measures spatial autocorrelation based on feature locations and attribute values. Learn more about how Spatial Autocorrelation: Moran's I works SpatialAutocorrelation_stats average_nearest_neighbor_spatial_statistics_ high_low_clustering_getis_ord_general_g_spatial_statistics_ cluster_and_outlier_analysis_colon_anselin_local_moran_s_i_spatial_statistics_ modeling_spatial_relationships what_is_a_z_score_qst_what_is_a_p_value_qst_
Attribute Similarity (Moran's I) illustration

The feature class for which spatial autocorrelation will be calculated. The feature class for which spatial autocorrelation will be calculated. The numeric field used in assessing spatial autocorrelation. The numeric field used in assessing spatial autocorrelation. Specifies whether the tool will display the Moran's I and Z score values graphically. True—The output will be displayed graphically. False—The output will not be displayed graphically. Specifies whether the tool will display the Moran's I and Z score values graphically. Checked—The output will be displayed graphically. Unchecked—The output will not be displayed graphically. Specifies how spatial relationships among features are conceptualized. Inverse Distance—All features impact/influence all other features, but the farther away something is, the smaller the impact it has. Inverse Distance Squared—Same as Inverse Distance except that the slope is sharper so influence drops off more quickly and only a target feature's closest neighbors will exert substantial influence in computations for that feature. Fixed Distance Band—Each feature is analyzed within the context of those neighboring features within some specified critical distance. Features outside the critical distance of a target feature do not influence calculations for that feature. Zone of Indifference—Features within the specified critical distance of a target feature are included in analyses for that feature. Once the critical distance is exceeded, the level of impact quickly drops off. Polygon Contiguity (First Order)—The neighbors of each feature are only those with which the feature shares a boundary. All other features have no influence on computations. Get Spatial Weights From File—Spatial relationships are defined in a spatial weights file. The pathname to the spatial weights file is specified in the Weights Matrix File parameter. Specifies how spatial relationships among features are conceptualized. Inverse Distance—All features impact/influence all other features, but the farther away something is, the smaller the impact it has. Inverse Distance Squared—Same as Inverse Distance except that the slope is sharper so influence drops off more quickly and only a target feature's closest neighbors will exert substantial influence in computations for that feature. Fixed Distance Band—Each feature is analyzed within the context of those neighboring features within some specified critical distance. Features outside the critical distance of a target feature do not influence calculations for that feature. Zone of Indifference—Features within the specified critical distance of a target feature are included in analyses for that feature. Once the critical distance is exceeded, the level of impact quickly drops off. Polygon Contiguity (First Order)—The neighbors of each feature are only those with which the feature shares a boundary. All other features have no influence on computations. Get Spatial Weights From File—Spatial relationships are defined in a spatial weights file. The pathname to the spatial weights file is specified in the Weights Matrix File parameter. Specifies how distances are calculated when measuring spatial autocorrelation. Euclidean (as the crow flies)—The straight-line distance between two points. Manhattan (city block)—The distance between two points measured along axes at right angles. Calculated by summing the (absolute) differences between point coordinates. Specifies how distances are calculated when measuring spatial autocorrelation. Euclidean (as the crow flies)—The straight-line distance between two points. Manhattan (city block)—The distance between two points measured along axes at right angles. Calculated by summing the (absolute) differences between point coordinates. Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme. None—No standardization of spatial weights is applied. Row—Spatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features). Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme. None—No standardization of spatial weights is applied. Row—Spatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features). Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance while those inside the distance threshold are equally considered. The value entered should match those of the Output Coordinate System.For the Inverse Distance conceptualizations of spatial relationships: A value of zero for this parameter indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value will be computed and applied.This parameter has no effect when "Polygon Contiguity" or "Get Spatial Weights From File" spatial conceptualizations are selected. Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance while those inside the distance threshold are equally considered. The value entered should match those of the Output Coordinate System.For the Inverse Distance conceptualizations of spatial relationships: A value of zero for this parameter indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value will be computed and applied.This parameter has no effect when "Polygon Contiguity" or "Get Spatial Weights From File" spatial conceptualizations are selected. The pathname to a file containing spatial weights that define spatial relationships between features. The pathname to a file containing spatial weights that define spatial relationships between features. This tool honors the environment output coordinate system even though no feature class output is created. Feature geometry is projected to the output coordinate system prior to analysis, so values entered for the Distance Band/Threshold Distance parameter should match those specified in the output coordinate system. All mathematical computations are based on the output coordinate system spatial reference. Calculations based on either Euclidean or Manhattan distance require projected data to accurately measure distances. If you will be running several analyses on a single dataset (e.g., analyzing several different fields) or if you have a dataset with more than 3000 features, it is recommended that you construct the spatial weights matrix file prior to analysis. The Moran's I value and associated Z score and p-value are written to the Command window and passed as derived output. Given a set of features and an associated attribute, Global Moran's I evaluates whether the pattern expressed is clustered, dispersed, or random. When the Z score or p-value indicates statistical significance, a positive Moran's I index value indicates tendency toward clustering while a negative Moran's I index value indicates tendency toward dispersion. The Global Moran's I tool calculates a Z score and p-value to indicate whether or not you can reject the null hypothsis. In this case, the null hypothesis states that feature values are randomly distributed across the study area. In this tool, the Z score is based on the Randomization Null Hypothesis computation. For more information on Z scores and p-values, see What is a Z score? What is a p-value?. For line and polygon features, true feature geometric centroids are used in computations. The input field should contain a variety of non-negative values. The math for this statistic requires some variation in the variable being analyzed; it cannot solve if all input values are 1, for example. If you have incident data, and want to analyze incident intensity, consider aggregating your incident data or using Integrate with the Collect Events tool prior to analysis. Whenever using shapefiles keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from non-shapefile inputs may store or interpret null values as zero. This can lead to unexpected results. The Conceptualization of Spatial Relationships used for analysis should be based on your understanding of spatial interaction among the features being analyzed. For Inverse Distance conceptualization options: when zero is entered for the "Distance Band or Threshold Distance" parameter all features are considered neighbors of all other features; when this parameter is left blank, a default threshold distance will be applied. When the spatial conceptualization is an Inverse Distance method (Inverse Distance, Inverse Distance Squared, or Zone of Indifference) any two points that are coincident will be given a weight of one to avoid zero division. This assures features are not excluded from analysis. With inverse distance conceptualizations, weights for distances less than 1 become unstable. The weighting for features separated by less than 1 unit of distance (common with Geographic Coordinate System projections), are given a weight of 1. Analysis on features with a Geographic Coordinate System projection is not recommended with any of the inverse distance based spatial conceptualization methods. The "Display Output Graphically" parameter will only work on the Windows operating system. When set to true it will display the results of the tool graphically. When output is shown graphically, a separate graphics dialog box will be displayed. If you use the tool in a script, set the Display_Output_Graphically parameter to "false", otherwise your script will not complete until you click"Close" on the popup graphic. In ArcGIS version 9.2, the "Global" standardization option was removed. Global standardization returns the same results as no standardization. Models built with previous versions of ArcGIS that use the Global standardization option may need to be rebuilt. See the Modeling Spatial Relationships help page for further explanation of this tool's parameters. Current map layers may be used to define the input feature class. When using layers, only the currently selected features are included in the analysis. Learn more about working with layers and table views workspace e:\project93\dataSpatialAutocorrelation cancernm.shp RATE true 'Inverse Distance' 'Euclidean Distance' None # # # Analyze crime data to determine if spatial patterns are statistically significant # Import system modules import arcgisscripting # Create the Geoprocessor object gp = arcgisscripting.create() # Local variables... workspace = "C:/project93/data"crime_data = "burglaries.shp" try: # Set the current workspace (to avoid having to specify the full path to the feature classes each time) gp.workspace = workspace # Obtain Nearest Neighbor Ratio and Z score # Process: Average Nearest Neighbor... nn_output = gp.AverageNearestNeighbor_stats(crime_data, "Euclidean Distance", "false", "#") nn_values = nn_output.split(";") print "The nearest neighbor index is: " + nn_values[0] print "The Z score of the nearest neighbor index is: " + nn_values[1] # Obtain General G and Z score # Process: High/Low Clustering (Getis-Ord General G)... hlc_output = gp.HighLowClustering_stats(crime_data, "Count", "false", "Inverse Distance", "Euclidean Distance", "None", "#", "#") hlc_values = hlc_output.split(";") print "The General G value is: " + hlc_values[0] print "the Z score of the General G value is: " + hlc_values[1] # Obtain Moran's Index and Z score # Process: Spatial Autocorrelation (Morans I)... sa_output = gp.SpatialAutocorrelation_stats(crime_data, "Count", "false", "Inverse Distance", "Euclidean Distance", "None", "#", "#") sa_values = sa_output.split(";") print "The Moran's I value is: " + sa_values[0] print "The Z score of the Moran's I value is: " + sa_values[1] except: # If an error occurred when running the tool, print out the error message. print gp.GetMessages() Metadata imported.C:\ArcGIS\help\gp\SpatialAutocorrelation_stats.xml2008042407472800  class ToolValidator: """Class for validating a tool's parameter values and controlling the behavior of the tool's dialog.""" def __init__(self): """Setup the Geoprocessor and the list of tool parameters.""" import arcgisscripting as ARC self.GP = ARC.create(9.3) self.params = self.GP.getparameterinfo() #### Check for Professional License for Contiguity #### self.productInfo = self.GP.ProductInfo() if self.productInfo in ["ArcInfo", "ArcServer"]: self.professional = 1 else: self.professional = 0 #### Set Lists of Spatial Concepts #### self.baseConcepts = ["Inverse Distance", "Inverse Distance Squared", "Fixed Distance Band", "Zone of Indifference", "Get Spatial Weights From File"] self.allConcepts = ["Inverse Distance", "Inverse Distance Squared", "Fixed Distance Band", "Zone of Indifference", "Polygon Contiguity (First Order)", "Get Spatial Weights From File"] self.distanceConcepts = self.baseConcepts[0:4] def initializeParameters(self): """Refine the properties of a tool's parameters. This method is called when the tool is opened.""" self.professionalCheck() #### Disable Input Table Unless Correct Concept #### self.params[7].Enabled = 0 return def updateParameters(self): """Modify the values and properties of parameters before internal validation is performed. This method is called whenever a parmater has been changed.""" #### Enable Type of Distance Measure if Appropriate #### if self.params[3].Altered: if self.params[3].Value in self.distanceConcepts: self.params[4].Enabled = 1 self.params[6].Enabled = 1 else: self.params[4].Enabled = 0 self.params[6].Enabled = 0 if self.params[3].Value == "Get Spatial Weights From File": self.params[7].Enabled = 1 self.params[5].Enabled = 0 else: self.params[7].Enabled = 0 self.params[5].Enabled = 1 if self.params[0].Altered: if not self.params[0].IsInputValueDerived(): self.checkContiguity(self.params[0].Value) return def updateMessages(self): """Modify the messages created by internal validation for each tool parameter. This method is called after internal validation.""" return def checkContiguity(self, inputFC): try: desc = self.GP.Describe(inputFC) if desc.ShapeType.upper() == "POLYGON": self.params[3].Filter.List = self.allConcepts self.professionalCheck() else: self.params[3].Filter.List = self.baseConcepts except: self.params[3].Filter.List = self.baseConcepts def professionalCheck(self): if self.professional: self.params[3].Filter.List = self.allConcepts else: self.params[3].Filter.List = self.baseConcepts Bde Data Typex/ D      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrsvyz{|}~ OrdinaryLeastSquaresOrdinary Least Squares@Computes a linear regression model using Ordinary Least Squares.Analysis Tools&A..\..\..\..\..\..\Program Files (x86)\ArcGIS\help\ArcInfoMain.chm>F..\..\..\..\..\..\Program Files (x86)\ArcGIS\ArcToolbox\Scripts\OLS.pyo1?GW:.N_Input_Feature_ClassInput Feature Class* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)m=CJbo1?GW:.N_Unique_ID_FieldUnique ID Field* +DkbVFieldFieldField Data Typex/ dmE[A۶Input_Feature_ClassאY(Hcj4uOߟ_|Bho1?GW:.N_Output_Feature_ClassOutput Feature Class* +DkbVDEFeatureClass Feature Class Feature Class Data Typex/ dmE[A۶@Q>dHl BDEFeatureClassA0gK-/MZX|O:5uOߟ_|BhM$wv-n>ꁫ~GDi]o1?GW:.N_Dependent_VariableDependent Variable* +DkbVFieldFieldField Data Typex/ dmE[A۶Input_Feature_ClassאY(Hcj4uOߟ_|Bho1?GW:.N_Explanatory_VariablesExplanatory Variablesp35g@~"K* +DkbVFieldFieldField Data Typex/ dmE[A۶Input_Feature_ClassאY(Hcj4jHfcģD* +DkbVFieldFieldField Data Typex/ dmE[A۶^5Bhbo1?GW:.N_Coefficient_Output_TableCoefficient Output Table* +DkbVDETableTableTable Data Typex/ dmE[A۶K-5KDETable A0gK-/MZX|O:5uOߟ_|BhM$wv-Output Optionsq:C@H́}+o1?GW:.N_Diagnostic_Output_TableDiagnostic Output Table* +DkbVDETableTableTable Data Typex/ dmE[A۶K-5KDETable A0gK-/MZX|O:5uOߟ_|BhM$wv-Output Optionsq:C@H́}+= 20081222 10485300 TRUE {251DAF4E-049F-447C-9194-4A87EB79C3D8} Ordinary Least Squares (Spatial Statistics) ESRI, Inc. 380 New York Street Redlands California 92373 - 8100 info@esri.com United States 909-793-2853 909-793-5953 010 Performs global Ordinary Least Squares linear regression to generate predictions or to model a dependent variable in terms of its relationships to a set of explanatory variables. 005 ArcToolBox Tool Performs global Ordinary Least Squares linear regression to generate predictions or to model a dependent variable in terms of its relationships to a set of explanatory variables. Learn more about how Ordinary Least Squares regression works OrdinaryLeastSquares_stats regression_analysis_basics interpreting_ols_results geographically_weighted_regression_spatial_statistics_ spatial_autocorrelation_morans_i_spatial_statistics_ hot_spot_analysis_getis_ord_gi_star_spatial_statistics_ what_is_a_z_score_what_is_a_p_value
OLS Regression

The feature class containing the dependent and independent variables for analysis. The feature class containing the dependent and independent variables for analysis. An integer field containing a different value for every feature in the Input Feature Class. An integer field containing a different value for every feature in the Input Feature Class. The output feature class to receive dependent variable estimates and residuals. Resize the tool progress window during execution for optimal display of OLS results. The output feature class to receive dependent variable estimates and residuals. Resize the tool progress window during execution for optimal display of OLS results. The numeric field containing values for what you are trying to model. The numeric field containing values for what you are trying to model. A list of fields representing explanatory variables in your regression model. A list of fields representing explanatory variables in your regression model. The full pathname to an optional table that will receive model coefficients, standard errors, and probabilities for each explanatory variable. Resize the tool progress window during execution for optimal display of OLS results. The full pathname to an optional table that will receive model coefficients, standard errors, and probabilities for each explanatory variable. Resize the tool progress window during execution for optimal display of OLS results. The full pathname to an optional table that will receive model summary diagnostics. Resize the tool progress window during execution for optimal display of OLS results. The full pathname to an optional table that will receive model summary diagnostics. Resize the tool progress window during execution for optimal display of OLS results. OLS output is printed to the Progress Window and to the Command Message Window. Please resize the progress window wide in order to view the OLS results properly lined up without wrapping. This tool honors the Environment output coordinate system. The Unique ID field is used to link output results to input feautres. Consequently, the Unique ID field values must be unique for every feature and typically should be a permanent field that will remain with the feature class. If you don't have a unique ID field, you can easily create one by adding a new integer field to your feature class table, and calculating the field values to be equal to the FID/OID field. You cannot use the FID/OID field directly for the Unique ID parameter. Whenever using shapefiles keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from non-shapefile inputs may store or interpret null values as zero. This can lead to unexpected results. When the result of a computation is infinity or undefined, the output for non-shapefiles will be Null; for shapefiles the output will be -DBL_MAX = -1.7976931348623158e+308. The optional coefficient and/or diagnostic output tables, if they already exist, will be overwritten when the Geoprocessing Option to overwrite the outputs of geoprocessing operations is checked ON. The OLS model is misspecified, and consequently results from OLS regression are unreliable, whenever there is statistically signficant spatial autocorrelation of the regression residuals. Be sure to run the Spatial Autocorrelation tool on your regression residuals to assess this potential problem. Statistically significant spatial autocorrelation of regression residuals usually indicates a key missing explanatory variable. When misspecification is the result of trying to model non-stationary variables using a global model (OLS is a global model), then Geographically Weighted Regression may be used to improve predictions and to better understand the non-stationarity (regional variation) inherent in your explanatory variables. You should visually inspect the over and under predictions evident in your regression residuals to see if they provide clues about potential missing variables from your regression model. It sometimes helps to run Hot Spot Analysis on the residuals to help you visualize spatial clustering of the over and under predictions. Results from OLS regression are only trustworthy if your data and regression model meet/satisfy all of the assumptions inherently required by this method. Consult the table, "Common Regression Problems, Consequences, and Solutions" in Regression Analysis Basics to ensure your model is properly specified. Metadata imported.C:\ArcGIS\help\gp\OrdinaryLeastSquares_stats.xml2008122210485300 class ToolValidator: """Class for validating a tool's parameter values and controlling the behavior of the tool's dialog.""" def __init__(self): """Setup the Geoprocessor and the list of tool parameters.""" import arcgisscripting as ARC self.GP = ARC.create(9.3) self.params = self.GP.getparameterinfo() def initializeParameters(self): """Refine the properties of a tool's parameters. This method is called when the tool is opened.""" self.params[5].Category = "Output Options" self.params[6].Category = "Output Options" return def updateParameters(self): """Modify the values and properties of parameters before internal validation is performed. This method is called whenever a parmater has been changed.""" return def updateMessages(self): """Modify the messages created by internal validation for each tool parameter. This method is called after internal validation.""" return  dOutput_Feature_Class[..\..\..\..\..\..\Program Files (x86)\ArcGIS\ArcToolbox\templates\layers\StdResidPoints.lyrIf you specify a threshold distance of 10 miles and 3 for the number of neighbors, all features will receive a minimum of 3 neighbors even if the threshold has to be increased to find them. Whenever using shapefiles keep in mind that they cannot  HotSpots!Hot Spot Analysis (Getis-Ord Gi*)[Calculates the Getis-Ord Gi* statistic to identify spatial clusters of high and low values.Analysis Tools%ArcInfoMain.chmqɉE..\..\..\..\..\..\Program Files (x86)\ArcGIS\ArcToolbox\Scripts\Gi.py o1?GW:.N_Input_Feature_ClassInput Feature Class* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)m=CJbo1?GW:.N_ Input_Field Input Field* +DkbVFieldFieldField Data Typex/ dmE[A۶Input_Feature_ClassאY(Hcj4uOߟ_|Bho1?GW:.N_Output_Feature_ClassOutput Feature Class* +DkbVDEFeatureClass Feature Class Feature Class Data Typex/ dmE[A۶Input_Feature_Class@Q>dHl BDEFeatureClassA0gK-/MZX|O:5uOߟ_|BhM$wv-n>ꁫ~GDi]o1?GW:.N_*Conceptualization_of_Spatial_Relationships*Conceptualization of Spatial Relationships* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5Y"Inverse Distance2Inverse Distance Squared(Fixed Distance Band*Zone of IndifferenceBPolygon Contiguity (First Order)<Get Spatial Weights From File34jgJ$ "Inverse Distance34jgJ$ 2Inverse Distance Squared34jgJ$ (Fixed Distance Band34jgJ$ *Zone of Indifference34jgJ$ BPolygon Contiguity (First Order)34jgJ$ <Get Spatial Weights From File34jgJ$ (Fixed Distance Bando1?GW:.N_Distance_MethodDistance Method* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5Y&Euclidean Distance&Manhattan Distance34jgJ$ &Euclidean Distance34jgJ$ &Manhattan Distance34jgJ$ &Euclidean Distanceo1?GW:.N_StandardizationStandardization* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5Y NoneRow34jgJ$  None34jgJ$ Row34jgJ$  Noneo1?GW:.N_#Distance_Band_or_Threshold_Distance#Distance Band or Threshold Distance* +DkbVGPDoubleDoubleDouble Data Typex/ DAW+OIF)A"[2&3&k CtEL7n]wMgTƻ2o1?GW:.N_Self_Potential_FieldSelf Potential Field* +DkbVFieldFieldField Data Typex/ dmE[A۶Input_Feature_ClassאY(Hcj4uOߟ_|Bho1?GW:.N_Weights_Matrix_FileWeights Matrix File* +DkbVDEFileFileFile Data Typex/ DIN 20080424 07475800 TRUE {E28C05D4-1909-4A08-957A-CDFE6D8AA1AD} Hot Spot Analysis (Getis-Ord Gi*) (Spatial Statistics) ESRI, Inc. 380 New York Street Redlands California 92373 - 8100 info@esri.com United States 909-793-2853 909-793-5953 010 Calculates the Getis-Ord Gi* statistic for hot spot analysis. 005 spatial statistics;mapping clusters;hot-spot analysis;gi* statistic;gi statistic ArcToolBox Tool Calculates the Getis-Ord Gi* statistic for hot spot analysis. Learn more about how Hot Spot Analysis: Getis-Ord Gi* works HotSpots_stats cluster_and_outlier_analysis_colon_anselin_local_moran_s_i_spatial_statistics_ modeling_spatial_relationships what_is_a_z_score_qst_what_is_a_p_value_qst_ spatial_autocorrelation_morans_i_spatial_statistics_
Hot Spot Analysis Illustration

The feature class for which hot spot analysis will be performed. The feature class for which hot spot analysis will be performed. The numeric count field (number of victims, crimes, jobs, and so on) to be evaluated. The numeric count field (number of victims, crimes, jobs, and so on) to be evaluated. The output feature class to receive the Results field and Gi z score. The output feature class to receive the Results field and Gi z score. Specifies how spatial relationships between features are conceptualized. Inverse Distance—The impact of one feature on another feature decreases with distance. Inverse Distance Squared—Same as Inverse Distance, but the impact decreases more sharply over distance. Fixed Distance Band—Everything within a specified critical distance is included in the analysis; everything outside the critical distance is excluded. Zone of Indifference—A combination of Inverse Distance and Fixed Distance Band. Anything up to a critical distance has an impact on your analysis. Once that critical distance is exceeded, the level of impact quickly drops off. Polygon Contiguity (First Order)—The neighbors of each feature are only those with which the feature shares a boundary. All other features have no influence. Get Spatial Weights From File—Spatial relationships are defined in a spatial weights file. The pathname to the spatial weights file is specified in the Weights Matrix File parameter. Polygon contiguity is only available with an ArcInfo license. Specifies how spatial relationships between features are conceptualized. Inverse Distance—The impact of one feature on another feature decreases with distance. Inverse Distance Squared—Same as Inverse Distance, but the impact decreases more sharply over distance. Fixed Distance Band—Everything within a specified critical distance is included in the analysis; everything outside the critical distance is excluded. Zone of Indifference—A combination of Inverse Distance and Fixed Distance Band. Anything up to a critical distance has an impact on your analysis. Once that critical distance is exceeded, the level of impact quickly drops off. Polygon Contiguity (First Order)—The neighbors of each feature are only those with which the feature shares a boundary. All other features have no influence. Get Spatial Weights From File—Spatial relationships are defined in a spatial weights file. The pathname to the spatial weights file is specified in the Weights Matrix File parameter. Polygon contiguity is only available with an ArcInfo license. Specifies how distances are calculated when measuring concentrations. Euclidean (as the crow flies)—The straight-line distance between two points. Manhattan (city block)—The distance between two points measured along axes at right angles. Calculated by summing the (absolute) differences between point coordinates. Specifies how distances are calculated when measuring concentrations. Euclidean (as the crow flies)—The straight-line distance between two points. Manhattan (city block)—The distance between two points measured along axes at right angles. Calculated by summing the (absolute) differences between point coordinates. The standardization of spatial weights provides more accurate results. None—No standardization of spatial weights is applied. This is the default. Row—Spatial weights are standardized by row. Each weight is divided by its row sum. The standardization of spatial weights provides more accurate results. None—No standardization of spatial weights is applied. This is the default. Row—Spatial weights are standardized by row. Each weight is divided by its row sum. Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance while those inside the distance threshold are equally considered. The value entered should match those of the Output Coordinate System.For the Inverse Distance conceptualizations of spatial relationships: A value of zero for this parameter indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value will be computed and applied.This parameter has no effect when "Polygon Contiguity" or "Get Spatial Weights From File" spatial conceptualizations are selected. Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance while those inside the distance threshold are equally considered. The value entered should match those of the Output Coordinate System.For the Inverse Distance conceptualizations of spatial relationships: A value of zero for this parameter indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value will be computed and applied.This parameter has no effect when "Polygon Contiguity" or "Get Spatial Weights From File" spatial conceptualizations are selected. The field representing self-potential: The distance or weight between a feature and itself. The field representing self-potential: The distance or weight between a feature and itself. The pathname to a file containing spatial weights that define spatial relationships between features. The pathname to a file containing spatial weights that define spatial relationships between features. This tool honors the environment output coordinate system. Feature geometry is projected to the output coordinate system prior to analysis, so values entered for the Distance Band/Threshold Distance parameter should match those specified in the output coordinate system. All mathematical computations are based on the output coordinate system spatial reference. Calculations based on either Euclidean or Manhattan distance require projected data to accurately measure distances. If you will be running several analyses on a single dataset (e.g., analyzing several different fields) or if you have a dataset with more than 3000 features, it is recommended that you construct the spatial weights matrix file prior to analysis. Given a set of weighted features, the Getis-Ord Gi* statistic identifies spatial clusters of high values (hot spots) and spatial clusters of low values (cold spots). This tool creates as derived output the Z score and p-value fieldnames. The output from the Hot Spot Analysis tool is a Z score and p-value for each feature. These values represent the statistical significance of the spatial clustering of values, given the conceptualization of spatial relationships and the scale of analysis (distance parameter). A high Z score and small p-value (probability) for a feature indicates a spatial clustering of high values. A low negative Z score and small p-value indicates a spatial clustering of low values. The higher (or lower) the Z score, the more intense the clustering. A Z score near zero indicates no apparent spatial clustering. The Z score is based on the Randomization Null Hypothesis computation. For more information on Z scores, see What is a Z score? What is a p-value?. The input field should contain a variety of non-negative values. The math for this statistic requires some variation in the variable being analyzed; it cannot solve if all input values are 1, for example. If you have incident data, and want to analyze incident intensity, consider aggregating your incident data or using Integrate with the Collect Events tool prior to analysis. Whenever using shapefiles keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from non-shapefile inputs may store or interpret null values as zero. This can lead to unexpected results. The Conceptualization of Spatial Relationships used for analysis should be based on your understanding of spatial interaction among the features being analyzed. For this tool the fixed distance or contiguity spatial conceptualization methods are generally more appropriate than the inverse distance conceptualization methods. For the Fixed Distance option, the distance band used for analysis should be based on your understanding of spatial interaction among the features being analyzed. Alternatively, features may be evaluated for a range of distance values or at the specific distance where spatial autocorrelation is maximized. Use a Conceptualization of Spatial Relationships and/or Distance Band value that will ensure every feature has at LEAST one neighbor. Especially if the input data is skewed (does not create a nice bell curve when you plot the values as a histogram), you want to make sure that the number of neighbors is neither too small (most features have only one or two neighbors) nor too large (several features include all other features as neighbors), because that would make resultant Z scores less reliable. The Z scores are reliable (even with skewed data) as long as each feature is associated with several neighbors (approximately 8, as a rule of thumb). This tool can be applied to skewed data because it is "asymptotically normal". For Inverse Distance conceptualization options: when zero is entered for the "Distance Band or Threshold Distance" parameter all features are considered neighbors of all other features; when this parameter is left blank, a default threshold distance will be applied. When the spatial conceptualization is an Inverse Distance method (Inverse Distance, Inverse Distance Squared, or Zone of Indifference) any two points that are coincident will be given a weight of one to avoid zero division. This assures features are not excluded from analysis. With inverse distance conceptualizations, weights for distances less than 1 become unstable. The weighting for features separated by less than 1 unit of distance (common with Geographic Coordinate System projections), are given a weight of 1. Analysis on features with a Geographic Coordinate System projection is not recommended with the inverse distance spatial conceptualization methods. This tool computes the Gi* statistic where each feature is its own neighbor; however, if you specify a Self Potential field in which all values are zero, the tool performs the Gi statistic (local calculations for a feature exclude the feature's own value). In ArcGIS version 9.2, the "Global" standardization option was removed. Global standardization returns the same results as no standardization. Models built with previous versions of ArcGIS that use the Global standardization option may need to be rebuilt. See the Modeling Spatial Relationships help page for further explanation of this tool's parameters. Current map layers may be used to define the input feature class. When using layers, only the currently selected features are included in the analysis. Learn more about working with layers and table views When this tool runs in ArcMap, the output feature class is automatically added to the Table of Contents (TOC) with default rendering applied to the Z Score field. The hot to cold rendering applied is defined by a layer file in <ArcGIS>/ArcToolbox/Templates/Layers. You can reapply the default rendering, if needed, by importing the template layer symbology. workspace e:\project93\dataHotSpot tracts.shp AGE_65_UP tract65.shp 'Inverse Distance' 'Euclidean Distance' None # # # # Perform Hot Spot Analysis for assault incidents # Import system modules import arcgisscripting # Create the Geoprocessor object gp = arcgisscripting.create() # Local variables... workspace = "C:/project93/data"input = "assaults.shp"collect_output = "collect_output.shp"collect_count_field = "Count"hotspot_output = "hotspot_output.shp"hotspot_output_rendered = "hotspot_output_rendered.lyr"z_score_field_name = "GiInvDst" try: # Set the current workspace (to avoid having to specify the full path to the feature classes each time) gp.workspace = workspace # Convert assault incidents into weighted point data # Process: Collect Events... gp.CollectEvents_stats(input, collect_output) # Calculate Getis-Ord Gi* statistic # Process: Hot Spot Analysis (Getis-Ord Gi*)... gp.HotSpots_stats(collect_output, collect_count_field, hotspot_output, "Inverse Distance", "Euclidean Distance", "None", "#", "#", "#") # Render hot spot analysis # Process: Z Score Rendering... gp.ZRenderer_stats(hotspot_output, z_score_field_name, hotspot_output_rendered) except: # If an error occurred when running the tool, print out the error message. print gp.GetMessages(2) Metadata imported.C:\ArcGIS\help\gp\HotSpots_stats.xml2008042407475800  class ToolValidator: """Class for validating a tool's parameter values and controlling the behavior of the tool's dialog.""" def __init__(self): """Setup the Geoprocessor and the list of tool parameters.""" import arcgisscripting as ARC self.GP = ARC.create(9.3) self.params = self.GP.getparameterinfo() #### Check for Professional License for Contiguity #### self.productInfo = self.GP.ProductInfo() if self.productInfo in ["ArcInfo", "ArcServer"]: self.professional = 1 else: self.professional = 0 #### Set Lists of Spatial Concepts #### self.baseConcepts = ["Inverse Distance", "Inverse Distance Squared", "Fixed Distance Band", "Zone of Indifference", "Get Spatial Weights From File"] self.allConcepts = ["Inverse Distance", "Inverse Distance Squared", "Fixed Distance Band", "Zone of Indifference", "Polygon Contiguity (First Order)", "Get Spatial Weights From File"] self.distanceConcepts = self.baseConcepts[0:4] def initializeParameters(self): """Refine the properties of a tool's parameters. This method is called when the tool is opened.""" self.professionalCheck() #### Disable Standardization #### self.params[5].Enabled = 0 #### Disable Input Table Unless Correct Concept #### self.params[8].Enabled = 0 return def updateParameters(self): """Modify the values and properties of parameters before internal validation is performed. This method is called whenever a parmater has been changed.""" #### Enable Type of Distance Measure if Appropriate #### if self.params[3].Altered: if self.params[3].Value in self.distanceConcepts: self.params[4].Enabled = 1 self.params[6].Enabled = 1 else: self.params[4].Enabled = 0 self.params[6].Enabled = 0 if self.params[3].Value == "Get Spatial Weights From File": self.params[8].Enabled = 1 else: self.params[8].Enabled = 0 if self.params[0].Altered: if not self.params[0].IsInputValueDerived(): self.checkContiguity(self.params[0].Value) return def updateMessages(self): """Modify the messages created by internal validation for each tool parameter. This method is called after internal validation.""" return def checkContiguity(self, inputFC): try: desc = self.GP.Describe(inputFC) if desc.ShapeType.upper() == "POLYGON": self.params[3].Filter.List = self.allConcepts self.professionalCheck() else: self.params[3].Filter.List = self.baseConcepts except: self.params[3].Filter.List = self.baseConcepts def professionalCheck(self): if self.professional: self.params[3].Filter.List = self.allConcepts else: self.params[3].Filter.List = self.baseConcepts (=dOutput_Feature_Class[..\..\..\..\..\..\Program Files (x86)\ArcGIS\ArcToolbox\templates\layers\LocalGPolygons.lyrlass for which cluster/outlier analysis will be performed. The feature class for which cluster/outlier analysis will be performed. The  !"#%&'()*+,-/012345689:;<=>@ABCDEFGIJKLMNOPQ GeographicallyWeightedRegression"Geographically Weighted Regression"Geographically Weighted RegressionAnalysis Tools$B/37BKȹF GeographicallyWeightedRegression"Geographically Weighted Regression"Geographically Weighted Regressionx/ KKaWdArcInfoMain.chm>tial relationships among featuresClipClipclips a rasterRaster Processing Tools B/37BKȹFClipClipclips a raster dataset.Raster\Raster Processingx/ 356NQ>HdArcInfoMain.chm0st  GenerateSpatialWeightsMatrixGenerate Spatial Weights MatrixJCreates a spatial weights matrix (*.swm) file from an input feature class.Analysis Tools#A..\..\..\..\..\..\Program Files (x86)\ArcGIS\help\ArcInfoMain.chm>J..\..\..\..\..\..\Program Files (x86)\ArcGIS\ArcToolbox\Scripts\Weights.py o1?GW:.N_Input_Feature_ClassInput Feature Class* +DkbVDEFeatureClass Feature Class Feature Class Data Typex/ dmE[A۶@Q>dHl BDEFeatureClassA0gK-/MZX|O:5uOߟ_|BhM$wv-o1?GW:.N_Unique_ID_FieldUnique ID Field* +DkbVFieldFieldField Data Typex/ dmE[A۶Input_Feature_ClassאY(Hcj4uOߟ_|Bho1?GW:.N_"Output_Spatial_Weights_Matrix_File"Output Spatial Weights Matrix File* +DkbVDEFileFileFile Data Typex/ DING4 L-swm\:A= DEFileo1?GW:.N_*Conceptualization_of_Spatial_Relationships*Conceptualization of Spatial Relationships* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5Y"INVERSE_DISTANCEFIXED_DISTANCE(K_NEAREST_NEIGHBORS,CONTIGUITY_EDGES_ONLY2CONTIGUITY_EDGES_CORNERS.DELAUNAY_TRIANGULATIONCONVERT_TABLE34jgJ$ "INVERSE_DISTANCE34jgJ$ FIXED_DISTANCE34jgJ$ (K_NEAREST_NEIGHBORS34jgJ$ ,CONTIGUITY_EDGES_ONLY34jgJ$ 2CONTIGUITY_EDGES_CORNERS34jgJ$ .DELAUNAY_TRIANGULATION34jgJ$ CONVERT_TABLE34jgJ$ o1?GW:.N_Distance_MethodDistance Method* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5YEUCLIDEANMANHATTAN34jgJ$ EUCLIDEAN34jgJ$ MANHATTAN34jgJ$ EUCLIDEANo1?GW:.N_ExponentExponent* +DkbVGPDoubleDoubleDouble Data Typex/ DAW+OIF)tEL7n]wMgTƻ2?o1?GW:.N_Threshold_DistanceThreshold Distance* +DkbVGPDoubleDoubleDouble Data Typex/ DAW+OIF)A"[2&_BtEL7n]wMgTƻ2o1?GW:.N_Number_of_NeighborsNumber of Neighbors* +DkbVGPLongLong Long integer Data Typex/ DAW+OIF)G0Lz to1?GW:.N_Row_StandardizationRow Standardization* +DkbV GPBooleanBooleanBoolean data type Data Typex/ DAW+OIF)qfK5Y(ROW_STANDARDIZATION&NO_STANDARDIZATIONYf^EkIYf^EkIYf^EkIo1?GW:.N_ Input_Table Input Table* +DkbVDETableTableTable Data Typex/ dmE[A۶K-5KDETable A0gK-/MZX|O:5uOߟ_|BhM$wv-a 20080424 07492900 TRUE {B79F83F8-C789-4084-A074-95C963AE470D} Generate Spatial Weights Matrix (Spatial Statistics) ESRI, Inc. 380 New York Street Redlands California 92373 - 8100 info@esri.com United States 909-793-2853 909-793-5953 010 Constructs a spatial weights matrix (.swm) file to represent the spatial relationships among features in a dataset. 005 Spatial Weights Matrix ArcToolBox Tool Constructs a spatial weights matrix (.swm) file to represent the spatial relationships among features in a dataset. Learn more about how Generate Spatial Weights Matrix works GenerateSpatialWeightsMatrix_stats spatial_autocorrelation_morans_i_spatial_statistics_ high_low_clustering_getis_ord_general_g_spatial_statistics_ cluster_and_outlier_analysis_colon_anselin_local_moran_s_i_spatial_statistics_ hot_spot_analysis_getis_ord_gi_star_spatial_statistics_ modeling_spatial_relationships generate_network_spatial_weights_spatial_statistics_
Spatial Weights based on Polygon Contiguity.

The feature class for which spatial relationships of features will be assessed. The feature class for which spatial relationships of features will be assessed. An integer field containing a different value for every feature in the Input Feature Class. An integer field containing a different value for every feature in the Input Feature Class. The full pathname for the spatial weights matrix file (.swm) you want to create. The full pathname for the spatial weights matrix file (.swm) you want to create. Specifies how spatial relationships among features are conceptualized. Inverse Distance—The impact of one feature on another feature decreases with distance. Fixed Distance—Everything within a specified critical distance of each feature is included in the analysis; everything outside the critical distance is excluded. K Nearest Neighbors—The closest "k" features are included in the analysis; k is a specified numeric parameter. Polygon Contiguity (Edges Only)—Polygon features that share a boundary are neighbors. Polygon Contiguity (Edges and Corners)—Polygon features that share a boundary and/or share a node are neighbors. Delaunay Triangulation—A mesh of non-overlapping triangles is created from feature centroids; features associated with triangle nodes that share edges are neighbors. Convert Table—Spatial relationships are defined in a table. Note: Polygon Contiguity methods are only available with an ArcInfo license. Specifies how spatial relationships among features are conceptualized. Inverse Distance—The impact of one feature on another feature decreases with distance. Fixed Distance—Everything within a specified critical distance of each feature is included in the analysis; everything outside the critical distance is excluded. K Nearest Neighbors—The closest "k" features are included in the analysis; k is a specified numeric parameter. Polygon Contiguity (Edges Only)—Polygon features that share a boundary are neighbors. Polygon Contiguity (Edges and Corners)—Polygon features that share a boundary and/or share a node are neighbors. Delaunay Triangulation—A mesh of non-overlapping triangles is created from feature centroids; features associated with triangle nodes that share edges are neighbors. Convert Table—Spatial relationships are defined in a table. Note: Polygon Contiguity methods are only available with an ArcInfo license. Specifies how distances among features are calculated. Euclidean (as the crow flies)—The straight-line distance between two points. Manhattan (city block)—The distance between two points measured along axes at right angles. Calculated by summing the (absolute) differences between point coordinates. Specifies how distances among features are calculated. Euclidean (as the crow flies)—The straight-line distance between two points. Manhattan (city block)—The distance between two points measured along axes at right angles. Calculated by summing the (absolute) differences between point coordinates. Parameter for inverse distance calculation. Typical values are 1 or 2. Parameter for inverse distance calculation. Typical values are 1 or 2. Specifies a cutoff distance for Inverse Distance and Fixed Distance conceptualizations of spatial relationships. Enter this value using the units specified in the environment output coordinate system. A value of zero indicates that no threshold distance is applied. When this parameter is left blank, a default threshold value is computed based on output feature class extent and the number of features. Specifies a cutoff distance for Inverse Distance and Fixed Distance conceptualizations of spatial relationships. Enter this value using the units specified in the environment output coordinate system.A value of zero indicates that no threshold distance is applied. When this parameter is left blank, a default threshold value is computed based on output feature class extent and the number of features. An integer reflecting either the minimum or the exact number of neighbors. For K_NEAREST_NEIGHBORS, each feature will have exactly this specified number of neighbors. For INVERSE_DISTANCE or FIXED_DISTANCE each feature will have at least this many neighbors (the threshold distance will be extended to ensure this many neighbors, if necessary). When there are island polygons and one of the CONTIGUITY Conceptualizations of Spatial Relationships is selected, then this specified number of nearest polygons will be associated with those island polygons. An integer reflecting either the minimum or the exact number of neighbors. For K_NEAREST_NEIGHBORS, each feature will have exactly this specified number of neighbors. For INVERSE_DISTANCE or FIXED_DISTANCE each feature will have at least this many neighbors (the threshold distance will be extended to ensure this many neighbors, if necessary). When there are island polygons and one of the CONTIGUITY Conceptualizations of Spatial Relationships is selected, then this specified number of nearest polygons will be associated with those island polygons. Row standardization is recommended whenever feature distribution is potentially biased due to sampling design or to an imposed aggregation scheme. Row—Spatial weights are standardized by row. Each weight is divided by its row sum. None—No standardization of spatial weights is applied. Row standardization is recommended whenever feature distribution is potentially biased due to sampling design or to an imposed aggregation scheme. Row—Spatial weights are standardized by row. Each weight is divided by its row sum. None—No standardization of spatial weights is applied. A table containing numeric weights relating every feature to every other feature in the input feature class. Required fields are <input feature class Unique ID field>, NID (neighbor ID), and WEIGHT. A table containing numeric weights relating every feature to every other feature in the input feature class. Required fields are <input feature class Unique ID field>, NID (neighbor ID), and WEIGHT. This tool honors the Environment output coordinate system. Feature geometry is projected to the output coordinate system prior to analysis, so values entered for the Threshold Distance parameter use the same units as those specified in the output coordinate system. All mathematical computations are based on the output coordinate system. Consequently, if the output coordinate system does not match the input feature class spatial reference, either make sure, for all analyses using the spatial weights matrix file, that the output coordinate system matches the settings used when the spatial weights matrix file was created. Alternatively, project the feature class so that its spatial reference matches the spatial reference associated with the spatial weights matrix file. Whenever using a distance-based Conceptualization of Spatial Relationships, you should project your data using a Projected Coordinate System (rather than a Geographic Coordinate System based on degrees, minutes, and seconds) prior to analysis. To avoid confusion, this projection should match the Environment output coordinate system settings. The Unique ID field is used to relate features to one another (the relationship or weight between features 1 and 5, for example). Consequently, the Unique ID field values must be unique for every feature and typically should be a permanent field that will remain with the feature class. The Polygon Contiguity Conceptualizations of Spatial Relationships are only valid for polygon features. These options are available with the ArcInfo license only. The Number of Neighbors parameter may override the Threshold Distance parameter for Inverse or Fixed Distance Conceptualizations of Spatial Relationships. If you specify a threshold distance of 10 miles and 3 for the number of neighbors, all features will receive a minimum of 3 neighbors even if the threshold has to be increased to find them. Whenever using shapefiles keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from non-shapefile inputs may store or interpret null values as zero. This can lead to unexpected results. workspace e:\crestline\dataGenerateSpatialWeightsMatrix Hospital.shp MyID euclidean6Neighs.swm K_NEAREST_NEIGHBORS EUCLIDEAN 1 # 6 ROW_STANDARDIZATION # # # Create a Spatial Weights Matrix based on k-Nearest Neighbors and use# it to analyze the spatial distribution of per capita incomes in US# counties in 1969 and 2002 # Import system modules import arcgisscripting # Create the Geoprocessor object gp = arcgisscripting.create(9.3)gp.OverwriteOutput = 1 # Local variables... workspace = "C:\Data\USCounties\US"try: # Set the current workspace (to avoid having to specify the full path to the feature classes each time) gp.workspace = workspace # Create Spatial Weights Matrix (Can be based off input or output FC) # Process: Generate Spatial Weights Matrix... swm = gp.GenerateSpatialWeightsMatrix("USCounties.shp", "MYID", "euclidean6Neighs.swm", "K_NEAREST_NEIGHBORS", "#", "#", "#", 6) # Calculate Moran's Index of Spatial Autocorrelation for # per capita incomes in 1969 using a SWM File. # Process: Spatial Autocorrelation (Morans I)... moransI69 = gp.SpatialAutocorrelation("USCounties.shp", "PCR1969", "false", "Get Spatial Weights From File", "Euclidean Distance", "None", "#", "euclidean6Neighs.swm") # Calculate Moran's Index of Spatial Autocorrelation for # per capita incomes in 2002 using a SWM File. # Process: Spatial Autocorrelation (Morans I)... moransI02 = gp.SpatialAutocorrelation("USCounties.shp", "PCR2002", "false", "Get Spatial Weights From File", "Euclidean Distance", "None", "#", "euclidean6Neighs.swm")except: # If an error occurred when running the tool, print out the error message. print gp.GetMessages() Metadata imported.C:\ArcGIS\help\gp\GenerateSpatialWeightsMatrix_stats.xml2008042407492900  class ToolValidator: """Class for validating a tool's parameter values and controlling the behavior of the tool's dialog.""" def __init__(self): """Setup the Geoprocessor and the list of tool parameters.""" import arcgisscripting as ARC self.GP = ARC.create(9.3) self.params = self.GP.getparameterinfo() def initializeParameters(self): """Refine the properties of a tool's parameters. This method is called when the tool is opened.""" #### Disable Input Table Unless Correct Concept #### self.params[4].Enabled = 0 self.params[5].Enabled = 0 self.params[6].Enabled = 0 self.params[7].Enabled = 0 self.params[9].Enabled = 0 #### Disable Professional Options #### conceptList = ["INVERSE_DISTANCE", "FIXED_DISTANCE", "K_NEAREST_NEIGHBORS", "DELAUNAY_TRIANGULATION", "CONVERT_TABLE"] productInfo = self.GP.ProductInfo() if productInfo in ["ArcInfo", "ArcServer"]: conceptList.insert(3, "CONTIGUITY_EDGES_ONLY") conceptList.insert(4, "CONTIGUITY_EDGES_CORNERS") self.params[3].Filter.List = conceptList return def updateParameters(self): """Modify the values and properties of parameters before internal validation is performed. This method is called whenever a parmater has been changed.""" #### Enable Type of Distance Measure if Appropriate #### if self.params[3].Value == "INVERSE_DISTANCE" or \ self.params[3].Value == "FIXED_DISTANCE": self.params[4].Enabled = 1 self.params[6].Enabled = 1 else: self.params[4].Enabled = 0 self.params[6].Enabled = 0 #### Enable Exponent if Distance Measure Inverse #### if self.params[3].Value == "INVERSE_DISTANCE": self.params[5].Enabled = 1 else: self.params[5].Enabled = 0 #### Enable Table Input if Specified via Concept #### if self.params[3].Value == "CONVERT_TABLE": self.params[9].Enabled = 1 else: self.params[9].Enabled = 0 #### Disable Number of Neighbors #### if self.params[3].Altered: if self.params[3].Value == "DELAUNAY_TRIANGULATION" or \ self.params[3].Value == "CONVERT_TABLE" or \ self.params[3].Value == "": self.params[4].Enabled = 0 self.params[7].Enabled = 0 else: self.params[4].Enabled = 1 self.params[7].Enabled = 1 return def updateMessages(self): """Modify the messages created by internal validation for each tool parameter. This method is called after internal validation.""" return  cdExponentString Data Typex/ _DAW+OIF) DirectionalDistribution7Directional Distribution (Standard Deviational Ellipse)IMeasures whether a distribution of features exhibits a directional trend.Analysis Tools"ArcInfoMain.chmR..\..\..\..\..\..\Program Files (x86)\ArcGIS\ArcToolbox\Scripts\StandardEllipse.pyo1?GW:.N_Input_Feature_ClassInput Feature Class* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)m=CJbo1?GW:.N_Output_Ellipse_Feature_ClassOutput Ellipse Feature Class* +DkbVDEFeatureClass Feature Class Feature Class Data Typex/ dmE[A۶@Q>dHl BDEFeatureClassA0gK-/MZX|O:5uOߟ_|BhM$wv-n>ꁫ~GDi]o1?GW:.N_ Ellipse_Size Ellipse Size* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5Y*1 Standard Deviation,2 Standard Deviations,3 Standard Deviations34jgJ$ *1 Standard Deviation34jgJ$ ,2 Standard Deviations34jgJ$ ,3 Standard Deviations34jgJ$ *1 Standard Deviationo1?GW:.N_ Weight_Field Weight Field* +DkbVFieldFieldField Data Typex/ dmE[A۶Input_Feature_ClassאY(Hcj4uOߟ_|Bho1?GW:.N_ Case_Field Case Field* +DkbVFieldFieldField Data Typex/ dmE[A۶Input_Feature_ClassאY(Hcj4uOߟ_|Bh4 20080424 07482300 TRUE {86437344-979D-468C-90F2-0B3A0044B0C1} Directional Distribution (Standard Deviational Ellipse) (Spatial Statistics) ESRI, Inc. 380 New York Street Redlands California 92373 - 8100 info@esri.com United States 909-793-2853 909-793-5953 010 Measures whether a distribution of features exhibits a directional trend (whether features are farther from a specified point in one direction than in another direction). 005 spatial statistic;Measuring Geographic Distributions;concentration;dipsersion;standard ellipse;directional trend ArcToolBox Tool Measures whether a distribution of features exhibits a directional trend (whether features are farther from a specified point in one direction than in another direction). Learn about how Directional Distribution: Standard Deviational Ellipse works DirectionalDistribution_stats central_feature_spatial_statistics_ mean_center_spatial_statistics_ standard_distance_spatial_statistics_ Directional Distribution (Standard Deviational Ellipse) illustration A feature class containing a distribution of features for which the standard deviational ellipse will be calculated. A feature class containing a distribution of features for which the standard deviational ellipse will be calculated. A polygon feature class that will contain the output ellipse feature. A polygon feature class that will contain the output ellipse feature. The size of output ellipses in standard deviations. The default ellipse size is 1; valid choices are 1, 2, or 3 standard deviations. The size of output ellipses in standard deviations. The default ellipse size is 1; valid choices are 1, 2, or 3 standard deviations. The numeric field used to weight locations according to their relative importance. The numeric field used to weight locations according to their relative importance. Field used to group features for separate directional distribution calculations. The case field can be of numeric, date, or string type. Field used to group features for separate directional distribution calculations. The case field can be of numeric, date, or string type. This tool honors the environment output coordinate system. Feature geometry is projected to the output coordinate system prior to analysis. All mathematical computations are based on the output coordinate system spatial reference. Calculations based on either Euclidean or Manhattan distance require projected data to accurately measure distances. The Standard Deviational Ellipse tool creates a new feature class containing elliptical polygons, one for each Case (Case Field parameter). The attribute values for these ellipse polygons include X and Y coordinates for the mean or median center (Use the Median parameter), two standard distances (long and short axes), and the orientation of the ellipse. The fieldnames are CenterX, CenterY, XStdDist, YStdDist and Rotation. When a case field is provided, this field is added to the output feature class as well. If the underlying spatial pattern of the features is concentrated in the center with fewer features toward periphery (spatial normal distribution), a one standard deviation ellipse polygon will cover approximately 68 percent of the features; a two standard deviation ellipse will contain approximately 95 percent of the features; and three standard deviations will cover approximately 99 percent of the features in the cluster. The value in the Rotation field represents the rotation of the long axis measured clockwise from noon. If a case field is specified, then the input features are grouped according to case field values. A mean or median center, and standard ellipse is computed separately for each case. For line and polygon features, feature true geometric centroids are used in the computations. Whenever using shapefiles keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from non-shapefile inputs may store or interpret null values as zero. This can lead to unexpected results. Current map layers may be used to define the input feature class. When using layers, only the currently selected features are included in the analysis. Learn more about working with layers and table views workspace e:\project93\dataDirectionalDistribution assault.shp assault_ellipse.shp '1 Standard Deviation' # # Measure the geographic distribution of auto thefts # Import system modules import arcgisscripting # Create the Geoprocessor object gp = arcgisscripting.create() # Local variables... workspace = "C:/chris/data/"auto_theft_locations = "AutoTheft.shp"auto_theft_links = "AutoTheft_links.shp"auto_theft_sd = "auto_theft_SD.shp"auto_theft_se = "auto_theft_SE.shp"auto_theft_ldm = "auto_theft_LDM.shp" try: # Set the workspace (to avoid having to type in the full path to the data every time) gp.Workspace = workspace # Process: Standard Distance of auto theft locations... gp.StandardDistance_stats(auto_theft_locations, auto_theft_sd, "1 Standard Deviation", "#", "#") # Process: Directional Distribution (Standard Deviational Ellipse) of auto theft locations... gp.DirectionalDistribution_stats(auto_theft_locations, auto_theft_se, "1 Standard Deviation", "#", "#") # Process: Linear Directional Mean of auto thefts... gp.DirectionalMean_stats(auto_theft_links, auto_theft_ldm, "false", "#") except: # If an error occurred while running a tool, print the messages print gp.GetMessages() Metadata imported.C:\ArcGIS\help\gp\DirectionalDistribution_stats.xml2008042407482300 class ToolValidator: """Class for validating a tool's parameter values and controlling the behavior of the tool's dialog.""" def __init__(self): """Setup the Geoprocessor and the list of tool parameters.""" import arcgisscripting as ARC self.GP = ARC.create(9.3) self.params = self.GP.getparameterinfo() def initializeParameters(self): """Refine the properties of a tool's parameters. This method is called when the tool is opened.""" return def updateParameters(self): """Modify the values and properties of parameters before internal validation is performed. This method is called whenever a parmater has been changed.""" return def updateMessages(self): """Modify the messages created by internal validation for each tool parameter. This method is called after internal validation.""" return  d><charSet value="0"/><outPrecision value="0"/><clipPrecision value="0"/><quality value="0"/><pitchAndFamily value="0"/></font></fonts></resources><instances><graphs><graph id="5"><minimumMarginWidth value="0"/><minimumMarginHeight value="0"/>      !"#$%&'()*+,-./0123456789:;>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~ ClustersOutliers5Cluster and Outlier Analysis (Anselin Local Morans I)hGiven a set of weighted data points, identifies spatial clusters of extreme values and spatial outliers.Analysis Tools!ArcInfoMain.chmsɉM..\..\..\..\..\..\Program Files (x86)\ArcGIS\ArcToolbox\Scripts\LocalMoran.py o1?GW:.N_Input_Feature_ClassInput Feature Class* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)m=CJbo1?GW:.N_ Input_Field Input Field* +DkbVFieldFieldField Data Typex/ dmE[A۶Input_Feature_ClassאY(Hcj4uOߟ_|Bho1?GW:.N_Output_Feature_ClassOutput Feature Class* +DkbVDEFeatureClass Feature Class Feature Class Data Typex/ dmE[A۶@Q>dHl BDEFeatureClassA0gK-/MZX|O:5uOߟ_|BhM$wv-n>ꁫ~GDi]o1?GW:.N_*Conceptualization_of_Spatial_Relationships*Conceptualization of Spatial Relationships* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5Y"Inverse Distance2Inverse Distance Squared(Fixed Distance Band*Zone of IndifferenceBPolygon Contiguity (First Order)<Get Spatial Weights From File34jgJ$ "Inverse Distance34jgJ$ 2Inverse Distance Squared34jgJ$ (Fixed Distance Band34jgJ$ *Zone of Indifference34jgJ$ BPolygon Contiguity (First Order)34jgJ$ <Get Spatial Weights From File34jgJ$ "Inverse Distanceo1?GW:.N_Distance_MethodDistance Method* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5Y&Euclidean Distance&Manhattan Distance34jgJ$ &Euclidean Distance34jgJ$ &Manhattan Distance34jgJ$ &Euclidean Distanceo1?GW:.N_StandardizationStandardization* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5Y NoneRow34jgJ$  None34jgJ$ Row34jgJ$  Noneo1?GW:.N_#Distance_Band_or_Threshold_Distance#Distance Band or Threshold Distance* +DkbVGPDoubleDoubleDouble Data Typex/ DAW+OIF)A"[2&3&k CtEL7n]wMgTƻ2o1?GW:.N_Weights_Matrix_FileWeights Matrix File* +DkbVDEFileFileFile Data Typex/ DIN 20080424 07474600 TRUE {32D7BA61-6397-4825-AD66-C338A6CAF84C} Cluster and Outlier Analysis: Anselin Local Moran's I (Spatial Statistics) ESRI, Inc. 380 New York Street Redlands California 92373 - 8100 info@esri.com United States 909-793-2853 909-793-5953 010 Given a set of weighted features, identifies where high or low values cluster spatially, and features with values that are very different from surrounding feature values. 005 spatial statistics;mapping clusters;clusters;Local Moran's I;weighted data points;diversity analysis ArcToolBox Tool Given a set of weighted features, identifies where high or low values cluster spatially, and features with values that are very different from surrounding feature values. Learn more about how Cluster and Outlier Analysis: Anselin Local Moran's I works ClustersOutliers_stats hot_spot_analysis_getis_ord_gi_star_spatial_statistics_ spatial_autocorrelation_morans_i_spatial_statistics_ modeling_spatial_relationships what_is_a_z_score_qst_what_is_a_p_value_qst_
Cluster and Outlier Analysis Illustration

The feature class for which cluster/outlier analysis will be performed. The feature class for which cluster/outlier analysis will be performed. The numeric field to be evaluated. The numeric field to be evaluated. The output feature class to receive the results fields. The output feature class to receive the results fields. Specifies how spatial relationships among features are conceptualized. Inverse Distance—All features impact/influence all other features, but the farther away something is, the smaller the impact it has. Inverse Distance Squared—Same as Inverse Distance except that the slope is sharper so influence drops off more quickly and only a target feature's closest neighbors will exert substantial influence in computations for that feature. Fixed Distance Band—Each feature is analyzed within the context of those neighboring features within some specified critical distance. Features outside the critical distance of a target feature do not influence calculations for that feature. Zone of Indifference—Features within the specified critical distance of a target feature are included in analyses for that feature. Once the critical distance is exceeded, the level of impact quickly drops off. Polygon Contiguity (First Order)—The neighbors of each feature are only those with which the feature shares a boundary. All other features have no influence on computations. Get Spatial Weights From File—Spatial relationships are defined in a spatial weights file. The pathname to the spatial weights file is specified in the Weights Matrix File parameter. Specifies how spatial relationships among features are conceptualized. Inverse Distance—All features impact/influence all other features, but the farther away something is, the smaller the impact it has. Inverse Distance Squared—Same as Inverse Distance except that the slope is sharper so influence drops off more quickly and only a target feature's closest neighbors will exert substantial influence in computations for that feature. Fixed Distance Band—Each feature is analyzed within the context of those neighboring features within some specified critical distance. Features outside the critical distance of a target feature do not influence calculations for that feature. Zone of Indifference—Features within the specified critical distance of a target feature are included in analyses for that feature. Once the critical distance is exceeded, the level of impact quickly drops off. Polygon Contiguity (First Order)—The neighbors of each feature are only those with which the feature shares a boundary. All other features have no influence on computations. Get Spatial Weights From File—Spatial relationships are defined in a spatial weights file. The pathname to the spatial weights file is specified in the Weights Matrix File parameter. Specifies how distances are calculated when measuring spatial autocorrelation. Euclidean (as the crow flies)—The straight-line distance between two points. Manhattan (city block)—The distance between two points measured along axes at right angles. Calculated by summing the (absolute) differences between point coordinates. Specifies how distances are calculated when measuring spatial autocorrelation. Euclidean (as the crow flies)—The straight-line distance between two points. Manhattan (city block)—The distance between two points measured along axes at right angles. Calculated by summing the (absolute) differences between point coordinates. Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme. None—No standardization of spatial weights is applied. Row—Spatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features). Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme. None—No standardization of spatial weights is applied. Row—Spatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features). Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance while those inside the distance threshold are equally considered. The value entered should match those of the Output Coordinate System.For the Inverse Distance conceptualizations of spatial relationships: A value of zero for this parameter indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value will be computed and applied.This parameter has no effect when "Polygon Contiguity" or "Get Spatial Weights From File" spatial conceptualizations are selected. Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance while those inside the distance threshold are equally considered. The value entered should match those of the Output Coordinate System.For the Inverse Distance conceptualizations of spatial relationships: A value of zero for this parameter indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value will be computed and applied.This parameter has no effect when "Polygon Contiguity" or "Get Spatial Weights From File" spatial conceptualizations are selected. The pathname to a file containing spatial weights that define spatial relationships between features. The pathname to a file containing spatial weights that define spatial relationships between features. This tool honors the environment output coordinate system. Feature geometry is projected to the output coordinate system prior to analysis, so values entered for the Distance Band/Threshold Distance parameter should match those specified in the output coordinate system. All mathematical computations are based on the output coordinate system spatial reference. Calculations based on either Euclidean or Manhattan distance require projected data to accurately measure distances. If you will be running several analyses on a single dataset (e.g., analyzing several different fields) or if you have a dataset with more than 3000 features, it is recommended that you construct the spatial weights matrix file prior to analysis. The cluster and outlier analysis output is a Local Moran's I index value, Z score, P-value and cluster type code for each feature. This function creates as derived output the names of the index, z score, p-value, and cluster type result fields. The Z scores and p-values are measures of statistical significance which tell you whether or not to reject the null hypothesis, feature by feature. They, in effect, indicate whether the apparent similarity (or dissimilarity) in values for a feature and its neighbors is greater than one would expect in a random distribution. The Z Score is based on the Randomization Null Hypothesis computation. For more information on Z Scores, see What is a Z Score? What is a p-value?. A high positive Z score for a feature indicates that the surrounding features have similar values (either high values or low value). The COType field indicates HH for a statistically significant (0.05 level) cluster of high values and LL for a statistically significant (0.05 level) cluster of low values. A low negative Z score for a feature indicates a statistically significant (0.05 level) spatial outlier. The COType field indicates if the feature has high value and is surrounded by features with low values (HL) or if the feature has a low value and is surrounded by features with high values (LH). For line and polygon features, true feature geometric centroids are used in computations. The input field should contain a variety of non-negative values. The math for this statistic requires some variation in the variable being analyzed; it cannot solve if all input values are 1, for example. If you have incident data, and want to analyze incident intensity, consider aggregating your incident data or using Integrate with the Collect Events tool prior to analysis. Whenever using shapefiles keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from non-shapefile inputs may store or interpret null values as zero. This can lead to unexpected results. The Conceptualization of Spatial Relationships used for analysis should be based on your understanding of spatial interaction among the features being analyzed. For the Fixed Distance option, the distance band used for analysis should be based on your understanding of spatial interaction among the features being analyzed. Alternatively, features may be evaluated for a range of distance values or at the specific distance where spatial autocorrelation is maximized. For Inverse Distance conceptualization options: when zero is entered for the "Distance Band or Threshold Distance" parameter all features are considered neighbors of all other features; when this parameter is left blank, a default threshold distance will be applied. When the spatial conceptualization is an Inverse Distance method (Inverse Distance, Inverse Distance Squared, or Zone of Indifference) any two points that are coincident will be given a weight of one to avoid zero division. This assures features are not excluded from analysis. With inverse distance conceptualizations, weights for distances less than 1 become unstable. The weighting for features separated by less than 1 unit of distance (common with Geographic Coordinate System projections), are given a weight of 1. Analysis on features with a Geographic Coordinate System projection is not recommended with any of the inverse distance based spatial conceptualization methods. In ArcGIS version 9.2, the "Global" standardization option was removed. Global standardization returns the same results as no standardization. Models built with previous versions of ArcGIS that use the Global standardization option may need to be rebuilt. See the Modeling Spatial Relationships help page for further explanation of this tool's parameters. Current map layers may be used to define the input feature class. When using layers, only the currently selected features are included in the analysis. Learn more about working with layers and table views When this tool runs in ArcMap, the output feature class is automatically added to the Table of Contents (TOC) with default rendering applied to the Z Score field. The hot to cold rendering applied is defined by a layer file in <ArcGIS>/ArcToolbox/Templates/Layers. You can reapply the default rendering, if needed, by importing the template layer symbology. ClustersOutliers VotingDistricts.shp ProIncumbent VoteDiversity.shp 'Fixed Distance Band' 'Euclidean Distance' 'None' 1000 # # Perform cluster analysis for assault incidents # Import system modules import arcgisscripting # Create the Geoprocessor object gp = arcgisscripting.create() # Local variables... workspace = "C:/chris/data"input = "assaults.shp"collect_output = "collect_output.shp"collect_count_field = "Count"cluster_output = "cluster_output.shp"cluster_output_rendered = "cluster_output_rendered.lyr"z_score_field_name = "LMzInvDst" try: # Set the current workspace (to avoid having to specify the full path to the feature classes each time) gp.workspace = workspace # Convert assault incidents into weighted point data # Process: Collect Events... gp.CollectEvents_stats(input, collect_output) # Calculate Anselin Local Morans I statistic # Process: Cluster and Outlier Analysis (Anselin Local Morans I)... gp.ClustersOutliers_stats(collect_output, collect_count_field, cluster_output, "Inverse Distance", "Euclidean Distance", "None", "#", "#") # Render hot spot analysis # Process: Z Score Rendering... gp.ZRenderer_stats(cluster_output, z_score_field_name, cluster_output_rendered) except: # If an error occurred when running the tool, print out the error message. print gp.GetMessages(2) Metadata imported.C:\ArcGIS\help\gp\ClustersOutliers_stats.xml2008042407474600  class ToolValidator: """Class for validating a tool's parameter values and controlling the behavior of the tool's dialog.""" def __init__(self): """Setup the Geoprocessor and the list of tool parameters.""" import arcgisscripting as ARC self.GP = ARC.create(9.3) self.params = self.GP.getparameterinfo() #### Check for Professional License for Contiguity #### self.productInfo = self.GP.ProductInfo() if self.productInfo in ["ArcInfo", "ArcServer"]: self.professional = 1 else: self.professional = 0 #### Set Lists of Spatial Concepts #### self.baseConcepts = ["Inverse Distance", "Inverse Distance Squared", "Fixed Distance Band", "Zone of Indifference", "Get Spatial Weights From File"] self.allConcepts = ["Inverse Distance", "Inverse Distance Squared", "Fixed Distance Band", "Zone of Indifference", "Polygon Contiguity (First Order)", "Get Spatial Weights From File"] self.distanceConcepts = self.baseConcepts[0:4] def initializeParameters(self): """Refine the properties of a tool's parameters. This method is called when the tool is opened.""" self.professionalCheck() #### Disable Input Table Unless Correct Concept #### self.params[7].Enabled = 0 return def updateParameters(self): """Modify the values and properties of parameters before internal validation is performed. This method is called whenever a parmater has been changed.""" #### Enable Type of Distance Measure if Appropriate #### if self.params[3].Altered: if self.params[3].Value in self.distanceConcepts: self.params[4].Enabled = 1 self.params[6].Enabled = 1 else: self.params[4].Enabled = 0 self.params[6].Enabled = 0 if self.params[3].Value == "Get Spatial Weights From File": self.params[7].Enabled = 1 self.params[5].Enabled = 0 else: self.params[7].Enabled = 0 self.params[5].Enabled = 1 if self.params[0].Altered: if not self.params[0].IsInputValueDerived(): self.checkContiguity(self.params[0].Value) return def updateMessages(self): """Modify the messages created by internal validation for each tool parameter. This method is called after internal validation.""" return def checkContiguity(self, inputFC): try: desc = self.GP.Describe(inputFC) if desc.ShapeType.upper() == "POLYGON": self.params[3].Filter.List = self.allConcepts self.professionalCheck() else: self.params[3].Filter.List = self.baseConcepts except: self.params[3].Filter.List = self.baseConcepts def professionalCheck(self): if self.professional: self.params[3].Filter.List = self.allConcepts else: self.params[3].Filter.List = self.baseConcepts  dOutput_Feature_Class[..\..\..\..\..\..\Program Files (x86)\ArcGIS\ArcToolbox\templates\layers\LocalIPolygons.lyrStandardization4uOߟ_|Bh InfraRiskInfraRisk4uOߟ_|Bh DrugRiskDrugRisk4uOߟ_|BhSchoolRiskSchoolRisk4uOߟ_|BhNROppsNear Repeat OpportunitieshIF[akFe:2.E !Vector Risk Terrain (Input Layer)|YmI<,A/Ӳ NlCn2The value is empty.m=CJb* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)e:2.E Places In NR Bndwth|YmI<,A/m=CJb* +DkbV GPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)e:2.E  New Crime Incident (Input Layer)|YmI<,A/ Ӳ NlCn 2The value is empty.m=CJb* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)e:2.E Expected Near Repeat Bandwidth|YmI<,A/Ӳ NlCn2The value is empty.-AL!iޭ<$* +DkbV GPLinearUnit Linear unit Linear unit Data Typex/ DAW+OIF)ee:2.E  High Risk Places in NR Bndwth|YmI<,A/m=CJb\.gDo+UPFIDFIDShapeShape RiskValue RiskValue AnyP2Shoot AnyP2Shootidid@hCzt{nf* +DkbV GPTableView Table View Table View Data Typex/ DAW+OIF)* +DkbV GPRasterLayer Raster Layer Raster Layer Data Typex/ DAW+OIF)e:2.E  "Expression for High Risk Threshold|YmI<,A/!Ӳ NlCn"2The value is empty.34jgJ$ #* +DkbV$GPSQLExpressionSQL ExpressionSQL Expression Data Typex/ %DAW+OIF)ee:2.E& HName of New Layer Showing Places in Near Repeat Bandwidth (Output Layer)|YmI<,A/'m=CJb(\.gDo+UP)FIDFIDShapeShape RiskValue RiskValue AnyP2Shoot AnyP2Shootidid* +DkbV*GPFeatureLayer Feature Layer Feature Layer Data Typex/ +DAW+OIF)e:2.E, Select Attribute Output|YmI<,A/-m=CJb.@hCzt{nf/* +DkbV0 GPTableView Table View Table View Data Typex/ 1DAW+OIF)* +DkbV2 GPRasterLayer Raster Layer Raster Layer Data Typex/ 3DAW+OIF)g:M|I~ 4Select Layer By Location|YmI<,A/5Ӳ NlCn6d4ERROR 000735: Input Feature Layer: Value is requiredӲ NlCn7Ӳ NlCn8Ӳ NlCn9Ӳ NlCn:Ӳ NlCn;#c8Dæi<SelectLayerByLocationSelect Layer By Location-Update the selection of the layer by locationLayers and Table ViewsGKcI=Data Management Tools5ZqO:>rC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\ToolboxesTOOLBOX: Workspace = \\JOELCAPLAN\C$\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes;Toolbox DataZX|O:?DATABASErC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes1#4*)G9gK1ItrOG^aa-@in_layerInput Feature Layer@hCzt{nfA* +DkbVBGPFeatureLayer Feature Layer Feature Layer Data Typex/ CDAW+OIF)* +DkbVDGPRasterCatalogLayerRaster Catalog LayerRaster Catalog Layer Data Typex/ EDAW+OIF)* +DkbVF GPMosaicLayer Mosaic Layer Mosaic Layer Data Typex/ GDAW+OIF) M]mjHe:2.EItrOG^aa-I 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Feature Layer Feature Layer Data Typex/ DAW+OIF)m=CJb\.gDo+UPFIDFIDShapeShape RiskValue RiskValue AnyP2Shoot AnyP2Shootidid@ {7A295B8D-D268-4ACB-B89E-68DF02054EEE}2011081210363100TRUE20110813214205C:\Program Files\ArcGIS\Desktop10.0\Help\gpNear Repeat OpportunitiesCreates a new feature layer of the places from the vector risk terrain map that are within the expected near repeat bandwidth of the new crime incident location. 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GPRasterLayer Raster Layer Raster Layer Data Typex/ "DAW+OIRasterCalculatorRaster Calculator2Execute a map algebra statement to create a rasterRaster Processing ToolsB/37BKȹFRasterCalculatorRaster Calculator2Execute a map algebra statement to create a raster Map Algebrax/ 1TJ@?g$dArcInfoMain.chm{ culate Field%Calculate a fiZonalStatisticsZonal Statistics)Spatial Analyst ZonalStatistics FunctionsRaster Processing ToolsB/37BKȹFZonalStatisticsZonal StatisticsPCalculates statistics on values of a raster within the zones of another dataset.Zonalx/ 1TJ@?g$dArcInfoMain.chm%| Desktop10.0\ArcToo WeightedSum Weighted Sum%Spatial Analyst Weighted Sum functionRaster Processing ToolsB/37BKȹF WeightedSum Weighted SumZOverlays several rasters multiplying each by their given weight and summing them together.Overlayx/ 1TJ@?g$dArcInfoMain.chm4|  EucDistanceEuclidean Distance%Spatial Analyst EucDistance FunctionsRaster Processing ToolsB/37BKȹF EucDistanceEuclidean DistanceFCalculates the Euclidean distance to the closest source for each cell.Distancex/ 1TJ@?g$dArcInfoMain.chm8z ld Reclassify Reclassify#Spatial Analyst Reclassify functionRaster Processing ToolsB/37BKȹF Reclassify Reclassify1Reclassifies (or changes) the values in a raster.Reclassx/ 1TJ@?g$dArcInfoMain.chm| DAW+OIF) RasterToPointRaster to Point*Converts raster data to point feature dataRaster Processing ToolsB/37BKȹF RasterToPointRaster to Point+Converts raster data to point feature data. Conversionx/ 356NQ>HdArcInfoMain.chmnexpression_typeExpre KernelDensityKernel Density'Spatial Analyst KernelDensity FunctionsRaster Processing ToolsB/37BKȹF KernelDensityKernel Density]Calculates a magnitude per unit area from point or polyline features using a kernel function.Densityx/ 1TJ@?g$dArcInfoMain.chm!`z PDAW+OIF).Risk Terrain ToolsItrOG^aa-Q code_block Code Block* +DkbVRGPStringStringString Data Typex/ SDAW+OIF)      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~ NoteAnyEvents Add and Populate AnyEvents FieldCreates and populates a field in the attribute table of the vector grid that notes whether one or more outcome events are located within each grid cell.hIF[akFe:2.E Select Location Output|YmI<,A/m=CJb0Vector_Risk_Terrain_MapG8lGKv\vC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bh4uOߟ_|BhFIDFID4uOߟ_|Bh Shape ShapeR C9xgb*Q "sPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?MbP?MbP?4uOߟ_|Bh RiskValueRiskValue4uOߟ_|Bh AnyP2ShootAnyP2Shoot4uOߟ_|Bh GangRiskGangRisk4uOߟ_|Bh InfraRiskInfraRisk4uOߟ_|Bh DrugRiskDrugRisk4uOߟ_|BhSchoolRiskSchoolRisk4uOߟ_|BhBusRiskBusRisk4uOߟ_|BhWRiskValueWRiskValue4uOߟ_|BhWGangRiskWGangRisk4uOߟ_|BhWInfraRiskWInfraRisk4uOߟ_|BhWDrugRiskWDrugRisk4uOߟ_|BhWSchoolRskWSchoolRsk4uOߟ_|BhWBusRiskWBusRisk4uOߟ_|BhAnyEventsAnyEventsM$wv-'nM$wv- Shape5uOߟ_|Bh4uOߟ_|Bh Shape ShapeR C9xgb*Q "s Shaperp0R` ߜI!AK$A ߜIy!AK<%Agb*Q "sPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Aj@j@hqgj?MbP?MbP?gb*Q "sG8lGKv\vC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bh4uOߟ_|Bh FIDFID4uOߟ_|Bh! Shape ShapeR C9x"gb*Q "s4uOߟ_|Bh#RiskValueRiskValue4uOߟ_|Bh$AnyP2ShootAnyP2Shoot4uOߟ_|Bh%GangRiskGangRisk4uOߟ_|Bh&InfraRiskInfraRisk4uOߟ_|Bh'DrugRiskDrugRisk4uOߟ_|Bh(SchoolRiskSchoolRisk4uOߟ_|Bh)BusRiskBusRisk4uOߟ_|Bh*WRiskValueWRiskValue4uOߟ_|Bh+WGangRiskWGangRisk4uOߟ_|Bh,WInfraRiskWInfraRisk4uOߟ_|Bh-WDrugRiskWDrugRisk4uOߟ_|Bh.WSchoolRskWSchoolRsk4uOߟ_|Bh/WBusRiskWBusRiskM$wv-0'nM$wv-1 Shape5uOߟ_|Bh24uOߟ_|Bh3 Shape ShapeR C9x4gb*Q "s Shaperp0R`gb*Q "s\.gDo+UP5FIDFIDShapeShape RiskValue RiskValue AnyP2Shoot AnyP2ShootGangRiskGangRisk InfraRisk InfraRiskDrugRiskDrugRisk SchoolRisk SchoolRiskBusRiskBusRisk WRiskValue WRiskValue WGangRisk WGangRisk WInfraRisk WInfraRisk WDrugRisk WDrugRisk WSchoolRsk WSchoolRskWBusRiskWBusRisk* +DkbV6GPFeatureLayer Feature Layer Feature Layer Data Typex/ 7DAW+OIF)e:2.E8  $Study Area Vector Grid (Input Layer)|YmI<,A/9Ӳ NlCn:2The value does not exist.m=CJb;(StudyAreaVectorGrid* +DkbV<GPFeatureLayer Feature Layer Feature Layer Data Typex/ =DAW+OIF)e:2.E>  Add Field Output|YmI<,A/?m=CJb@0Vector_Risk_Terrain_MapG8lGKv\vAC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|BhB4uOߟ_|BhCFIDFID4uOߟ_|BhD Shape ShapeR C9xEgb*Q "s4uOߟ_|BhFRiskValueRiskValue4uOߟ_|BhGAnyP2ShootAnyP2Shoot4uOߟ_|BhHGangRiskGangRisk4uOߟ_|BhIInfraRiskInfraRisk4uOߟ_|BhJDrugRiskDrugRisk4uOߟ_|BhKSchoolRiskSchoolRisk4uOߟ_|BhLBusRiskBusRisk4uOߟ_|BhMWRiskValueWRiskValue4uOߟ_|BhNWGangRiskWGangRisk4uOߟ_|BhOWInfraRiskWInfraRisk4uOߟ_|BhPWDrugRiskWDrugRisk4uOߟ_|BhQWSchoolRskWSchoolRsk4uOߟ_|BhRWBusRiskWBusRisk4uOߟ_|BhSAnyEventsAnyEventsM$wv-T'nM$wv-U Shape5uOߟ_|BhV4uOߟ_|BhW Shape ShapeR C9xXgb*Q "s Shaperp0R`gb*Q "sG8lGKv\vYC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|BhZ4uOߟ_|Bh[FIDFID4uOߟ_|Bh\ Shape ShapeR C9x]gb*Q "s4uOߟ_|Bh^RiskValueRiskValue4uOߟ_|Bh_AnyP2ShootAnyP2Shoot4uOߟ_|Bh`GangRiskGangRisk4uOߟ_|BhaInfraRiskInfraRisk4uOߟ_|BhbDrugRiskDrugRisk4uOߟ_|BhcSchoolRiskSchoolRisk4uOߟ_|BhdBusRiskBusRisk4uOߟ_|BheWRiskValueWRiskValue4uOߟ_|BhfWGangRiskWGangRisk4uOߟ_|BhgWInfraRiskWInfraRisk4uOߟ_|BhhWDrugRiskWDrugRisk4uOߟ_|BhiWSchoolRskWSchoolRsk4uOߟ_|BhjWBusRiskWBusRiskM$wv-k'nM$wv-l Shape5uOߟ_|Bhm4uOߟ_|Bhn Shape ShapeR C9xogb*Q "s Shaperp0R`gb*Q "s\.gDo+UPpFIDFIDShapeShape RiskValue RiskValue AnyP2Shoot AnyP2ShootGangRiskGangRisk InfraRisk InfraRiskDrugRiskDrugRisk SchoolRisk SchoolRiskBusRiskBusRisk WRiskValue WRiskValue WGangRisk WGangRisk WInfraRisk WInfraRisk WDrugRisk WDrugRisk WSchoolRsk WSchoolRskWBusRiskWBusRisk@hCzt{nfq* +DkbVr GPTableView Table View Table View Data Typex/ sDAW+OIF)* +DkbVt GPRasterLayer Raster Layer Raster Layer Data Typex/ uDAW+OIF)e:2.Ev  Vector Grid with AnyEvents Field|YmI<,A/wm=CJbx0Vector_Risk_Terrain_MapG8lGKv\vyC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bhz4uOߟ_|Bh{FIDFID4uOߟ_|Bh| Shape ShapeR C9x}gb*Q "s4uOߟ_|Bh~RiskValueRiskValue4uOߟ_|BhAnyP2ShootAnyP2Shoot4uOߟ_|BhGangRiskGangRisk4uOߟ_|BhInfraRiskInfraRisk4uOߟ_|BhDrugRiskDrugRisk4uOߟ_|BhSchoolRiskSchoolRisk4uOߟ_|BhBusRiskBusRisk4uOߟ_|BhWRiskValueWRiskValue4uOߟ_|BhWGangRiskWGangRisk4uOߟ_|BhWInfraRiskWInfraRisk4uOߟ_|BhWDrugRiskWDrugRisk4uOߟ_|BhWSchoolRskWSchoolRsk4uOߟ_|BhWBusRiskWBusRisk4uOߟ_|BhAnyEventsAnyEventsM$wv-'nM$wv- Shape5uOߟ_|Bh4uOߟ_|Bh Shape ShapeR C9xgb*Q "s Shaperp0R`gb*Q "sG8lGKv\vC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bh4uOߟ_|BhFIDFID4uOߟ_|Bh Shape ShapeR C9xgb*Q "s4uOߟ_|BhRiskValueRiskValue4uOߟ_|BhAnyP2ShootAnyP2Shoot4uOߟ_|BhGangRiskGangRisk4uOߟ_|BhInfraRiskInfraRisk4uOߟ_|BhDrugRiskDrugRisk4uOߟ_|BhSchoolRiskSchoolRisk4uOߟ_|BhBusRiskBusRisk4uOߟ_|BhWRiskValueWRiskValue4uOߟ_|BhWGangRiskWGangRisk4uOߟ_|BhWInfraRiskWInfraRisk4uOߟ_|BhWDrugRiskWDrugRisk4uOߟ_|BhWSchoolRskWSchoolRsk4uOߟ_|BhWBusRiskWBusRiskM$wv-'nM$wv- Shape5uOߟ_|Bh4uOߟ_|Bh Shape ShapeR C9xgb*Q "s Shaperp0R`gb*Q "s\.gDo+UPFIDFIDShapeShape RiskValue RiskValue AnyP2Shoot AnyP2ShootGangRiskGangRisk InfraRisk InfraRiskDrugRiskDrugRisk SchoolRisk SchoolRiskBusRiskBusRisk WRiskValue WRiskValue WGangRisk WGangRisk WInfraRisk WInfraRisk WDrugRisk WDrugRisk WSchoolRsk WSchoolRskWBusRiskWBusRisk@hCzt{nfݩ* +DkbV GPTableView Table View Table View Data Typex/ ˫DAW+OIF)* +DkbV GPRasterLayer Raster Layer Raster Layer Data Typex/ ˭DAW+OIF)e:2.E $Outcome Event Features (Input Layer)|YmI<,A/m=CJbOutcome_EventsG8lGKv\vVC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\OtherFiles\Irvington_Shootings_Jan_June2007.shpJIrvington_Shootings_Jan_June2007.shpDEShapeFileFID5uOߟ_|Bh4uOߟ_|BhFIDFID4uOߟ_|Bh Shape ShapeR C9xgb*Q "sPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?MbP?MbP?4uOߟ_|BhStatusStatus4uOߟ_|Bh CRIME CRIME!4uOߟ_|Bh DATE_ DATE_4uOߟ_|BhDAYDAYM$wv- Shaperp0R`6,!A2%A^v!Aa[V2%Agb*Q "sgb*Q "sG8lGKv\v\.gDo+UPFIDFIDShapeShapeStatusStatusCRIMECRIMEDATE_DATE_DAYDAY* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ ˿DAW+OIF)e:2.E Calculate Field Output|YmI<,A/m=CJb0Vector_Risk_Terrain_MapG8lGKv\vC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bh4uOߟ_|BhFIDFID4uOߟ_|Bh Shape ShapeR C9xgb*Q "s4uOߟ_|BhRiskValueRiskValue4uOߟ_|BhAnyP2ShootAnyP2Shoot4uOߟ_|BhGangRiskGangRisk4uOߟ_|BhInfraRiskInfraRisk4uOߟ_|BhDrugRiskDrugRisk4uOߟ_|BhSchoolRiskSchoolRisk4uOߟ_|BhBusRiskBusRisk4uOߟ_|BhWRiskValueWRiskValue4uOߟ_|BhWGangRiskWGangRisk4uOߟ_|BhWInfraRiskWInfraRisk4uOߟ_|BhWDrugRiskWDrugRisk4uOߟ_|BhWSchoolRskWSchoolRsk4uOߟ_|BhWBusRiskWBusRisk4uOߟ_|BhAnyEventsAnyEventsM$wv-'nM$wv- Shape5uOߟ_|Bh4uOߟ_|Bh Shape ShapeR C9xgb*Q "s Shaperp0R`gb*Q "sG8lGKv\vC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bh4uOߟ_|BhFIDFID4uOߟ_|Bh Shape ShapeR C9xgb*Q "s4uOߟ_|BhRiskValueRiskValue4uOߟ_|BhAnyP2ShootAnyP2Shoot4uOߟ_|BhGangRiskGangRisk4uOߟ_|BhInfraRiskInfraRisk4uOߟ_|BhDrugRiskDrugRisk4uOߟ_|BhSchoolRiskSchoolRisk4uOߟ_|BhBusRiskBusRisk4uOߟ_|BhWRiskValueWRiskValue4uOߟ_|BhWGangRiskWGangRisk4uOߟ_|BhWInfraRiskWInfraRisk4uOߟ_|BhWDrugRiskWDrugRisk4uOߟ_|BhWSchoolRskWSchoolRsk4uOߟ_|BhWBusRiskWBusRiskM$wv-'nM$wv- Shape5uOߟ_|Bh4uOߟ_|Bh Shape ShapeR C9xgb*Q "s Shaperp0R`gb*Q "s\.gDo+UPFIDFIDShapeShape RiskValue RiskValue AnyP2Shoot AnyP2ShootGangRiskGangRisk InfraRisk InfraRiskDrugRiskDrugRisk SchoolRisk SchoolRiskBusRiskBusRisk WRiskValue WRiskValue WGangRisk WGangRisk WInfraRisk WInfraRisk WDrugRisk WDrugRisk WSchoolRsk WSchoolRskWBusRiskWBusRisk@hCzt{nf* +DkbV GPTableView Table View Table View Data Typex/ DAW+OIF)* +DkbV GPRasterLayer Raster Layer Raster Layer Data Typex/ DAW+OIF)e:2.E Select Attribute Output|YmI<,A/m=CJb0Vector_Risk_Terrain_MapG8lGKv\vC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bh4uOߟ_|BhFIDFID4uOߟ_|Bh Shape ShapeR C9xgb*Q "s4uOߟ_|BhRiskValueRiskValue4uOߟ_|BhAnyP2ShootAnyP2Shoot4uOߟ_|BhGangRiskGangRisk4uOߟ_|BhInfraRiskInfraRisk4uOߟ_|BhDrugRiskDrugRisk4uOߟ_|BhSchoolRiskSchoolRisk4uOߟ_|BhBusRiskBusRisk4uOߟ_|BhWRiskValueWRiskValue4uOߟ_|BhWGangRiskWGangRisk4uOߟ_|Bh WInfraRiskWInfraRisk4uOߟ_|Bh WDrugRiskWDrugRisk4uOߟ_|Bh WSchoolRskWSchoolRsk4uOߟ_|Bh WBusRiskWBusRisk4uOߟ_|Bh AnyEventsAnyEventsM$wv-'nM$wv- Shape5uOߟ_|Bh4uOߟ_|Bh Shape ShapeR C9xgb*Q "s Shaperp0R`gb*Q "sG8lGKv\vC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bh4uOߟ_|BhFIDFID4uOߟ_|Bh Shape ShapeR C9xgb*Q "s4uOߟ_|BhRiskValueRiskValue4uOߟ_|BhAnyP2ShootAnyP2Shoot4uOߟ_|BhGangRiskGangRisk4uOߟ_|BhInfraRiskInfraRisk4uOߟ_|BhDrugRiskDrugRisk4uOߟ_|BhSchoolRiskSchoolRisk4uOߟ_|BhBusRiskBusRisk4uOߟ_|BhWRiskValueWRiskValue4uOߟ_|Bh WGangRiskWGangRisk4uOߟ_|Bh!WInfraRiskWInfraRisk4uOߟ_|Bh"WDrugRiskWDrugRisk4uOߟ_|Bh#WSchoolRskWSchoolRsk4uOߟ_|Bh$WBusRiskWBusRiskM$wv-%'nM$wv-& Shape5uOߟ_|Bh'4uOߟ_|Bh( Shape ShapeR C9x)gb*Q "s Shaperp0R`gb*Q "s\.gDo+UP*FIDFIDShapeShape RiskValue RiskValue AnyP2Shoot AnyP2ShootGangRiskGangRisk InfraRisk InfraRiskDrugRiskDrugRisk SchoolRisk SchoolRiskBusRiskBusRisk WRiskValue WRiskValue WGangRisk WGangRisk WInfraRisk WInfraRisk WDrugRisk WDrugRisk WSchoolRsk WSchoolRskWBusRiskWBusRisk@hCzt{nf+* +DkbV, GPTableView Table View Table View Data Typex/ -DAW+OIF)* +DkbV. GPRasterLayer Raster Layer Raster Layer Data Typex/ /DAW+OIF)g:M|I~ 0Select Layer By Location (2)|YmI<,A/1Ӳ NlCn2Ӳ NlCn3Ӳ NlCn4d[ERROR 000732: Selecting Features: Dataset Outcome_Events does not exist or is not supportedӲ NlCn5Ӳ NlCn6Ӳ NlCn7#c8Dæi8SelectLayerByLocationSelect Layer By Location-Update the selection of the layer by locationLayers and Table ViewsGKcI9Data Management Tools5ZqO::rC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\ToolboxesTOOLBOX: Workspace = \\JOELCAPLAN\C$\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes;Toolbox DataZX|O:;DATABASErC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes1#4*)G9gK1ItrOG^aa-<in_layerInput Feature Layer@hCzt{nf=* +DkbV>GPFeatureLayer Feature Layer Feature Layer Data Typex/ ?DAW+OIF)* +DkbV@GPRasterCatalogLayerRaster Catalog LayerRaster Catalog Layer Data Typex/ ADAW+OIF)* +DkbVB GPMosaicLayer Mosaic Layer Mosaic Layer Data Typex/ CDAW+OIF) M]mjDe:2.EItrOG^aa-E overlap_type Relationship* +DkbVFGPStringStringString Data Typex/ 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Linear unit Linear unit Data Typex/ eDAW+OIF)ItrOG^aa-fselection_typeSelection type* +DkbVgGPStringStringString Data Typex/ hDAW+OIF)qfK5YiNEW_SELECTION"ADD_TO_SELECTION,REMOVE_FROM_SELECTION"SUBSET_SELECTION"SWITCH_SELECTION34jgJ$ jNEW_SELECTION34jgJ$ k"ADD_TO_SELECTION34jgJ$ l,REMOVE_FROM_SELECTION34jgJ$ m"SUBSET_SELECTION34jgJ$ n"SWITCH_SELECTIONe:2.Eo 34jgJ$ pNEW_SELECTION* +DkbVqGPStringStringString Data Typex/ rDAW+OIF)ItrOG^aa-sout_layer_or_viewOutput Layer Name@hCzt{nft* +DkbVuGPFeatureLayer Feature Layer Feature Layer Data Typex/ vDAW+OIF)* +DkbVwGPRasterCatalogLayerRaster Catalog LayerRaster Catalog Layer Data Typex/ xDAW+OIF)* +DkbVy GPMosaicLayer Mosaic Layer Mosaic Layer Data Typex/ zDAW+OIF)in_layer_or_viewe:2.ErS+*Fњ6{g:M|I~ | Add Field|YmI<,A/} Ӳ NlCn~dYERROR 000732: Input Table: Dataset StudyAreaVectorGrid does not exist or is not supportedӲ NlCnӲ NlCnӲ NlCnӲ NlCnӲ NlCnӲ NlCnӲ NlCnӲ NlCnӲ NlCnӲ NlCn#c8DæiAddField Add Field'Add a field to a table or feature 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"sPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?MbP?MbP?4uOߟ_|BhStatusStatus4uOߟ_|Bh CRIME CRIME!4uOߟ_|Bh DATE_ DATE_4uOߟ_|BhDAYDAYM$wv- Shaperp0R`6,!A2%A^v!Aa[V2%Agb*Q "sPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?MbP?MbP?gb*Q "sPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?MbP?MbP?G8lGKv\vVC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\OtherFiles\Irvington_Shootings_Jan_June2007.shpJIrvington_Shootings_Jan_June2007.shpDEShapeFileFID5uOߟ_|Bh4uOߟ_|BhFIDFID4uOߟ_|Bh Shape ShapeR C9xgb*Q "sPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?MbP?MbP?4uOߟ_|BhStatusStatus4uOߟ_|Bh CRIME CRIME!4uOߟ_|Bh DATE_ DATE_4uOߟ_|BhDAYDAYM$wv- Shaperp0R`6,!A2%A^v!Aa[V2%Agb*Q "sPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?MbP?MbP?gb*Q "sPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?MbP?MbP?\.gDo+UPFIDFIDShapeShapeStatusStatusCRIMECRIMEDATE_DATE_DAYDAY {942CEE24-235E-4391-AFD2-480F331CB82C}2011043002363600TRUE20110813211927C:\Program Files\ArcGIS\Desktop10.0\Help\gpAdd and Populate AnyEvents FieldArcToolbox Tool 0 0 0"/><justification value="CENTER"/><maxWidth value="32767"/><fillColor value="255 255 255"/><borderColor value="0 0 0"/><bo      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijkopqrstuvwxyz{|}~!PrepareTabularDataForSelectingRMLSpecify Risky 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PROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Aj@j@hqgj?h㈵>h㈵>4uOߟ_|Bh FIDFID4uOߟ_|Bh POINTIDPOINTID4uOߟ_|BhGRID_CODEGRID_CODEM$wv- Shaperp0R` ߜ!AK$A ߜIy!AK<%Agb*Q "sPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?gb*Q "s * +DkbVDEFeatureClass Feature Class Feature Class Data Typex/ dmE[A۶e:2.E RML_HighRisk_Points|YmI<,A/m=CJb.RasterT_rclass_1_LayerG8lGKv\vHC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\BackupRiskMapLayers\RasterT_rclass_1.shp*RasterT_rclass_1.shpDEShapeFileFID5uOߟ_|Bh4uOߟ_|Bh Shape ShapeR C9x2@@gb*Q "s 4uOߟ_|BhFIDFID4uOߟ_|BhPOINTIDPOINTID4uOߟ_|BhGRID_CODEGRID_CODEM$wv- Shaperp0R`gb*Q "s G8lGKv\vHC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\BackupRiskMapLayers\RasterT_rclass_1.shp*RasterT_rclass_1.shpDEShapeFileFID5uOߟ_|Bh 4uOߟ_|Bh! Shape ShapeR C9x"2@@gb*Q "s 4uOߟ_|Bh#FIDFID4uOߟ_|Bh$POINTIDPOINTID4uOߟ_|Bh%GRID_CODEGRID_CODEM$wv-& Shaperp0R`gb*Q "s \.gDo+UP'ShapeShapeFIDFIDPOINTIDPOINTID GRID_CODE GRID_CODE@hCzt{nf(* +DkbV) GPTableView Table View Table View Data Typex/ *DAW+OIF)* +DkbV+ GPRasterLayer Raster Layer Raster Layer Data Typex/ ,DAW+OIF)e:2.E-  Output Layer|YmI<,A/.m=CJb/.RasterT_rclass_1_LayerG8lGKv\v0HC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\BackupRiskMapLayers\RasterT_rclass_1.shp*RasterT_rclass_1.shpDEShapeFileFID5uOߟ_|Bh14uOߟ_|Bh2 Shape ShapeR C9x32@@gb*Q "s 4uOߟ_|Bh4FIDFID4uOߟ_|Bh5POINTIDPOINTID4uOߟ_|Bh6GRID_CODEGRID_CODEM$wv-7 Shaperp0R`gb*Q "s G8lGKv\v0\.gDo+UP8ShapeShapeFIDFIDPOINTIDPOINTID GRID_CODE GRID_CODE* +DkbV9GPFeatureLayer Feature Layer Feature Layer Data Typex/ :DAW+OIF)e:2.E; %1Reclassified Binary Valued Risk Map (Input Layer)|YmI<,A/<Ӳ NlCn=2The value is empty.y"1淇G%͔h>* +DkbV? GPRasterLayer Raster Layer Raster Layer Data Typex/ @DAW+OIF)e:2.EA '$Study Area Vector Grid (Input Layer)|YmI<,A/BӲ NlCnC2The value is empty.m=CJbD* +DkbVEGPFeatureLayer Feature Layer Feature Layer Data Typex/ FDAW+OIF)e:2.EG ?+Name of Field to Specify Highest Risk Place|YmI<,A/HӲ NlCnI2The value is empty.34jgJ$ J* +DkbVKGPStringStringString Data Typex/ LDAW+OIF)ee:2.EM DOutput Feature Class|YmI<,A/NϠJ+2SO@hCzt{nfP* +DkbVQ GPTableView Table View Table View Data Typex/ RDAW+OIF)* +DkbVS GPRasterLayer Raster Layer Raster Layer Data Typex/ TDAW+OIF)e:2.EU GRiskTerrain Map with New Field|YmI<,A/VϠJ+2SW@hCzt{nfX* +DkbVY GPTableView Table View Table View Data Typex/ ZDAW+OIF)* +DkbV[ GPRasterLayer Raster Layer Raster Layer Data Typex/ \DAW+OIF)e:2.E] H.Select the Field Name that You Specified Above|YmI<,A/^Ӳ NlCn_2The value is empty.4uOߟ_|Bh`* +DkbVaFieldFieldField Data Typex/ bdmE[A۶eg:M|I~ c Select Layer By Location (2)|YmI<,A/dӲ NlCned4ERROR 000735: Input Feature Layer: Value is requiredӲ NlCnfӲ NlCngӲ NlCnhӲ NlCniӲ NlCnj#c8DæikSelectLayerByLocationSelect Layer By Location-Update the selection of the layer by locationLayers and Table ViewsGKcIlData Management Tools5ZqO:mrC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\ToolboxesTOOLBOX: Workspace = \\JOELCAPLAN\C$\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes;Toolbox DataZX|O:nDATABASErC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes1#4*)G9gK1ItrOG^aa-oin_layerInput Feature Layer@hCzt{nfp* +DkbVqGPFeatureLayer Feature Layer Feature Layer Data Typex/ rDAW+OIF)* +DkbVsGPRasterCatalogLayerRaster Catalog LayerRaster Catalog Layer Data Typex/ tDAW+OIF)* +DkbVu GPMosaicLayer Mosaic Layer Mosaic Layer Data Typex/ vDAW+OIF) M]mjwe:2.EAItrOG^aa-x overlap_type Relationship* +DkbVyGPStringStringString Data Typex/ zDAW+OIF)qfK5Y{INTERSECTINTERSECT_3D$WITHIN_A_DISTANCE*WITHIN_A_DISTANCE_3DCONTAINS(COMPLETELY_CONTAINS(CONTAINS_CLEMENTINIWITHIN$COMPLETELY_WITHIN$WITHIN_CLEMENTINI"ARE_IDENTICAL_TO"BOUNDARY_TOUCHES4SHARE_A_LINE_SEGMENT_WITH4CROSSED_BY_THE_OUTLINE_OF*HAVE_THEIR_CENTER_IN34jgJ$ |INTERSECT34jgJ$ }INTERSECT_3D34jgJ$ ~$WITHIN_A_DISTANCE34jgJ$ *WITHIN_A_DISTANCE_3D34jgJ$ CONTAINS34jgJ$ (COMPLETELY_CONTAINS34jgJ$ (CONTAINS_CLEMENTINI34jgJ$ WITHIN34jgJ$ $COMPLETELY_WITHIN34jgJ$ $WITHIN_CLEMENTINI34jgJ$ "ARE_IDENTICAL_TO34jgJ$ "BOUNDARY_TOUCHES34jgJ$ 4SHARE_A_LINE_SEGMENT_WITH34jgJ$ 4CROSSED_BY_THE_OUTLINE_OF34jgJ$ *HAVE_THEIR_CENTER_INe:2.E 34jgJ$ INTERSECT* +DkbVGPStringStringString Data Typex/ ˎDAW+OIF)ItrOG^aa-select_featuresSelecting Features* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ ˑDAW+OIF)e:2.EItrOG^aa-search_distanceSearch Distance* +DkbV GPLinearUnit Linear unit Linear unit Data Typex/ ˔DAW+OIF)in_layere:2.E -AL!iޭ<$* +DkbV GPLinearUnit Linear unit Linear unit Data Typex/ ˘DAW+OIF)ItrOG^aa-selection_typeSelection type* +DkbVGPStringStringString Data Typex/ ˛DAW+OIF)qfK5YNEW_SELECTION"ADD_TO_SELECTION,REMOVE_FROM_SELECTION"SUBSET_SELECTION"SWITCH_SELECTION34jgJ$ NEW_SELECTION34jgJ$ "ADD_TO_SELECTION34jgJ$ ,REMOVE_FROM_SELECTION34jgJ$ "SUBSET_SELECTION34jgJ$ "SWITCH_SELECTIONe:2.E 34jgJ$ NEW_SELECTION* +DkbVGPStringStringString Data Typex/ ˥DAW+OIF)ItrOG^aa-out_layer_or_viewOutput Layer Name@hCzt{nfݧ* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ ˩DAW+OIF)* +DkbVGPRasterCatalogLayerRaster Catalog LayerRaster Catalog Layer Data Typex/ ˫DAW+OIF)* +DkbV GPMosaicLayer Mosaic Layer Mosaic Layer Data Typex/ ˭DAW+OIF)in_layer_or_viewe:2.ErS+*Fњ6g:M|I~ ЯRaster to Point|YmI<,A/Ӳ NlCnd[gERROR 000859: The required parameter Input raster is empty, or is not the type of Composite Geodataset.Ӳ NlCnӲ NlCn#c8Dæi RasterToPointRaster to Point*Converts raster data to point feature data From RasterGKcIConversion Tools5ZqO:rC:\Program 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Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes;Toolbox DataZX|O:DDATABASErC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes1#4*)G9gK ItrOG^aa-Ein_table Input Table@hCzt{nfF* +DkbVG GPTableView Table View Table View Data Typex/ HDAW+OIF)* +DkbVI GPRasterLayer Raster Layer Raster Layer Data Typex/ JDAW+OIF)[t%.IU-XKe:2.EItrOG^aa-L field_name Field Name* +DkbVMGPStringStringString Data Typex/ NDAW+OIF)e:2.EGItrOG^aa-O field_type Field Type* +DkbVPGPStringStringString Data Typex/ QDAW+OIF)qfK5YR TEXT FLOATDOUBLE SHORT LONG DATE BLOBRASTER34jgJ$ S TEXT34jgJ$ T FLOAT34jgJ$ UDOUBLE34jgJ$ V SHORT34jgJ$ W LONG34jgJ$ X DATE34jgJ$ Y BLOB34jgJ$ ZRASTERe:2.E[ 34jgJ$ V* +DkbV\GPStringStringString Data Typex/ ]DAW+OIF)ItrOG^aa-^field_precisionField Precision* +DkbV_GPLongLong Long integer Data Typex/ `DAW+OIF)e:2.Ea G0Lz tb* +DkbVcGPLongLong Long integer Data Typex/ dDAW+OIF)ItrOG^aa-e field_scale Field Scale* +DkbVfGPLongLong Long integer Data Typex/ gDAW+OIF)e:2.Eh G0Lz ti* +DkbVjGPLongLong 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Layer Data Typex/ DAW+OIF)y"1淇G%͔ho1?GW:.N_+Name_of_Field_to_Specify_Highest_Risk_Place+Name of Field to Specify Highest Risk Place* +DkbVGPStringStringString Data Typex/ DAW+OIF)34jgJ$ o1?GW:.N_.Select_the_Field_Name_that_You_Specified_Above.Select the Field Name that You Specified Above* +DkbVFieldFieldField Data Typex/ dmE[A۶Output Feature ClassאY(Hcj4uOߟ_|Bh {709EF2EA-A998-4822-8F26-09BF75B22F0E}2011042921585100TRUE20110813221402C:\Program Files\ArcGIS\Desktop10.0\Help\gpSpecify Risky PlacesArcToolbox Tool asterCatalogLayerRaster Catalog LayerRaster Catalog Layer      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnorstuvwxyz{|}~ProduceValuesForRSICalculationRSI CalculationThis tools selects features from two layers in ArcMap's Table of Contents to present the following information: (1) The number of cells in the "Vector Grid" that are influenced by the highest-risk places in the "Reclassified Binary Valued Risk Map Layer" AND that intersect with the "Outcome Event Bounds"; and, (2) The number of "Outcome Event" features that are located within with the selected cells of the "Vector Grid". ***** After running this tool, click the "Selection" tab in ArcMap's Table of Contents to see the number of selected features for each layer. ***** To calculate the Relative Spatial Influence (RSI) of the risk factor--e.g., for inter-risk map layer weighting (Step 8), divide the number of selected outcome event features by the number of selected cells in the risk terrain map. ***** Run this tool and repeat these steps for each risk map layer (RML) in your risk terrain model (RTM). For example, if your RTM has 5 risk map layers, run this tool 5 times--once for each RML. ***** Calculate weights for each risk map layer by dividing the RSI value of each risk map layer by the smallest RSI. Reclassify each risk map layer to assign the newly computed weights, respectively. hIF[akF1 e:2.E Select Location Outcome|YmI<,A/m=CJb* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)e:2.E  3RiskTerrainMap_for_OutcomeEventBounds_withSelection|YmI<,A/m=CJbNOutcome_Event_Bounded_Risk_Terrain_MapG8lGKv\vC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bh 4uOߟ_|Bh FIDFID4uOߟ_|Bh  Shape ShapeR C9x gb*Q "s PROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?MbP?MbP?4uOߟ_|BhRiskValueRiskValue4uOߟ_|BhAnyP2ShootAnyP2Shoot4uOߟ_|BhGangRiskGangRisk4uOߟ_|BhInfraRiskInfraRisk4uOߟ_|BhDrugRiskDrugRisk4uOߟ_|BhSchoolRiskSchoolRisk4uOߟ_|BhBusRiskBusRisk4uOߟ_|BhWRiskValueWRiskValue4uOߟ_|BhWGangRiskWGangRisk4uOߟ_|BhWInfraRiskWInfraRisk4uOߟ_|BhWDrugRiskWDrugRisk4uOߟ_|BhWSchoolRskWSchoolRsk4uOߟ_|BhWBusRiskWBusRiskM$wv-'nM$wv- Shape5uOߟ_|Bh4uOߟ_|Bh Shape ShapeR C9xgb*Q "s  Shaperp0R`  ߜI!AK$A ߜIy!AK<%Agb*Q "s!PROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Aj@j@hqgj?MbP?MbP?gb*Q "s G8lGKv\v"C:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bh#4uOߟ_|Bh$FIDFID4uOߟ_|Bh% Shape ShapeR C9x&gb*Q "s 4uOߟ_|Bh'RiskValueRiskValue4uOߟ_|Bh(AnyP2ShootAnyP2Shoot4uOߟ_|Bh)GangRiskGangRisk4uOߟ_|Bh*InfraRiskInfraRisk4uOߟ_|Bh+DrugRiskDrugRisk4uOߟ_|Bh,SchoolRiskSchoolRisk4uOߟ_|Bh-BusRiskBusRisk4uOߟ_|Bh.WRiskValueWRiskValue4uOߟ_|Bh/WGangRiskWGangRisk4uOߟ_|Bh0WInfraRiskWInfraRisk4uOߟ_|Bh1WDrugRiskWDrugRisk4uOߟ_|Bh2WSchoolRskWSchoolRsk4uOߟ_|Bh3WBusRiskWBusRiskM$wv-4'nM$wv-5 Shape5uOߟ_|Bh64uOߟ_|Bh7 Shape ShapeR C9x8gb*Q "s  Shaperp0R` gb*Q "s \.gDo+UP9FIDFIDShapeShape RiskValue RiskValue AnyP2Shoot AnyP2ShootGangRiskGangRisk InfraRisk InfraRiskDrugRiskDrugRisk SchoolRisk SchoolRiskBusRiskBusRisk WRiskValue WRiskValue WGangRisk WGangRisk WInfraRisk WInfraRisk WDrugRisk WDrugRisk WSchoolRsk WSchoolRskWBusRiskWBusRisk* +DkbV:GPFeatureLayer Feature Layer Feature Layer Data Typex/ ;DAW+OIF)e:2.E< Output point features|YmI<,A/=G8lGKv\v>@C:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\BackupRiskMapLayers\RasterT_rclass_1"RasterT_rclass_1DEShapeFileFID5uOߟ_|Bh?4uOߟ_|Bh@ Shape ShapeR C9xA2@@gb*Q "sBPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Aj@j@hqgj?h㈵>h㈵>4uOߟ_|BhCFIDFID4uOߟ_|BhDPOINTIDPOINTID4uOߟ_|BhEGRID_CODEGRID_CODEM$wv-F Shaperp0R`G ߜ!AK$A ߜIy!AK<%Agb*Q "sHPROJCS["NAD_1983_StatePlane_New_Jersey_FIPS_2900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",492125.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-74.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",38.83333333333334],UNIT["Foot_US",0.3048006096012192]]rqN_0+;\Ahqgj?gb*Q "sB* +DkbVIDEFeatureClass Feature Class Feature Class Data Typex/ JdmE[A۶e:2.EK RML_HighRisk_Points|YmI<,A/Lm=CJbM.RasterT_rclass_1_LayerG8lGKv\vN@C:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\BackupRiskMapLayers\RasterT_rclass_1"RasterT_rclass_1DEShapeFileFID5uOߟ_|BhO4uOߟ_|BhP Shape ShapeR C9xQ2@@gb*Q "sB4uOߟ_|BhRFIDFID4uOߟ_|BhSPOINTIDPOINTID4uOߟ_|BhTGRID_CODEGRID_CODEM$wv-U Shaperp0R`Ggb*Q "sBG8lGKv\vV@C:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\BackupRiskMapLayers\RasterT_rclass_1"RasterT_rclass_1DEShapeFileFID5uOߟ_|BhW4uOߟ_|BhX Shape ShapeR C9xY2@@gb*Q "sB4uOߟ_|BhZFIDFID4uOߟ_|Bh[POINTIDPOINTID4uOߟ_|Bh\GRID_CODEGRID_CODEM$wv-] Shaperp0R`Ggb*Q "sB\.gDo+UP^ShapeShapeFIDFIDPOINTIDPOINTID GRID_CODE GRID_CODE@hCzt{nf_* +DkbV` GPTableView Table View Table View Data Typex/ aDAW+OIF)* +DkbVb GPRasterLayer Raster Layer Raster Layer Data Typex/ cDAW+OIF)e:2.Ed  Output Layer|YmI<,A/em=CJbf.RasterT_rclass_1_LayerG8lGKv\vg@C:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\BackupRiskMapLayers\RasterT_rclass_1"RasterT_rclass_1DEShapeFileFID5uOߟ_|Bhh4uOߟ_|Bhi Shape ShapeR C9xj2@@gb*Q "sB4uOߟ_|BhkFIDFID4uOߟ_|BhlPOINTIDPOINTID4uOߟ_|BhmGRID_CODEGRID_CODEM$wv-n Shaperp0R`Ggb*Q "sBG8lGKv\vg\.gDo+UPoShapeShapeFIDFIDPOINTIDPOINTID GRID_CODE GRID_CODE* +DkbVpGPFeatureLayer Feature Layer Feature Layer Data Typex/ qDAW+OIF)e:2.Er @Selected Outcome Events that Intersect with High Risk RML Places|YmI<,A/sm=CJbt* +DkbVuGPFeatureLayer Feature Layer Feature Layer Data Typex/ vDAW+OIF)e:2.Ew  )Risk Terrain Map for Outcome Event Bounds|YmI<,A/xm=CJbyNOutcome_Event_Bounded_Risk_Terrain_MapG8lGKv\vzC:\Users\jcaplan\Desktop\Joel\Rutgers\Presentations\JJay RTM Workshop\RTMWorkshop_MaterialForDistribution\RTMworkshop_Data\DataForWeightingExample\Irvington_VectorRiskTerrainMap_ForWeighting.shp`Irvington_VectorRiskTerrainMap_ForWeighting.shpDEShapeFileFID5uOߟ_|Bh{4uOߟ_|Bh|FIDFID4uOߟ_|Bh} Shape ShapeR C9x~gb*Q "s 4uOߟ_|BhRiskValueRiskValue4uOߟ_|BhAnyP2ShootAnyP2Shoot4uOߟ_|BhGangRiskGangRisk4uOߟ_|BhInfraRiskInfraRisk4uOߟ_|BhDrugRiskDrugRisk4uOߟ_|BhSchoolRiskSchoolRisk4uOߟ_|BhBusRiskBusRisk4uOߟ_|BhWRiskValueWRiskValue4uOߟ_|BhWGangRiskWGangRisk4uOߟ_|BhWInfraRiskWInfraRisk4uOߟ_|BhWDrugRiskWDrugRisk4uOߟ_|BhWSchoolRskWSchoolRsk4uOߟ_|BhWBusRiskWBusRiskM$wv-'nM$wv- Shape5uOߟ_|Bh4uOߟ_|Bh Shape ShapeR C9xgb*Q "s  Shaperp0R` gb*Q "s G8lGKv\vz\.gDo+UPFIDFIDShapeShape RiskValue RiskValue AnyP2Shoot AnyP2ShootGangRiskGangRisk InfraRisk InfraRiskDrugRiskDrugRisk SchoolRisk SchoolRiskBusRiskBusRisk WRiskValue WRiskValue WGangRisk WGangRisk WInfraRisk WInfraRisk WDrugRisk WDrugRisk WSchoolRsk WSchoolRskWBusRiskWBusRisk* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ ˓DAW+OIF)e:2.E %7Reclassified Binary Valued Risk Map Layer (Input Layer)|YmI<,A/Ӳ NlCn2The value is empty.y"1淇G%͔h* +DkbV GPRasterLayer Raster Layer Raster Layer Data Typex/ ˙DAW+OIF)e:2.E &"Outcome Event Bounds (Input Layer)|YmI<,A/Ӳ NlCn2The value is empty.m=CJb* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ ˟DAW+OIF)e:2.E 'Vector Grid (Input Layer)|YmI<,A/Ӳ NlCn2The value is empty.m=CJb* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ ˥DAW+OIF)e:2.E (Outcome Events (Input Layer)|YmI<,A/Ӳ NlCn2The value is empty.m=CJb* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ ˫DAW+OIF)e:2.E .Select Location 4 Output|YmI<,A/m=CJb* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ ˰DAW+OIF)g:M|I~ бSelect Layer By Location|YmI<,A/Ӳ NlCnd4ERROR 000735: Input Feature Layer: Value is requiredӲ NlCnӲ NlCnӲ NlCnӲ NlCnӲ NlCn#c8DæiSelectLayerByLocationSelect Layer By Location-Update the selection of the layer by locationLayers and Table ViewsGKcIData Management Tools5ZqO:rC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\ToolboxesTOOLBOX: Workspace = \\JOELCAPLAN\C$\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes;Toolbox DataZX|O:DATABASErC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes1#4*)G9gK1ItrOG^aa-in_layerInput Feature Layer@hCzt{nfݾ* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)* +DkbVGPRasterCatalogLayerRaster Catalog LayerRaster Catalog Layer Data Typex/ DAW+OIF)* +DkbV GPMosaicLayer Mosaic Layer Mosaic Layer Data Typex/ DAW+OIF) M]mje:2.EItrOG^aa- overlap_type Relationship* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5YINTERSECTINTERSECT_3D$WITHIN_A_DISTANCE*WITHIN_A_DISTANCE_3DCONTAINS(COMPLETELY_CONTAINS(CONTAINS_CLEMENTINIWITHIN$COMPLETELY_WITHIN$WITHIN_CLEMENTINI"ARE_IDENTICAL_TO"BOUNDARY_TOUCHES4SHARE_A_LINE_SEGMENT_WITH4CROSSED_BY_THE_OUTLINE_OF*HAVE_THEIR_CENTER_IN34jgJ$ INTERSECT34jgJ$ INTERSECT_3D34jgJ$ $WITHIN_A_DISTANCE34jgJ$ *WITHIN_A_DISTANCE_3D34jgJ$ CONTAINS34jgJ$ (COMPLETELY_CONTAINS34jgJ$ (CONTAINS_CLEMENTINI34jgJ$ WITHIN34jgJ$ $COMPLETELY_WITHIN34jgJ$ $WITHIN_CLEMENTINI34jgJ$ "ARE_IDENTICAL_TO34jgJ$ "BOUNDARY_TOUCHES34jgJ$ 4SHARE_A_LINE_SEGMENT_WITH34jgJ$ 4CROSSED_BY_THE_OUTLINE_OF34jgJ$ *HAVE_THEIR_CENTER_INe:2.E 34jgJ$ INTERSECT* +DkbVGPStringStringString Data Typex/ DAW+OIF)ItrOG^aa-select_featuresSelecting Features* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)e:2.EItrOG^aa-search_distanceSearch Distance* +DkbV GPLinearUnit Linear unit Linear unit Data Typex/ DAW+OIF)in_layere:2.E -AL!iޭ<$* +DkbV GPLinearUnit Linear unit Linear unit Data Typex/ DAW+OIF)ItrOG^aa-selection_typeSelection type* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5YNEW_SELECTION"ADD_TO_SELECTION,REMOVE_FROM_SELECTION"SUBSET_SELECTION"SWITCH_SELECTION34jgJ$ NEW_SELECTION34jgJ$ "ADD_TO_SELECTION34jgJ$ ,REMOVE_FROM_SELECTION34jgJ$ "SUBSET_SELECTION34jgJ$ "SWITCH_SELECTIONe:2.E 34jgJ$ NEW_SELECTION* +DkbVGPStringStringString Data Typex/ DAW+OIF)ItrOG^aa-out_layer_or_viewOutput Layer Name@hCzt{nf* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)* +DkbVGPRasterCatalogLayerRaster Catalog LayerRaster Catalog Layer Data Typex/ DAW+OIF)* +DkbV GPMosaicLayer Mosaic Layer Mosaic Layer Data Typex/ DAW+OIF)in_layer_or_viewe:2.ErS+*Fњ6g:M|I~  Select Layer By Location (2)|YmI<,A/Ӳ NlCnӲ NlCnӲ NlCnӲ NlCnӲ NlCnӲ NlCn#c8DæiSelectLayerByLocationSelect Layer By Location-Update the selection of the layer by locationLayers and Table ViewsGKcIData Management Tools5ZqO:rC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\ToolboxesTOOLBOX: Workspace = \\JOELCAPLAN\C$\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes;Toolbox DataZX|O:DATABASErC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes1#4*)G9gK1ItrOG^aa- in_layerInput Feature Layer@hCzt{nf * +DkbV GPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)* +DkbV GPRasterCatalogLayerRaster Catalog LayerRaster Catalog Layer Data Typex/ DAW+OIF)* +DkbV GPMosaicLayer Mosaic Layer Mosaic Layer Data Typex/ DAW+OIF) M]mje:2.EwItrOG^aa- overlap_type Relationship* +DkbVGPStringStringString Data Typex/ DAW+OIF)qfK5Ye:2.E 34jgJ$ INTERSECT* +DkbVGPStringStringString Data Typex/ DAW+OIF)ItrOG^aa-select_featuresSelecting Features* +DkbVGPFeatureLayer Feature Layer Feature Layer Data Typex/ DAW+OIF)e:2.EKItrOG^aa-search_distanceSearch Distance* +DkbV GPLinearUnit Linear unit Linear unit Data Typex/ DAW+OIF)in_layere:2.E -AL!iޭ<$ * +DkbV! GPLinearUnit Linear unit Linear unit Data Typex/ "DAW+OIF)ItrOG^aa-#selection_typeSelection type* +DkbV$GPStringStringString Data Typex/ %DAW+OIF)qfK5Y&NEW_SELECTION"ADD_TO_SELECTION,REMOVE_FROM_SELECTION"SUBSET_SELECTION"SWITCH_SELECTION34jgJ$ 'NEW_SELECTION34jgJ$ ("ADD_TO_SELECTION34jgJ$ ),REMOVE_FROM_SELECTION34jgJ$ *"SUBSET_SELECTION34jgJ$ +"SWITCH_SELECTIONe:2.E, 34jgJ$ -NEW_SELECTION* +DkbV.GPStringStringString Data Typex/ /DAW+OIF)ItrOG^aa-0out_layer_or_viewOutput Layer Name@hCzt{nf1* +DkbV2GPFeatureLayer Feature Layer Feature Layer Data Typex/ 3DAW+OIF)* +DkbV4GPRasterCatalogLayerRaster Catalog LayerRaster Catalog Layer Data Typex/ 5DAW+OIF)* +DkbV6 GPMosaicLayer Mosaic Layer Mosaic Layer Data Typex/ 7DAW+OIF)in_layer_or_viewe:2.ErS+*Fњ68g:M|I~ 9Raster to Point|YmI<,A/:Ӳ NlCn;d[gERROR 000859: The required parameter Input raster is empty, or is not the type of Composite Geodataset.Ӳ NlCn<Ӳ NlCn=#c8Dæi> RasterToPointRaster to Point*Converts raster data to point feature data From RasterGKcI?Conversion Tools5ZqO:@rC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\ToolboxesTOOLBOX: Workspace = \\JOELCAPLAN\C$\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes;Toolbox DataZX|O:ADATABASErC:\Program Files\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes1#4*)G9gK ItrOG^aa-B in_raster Input rastertIjtI O_C_oamuFpv@D* +DkbVEDERasterDatasetRaster DatasetRaster Dataset Data Typex/ F J~Jq;;* +DkbVG DERasterBand Raster Band Raster Band Data Typex/ H J~Jq;;* +DkbVI GPRasterLayer Raster Layer Raster Layer Data Typex/ JDAW+OIF)* +DkbVKGPRasterFormulatedFormulated RasterFormulated Raster Data Typex/ L5@;Iz[|* +DkbVMDEFeatureClass Feature Class Feature Class Data Typex/ NdmE[A۶* +DkbVOGPFeatureLayer Feature Layer Feature Layer Data Typex/ PDAW+OIF)* +DkbVQDETinTINTIN Data Typex/ RDINDAW+OIF)ItrOG^aa-?select_featuresSelecting Features* +DkbV@GPFeatureLayer Feature Layer Feature Layer Data Typex/ ADAW+OIF)e:2.EwItrOG^aa-Bsearch_distanceSearch Distance* +DkbVC GPLinearUnit Linear unit 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Toolbox #c8Dæi JoinOutcomes+Join Count of Outcome Events to Vector GridAssigns a new field to the Vector Grid and populates it with the number of outcome event features that intersect with each grid cell.GKcIRiskTerrainTools_V105ZqO:`C:\Users\jcaplan\Desktop\Rutgers\GIS_Data\ToolsTOOLBOX: Workspace = \\JCAPLAN-PC\Users\jcaplan\Desktop\Rutgers\GIS_Data\Tools;Toolbox DataZX|O:DATABASE`C:\Users\jcaplan\Desktop\Rutgers\GIS_Data\Tools1#4*)G9gK#c8Dæi ClipToStreetsClip Vector Grid to StreetsUse this tool to remove all cells from the vector grid that do not intersect with the street centerline features to which outcome events were geocoded to. (Important for testing predictive validity).GKcI#c8DæiClipToStudyAreaClip Vector Grid to Study AreakUse this tool to remove all cells from the vector grid that are located outside of the study area boundary.GKcI #c8DæiModel&Create Blank Vector Grid of Study AreaCreates a blank vector grid, with cells of the user specified value, that covers the area of the features in the study area extent layer.GKcI #c8DæiProduceValuesForRSICalculationRSI CalculationThis tools selects features from two layers in ArcMap's Table of Contents to present the following information: (1) The number of cells in the "Vector Grid" that are influenced by the highest-risk places in the "Reclassified Binary Valued Risk Map Layer" AND that intersect with the "Outcome Event Bounds"; and, (2) The number of "Outcome Event" features that are located within with the selected cells of the "Vector Grid". ***** After running this tool, click the "Selection" tab in ArcMap's Table of Contents to see the number of selected features for each layer. ***** To calculate the Relative Spatial Influence (RSI) of the risk factor--e.g., for inter-risk map layer weighting (Step 8), divide the number of selected outcome event features by the number of selected cells in the risk terrain map. ***** Run this tool and repeat these steps for each risk map layer (RML) in your risk terrain model (RTM). For example, if your RTM has 5 risk map layers, run this tool 5 times--once for each RML. ***** Calculate weights for each risk map layer by dividing the RSI value of each risk map layer by the smallest RSI. Reclassify each risk map layer to assign the newly computed weights, respectively.GKcI #c8Dæi!PrepareTabularDataForSelectingRMLSpecify Risky PlacesUse this tool to identify the places in the vector grid that are the highest risk according to the high risk places of the operationalized risk map layer.GKcI#c8Dæi  NoteAnyEvents Add and Populate AnyEvents FieldCreates and populates a field in the attribute table of the vector grid that notes whether one or more outcome events are located within each grid cell.GKcI#c8Dæi  KernelDensityKernel Density'Spatial Analyst KernelDensity FunctionsRaster Processing ToolsGKcI#c8Dæi  RasterToPointRaster to Point*Converts raster data to point feature dataRaster Processing ToolsGKcI#c8Dæi  Reclassify Reclassify#Spatial Analyst Reclassify functionRaster Processing ToolsGKcI#c8Dæi  EucDistanceEuclidean Distance%Spatial Analyst EucDistance FunctionsRaster Processing ToolsGKcI#c8Dæi WeightedSum Weighted Sum%Spatial Analyst Weighted Sum functionRaster Processing ToolsGKcI#c8DæiZonalStatisticsZonal Statistics)Spatial Analyst ZonalStatistics FunctionsRaster Processing ToolsGKcI#c8DæiRasterCalculatorRaster Calculator2Execute a map algebra statement to create a rasterRaster Processing ToolsGKcI#c8DæiNROppsNear Repeat OpportunitiesGKcI#c8DæiClipClipclips a rasterRaster Processing ToolsGKcI #c8DæiClustersOutliers5Cluster and Outlier Analysis (Anselin Local Morans I)hGiven a set of weighted data points, identifies spatial clusters of extreme values and spatial outliers.Analysis ToolsGKcI!#c8DæiDirectionalDistribution7Directional Distribution (Standard Deviational Ellipse)IMeasures whether a distribution of features exhibits a directional trend.Analysis ToolsGKcI"#c8DæiGenerateSpatialWeightsMatrixGenerate Spatial Weights MatrixJCreates a spatial weights matrix (*.swm) file from an input feature class.Analysis ToolsGKcI##c8Dæi GeographicallyWeightedRegression"Geographically Weighted Regression"Geographically Weighted RegressionAnalysis ToolsGKcI$#c8DæiHotSpots!Hot Spot Analysis (Getis-Ord Gi*)[Calculates the Getis-Ord Gi* statistic to identify spatial clusters of high and low values.Analysis ToolsGKcI%#c8DæiOrdinaryLeastSquaresOrdinary Least Squares@Computes a linear regression model using Ordinary Least Squares.Analysis ToolsGKcI&#c8DæiSpatialAutocorrelation"Spatial Autocorrelation (Morans I)QMeasures spatial autocorrelation based on feature locations and attribute values.Analysis ToolsGKcI'#c8Dæi"ConvertSpatialWeightsMatrixtoTable(Convert Spatial Weights Matrix to Table >Converts a Spatial Weights Matrix (*.swm) to a database table.Analysis ToolsGKcI(#c8DæiModel1ModelRaster Processing ToolsGKcI*#c8DæiModel2Model 2Raster Processing ToolsGKcI+#c8DæiModel3 Model 3_goodRaster Processing ToolsGKcI,#c8Dæi"ConvertRastertoVectorGridRev042012#Convert Raster Layer To Vector GridThis tool converts a raster map layer into a vector grid of equally-sized polygonal cells and records the value for each cell (i.e., as the "GRID_CODE" variable). IMPORTANT: In the "Cell Size" text boxes, enter the exact same cell size that exists for the input raster.GKcI-