Latest Contributions by ShitijMehta

In ModelBuilder, you can substitute the value or dataset path of a variable by enclosing the variable name between percent signs (%Variable Name%). Substituting variables in this manner is called inline variable substitution. There are three categories of such variables Any variable existing in a model System variables that are added in the product such as %scratchGDB%, %scratchFolder%, %t% for time or %n% for iteration number Workspace Environments such as %scratchworkspace% or %workspace% Examples of where inline variable substitution can be used includes: In an SQL query when the variable is exposed as a model parameter and the value is unknown until provided by the user. In any tool where there is no direct data type compatibility between an existing variable and a tool parameter. For example, In Calculate Field tool, when the value of the expression comes from a string variable which cannot be directly connected to the expression parameter. In Calculate Value tool where a variable data type can be casted to a different data type before passing it to another tool. In output path using system variables such as %scratchGDB% and %scratchFolder% that point to the project workspace. In output path using ModelBuilder workspace variables %scratchworkspace% and %workspace%, that point to the environment set in the model. In the output name if it needs a current time stamp %t% or an iteration number %n%. To understand more, check out this video created by our summer intern, Atreyi Guin.  Additional resources: Inline variable substitution help ... View more

ModelBuilder: Iteration, Nesting and Branching   This session will introduce Iteration, often referred to as looping, in ModelBuilder. Using examples, we will demonstrate how a process can be executed over and over using iteration with different settings or datasets, how to correctly use inline variable substitution and how to integrate an iterating model within another model. Do’s and Don’ts about iteration will be highlighted and we will introduce if-then-else logic, often referred to as branching. Finally, we will discuss how a process can be separated into two conditional branches and then merged at the end of your model.   Session Type: Technical Workshop   Topic: Spatial Analysis and Data Science Session Level: Advanced Wednesday, Jul 121:00 PM - 2:00 PM PDT Room 15 A | SDCC Friday, Jul 149:00 AM - 10:00 AM PDT Room 4 | SDCC ... View more

ModelBuilder: Tips and Tricks   This session will present various tips and tricks when using ModelBuilder with an emphasis on how to think about modeling problems and understanding which tools are best suited for the workflow with real case examples. Major topics covered are: inline variable substitution, creating choice lists and restricting input type, understanding compatible data type of a variable and the actual data, recasting data types, using model specific tools, creating finished outputs, finding help and finally, best practices for sharing models.   Session Type: Demo Theater Presentation   Topic: Spatial Analysis and Data Science Session Level: Intermediate Wednesday, Jul 1210:00 AM - 10:45 AM PDT  Expo Demo Theater 7 | SDCC ... View more

ModelBuilder: An Introduction and Best Practices   This session introduces you to ModelBuilder and shows how it can be used to create automated workflows by adding data & tools and connecting them or creating workflows from an existing project history. You will learn about model elements, types of tools, connections, environments and best practices to organize and document the model. The session also covers migration best practices, editing model schema, debugging methods, working through model reports and efficiency tips that can help you be more productive with your analytical workflows using ModelBuilder.   Session Type: Technical Workshop   Topic: Spatial Analysis and Data Science   Session Level: Beginner Tuesday, Jul 112:30 PM - 3:30 PM PDT Room 30 C | SDCC Thursday, Jul 138:30 AM - 9:30 AM PDT Room 3 | SDCC   ... View more