Model data connections and make predictions

Insights for ArcGIS 2.3 released!

Available now and hot off the software press! Insights for ArcGIS 2.3 is available with ArcGIS Online and ArcGIS Enterprise. What’s new? Great question. Glad you asked. Here are the key features…

Link analysis allows you to establish relationships and view connections between related data elements. This lets you make sense of associations between people, places, events, and other entities. The relationships can be described by degrees, closeness, and betweenness.  A link map incorporates both the ability to see spider lines to show connected locations or, when it shows directionality, a flow map.  Here are some examples where link charts and link maps can be useful:

• Health: Where did patient zero come from, who infected whom, and how did the disease spread?
• Police intelligence: Where do gang members live versus get arrested? Using ballistics and casings – how are guns reused and circulated? Who is communicating through social media, and how well are they connected?
• Commercial: How is the salesforce supporting customers? What locations does a store or location serve?
• Manufacturing: What is the supply chain, what parts are used in the final product, and where have goods traveled?

See link charts and link maps in action:

Regression analysis, scatter plot matrix, and make predictions.  These three new capabilities are most powerful when used together within a workflow.

Regression analysis allows you to make predictions.  It’s one of the most standard and widely used statistical and regression models that analysts adhere to.  Insights allows you to create a scatterplot matrix of variables as part of the regression model building, to help you quickly determine a good model for explaining a dependent variable.  Then, Insights maps the correlation output statistics, providing a powerful way to view results.  Regressions models built in Insights can easily be used to make predictions of your dependent variable, using a simple drag and drop experience.  Here are some examples where regression could provide value to you:

• Natural resources: better understand what physical features produce a successful oil well, and then predict where a similar well could be drilled. Model when infrastructure needs to be repaired or replaced, and then predict maintenance before it breaks.
• Environmental:  what factors contribute to air pollution, and how much pollution is there going to be in the future?
• Local government:  understand how home values are related to demographics, crime, income, and schools.  Determine the economic factors influencing businesses to be successful, and where to locate new businesses

See regression in action: