This workshop will show how Esri Community Analyst can provide a deep understanding of a community’s demographics and health needs.  Community Analyst is an online application hosted by Esri that contains thousands of demographic, business, behavioral and health variables that can be visualized and summarized in reports for any area desired. This is useful for anticipating the changing health needs of the community, determining whether underserved populations are being addressed, and creating new ways to reach those in need of services.

Attendees will learn about the different types of data available, as well as how to leverage the data for multiple purposes, including using Tapestry segmentation data to improve outreach, substantiating grant funding applications with demographic data, determining where to target health programs to have the most impact, and being able show that vulnerable populations are being served.

This session will introduce you to basic concepts of spatial pattern analysis using tools in the Spatial Statistics toolbox. You’ll learn how these tools can help you summarize and evaluate geographic distributions, identify statistically significant spatial outliers and spatial clusters (hot spots), and assess broad geographic patterns and trends over time. With examples from a range of application areas such as epidemiology and demographics, the tools will help you to find patterns and relationships in your data, facilitating discussion, contributing to research, and informing decision making.

Regression analysis is a set of statistical methods used in many application areas (e.g., business, defense, education, health and human services, natural resources, and public safety.). Ordinary least squares regression (OLS) and geographically weighted regression (GWR) allow you to examine, model, and explore data relationships. Ultimately, regression analysis helps you answer “why?” questions: “why do we see so much disease in particular areas?”, “what are the factors that contribute to consistently high fitness rates?”, “why are screening rates so low in particular regions of the country?” Regression analysis also allows you to predict spatial outcomes for other places or other time periods: “how will improvements in road conditions impact traffic fatalities?”, “how will projected population growth affect the demand for health services?”. We will cover basic regression analysis concepts and workflows as they relate to the analysis of geographic data. You will learn how to build a properly specified OLS model, interpret regression results and diagnostics, and potentially use the results of regression analysis to design targeted interventions.

More detailed than the first two sessions, this workshop will present an analytical workflow from start to finish. You’ll begin with hot spot analysis to answer “where?”—creating a map of statistically significant hot and cold spots. Then you’ll move to regression analysis to answer “why?”—Identifying key factors contributing to the observed spatial pattern. Your focus, however, is in the details. Before you run a hot spot analysis, you must determine a proper analytical scale for your research. We’ll discuss the art and science of finding a proper scale, and introduce you to tools that help accomplish this. Before you can trust results from regression analysis, you need to find all the key explanatory variables for what you are trying to model. We’ll give you strategies and tools to for finding them so you can build a model that meets all assumptions of the regression method. You’ll learn, at a deeper level, how spatial pattern analysis and regression tools can be applied to your own work. While not required, first attending the Spatial Pattern and Regression Analysis workshops will help you get more out of thi