A geographic information system (GIS) can be used effectively for activities, programs, and analyses focused on equity and social justice (ESJ). Many types of inequities exist in society, but race and space are key predictors of inequity. A key concept of social justice is that any person born into society, no matter where they were born or live, will have an equitable opportunity to achieve successful life outcomes and to thrive. Geographic information science and its technologies (GIS&T) provide powerful tools to analyze equity and social justice issues and help government agencies apply an equity lens to every aspect of their administration. Given the reliance on spatial data to represent and analyze matters of ESJ, the use of these tools is necessary, logical, and appropriate. Some types of analyses and mapping commonly used with ESJ programs require careful attention to how data are combined and represented, risking misleading or false conclusions otherwise. Such outcomes could build mistrust when trust is most needed. A GIS-supported lifecycle for ESJ is presented that includes stages of exploratory issue analysis, community feedback, pro-equity programs analysis, management monitoring and stakeholder awareness, program performance metrics, and effectiveness analysis.
Regression analysis is a common statistical tool used to model relationships between variables and to explore the influencing factors underlying observed spatial data patterns. This entry focuses on the most basic form of regression model: linear regression. The notations, inference, assumptions, and diagnostics of linear regression are introduced, and interpretations of linear regression results are demonstrated using an empirical example in R software. The entry concludes with a brief discussion of the challenges of applying standard linear regression to spatial data.