Unplanned hospital readmission after a recent hospitalization is an indication of poor healthcare quality and a waste of healthcare resources. The Centers for Medicare and Medicaid Services (CMS) initiated the Hospital Readmission Reduction Program (HRRP) to improve healthcare quality and reduce costs; however, studies found the risk adjustment method used in calculating the standardized readmission rate was less accurate without hospital region or community factors. Accordingly, this cross-sectional quantitative study was designed to examine spatial patterns in hospital readmission rates following Andersen's behavioral model of health service utilization. This study was the first geospatial analysis on risk standardized hospital readmissions (RSRR) based on hospital geographic locations. Secondary data from the CMS was used in assessing the global and local geospatial cluster patterns using Global Moran's Index, Anselin local Moran's Index, and graphical analysis tool to identify cluster groups. The study found hospital-wide RSRR was significantly clustered across the country or at the local level. A total of 15 optimal cluster groups were identified with wide variability in cluster size. The hospital-wide and other seven CMS published RSRRs were significantly different among all clusters. The geographically bounded hospital RSRRs provided evidence in support of adding community or regional layer to risk adjustment of RSRR. The specific cluster groups with extremely high or low readmission rates can assist national and local policymakers and hospital administrators to identify specific targets to take actions. This research has social change implications for reducing hospital readmission rates and saving healthcare costs.