Notwithstanding the astounding growth achieved by Geographic Information Systems (GIS) in recentdecades,some criticalbottlenecks still continue to pose challenges to the advancement of this technology.The inability to handle large and diverse datasets and the lack of functionalities to solve complex spatial problems such as combinatorial location problems is essential one among these hurdles.Recent advances in Computational Intelligence (CI) and Operations Research have, however, opened up new avenues to overcome these obstacles to the development of GIS cience.Judicious integration of the aforementioned techniques into GIS cience could possibly lead to an innovative discipline,namely spatial intelligence. This paper presents our prelmi inary investigation on this subject, including its framework,major acquisitionmethods, and sample applications. The basic concept of spatial intelligence derived from psychology refers to themental process associated with the brain’s attempts to interpret certain types of information received. This\ninformation basically includes anykind ofmental input, such asvisualpictures, maps, and plans. Based on this concept,we propose that the introduction of spatial intelligencewithin the domain of GIS cience is an ability to discover and apply spatial patterns, which is usually elicited through analysis/mining, optmi ization, and smi ulation. Two characteristics of spatial intelligence are highlighted here. One is the ability of spatial cognition, and the other is the self learning capability. Spatialcognition refers to the processofrecognizing, encoding, saving, expressing, decomposing, constructing and generalizing spatialobjects, which can be obtained from spatialobservation, spatial perception, spatial indexing, and spatial deductive inference. Self learning includes enforcing learning, adaptive learning, and knowledge acquiring abilities to actively dig up knowledge from observation data. Promoting spatial intelligence is a logical requirement forhigher level analysis and application ofGIScience. Through active learning and searching process in complex spatiotemporal data,spatial intelligence discovers unknown spatial patterns, trends, and regularities.From the technical perspective, we emphasize the use ofmeta-heuristic and other intelligent algorithms to address complex geospatial problems. A three-tiered structure is proposed for the spatial intelligence framework. At the bottom of the framework, spatial statistics,programming, and intelligence computation are used to provide the foundation of spatial analysis, smi ulation, and optmi ization. The middle level consists of spatial intelligence, self learning, and spatial cognition foranalyzing, smi ulating, interpreting, and decisionmaking forgeospatialprocesses and phenomena. At the top of the framework, GIScience laws and regularities are used to mine unknown patterns. To realize the goal of spatial intelligence, a solid research that integrates key topics with the concepts and modeling approaches derived from Information Science andOperationsResearch to advanceGIS theories have been developed. The core supportingmethods and techniques pertinent to the proposed framework include spatial analysis, smi ulation, and optmi ization. The development of spatial analyticalmodels to represent spatial and temporal features and their relationships forms a vital aspectof this research. Spatial optmi ization is employed tomaxmi ize orminmi ize a planning objective, given the lmi ited area, finite resources, and spatial relationships for a location-specific problem,once spatiotemporal patterns have been discovered. Smi ulation is an mi portant tool to evaluate and mi provemodels and spatial patterns.Some successfulapplicationsofspatialanalysis, optmi ization, and smi ulation are also reported in thispaper. Logistic regression models,e.g.,binomia,l multinomia,l and Nested Logit,are applied and examined to predict various spatiotemporal changes, including rural to commercia,l rural to recreationa,l and rural to other land uses.A range of heuristic algorithms, such as Geneti Algorithms (GA) and Ant Colony Systems (Ant), to troubleshoot complex routing and location problems,andmulti-objective optmi ization forspatialdecisionmaking are studied.A case study of integrating agent-basedmodelingwith analyticalmodelsby drawing uponmicroscopic traffic smi ulations for emulating real-tmi e traffic conditions is also conducted.