Notwithstanding the astounding growth achieved by Geographic Information Systems（GIS） in recent decades

some critical bottlenecks 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 GIScience.Judicious integration of the aforementioned techniques into GIScience could possibly lead to an innovative discipline

namely spatial intelligence.This paper presents our preliminary investigation on this subject

including its framework

major acquisition methods

and sample applications.The basic concept of spatial intelligence derived from psychology refers to the mental process associated with the brain’s attempts to interpret certain types of information received.This information basically includes any kind of mental input

such as visual pictures

maps

and plans.Based on this concept

we propose that the introduction of spatial intelligence within the domain of GIScience is an ability to discover and apply spatial patterns

which is usually elicited through analysis/mining

optimization

and simulation.Two characteristics of spatial intelligence are highlighted here.One is the ability of spatial cognition

and the other is the self learning capability.Spatial cognition refers to the process of recognizing

encoding

saving

expressing

decomposing

constructing and generalizing spatial objects

which can be obtained from spatial observation

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 for higher level analysis and application of GIScience.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 of meta-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

simulation

and optimization.The middle level consists of spatial intelligence

self learning

and spatial cognition for analyzing

simulating

interpreting

and decision making for geospatial processes 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 and Operations Research to advance GIS theories have been developed.The core supporting methods and techniques pertinent to the proposed framework include spatial analysis

simulation

and optimization.The development of spatial analytical models to represent spatial and temporal features and their relationships forms a vital aspect of this research.Spatial optimization is employed to maximize or minimize a planning objective

given the limited area

finite resources

and spatial relationships for a location-specific problem

once spatiotemporal patterns have been discovered.Simulation is an important tool to evaluate and improve models and spatial patterns.Some successful applications of spatial analysis

optimization

and simulation are also reported in this paper.Logistic regression models

e.g.

binomial

multinomial

and Nested Logit

are applied and examined to predict various spatiotemporal changes

including rural to commercial

rural to recreational

and rural to other land uses.A range of heuristic algorithms

such as Genetic Algorithms（GA） and Ant Colony Systems（Ant）

to troubleshoot complex routing and location problems

and multi-objective optimization for spatial decision making are studied.A case study of integrating agent-based modeling with analytical models by drawing upon microscopic traffic simulations for emulating real-time traffic conditions is also conducted.