Intelligent spatial decision support systems

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In the past fifty years, two significant methodological developments have transformed the study of human behavior in space and time. The first was the "quantitative revolution," which shifted geography from a descriptive to a scientific discipline, employing formal methods such as probability, statistics, and mathematical techniques to analyze spatial structures and processes under varying degrees of certainty. The second wave involves advancements in geographical information systems (GIS), which enhance geographers' capabilities in data storage, retrieval, analysis, and visualization. Both developments have profoundly influenced geographical studies and provided solutions to real-world spatio-temporal issues, finding applications in areas such as urban and regional planning, automated mapping, transportation management, and environmental management. A common thread between these advancements is their reliance on computer technology for data processing and analysis. However, until recently, there has been limited interaction between the two. Quantitative models have been largely developed without integrating the data models that represent spatial phenomena, leading to primitive displays of analysis results and communication challenges. Conversely, GIS was initially developed with minimal focus on incorporating robust analytical models.