<![CDATA[Geographic Artificial Intelligence (GeoAI) and Natural Language Processing (NLP) are two rapidly advancing technologies in spatial data analysis. GeoAI integrates artificial intelligence with geographic data to identify patterns, make predictions, and support decision-making. Meanwhile, NLP enables the processing and analysis of textual data related to spatial information, such as documents, reports, or geographic descriptions. The objective of this research is to obtain a representation of spatial data patterns through a series of processes, including problem identification, needs analysis, data collection and processing, document representation, and the application of Geographic Artificial Intelligence (GeoAI), Natural Language Processing (NLP), and Fuzzy Similarity methods to spatial or textual tax data and textual land data to identify data similarities. The research explores the integration of GeoAI and NLP in spatial data analysis to enhance the efficiency and accuracy of geographic data interpretation. The methods used in this study are based on artificial intelligence, which extracts spatial information from text and performs machine learning-based spatial analysis. The results demonstrate that the combination of GeoAI and NLP can improve the understanding of spatial patterns in unstructured data and support location-based decision-making processes. This research contributes to the development of more accurate spatial data analysis techniques that can be applied in various fields, such as urban planning, disaster management, and environmental analysis.]]>
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