Abstract

This study aims to build a spatial model for selecting the optimal site for a radio astronomy observatory in southern Jordan. Geographic Information Systems (GISs) and Multi-Criteria Decision Analysis (MCDA)-based methodology were used in this study to develop a spatial model for choosing the best location for a radio astronomy observatory in southern Jordan. The criteria were weighted using a hybrid framework that combined the Analytic Hierarchy Process (AHP) and the entropy method to account for the actual spatial diversity of the data, in addition to expert judgment. The study assesses site suitability by considering several environmental and logistical factors that mitigate radio frequency interference (RFI), including elevation, cloud cover, artificial light pollution, and accessibility. A final map highlighting the optimal areas for radio astronomy observatories in southern Jordan has been created. The study methodology started with MCDA, and was followed by several stages, including visual evaluation, overlay analysis, establishment of 500 m buffer zones, extraction of the “Very High Suitability” class, and conversion to a transparent vector layer that is free from urban overlap and electromagnetic interference. The results show that the majority of large observatories (10 km2; equivalent to ≥10,000,000 m2) are located in Aqaba and Ma’an, which offer natural isolation and wide expanses ideal for global projects. Medium observatories (0.5–10 km2; equivalent to 500,000–10,000,000 m2) were generally identified at a reasonable cost in Ma’an and Aqaba, with the possibility of radio surveillance and infrastructure expansion. Many small observatories (0.01–0.5 km2; equivalent to 10,000–500,000 m2) were constructed near academic institutions, providing viable, easily accessible places for university research with little regulatory restraints. This research contributes to national astronomy infrastructure planning and serves as a model for other countries experiencing dry or semi-arid climates. It also offers decision-makers a useful spatial database.

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