AI Summary & Key Takeaways
AI InsightResults of the study showed that the methodology is capable of dealing with spatial and non-spatial characteristics of the data-set and determine the convenient alternatives.
📌 3 Key Takeaways
Özet
Fishing ports are the vital constituent of the fishery industry of Turkey. With governmental contribution, the number ofthe fishing ports reached to 366 alongside of the shores and the collected fish volume has been in increasing trend. However, there are differences in the location characteristics and technical infrastructure so that each facility’s success level is measured differently with convenient parameters. To increase the performance of fishing ports, for their better utilization, and also to understand which of them can be transformed to regional centers a classification is needed. With the transformation to the regional centers, i.e. the infrastructure improvements of the facilities and providing multiple services such as tourism and transportation activities; the efficiency of the ports can be increased. In this paper, a classification methodology is developed and it is tested. While applying the methodology, expert workshops are carried out to represent current fishery environment in Turkey. In the meetings, decision criteria are discussed by participating over 200 experts in the field. f-AHP and GIS/Spatial Analysis is used to analyze spatial suitability. Results of the study showed that the methodology is capable of dealing with spatial and non-spatial characteristics of the data-set and determine the convenient alternatives.