Discovering Sericulture Clusters Through the Use of Local Indicators of Spatial Association (LISA)

Abstract

The aim of this study is to analyze the spatial pattern of sericulture in Türkiye with spatial analysis approaches, taking into account the data of 81 provinces, by creating a database based on Geographic Information Systems (GIS). This study employed data from the TURKSTAT, which included the amount of fresh cocoon produced and the silkworm egg boxes opened, between 2004-2022. In order to evaluate the relationship between the cocoon yield in each province and the cocoon yield in neighboring provinces, Moran's I measurements were made for the relevant time periods, and each period's Moran's I scatter plot was created. The Local Spatial Association Indicator (LISA) was used to assess the spatial structure of fresh cocoon yield. There was determined spatial clustering and a positive spatial association for cocoon yield, and all of the Moran's I values were found to be more than 0. The LISA maps were demonstrated the distinct local clustering patterns in the cocoon yield. More local clusters were identified (High-High or Low-Low) compared to local spatial outliers (High-Low or Low-High). High-High clusters were generally identified in the Marmara Region. Important implications have been obtained in terms of sustainability, such as how to take advantage of hotspot spillover effects and prioritize hotspot locations when allocating resources. As a result, spatial relations should be taken into account in the policies to be developed for sericulture and more activities should be carried out on sericulture.