TWO REGIONALIZATIONS OF ARTISANAL FISHERIES IN MEXICO USING THE KRIGING METHODOLOGY USING SOCIAL AND ECONOMIC VARIABLES

Abstract

Objective: Generate two regionalization proposals for the coastal areas of Mexico using social and economic variables of fishing.

Design/methodology/approach: Socioeconomic data from fisheries in Mexico are analyzed, generating two regionalization works with different variables. The first study makes use of the data from the Statistical Yearbook of Aquaculture and Fisheries proper from the production value section. The second work uses data collected in the field of the situation of artisanal fishing cooperativism in BCS. Subsequently, in both works, a visual georeferencing methodology linked to a database was applied, which was recategorized into nominal and ordinal statistical values, as appropriate, using a Geographical Information System (GIS) ArcView 3.2. Lastly, apply a geostatistical analysis derived from the Kriging tool.

Results: Two regionalizations of artisanal fishing in Mexico are presented, visualized on maps made up of vector data. The first regionalization map is based on the economic criterion of production value by federative entity at national level classified in four zones with different fishing priority. The second regionalization is of a social and organizational nature, showing a classification of artisanal fishing cooperativism present in the fishing towns of northern Baja California Sur (BCS)

Limitations/implications: The lack of socioeconomic data of the fisheries in Mexico has been an important limitation to generate a deeper regionalization of the Mexican coasts. It is necessary to lay the foundations to create analyzes with a systemic approach that combine the multiplicity of quantitative and qualitative variables that are limited to the context of fishing.

Finding/conclusions: Analyzing the social and organizational factors of artisanal fishing is necessary for understanding the marine socio-ecosystems of our country. These criteria together with the use of computer tools allowed the regional geolocation of areas that share similar characteristics. Both regionalizations presented here share the same methodology with a different use of variables, so the efficiency of using Kriging as a multi-specific analysis tool can be verified.