The maintenance of a sustainable mechanism to curb the rate of overfishing in similar water zones is one of the major challenges facing artisan fisheries in small island regions. Several factors instigate the decision by local fishermen to potentially choose a location to explore. Obviously, the demand to address these triggers, particularly those with negative impact tendencies, has become paramount. This study, therefore, is aimed at mapping out fishing heat spots based on arrival, background of the fishermen, and available species. Subsequently, an evaluation is conducted to ascertain the criteria influencing the decision on a specific position and also to determine the consequences of their negative impact on marine resource sustainability and coastal community welfare. For these reasons, data from fishermen, sellers and local distributors were collected by using questionnaires, and analyzed to generate useful information on related fishing ground. Furthermore, experts were involved in providing expert assessment for Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL). The results showed significant insights and therefore offer support to help regulators preserve marine resources, especially by updating their decision making capacity for increased coastal community welfare. The direct implication of this research also serves as a basis for conducting traditional fisheries, estimated not only to meet economic impact, but also promote sustainable environment.

One time series model developed to predict economic data is Spatial Vector Autoregressive with Exogenous Variables (SpVARX). This model can accommodate simultaneously the interrelationships between variables, the impact of exogenous variables, and Inter-regional linkages. However, this model has not adjusted the nonlinearity relationships between variables. The relationship between economic variables is usually not linear. Therefore, we introduce the Threshold Spatial Vector Autoregressive with exogenous variables (TSpVARX). This paper assesses the forecasting performance of TSpVARX and compares it with SpVARX models. We conducted a simulation study by generating 100 times the simulation data with twelve scenarios. We found that the forecasting performance of the TSpVARX model is better than SpVARX when there is a nonlinear relationship between variables. In addition, we find that the forecasting performance of TSpVARX models will improve as the sample size increases.