Spatial modeling of migration using GIS‐based multi‐criteria decision analysis: A case study of Iran

Abstract

Spatial modeling of migration and the identification of the effective parameters are imperative for planning and managing demographic, economic, social, and environmental changes on various geographical scales. The recent climate change stressors as well as inequality in terms of education and life quality have triggered internal mass migrations in Iran, causing pressure on housing, the job market, and potential slums around large cities. This study proposes a new approach to modeling migration patterns in Iran based on multi‐criteria decision analysis. For this purpose, a total of 23 individual criteria embedded within four criteria groups (economic, socio‐cultural, welfare, and environmental) affecting national migration were used. The analytic hierarchy process was employed to determine weights for the input factors and the weighted linear combination (WLC) model was used for the integration of criteria, based on which maps of migration potential were produced. The model applied was evaluated based on the correlation coefficient between migration potential values obtained from the WLC model and the actual net migration rate. Among the input individual criteria, unemployment, higher education centers, number of physicians, and dust storms were found to influence national migration. Furthermore, our findings reveal that the potential for migration across Iranian provinces is heterogeneous, with the spatial potential for emigration being the highest and lowest in the border and central provinces, respectively. The correlation coefficient calculated between outputs from the WLC model and the net migration rate from 2011 to 2016, was .81, indicating the relatively high performance of the proposed model in producing a migration spatial potential map. Our proposed approach, along with the results achieved, can be useful to decision‐makers and planners in designing data‐driven policies against inequality‐ and climate‐induced stressors.

Quantifying the Impact of Dust Sources on Urban Physical Growth and Vegetation Status: A Case Study of Saudi Arabia

“…In the third step, standardized values of effective criteria and corresponding weights were aggregated by means of the WLC model [68][69][70] to identify SADSF (Equation ( 1)).…”

Optimizing Emergency Shelter Selection in Earthquakes Using a Risk-Driven Large Group Decision-Making Support System

“…GIS can store, manipulate, analyze, and visualize geospatial information in creative ways. However, multi-criteria decision making provides a set of methods for analyzing, evaluating, and prioritizing options that can be used to solve decision-making problems [23][24][25][26]. It is relevant to note that criteria selection may vary, but generally include safety (distance from hazardous facilities), accessibility (road network, distance from critical facilities), and operational efficiency (capacity, area) [27][28][29].…”

A Scenario-Based Multi-Criteria Decision-Making Approach for Allocation of Pistachio Processing Facilities: A Case Study of Zarand, Iran

“…The combination of GIS-MCDA has been used for various purposes such as the assessment of land suitability [11,[22][23][24][25], renewable energy [26][27][28][29], vulnerability to natural hazards [30,31], socio-environmental resilience [32], agricultural management [33], habitat suitability [34], spatial modeling of migration [35], and urban management [1,36,37]. In past studies, various GIS-MCDA methods such as AHP, Fuzzy AHP, Fuzzy TOPSIS, ANP, VIKOR, ELECTRE, and PROMETHEE have been used for these applications.…”