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<p>Understanding spatial distribution of infertility risk is essential for identifying environmental and infrastructural factors affecting reproductive health. Despite its relevance, infertility has rarely been examined through a geographic lens. This study highlights the need for integrating spatial analysis into infertility research to uncover hidden patterns linked to environmental exposures. This study aims to review the effect of environmental and genetic risk factors on infertility in individuals and provide a continuous model of infertility probability. This study analyzes infertility factors in Tehran province using Geographic Information System (GIS) and fuzzy logic. Initially, we estimate infertility probabilities with non-spatial (genetic) factors using fuzzy logic rules. Subsequently, we assess the influence of environmental factors on infertility through three fuzzy rule scenarios. The results of this study suggest that spatial factors, such as air pollution, high-voltage towers, power lines, and telecommunication towers, are associated with infertility, accounting for approximately 63% of the explanatory power in our model, while the genetic and behavioral (non-spatial) factors contribute about 37%. These percentages reflect the relative performance of spatial vs. non-spatial fuzzy inference systems and do not imply causal proportions. Given the stronger influence of spatial factors, we produced a probability distribution map of infertility across Tehran Province.</p>

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Modeling infertility based on spatial and non-spatial factors using GIS

  • 期刊论文

  • 发表日期：

    2025年6月28日

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  • Ziba Abbasian,
  • Saeed Behzadi,
  • Hossein Naderi,
  • Alireza Vafaeinejad,
  • Alireza Sharifi

摘要

Understanding spatial distribution of infertility risk is essential for identifying environmental and infrastructural factors affecting reproductive health. Despite its relevance, infertility has rarely been examined through a geographic lens. This study highlights the need for integrating spatial analysis into infertility research to uncover hidden patterns linked to environmental exposures. This study aims to review the effect of environmental and genetic risk factors on infertility in individuals and provide a continuous model of infertility probability. This study analyzes infertility factors in Tehran province using Geographic Information System (GIS) and fuzzy logic. Initially, we estimate infertility probabilities with non-spatial (genetic) factors using fuzzy logic rules. Subsequently, we assess the influence of environmental factors on infertility through three fuzzy rule scenarios. The results of this study suggest that spatial factors, such as air pollution, high-voltage towers, power lines, and telecommunication towers, are associated with infertility, accounting for approximately 63% of the explanatory power in our model, while the genetic and behavioral (non-spatial) factors contribute about 37%. These percentages reflect the relative performance of spatial vs. non-spatial fuzzy inference systems and do not imply causal proportions. Given the stronger influence of spatial factors, we produced a probability distribution map of infertility across Tehran Province.