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Journals / Bölgesel Kalkınma Dergisi / 2026 / Cilt: 04 Sayı: 01

DATA-DRIVEN INSIGHTS INTO TOURISM INVESTMENT TRENDS: A FORECASTING APPROACH USİNG E-EIA SYSTEM DATA IN THE TR32 REGION

ÇEVRESEL ETKİ DEĞERLENDİRMESİ BAŞVURULARININ MEKÂNSAL VE ZAMANSAL ANALİZİ: TR32 BÖLGESİ TURİZM SEKTÖRÜNDE ÖNGÖRÜCÜ MODELLERİN KARŞILAŞTIRILMASI

Armağan Aydın(T.C. GÜNEY EGE KALKINMA AJANSI)0000-0003-0251-4913
Pages
49–62
Publication Date
2026-04-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Practically, the findings provide local authorities and planners with robust, low-cost forecasting tools to support data-driven decision-making in tourism investment planning.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Tourism investments play a critical role in regional growth and environmental sustainability but require complex planning and assessment processes.
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Key Findings & Data:In Türkiye, the digitization of Environmental Impact Assessment (EIA) procedures through the Electronic EIA (E-EIA) system has increased transparency and enabled data-driven analyses.
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Conclusion & Impact:Practically, the findings provide local authorities and planners with robust, low-cost forecasting tools to support data-driven decision-making in tourism investment planning.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Turizm Yatırımları · Çevresel Etki · Zaman Serisi Modelleri

Abstract

Tourism investments play a critical role in regional growth and environmental sustainability but require complex planning and assessment processes. In Türkiye, the digitization of Environmental Impact Assessment (EIA) procedures through the Electronic EIA (E-EIA) system has increased transparency and enabled data-driven analyses. This study aims to fill the gap in the literature by investigating tourism investment trends in the TR32 region using E-EIA data. Trend analysis was conducted to capture historical patterns, and both the Holt-Winters exponential smoothing method and machine learning algorithms were employed to forecast future developments. Results reveal significant spatial concentration and sectoral diversification in tourism investments, with the Holt-Winters method outperforming machine learning techniques, especially in data with strong trend and seasonality components. Theoretically, the study challenges the common assumption of machine learning superiority by demonstrating the predictive strength of classical time-series models. Practically, the findings provide local authorities and planners with robust, low-cost forecasting tools to support data-driven decision-making in tourism investment planning.

Özet

Turizm yatırımları, hem bölgesel kalkınma hem de çevresel sürdürülebilirlik üzerinde belirleyici bir etkiye sahip olup, aynı zamanda çok katmanlı ve karmaşık bürokratik değerlendirme süreçlerini gerektirmektedir. Türkiye'de Çevresel Etki Değerlendirmesi (ÇED) süreçlerinin Elektronik ÇED (E-ÇED) sistemi aracılığıyla dijitalleştirilmesi şeffaflığı artırmış ve veriye dayalı analizlere olanak sağlamıştır. Bu çalışma, E-ÇED verileri kullanılarak TR32 bölgesindeki turizm yatırım eğilimlerini inceleyerek literatürdeki boşluğu doldurmayı amaçlamaktadır. Tarihsel örüntüleri yakalamak için trend analizi yapılmış ve gelecekteki gelişmeleri tahmin etmek için hem Holt-Winters üstel düzeltme yöntemi hem de makine öğrenimi algoritmaları kullanılmıştır. Sonuçlar, turizm yatırımlarında önemli bir mekânsal yoğunlaşma ve sektörel çeşitlenme olduğunu ortaya koymaktadır. Holt-Winters yöntemi, özellikle güçlü trend ve mevsimsellik bileşenleri olan verilerde makine öğrenimi tekniklerinden daha iyi performans göstermektedir. Teorik olarak çalışma, klasik zaman serisi modellerinin tahmin gücünü göstererek makine öğreniminin üstünlüğüne ilişkin yaygın varsayımı sorgulamaktadır. Pratik olarak ise bulgular, yerel yönetimlere ve planlamacılara turizm yatırım planlamasında veriye dayalı karar almayı desteklemek için sağlam ve düşük maliyetli tahmin araçları sağlamaktadır.

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Cite This Article

Aydın, A. (2026). DATA-DRIVEN INSIGHTS INTO TOURISM INVESTMENT TRENDS: A FORECASTING APPROACH USİNG E-EIA SYSTEM DATA IN THE TR32 REGION. Bölgesel Kalkınma Dergisi, Cilt: 04 Sayı: 01, 49–62. https://doi.org/10.61138/bolgeselkalkinmadergisi.1814342

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