AI can help identify real estate gaps in Oman by analyzing geographic, demographic, and economic data. Machine learning models like clustering (K-Means) can spot underdeveloped areas, regression models can predict demand, and CNNs can analyze satellite imagery for land use.

Existing studies have used AI to predict property prices, track urban expansion, and map underserved communities. Tools like Google Earth Engine (satellite analysis), QGIS/ArcGIS (GIS mapping), and Scikit-learn/TensorFlow (ML models) can support your research. Let me know if you need specific case studies!

Your research focus is fascinating and aligns well with the growing trend of leveraging AI for spatial and socioeconomic analysis.

1. Relevant Methodologies

• Spatial Data Integration:Use Geographic Information Systems (GIS) to integrate spatial data with socioeconomic and demographic datasets. Machine learning models can leverage this integration for analysis and prediction. Techniques like spatial clustering (e.g., K-Means or DBSCAN) can help identify patterns and gaps in real estate development across regions.
• Predictive Modeling:Use regression models (linear, random forest regression) for estimating real estate demand based on demographic and economic indicators. Deep learning models like CNNs (applied to geospatial imagery) and LSTMs (for temporal trends in data) can provide advanced predictive capabilities.
• Multi-Criteria Decision Analysis (MCDA):Combine AI models with MCDA for prioritizing underdeveloped regions by weighting various factors (e.g., income levels, population density, infrastructure availability).
• Explainable AI (XAI):Use XAI tools (e.g., SHAP, LIME) to interpret machine learning model outputs, ensuring actionable insights for decision-makers.

2. Case Studies and Research

• Urban Development:AI in Urban Planning and Real Estate Development (ArXiv, 2020): Focuses on predicting urban expansion using ML models and remote sensing data. Application of GIS and AI in Smart City Development (Elsevier, 2021): Explores integrating GIS-based datasets with AI for identifying growth opportunities.
• Real Estate Price Prediction:Using Machine Learning to Predict Real Estate Prices (Springer, 2021): Discusses how socio-economic data and ML models can be utilized to predict property values and identify undervalued regions.
• Oman-Specific Insights:Review studies or reports by Omani government entities or regional planning bodies that provide data on infrastructure and economic growth trends.

3. Tools and Platforms

• GIS and Spatial Analysis:ArcGIS: Comprehensive spatial analysis tool with AI integration capabilities. QGIS: Open-source alternative with plugins for machine learning and demographic analysis.
• Data Science Platforms:Google Earth Engine: Excellent for processing satellite imagery and analyzing spatial data. Tableau or Power BI: For creating interactive visualizations of your findings.
• ML Frameworks:TensorFlow or PyTorch: For building and training custom AI models. Scikit-learn: For traditional machine learning algorithms with user-friendly APIs.
• GeoAI Libraries:GeoPandas: Python library for spatial data manipulation. Kepler.gl: Web-based visualization tool for large-scale geographic data.

Recommendations:

To address the integration of AI and machine learning in identifying real estate development gaps in Oman's geographic regions, consider the following:

1. Methodologies for Data Integration:

- Use Geospatial AI combining geographic information systems (GIS) and machine learning algorithms to process spatial and demographic data.

- Apply Supervised Learning Models (e.g., Random Forest, Gradient Boosting) for predictive analytics based on labeled data.

- Utilize Clustering Techniques (e.g., K-Means, DBSCAN) for identifying underdeveloped areas by grouping regions with similar characteristics.

- Integrate socioeconomic data (e.g., income levels, population density) with satellite imagery and mobility data using platforms like Google Earth Engine.

2. Case Studies/Research:

- Study applications like Smart Dubai’s AI-driven real estate analysis or China’s use of deep learning models for urban planning.

- Refer to research on spatial-temporal analysis in real estate development, particularly leveraging urbanization prediction models.

- Analyze existing works like the integration of AI in the US housing market for zoning and demand-supply alignment.

3. Tools and Platforms:

- ArcGIS: For spatial data visualization and analysis.

- Google Earth Engine: For large-scale geospatial data processing.

- QGIS with Python: For open-source spatial data analysis.

- AI frameworks like TensorFlow or PyTorch, combined with libraries like GeoPandas for geospatial data handling.

You can systematically start by mining multi-layered datasets ranging from demographic profiles and socioeconomic indicators to property transactions, geospatial infrastructure information, and macroeconomic parameters. Then the chosen models which may include cluster based market segmentation, spatial regression, or time series forecasting, will enable a nuanced understanding of the supply-demand equilibrium, highlight emergent development hotspots, and anticipate future shifts in regional demand. So such predictive analytics, combined with domain expertise, inform strategic investment decisions and policy formulation, ultimately facilitating more efficient resource allocation and fostering balanced, sustainable urban expansion throughout Oman’s diverse geographies.

To identify geographical location we include Data Mining and Big Data concepts relating it to Google Map.

To find land area and their climatic condition we use Municipality database of that city, village.

Various GIS tools are gvSIG, grassSIG in use.