Data modeling in geographical data warehousing aims to create a conceptual representation of the real-world entities and their relationships relevant to the domain of Indian Geography. This structured representation serves as the foundation for designing the schema and organizing the data in the data warehouse.

Spatial data modeling is a specialized technique used to represent and manage spatial data, such as geographical features, locations, and their relationships. It allows for the storage, retrieval, and analysis of spatial data in a structured and efficient manner.

A schema in geographical data warehousing serves multiple purposes. It defines the structure of the data warehouse by specifying the tables, columns, and their data types. It also defines constraints to ensure data integrity and consistency. Additionally, it establishes relationships between different entities in the data warehouse, allowing for efficient data retrieval and analysis.

Third Normal Form (3NF) is the normalization form that ensures that each attribute in a table is dependent on the primary key and not on any other attribute. This eliminates transitive dependencies and ensures data integrity and consistency.

Surrogate keys are artificial keys used to uniquely identify records in a table. They are often used in geographical data warehousing to improve data integrity and consistency by eliminating the risk of duplicate or missing values in the primary key column.

Hierarchical data modeling is a technique that is well-suited for representing hierarchical relationships in geographical data warehousing. It allows for the organization of data in a tree-like structure, where each parent node can have multiple child nodes.

The primary goal of schema design in geographical data warehousing is to create a logical representation of the data in the data warehouse. This involves defining the tables, columns, data types, and relationships between them. The logical schema serves as a blueprint for the physical implementation of the data warehouse.

In a relational data model, a one-to-many (1:M) relationship represents a scenario where one record in a table can be related to multiple records in another table. This type of relationship is often used to model parent-child relationships or hierarchical structures.

Fact tables in geographical data warehousing are used to summarize data from multiple dimensions for analysis. They contain measures or metrics that are of interest to the business, along with the foreign keys that link them to the dimension tables. Fact tables enable efficient data aggregation and analysis across different dimensions.

Temporal data modeling is a specialized technique used to represent and manage temporal data, such as historical records or time-series data. It allows for the storage, retrieval, and analysis of data over time, which is crucial for many applications in geographical data warehousing.

Dimension tables in geographical data warehousing are used to establish relationships between different dimensions in the data warehouse. They contain descriptive attributes that provide context and meaning to the data in the fact tables. Dimension tables enable efficient data navigation and analysis across different dimensions.

In a relational data model, a many-to-many (M:N) relationship represents a scenario where multiple records in one table can be related to multiple records in another table. This type of relationship is often used to model associative entities or intersection entities.

Materialized views in geographical data warehousing are pre-computed and stored copies of query results. They are used to improve the performance of data retrieval and analysis queries by reducing the need to re-execute complex queries against the main data warehouse tables. Materialized views can also be used to support near-real-time reporting and analysis.

Network data modeling is a specialized technique used to represent and manage network data, such as transportation networks or communication networks. It allows for the storage, retrieval, and analysis of data related to nodes, edges, and their relationships. Network data modeling is particularly useful for applications involving routing, shortest path analysis, and network optimization.