🗺️Geospatial Engineering Unit 6 – Spatial Data Modeling & Management

Key Concepts and Definitions

• Spatial data represents information about the location, shape, and relationships of geographic features
• Geospatial data includes both spatial and non-spatial attributes (elevation, temperature, population)
• Vector data represents discrete features using points, lines, and polygons
  • Points represent single locations (cities, landmarks)
  • Lines represent linear features (roads, rivers)
  • Polygons represent area features (buildings, land parcels)
• Raster data represents continuous surfaces using a grid of cells (satellite imagery, digital elevation models)
• Topology describes the spatial relationships between features (connectivity, adjacency, containment)
• Geodatabases are specialized databases designed to store, manage, and analyze spatial data
• Metadata provides information about the content, quality, and characteristics of spatial data

Spatial Data Types and Structures

• Vector data structures include spaghetti, topological, and network models
  • Spaghetti model stores geometry without explicit topological relationships
  • Topological model maintains spatial relationships between features (shared boundaries, connectivity)
  • Network model represents linear features as nodes and edges for network analysis (transportation networks, utility networks)
• Raster data structures include regular grids, quadtrees, and triangulated irregular networks (TINs)
  • Regular grids divide space into equal-sized cells (digital elevation models, land cover maps)
  • Quadtrees recursively subdivide space into quadrants for efficient data storage and retrieval
  • TINs represent surfaces using irregularly spaced points connected by triangles (terrain modeling, slope analysis)
• Spatial indexing techniques (R-trees, quadtrees) optimize spatial queries and improve performance
• Spatial data compression methods (run-length encoding, wavelet compression) reduce storage requirements

Coordinate Systems and Projections

• Geographic coordinate systems (GCS) use latitude and longitude to define locations on the Earth's surface
• Projected coordinate systems (PCS) transform geographic coordinates onto a flat surface for mapping and analysis
• Map projections (Mercator, UTM, Lambert Conformal Conic) balance distortions in area, shape, distance, and direction
  • Mercator projection preserves shape but distorts area, commonly used for web mapping
  • UTM projection divides the Earth into 60 zones for large-scale mapping and military applications
  • Lambert Conformal Conic projection minimizes distortion for mid-latitude regions
• Datum (WGS84, NAD83) defines the reference surface for coordinate systems
• Coordinate transformations convert data between different coordinate systems and datums

Data Modeling Techniques

• Conceptual data models (entity-relationship diagrams, UML) represent high-level entities and relationships
• Logical data models (relational, object-oriented) define the structure and constraints of the database
• Physical data models specify the implementation details (tables, indexes, storage)
• Normalization reduces data redundancy and ensures data integrity
  • First normal form (1NF) eliminates repeating groups and ensures atomic values
  • Second normal form (2NF) removes partial dependencies on composite keys
  • Third normal form (3NF) eliminates transitive dependencies
• Spatial data modeling extensions (spatial data types, spatial relationships) capture the unique characteristics of geospatial data

Database Management Systems for Spatial Data

• Relational database management systems (RDBMS) store spatial data using tables and relationships (PostgreSQL/PostGIS, Oracle Spatial)
• Object-relational database management systems (ORDBMS) extend RDBMS with object-oriented features (Esri Geodatabase)
• NoSQL databases (MongoDB, Cassandra) provide scalability and flexibility for big spatial data
• Spatial indexing (R-trees, grid indexes) improves query performance for large datasets
• Spatial SQL extensions (ST_Geometry, ST_Intersects) enable spatial queries and analysis within the database
• Versioning and long transactions support collaborative editing and data history management

Data Quality and Accuracy

• Spatial data quality encompasses completeness, accuracy, consistency, and timeliness
• Positional accuracy measures the closeness of feature locations to their true positions
• Attribute accuracy assesses the correctness of non-spatial attributes
• Logical consistency ensures adherence to topological rules and data model constraints
• Temporal accuracy considers the currency and validity of time-dependent data
• Metadata standards (ISO 19115, FGDC) provide a framework for documenting data quality
• Data quality assessment techniques (visual inspection, automated checks, ground truthing) identify and quantify errors
• Error propagation analysis estimates the impact of input data uncertainties on analysis results

Analysis and Visualization Methods

• Spatial analysis techniques (overlay, buffer, proximity) derive new information from spatial relationships
  • Overlay analysis combines multiple layers to identify spatial coincidence or intersection
  • Buffer analysis creates zones of influence around features based on distance or time
  • Proximity analysis measures distances and relationships between features
• Network analysis (shortest path, service area, location-allocation) solves transportation and logistics problems
• Terrain analysis (slope, aspect, viewshed) derives information from digital elevation models
• Spatial interpolation (kriging, IDW) estimates values at unsampled locations based on nearby observations
• Spatial statistics (Moran's I, Getis-Ord Gi*) measure spatial patterns and relationships
• Geovisualization techniques (choropleth maps, heat maps, 3D visualization) communicate spatial information effectively

Practical Applications and Case Studies

• Urban planning and land use management
  • Suitability analysis identifies optimal locations for development based on multiple criteria
  • Zoning and land use mapping ensures compatible and sustainable land use patterns
• Environmental monitoring and natural resource management
  • Habitat modeling predicts species distributions based on environmental factors
  • Watershed analysis assesses water quality and identifies sources of pollution
• Emergency response and disaster management
  • Evacuation routing and shelter location optimization improve emergency response efficiency
  • Risk assessment and vulnerability mapping prioritize areas for mitigation and preparedness
• Public health and epidemiology
  • Disease surveillance and outbreak detection identify spatial clusters of health events
  • Accessibility analysis measures population access to healthcare services
• Transportation and logistics
  • Fleet management and vehicle routing optimize delivery routes and minimize costs
  • Transit network analysis evaluates the performance and coverage of public transportation systems