10.1 GIS data models and spatial analysis for transportation

Geographic Information Systems (GIS) are crucial for transportation planning and analysis. They allow us to map, analyze, and visualize complex transportation networks and data. GIS helps us understand spatial relationships, optimize routes, and make informed decisions about infrastructure and services.

In this section, we'll explore GIS data models and spatial analysis techniques specific to transportation. We'll cover vector and raster data types, network analysis, and visualization methods that help planners and engineers tackle real-world transportation challenges.

GIS Data Models for Transportation

Vector and Raster Data Models

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• Geographic Information Systems (GIS) integrate hardware, software, and data for capturing, managing, analyzing, and displaying geographically referenced information
• Vector data models represent geographic features as points, lines, and polygons
  • Useful for discrete transportation elements (road networks, intersections, transit stops)
  • Example: Representing a city's bus routes as lines and bus stops as points
• Raster data models divide space into a regular grid of cells, each containing a value
  • Used for continuous data (elevation, population density, traffic flow intensity)
  • Example: Creating a heat map of traffic congestion levels across a city

Topological Relationships and Attribute Data

• Topological relationships define how spatial features relate to each other
  • Crucial for network analysis in transportation systems
  • Example: Determining connectivity between road segments at intersections
• Attribute data provides additional information about transportation elements
  • Includes details like road classifications, speed limits, transit schedules
  • Example: Storing the number of lanes and speed limit for each road segment
• Metadata provides information about the data itself
  • Includes source, accuracy, coordinate system
  • Essential for assessing data quality and compatibility
  • Example: Recording the date of data collection for a road network dataset

Spatial Analysis in Transportation

Network and Overlay Analysis

• Network analysis used for routing, service area delineation, and accessibility studies
  • Example: Calculating the shortest path between two locations in a city
• Overlay analysis combines multiple data layers to identify spatial relationships and patterns
  • Used for determining optimal locations for new transportation infrastructure
  • Example: Overlaying population density, existing transit routes, and land use data to identify areas for new bus stops

Proximity and Cluster Analysis

• Proximity analysis techniques include buffer and distance calculations
  • Assess the influence of transportation facilities on surrounding areas
  • Example: Creating a 500-meter buffer around schools to analyze pedestrian safety
• Cluster analysis identifies spatial patterns and hotspots in transportation data
  • Useful for locating accident-prone areas or high-demand transit zones
  • Example: Identifying clusters of traffic accidents to prioritize safety improvements

Spatial Regression and Multi-criteria Decision Analysis

• Spatial regression models analyze relationships between transportation variables and other spatial factors
  • Account for geographic dependencies
  • Example: Analyzing the impact of land use patterns on traffic congestion
• Multi-criteria decision analysis (MCDA) integrates various spatial and non-spatial factors
  • Supports complex transportation planning and policy decisions
  • Example: Evaluating potential locations for a new transit hub based on accessibility, cost, and environmental impact

Visualizing Transportation Data

Thematic Mapping and Flow Visualization

• Common GIS data formats for transportation include shapefiles, geodatabases, and KML files
• Thematic mapping techniques visualize quantitative transportation data across geographic areas
  • Choropleth maps use color gradients to represent data values
  • Graduated symbol maps vary symbol size based on data values
  • Example: Creating a choropleth map of traffic volumes on different road segments
• Flow maps and desire lines represent movement patterns and interactions
  • Illustrate origins and destinations in transportation systems
  • Example: Visualizing commuter flows between residential areas and employment centers

Advanced Visualization Techniques

• 3D visualization enhances representation of transportation infrastructure and terrain
  • Aids in impact assessments and public communication
  • Example: Creating a 3D model of a proposed highway overpass to evaluate visual impact
• Time-series mapping and animation visualize temporal changes in transportation patterns
  • Example: Animating traffic flow changes throughout a day
• Web-based GIS platforms enable dynamic visualization and sharing of transportation data
  • Facilitate stakeholder engagement and public participation
  • Example: Creating an interactive online map of proposed bike lane locations for public feedback

Data Quality for Transportation Analysis

Spatial and Attribute Accuracy

• Spatial accuracy measures positional correctness of geographic features
  • Critical for precise transportation network analysis and infrastructure planning
  • Example: Ensuring road intersections are accurately located for turn-by-turn navigation
• Attribute accuracy refers to correctness of non-spatial information
  • Includes road classifications, transit schedules, speed limits
  • Example: Verifying that posted speed limits match the values in the GIS database

Completeness and Consistency

• Completeness assessment determines if dataset contains all necessary features and attributes
  • Example: Checking if all bus stops in a transit network are included in the dataset
• Logical consistency evaluates internal coherence of the dataset
  • Includes topological relationships and attribute dependencies
  • Example: Ensuring all road segments connect properly at intersections

Temporal Accuracy and Data Lineage

• Temporal accuracy considers currency and relevance of GIS data
  • Important for time-sensitive transportation applications
  • Example: Using up-to-date traffic count data for congestion analysis
• Resolution and scale suitability assess if data detail matches analysis requirements
  • Example: Determining if the spatial resolution of a digital elevation model is sufficient for road gradient analysis
• Data lineage and metadata evaluation provide insights into data origin and processing history
  • Inform decisions about data suitability for transportation applications
  • Example: Reviewing metadata to understand the collection methods used for traffic survey data