Spatial Modeling

Modeling is an overloading term. In general, a model is a simplified description of reality and can also be considered a process.

Modeling as a Process

  • What is the model to tell us? We need to understand the problem’s significance.
  • What type of data is needed? Does the data reflect the requirements and precision?
  • How to create a design to put the model together?
  • how to apply existing tools to derive meaningful models?

Validation and Verification

How well does the model actually reflect reality? This can be verified via groundtruth model data.

Thinking Spatially

  • Visible / Functional Patterns
  • Spatial Correlation: closer events are more similar

Types of Models

There are different definitions and methods of thinking for how models can be described.

Cartographic Model

A cartographic model is a temporally static interpretation of combined spatial datasets, containing operations and functions for problem solving.

Spatio-temporal Model

Dynamics in space and time, time-driven processes.

Network Models

Modeling of resources (flow, accumulation) as limited to networks.

  • A set of connected features (centers of demand and/or supply)
  • Centers connected to at least one network link
  • Links form a network and may attributes that affect the flow (i.e. transit cost)
  • Examples:
    • route selection
    • resource allocation
    • traffic modeling
    • hydrology

Data Models

Entities and fields as conceptual models.

Static Modeling

Taking inputs to transform and then into outputs using sets of tools and functions, its a generally defined model.

Dynamic Modeling

Iterative, sets of initial conditions, apply transformations to obtain a series of predictions at time intervals.

Based on Purpose

  • Descriptive: passive, description of the study area
  • Prescriptive: active, imposing best solution

Based on Methodology

  • Stochastic: based on statistical properties
  • Deterministic: based on known functional linkages and interactions

Based on Logic

  • Inductive: specific premises to form general conclusions
  • Deductive: form general to specific using known factors and relationships

Cartographic Modeling (Main Focus)

  • Combine data sets and operations in a sequence to answer questions, typically producing an output map from various input maps
  • Based on “criteria”; often as a suitability analysiss
  • Examples:
    • distribution of suitable habitats, viable populations
    • migration route/corridor studies
    • water distribution systems, natural and constructed
    • species invasions
    • mill site selection
    • harvest scheduling
    • pollution response planning

Suitability Analysis

  • Classification of land according to its utility for a specific use
  • Often temporally static (spatial features fixed over time)
  • Change models may include a temporal component
  • Uses Operational Sequencing in workflows, including:
    • buffers
    • overlays (intersections, unions, etc.)
    • reclassifications
  • Overall, uses intermediate data creation in a decision-making process

Criteria Design

  • conversion of qualitative terms into quantitative measures
    • i.e. not too steep into slopes less than a certain degree
    • requires checking with the planning committee to specify these terms and measures
  • different combined criteria could have varying relative importance
  • require an explicitly weights of relative importance (ranking and weighting) of layers and criteria
    • Weighting and Ranking is the name of the qualitative to quantitative process

Rankings

  • Assignment of relative values within the same layer (discrete or continuous)
  • Relationships can be complex and should be justified
  • Suitability assignment function

Weightings

  • assignment of relative values to different layers
  • difficulties in deciding weights from non-quantitative criteria

Model-Building Tools

  • connectivity: input data-processes-output data
  • specification: parameters, variables
  • accessibility of input data and data organization
  • automation, portability, extensibility, reusability, documentation
  • sensitivity and scenario testing (iterations)

Summary

  • Many different definitions and taxonomies of modeling
  • Understanding the methods (exploring / explaining) and the problem that is faced (modeling)
  • The aim of spatial modeling is to derive a meaningful representation of events, occurrences or processes by making use of the power of spatial analysis
  • Modelbuilder is a good example of how to combine functionalities for creating cartographic models.