Chapter 11: Paradigms of Discrete Spatial Modeling¶

Part III — Discrete Spatial Modeling

Learning Objectives¶

By the end of this chapter you will be able to:

• Survey existing approaches: Dinamica EGO, TerraME/LuccME, NetLogo
• Understand the gap between GIS tools and simulation frameworks
• Motivate a Python-native approach

{note}
Keep this chapter conceptual — no code. Sets the stage for Ch 12–20.

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# Standard imports — add chapter-specific imports below
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Standard imports — add chapter-specific imports below import numpy as np import pandas as pd import matplotlib.pyplot as plt

What Is Discrete Spatial Modeling?¶

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# Code for section: What Is Discrete Spatial Modeling?

# Code for section: What Is Discrete Spatial Modeling?

Visual Data-Flow: Dinamica EGO¶

TODO: write content.

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# Code for section: Visual Data-Flow: Dinamica EGO

# Code for section: Visual Data-Flow: Dinamica EGO

General-Purpose Engines: TerraME and LuccME¶

TODO: write content.

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# Code for section: General-Purpose Engines: TerraME and LuccME

# Code for section: General-Purpose Engines: TerraME and LuccME

Agent-Based Frameworks: NetLogo and MASON¶

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# Code for section: Agent-Based Frameworks: NetLogo and MASON

# Code for section: Agent-Based Frameworks: NetLogo and MASON

The Python Gap¶

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# Code for section: The Python Gap

# Code for section: The Python Gap

Summary¶

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# Code for section: Summary

# Code for section: Summary

Summary¶

TODO: summarise key takeaways.

Key concepts introduced¶

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Further Reading¶

• TODO: add references.