Spatial Data and Process Modelling

Introduction


This module introduces the concepts of spatial data and spatial data standards. It also covers the concepts of object orientation and UML that are the key issues required for the modelling of spatial data and processes. Finally, Cellular Automata (CA) and Agent-Based Models (ABM) are covered, which are the most common models for the simulation of dynamic phenomena.

 


Learning outcomes


By the end of the module, students will be able to understand spatial data and processes, along with the modelling tools to define and specify them. In addition, they can describe CA and ABM models and are able to design simple dynamic models for simulating spatial processes. Particularly, the students are supposed to:


  • Understand and explain the main concepts of spatial databases;
  • Understand and explain the main concepts of object-orientation;
  • Use UML for user requirements analysis;
  • Use UML to specify the data and processes of a system.
  • Understand and explain CA and ABM, and their applications
  • Design CA and ABM for representing dynamic spatial processes

 


Content


The following issues comprise the content of this module:


  • Spatial databases and spatial data standards;
  • Concepts of object-orientation;
  • System modelling using UML;
  • User requirements analysis
  • Cellular automata model and agent-based simulation;
  • Agent-based modelling using ODD;

 


Allocated time per teaching and learning method

 

Teaching / learning method

Allocated Hours

Lectures

36

Supervised practicals

30

Unsupervised practicals

12

Individual assignment

12

Group assignment

20

Self-study

30

Examination

4

Graduation project supervision

0

MSc thesis supervision

0

Development time

0



Examination


There will be a midterm and a final examination. All examinations will be closed book and closednotes. There will be a group assignment, in which students design a solution for a problem using UML, including both the data and the data processing parts. In another individual assignment, the main characteristics of an agent based model will be defined by each student, to represent a dynamic spatial phenomenon.

 


Final Grade Determination


The weight of written exam will be 60% and weight of the assignments will be 40% each.

Components

Weight

Group assignment

15 %

Individual assignment

15 %

Midterm Examination

30 %

Final Examination

40 %

 


Prerequisites


Good basics in mathematics and programming and modules 1-5.

 


Reference


Castle, C. J., & Crooks, A. T. (2006). Principles and concepts of agent-based modelling for
developing geospatial simulations, CASA Working Papers 110, UCL, London, UK.
Reader containing compilations of slides and reading material related to object orientation, spatial data modelling and UML

Date:
2018/06/02
review:
414
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