Towards the integration of complex systems theory, geographic information science, and network science for modelling geospatial phenomena
Bibliographic record
Abstract
A complex systems approach conceptualizes spatial systems from the bottom-up to better understand how local spatial interactions generate emergent system-level behavior and spatial patterns at large spatial extents.This approach can be applied to examine ecological, urban, and social systems within contexts of geographic space and time.Geographic automata systems (GAS) including cellular automata (CA) and agentbased models (ABM) are spatio-temporal modelling frameworks that are rooted in complex systems theory.In a similar manner, network theory uses a complex systems approach to represent and analyze spatial systems as sets of georeferenced nodes and links that form measurable spatial networks.Separately, GAS and network-based approaches offer unique advantages in exploring and analyzing complex systems, however the two approaches are rarely integrated.Therefore, the purpose of this dissertation is to explore the intersection of complex systems theory, geographic information science, and network theory to leverage the advantages of each field for better understanding a variety of complex spatial systems.The main objective is to develop a suite of novel network-based automata modelling approaches that simulate complex dynamic spatial systems as measurable, evolving, spatial networks.Three novel modelling approaches are developed including: a geographic network automata (GNA) model that uses spatial networks, network-based transition rules, and network analysis for the representation of complex spatial systems; a network-based ABM (N-ABM) that integrates networks not as inputs for the ABM, but as a novel way to conceptualize, analyze, and communicate the model and model results; and a network based validation approach for the testing of ABMs.Obtained results demonstrate that the integration of complex systems theory, geographic information science, and network theory offers new means for the representation, analysis, communication, and testing of GAS and the complex systems they represent, thus helping to thus helping to "open the black box".Furthermore, the presentation of modelling results in application to insect infestation and disease transmission contribute to the enhancement of decision-making processes by providing tools that can be used in forecasting and scenario testing.This dissertation contributes new methodological frameworks to the fields of geographic information science, GAS, and network theory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".