Big Data (R)evolution in Geography: Complexity Modelling in the Last Two Decades
Bibliographic record
Abstract
ABSTRACT The use of data and statistics along with computational systems heralded the beginning of a quantitative revolution in Geography. Use of simulation models (Cellular Automata and Agent‐Based Models) followed in the late 1990s, with ontology and epistemology of complexity theory and modelling being defined a little less than two decades ago. We are, however, entering a new era where sensors regularly collect and update large amounts of spatio‐temporal data. We define this ‘Big Data’ as geolocated data collected in sufficiently high volume (exceeding storage capacities of the largest personal hard drives currently available), that is updated at least daily, from a variety of sources in different formats, often without recourse to verification of its accuracy. We then identify the exponential growth in the use of complexity simulation models in the past two decades via an extensive literature review (broken down by application area), but also notice a recent slowdown. Further, a gap in the utilisation of Big Data by modellers to calibrate and validate their models is noted, which we attribute to data availability issues. We contend that Big Data can significantly boost simulation modelling, if certain constraints and issues are managed properly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".