Calibration and Validation of The Population Mobility and Housing Price Sub-Modules of The Smartplans Integrated Urban Model
Bibliographic record
Abstract
Since the 1960s, Integrated Urban Models (IUMs) have consistently been applied to simulate the future of cities. Technological advancement in recent years has opened the doors for sophisticated IUMs to be developed, ones requiring extreme computing power. The SMARTPLANS IUM is one example. While the development and application of SMARTPLANS exists in the literature, exploring potential improvements in the model’s predictive ability is lacking. This dissertation aims to fill the gap in the literature by focusing on two sub-modules of SMARTPLANS to test and ultimately advance their performance. The research conducted in this thesis explores the population mobility and land price submodules within the Land Use Module of SMARTPLANS. The models were estimated using relevant parameters, compared over time, and validated with Canadian census data. The results show that the population aged 24-35 is the primary influencing factor to impact population mobility in all study areas. Additionally, the number of detached dwellings and household income were found to positively impact house prices in all models. Further, the number of row houses and the distance from the central business district (CBD) negatively influenced prices. The estimated models for the two sub-modules suggest stable transferability over time in regions experiencing steady pace growth. Furthermore, the analysis confirms a strong spatial influence present in the data associated with both submodules. As such, the utilization of spatially oriented techniques, namely the Simultaneous Auto-Regressive (SAR) model, resulted in superior predictions when compared to the predictions obtained from Ordinary Least Squares (OLS) regression models. The implementation of SAR models within SMARTPLANS will therefore improve its predictive ability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".