Probabilistic Modelling of Demographic Changes in Singapore’s Neighbourhoods
Bibliographic record
Abstract
Abstract Predicting the temporal evolution of the demography and the residents’ spatial movements would immensely aid the estate development and urban planning. The evolution of population in three townships of Singapore is simulated at neighbourhood scale using a novel agent-based probabilistic approach with inputs from large-scale survey and statistical data. The demographic changes due to age-dependent rates of death and fertility are studied by considering the inter-ethnic marriages that has a varying probability depending on the ethnicities of the male and female partners. The predicted changes in the age and household compositions and family types have been found to reflect the population trends in Singapore over the past years. The decline in family types that contain children and the structure of age composition over years underline the issue of prevailing low fertility rates. The strategies for incorporating the population relocation to consider the long-term spatial movement are also discussed. In Singapore’s context, we consider in the relocation model an added complexity of ethnic quota for the residential units developed by public housing board. The ethnicity dependent parameter coupled with other parameters that represent the number of children in a household besides their size, the household income, the proximity of children’s schools, and the places of employment could play a strong role in predicting the spatial evolution of the residents. These predictions can be used by the urban planners and policy makers to improve the quality of life in Singapore.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".