Integrating Lotka-Volterra dynamics and gravity modeling for regional population forecasting
Bibliographic record
Abstract
Introduction Forecasting population dynamics is crucial for effective urban and regional planning. Traditional demographic methods, such as Cohort Component Analysis, often do not capture nonlinear interactions and spatial dependencies among regions. To address these limitations, this study integrates Lotka—Volterra prey—predator equations with a probabilistic adaptation of the Gravity model, providing a more robust theoretical and methodological framework for regional population forecasting. Methods We adapt the Lotka—Volterra model—originally rooted in ecological theory—by introducing carrying capacities and region-specific parameters, then embed a probabilistic Gravity model to capture interregional mobility. This unified approach leverages population data and migration flows from three major clusters in Quebec, Canada, calibrating model parameters to reflect observed demographic trends. The resulting system of equations was iteratively solved and tested using population data from 2021 through 2023. Results The combined model effectively captured competitive and cooperative population interactions, revealing how spatial connectivity and resource constraints shape long-term growth patterns across the three regions. Calibrated forecasts aligned well with observed trends, demonstrating the framework’s capacity to reflect real-world interdependencies in regional population flows. Key findings highlight the importance of prey—predator—like dynamics in producing stable or shifting equilibria, offering deeper insights into regional competition, cooperation, and demographic sustainability. Discussion By merging ecological modeling principles with spatial interaction theories, this work underscores the added value of grounding demographic forecasting in well-established theoretical constructs. Compared to more traditional approaches, the integrated Lotka–Volterra and Gravity model provides a clearer picture of how regional populations evolve under nonlinear and spatially linked influences. This approach is readily adaptable to diverse contexts, potentially enhancing forecast precision and guiding policy interventions in urban development, resource allocation, and strategic planning on a broader scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".