Simulating a Land Development Planning Process through Agent-Based Modeling
Bibliographic record
Abstract
Like most urban centers in North America, the City of Calgary has been experiencing steady population and unprecedented land-cover growth over the past six decades due to the strong Alberta economy, natural increase, and net migration (City of Calgary 2009). Between 1951 and 1961 the population exploded by 93% from 129,060 to 249,641 inhabitants and the city's jurisdiction swelled by 276% from 104 to 392 km 2 . In each decade since, the population has increased by approximately 35% to the current population of 1,043,000 inhabitants and the city's municipal lands have expanded by 14% to the current area of 745 km 2 (Applied History Research Group 1997 The City of Calgary's " Population Picture" predicts a population of 1.6 million by 2037 and just fewer than two million inhabitants by the 22 nd century (City of Calgary 2009). If these population predictions are correct and planning and land development decisions continue to be made based on current planning policy and developers' self-interest, the City of Calgary will continue to sprawl and housing costs will continue to climb. This competition and cost increase for land within the City of Calgary force young families unable to afford a house in the city to purchase outside of the city and commute to their place of employment. It also encourages retirees wanting to get away from the city to move out to the country. This creates demands on towns located in the vicinity of Calgary to accommodate this net migration. This is the case for the Town of Strathmore, located at about 40 km east of Calgary on the Trans-Canada Highway. It currently has a population of 11,335 inhabitants and covers approximately 15.5 km 2 . The population is expected to reach 56,731 inhabitants by 2056 and to cover 26.8 km 2 , an increase of 38% in population and 12% in area per decade (Brown and Associates Planning Group 2008). Several environmental, social and economic problems are associated to population growth, including: (a) increased demand on and cost for resources such as land and water; (b) increased intensity of use on and competition for land; (c) change in settlement patterns; (d) increased interaction, and conflict or required cooperation with adjacent municipalities; (e) increased demand on existing infrastructure, such as roads, utility distribution, collection and treatment facilities; (f) increased cost for new infrastructure like roads, utilities, schools, and other community facilities; (g) increased environmental ground, water and air pollution; and (h) increased health and emergency costs. Some of these costs can be avoided; www.intechopen.com Multi-Agent Systems -
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".