A Review of Trends in Modelling Residential Location Choice in Peri-Urban Settlements and the Potentials for Sustainable Development
Bibliographic record
Abstract
Urbanization occurring in most developing countries has caused urban expansion that has involved changes in structural and population growth in the surrounding territory, commonly known as peri-urban. The peri-urban area has undergone a transition process from rural agricultural land into the built environment, dominated by housing settlements serving the growing population not just from the city center, but also from rural areas. These new dynamics and complex spaces were shaped by householders' preference for their residences. Modelling residential location choice in peri-urban settlements can enable researchers to obtain a clear understanding of households' behaviour and the determinants that influence the decisions made and can shed light on the spatial variability and characteristics of localities. Most researchers recently used residential location choice model in developing integrated land use and transportation models, but minor attention has been given to its potentials for sustainable development. Therefore, this paper reviews the current trends in modelling residential location choice in peri-urban areas viewed through the prism of an economic and spatial interaction modelling approach and its trends in modelling urban simulations by integrating land use and the transportation system. Finally, this paper aims to address the gap by investigating its potentials for sustainable development. There is, the paper concludes, a significant potential for achieving sustainable development with modelling residential location choice. The highest potential is in regard to the social aspect through accessibility; which is one of the significant factors in explaining residential location choice. This review also enables planners to optimize residential location choice in modelling integrated land use and transportation beyond city boundaries so as to include peri-urban areas and treat them as a whole urban system.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".