The residential location choice of immigrants: a systematic review and future directions
Bibliographic record
Abstract
Immigration is one of the drivers of the demographic, economic, social and physical landscapes of countries like the United States, Canada, Australia, and New Zealand. Understanding how and why immigrants choose their residential locations and how urban infrastructure, especially transportation, influences the decision remain a research area that is critical but under-explored. Residential Location Choice (RLC) is a crucial focus in transportation planning research, as both land use and residential patterns significantly shape travel behaviour and transportation infrastructure. This study has three main goals based on a systematic review of 84 scientific publications. First, it examines the factors influencing immigrant location decisions, including socio-demographic characteristics, economic opportunities, social networks, housing affordability, transportation networks and institutional policies. Second, it assesses the methodologies and models used in the studies on immigrant residential location choice, and thirdly, it identifies critical research gaps and offers recommendations for future research. The findings reveal that social networks and economic factors facilitate immigrant settlement. We emphasise the need to better understand how immigrants choose where to live based on transportation networks through integrated land use and transport models and the need for a more nuanced understanding of diverse immigrant needs, which is crucial for creating inclusive and considerate policies. We also highlight the need for longitudinal studies and better predictive models to further our understanding of immigrant settlement patterns.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".