Understanding Homelessness among Migrants to Thunder Bay using Machine Learning (Preprint)
Bibliographic record
Abstract
BACKGROUND Over the past years, homelessness has become a significant issue around the globe. The largest social services organization in Thunder Bay, Ontario observed the majority of people experiencing homelessness in the city were from outside of the city or province. Thus, to improve programming and resource allocation for homelessness in the city, it was important to investigate the trends associated with homelessness and migration. OBJECTIVE This paper aims to address three research questions related to homelessness and migration in Thunder Bay: (1) What factors predict if a person experiences homelessness stays or leaves?; (2) If an individual stays, how long are they likely to stay? and (3) What factors predict their stay duration? METHODS We collected the required data from two sources; one through a survey conducted with people experiencing homelessness at three homeless shelters in Thunder Bay and the other from a database of a homeless information management system. In total, records of 151 migrants were used for the analysis. Two feature selection techniques were used in addressing the first and third research questions and to predict the stay duration of homeless migrants at shelters, eight machine learning models were used. In addition, data augmentation was performed to improve the size of the dataset and a cross-validation technique was used to avoid possible model overfitting. RESULTS Availability of family or friends and hospitalizations were identified as the most important factors predicting the stays or leaves of an individual migrant at Thunder Bay. Next, among the eight classification models developed for predicting the stay duration of migrants, the random forest and gradient boosting tree presented better results with 0.96 and 0.97 of area under the curve values. Finally, band membership, status card, age, sex, ethnicity, support for drugs and/or alcohol, home community and personal reasons were recognized as the critical factors in predicting the stay duration of migrants. CONCLUSIONS The application of machine learning to make predictions related to migrants’ homelessness and to investigate how various factors become determinants of them is demanded. We hope that the findings of this study will aid future policy-making and resource allocation to better serve people experiencing homelessness. However, interpretation of the identified factors in decision-making is further required.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".