Exploratory Data Analysis of Community Behavior Towards the Generation of Solid Waste Using K-Means and Social Indicators
Bibliographic record
Abstract
In Peru, solid waste accumulation has been constant for decades and impacts 72% of local governments, affecting 42% of the population. These numbers show new tools are required to better understand this phenomenon and develop appropriate mitigation methods. In this light, this research proposes an exploratory analysis of the study population against the accumulation of solid waste. For this, the study proposes the segmentation of a specific population through a set of social indicators grouped into three categories of analysis (i.e., sociocultural, sociodemographic, and socioeconomic) and, in turn, assess the geographic proximity between each group of people segmented according to the parameters used for this study, and the informal points of accumulation of MSW. To segment the study population, an unsupervised classification model (i.e., K-means) was used. For methodological purposes, the Puente Piedra district was chosen as a case study. The results show that the predominant population is framed between the ages of 36 to 45, with an intermediate educational level (i.e., secondary school) and an approximate monthly income of $ 300. In addition, the predominant family structure includes up to four members living in the same household. Finally, it is observed that the behavior of people who live close as neighbors is similar and is also related to the geographic location of the dumps.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".