Spatiotemporal Analysis of Chicago Ridesharing Demand using Modified Spatial Error Model
Bibliographic record
Abstract
Ridesharing has transformed urban transportation by altering the mobility patterns in major cities.To understand the complex interplay of demographic, socioeconomic, and infrastructural factors, it is necessary to employ a spatial and temporal analytical approach.This study utilized a modified version of the Spatial Error Model (SEM) to evaluate the dynamics of rideshare demand in Chicago by analyzing data from 77 community areas over a 60-day period in 2022.Our modified SEM accounts for both spatial dependencies and temporal correlations.This study provides a nuanced understanding of how demographic, socioeconomic, and infrastructural factors impact rideshare usage.Our results indicate that population size, crime rates, and educational attainment are positively correlated with rideshare demand, whereas median age has a negative impact.Additionally, high transit accessibility enhances rideshare usage, suggesting a synergistic relationship with public-transportation systems.However, regions with high walkability showed reduced demand, indicating a preference for walking, or cycling over ridesharing in easily navigable areas.These insights are essential for urban planners and policymakers aiming to improve urban mobility and effectively integrate ridesharing into transportation ecosystems.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".