A table presenting a multinomial logistic regression analysis of the potential impact of cycling infrastructure. The model uses difficulties commonly found when cycling as explanatory variables to predict the outcome of new infrastructure experiments. The dataset is associated with an Open Access paper by author Leo Ordínez.
Use Cases
- Modeling the impact of cycling infrastructure based on reported cycling difficulties.
- Analyzing the relative effect of different explanatory variables on a categorical outcome in urban planning.
- Predicting the potential outcome of new cycling infrastructure projects based on pre-existing difficulty factors.
Strengths
- Analysis is based on a structured multinomial logistic regression model.
- The dataset is associated with an Open Access (green) license, facilitating reuse.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- paperswithcode
- Collection Method
- Likely derived from a research experiment or survey analyzing cycling infrastructure.
- Geography
- Puerto Madryn