Search

This paper presents a methodology to estimate the multidimensional poverty index using spatial data at the street block level. The data used in this study were obtained from Open Street Maps and ESA's land use cover, which are freely available sources of spatial information. The study employs five machine-learning algorithms, including Catboost, Lightboost, and Random Forest, to estimate the multidimensional poverty index with spatial granularity. The results indicate that these models achieve promising performance in predicting poverty levels in Medellín, Colombia. The results showed that the Random Forest algorithm achieved the highest performance, with an MAE of 0.07504. Furthermore, the spatial distribution of the multidimensional poverty estimate was highly correlated with the true values of the distribution. This work contributes to predicting multidimensional poverty by demonstrating the potential of machine learning algorithms to utilize accessible spatial data. By providing evidence of the feasibility of estimating poverty levels at a granular spatial level, this methodology offers a powerful tool for policymakers to make poverty social interventions with low-cost evidence. Furthermore, this study has important implications for poverty eradication efforts in developing countries, where access to reliable data remains challenging. [ABSTRACT FROM AUTHOR]

Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas. [ABSTRACT FROM AUTHOR]