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Within feminist geography, there is a growing consensus on the need for research to contribute to social change and transformation beyond the academy, and increased emphasis on the co‐production of impact. In this paper I critically reflect and report on how I co‐produced impact with a participatory audio‐visual research project, conducted in collaboration with women in Bogotá and Medellín and researchers and filmmakers based in the UK and Colombia. I focus particularly on co‐producing 'impact‐in‐process', which builds participants' capacities, creates spaces of reciprocal learning and increases participants' confidence and sense of ownership both during and beyond the research process. Yet, while co‐producing impact‐in‐process benefits research participants and has the potential to contribute to social change and transformation, this form of impact is rarely recognised as such. [ABSTRACT FROM AUTHOR]

Currently, sewer utility systems require extending management activities by developing tools, such as deterioration models, to face their aging problem. In the literature on sewer asset management, Support Vector Machines (SVM) have been a useful tool to predict and forecast pipe's structural conditions. In this paper, the differential evolution method was implemented as an optimization tool for the hyper-parameters combinations to use in SVM-based models for two different management objectives (network and pipe levels). These models were applied to Colombia's main cities of Bogotá and Medellin, resulting in a less than 6% deviation in the prediction of structural conditions in both cities at a network level. The DE-optimized SVM models at the pipe level show higher percentages of correct predictions in all structural conditions than non-optimized SVM models (conventional SVM model) for specific management objectives. Therefore, the relevance of optimizing the hyper-parameters of SVM models to improve predictions of the structural condition of unspecified sewer assets became apparent. [ABSTRACT FROM AUTHOR]

On October 26, 2016, a large-scale mass movement occurred surrounding the Medellín-Bogotá highway, near the municipality of Copacabana. This paper investigates the physical and legal conditions of the place where the landslide occurred, and thus, to corroborate the causes, the possibility of avoiding or preventing the phenomenon and the influence of anthropic activities in the sector. To determine the susceptibility to mass movements and verify the physical conditions, it was carried out by means of geological mapping, geomorphology and interpretation of satellite images. The legal and bibliographic review was consulted in the reports of the event, the PBOT of the municipality of Copacabana and the mining file of the Cantera Las Nieves. The area was found to be highly susceptible to mass movements, and exploitation in the Cantera Las Nieves triggered the landslide, which could be avoided. Similar phenomena could occur in the near future in the area. [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]

This paper describes the design of a fiber optic network between Bogotá and Medellin, whose main objective is to optimize the bandwidth by DWDM technology. The design was done with optical transmission equipment OSN 6800 with the latest technology, which can handle up to 80 wavelengths. Moreover, this network utilizes control nodes and network management software through T2000, which is used in the provision of services in the real optical networks. The most important contribution of this article is its practical character, because it can be used by the readers as a tool to guide the design of optical networks for data transmission, using commercial devices and the key parameters that are considered in any design. [ABSTRACT FROM AUTHOR]

This paper focuses on the evolution of violence trends in the three biggest Colombian cities, Bogotá, Medellín, and Cali. Two of those cities, Bogotá and Medellín, were able to cut dramatically their homicide rates, while Cali did not. The taming of violence in Bogotá and Medellín challenges structural explanations of urban violence in Latin America, which suggest that the effects of neoliberalism, juxtaposed to already very high levels of inequality and social exclusion, should “overwhelm” municipal administrations. The systematic comparison of Bogotá and Medellín, on the one hand, and Cali, on the other, suggests that any explanation of urban violence reduction should take into account political dimensions, including in particular city-level coalition building. [ABSTRACT FROM PUBLISHER]