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- ItemPandemic-related streets transformations: accelerating sustainable mobility transitions in Latin America(2021) Vecchio, Giovanni; Tiznado Aitken, Ignacio Andrés; Mora Vega, Rodrigo; Centro de Desarrollo Urbano Sustentable (Chile)The COVID-19 pandemic and the need for physical distancing in public spaces have significant effects on sustainable mobility initiatives. Many cities around the world promoted temporary transformations of streets, redistributing road space to create emergency cycleways and expand sidewalks to allow the movement of people while granting a certain physical distance between people. While Europe and, to a lesser extent, North America have initially led these initiatives, Latin America has soon followed suit, without much of their technical experience, governance, resources and sometimes public acceptance for sustainable mobility. The paper examines if and how the major disruptive event posed by the COVID-19 has reconfigured sustainable mobility initiatives in Latin American cities. Interviewing key decision-makers, the paper reviews five initiatives across the region to examine the typology of interventions promoted and the spaces involved, the governance schemes that allowed them and the public acceptance towards such measures. The analysis demonstrates that the COVID-19 pandemic has fostered a faster adoption of sustainable mobility measures such as emergency cycleways, street pedestrianisations and other traffic calming initiatives, facilitating the creation of new coalitions and the public acceptance of these interventions thanks to the sense of urgency generated by the pandemic.
- ItemMeasuring heterogeneous perception of urban space with massive data and machine learning: An application to safety(2021) Ramírez Sarmiento, Tomás Ignacio; Hurtubia, Ricardo; Löbel Díaz, Hans-Albert; Rossetti, T.; Centro de Desarrollo Urbano Sustentable (Chile)Urban space safety Machine learning Heterogeneous perception Built environment In the last decade, large street imagery data sets and machine learning developments have allowed increasing scalability of methodologies to understand the effects of landscape attributes on the way they are perceived. However, these new methodologies have not incorporated individual heterogeneity in their analysis, even though differences by gender and other sociodemographic characteristics in the perception of safety and other aspects of landscapes and public spaces have been widely studied in social sciences and urban planning in lower scale studies. In the present study, we combine computational and statistical tools to develop a methodological proposal with high scalability and low implementation cost, which helps to identify and measure heterogeneous perception and its correlation to the presence of elements in the landscape. To achieve this, we implement a survey of perception of public spaces, collecting sociodemographic information of respondents. Then, we fit a discrete choice model to quantify perceptions of these spaces using a parametrization of images that jointly considers semantic segmentation and object detection as input. Our results show heterogeneity in the perception of safety in public spaces according to gender and the observer’s habitual mobility choices. The model is then applied to the city of Santiago, Chile. This produces a map of safety perception for different types of users. The proposed method and the obtained results can be a relevant input for the design of public spaces and decision making in the urban planning process.
- ItemThe policy implications of enabling mobilities(2019) Pucci, P.; Vecchio, Giovanni; Centro de Desarrollo Urbano Sustentable (Chile)
- ItemStations: Nodes and places of everyday life(2019) Pucci, P.; Vecchio, Giovanni; Centro de Desarrollo Urbano Sustentable (Chile)
- ItemBig data: hidden challenges for a fair mobility planning(2019) Pucci, P.; Vecchio, Giovanni; Centro de Desarrollo Urbano Sustentable (Chile)