Browsing by Author "Hurtubia, Ricardo"

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    Analyzing the Determinants of Teleworking during the COVID-19 Pandemic in Chile
    (2024) Astroza, Sebastian; Hurtubia, Ricardo; Tirachini, Alejandro; Guevara, C. Angelo; Carrasco, Juan Antonio; Salas, Patricio; Munizaga, Marcela
    The COVID-19 pandemic triggered an unprecedented increase in telework, a trend expected to have lasting effects on the labor market and commuting patterns, including location preferences. Understanding the demand for telework is critical to face the challenges that may come in present and future scenarios with hybrid work arrangements. In this paper, a model for the probability of telework is proposed and estimated with data collected during two different periods of the COVID-19 pandemic, in 2020, in Chile. The model measures the correlation between several socioeconomic characteristics, and latent variables related to concerns about health and the economy, with the probability of teleworking. We find that low-income workers are less likely to telework, and that females are more likely to work from home. Latent variables also played a relevant role at the beginning of the pandemic: a greater concern about health issues increased the probability of teleworking. In comparison, a greater concern about the economic effects of the pandemic had the opposite impact. However, these effects shifted 10 weeks into the pandemic, when a total lockdown was imposed in the largest city. The implications of our findings for both policy and research after the pandemic are discussed.
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    Latent Segmentation of Urban Space through Residential Location Choice
    (2021) Cox Campos, Tomás; Hurtubia, Ricardo; CEDEUS (Chile)
    Understanding the preferences of households in their location decisions is key for residential demand forecast and urban policy making. Accounting for preference heterogeneity across agents is useful for the modelling process but not enough to completely describe location choice behavior. Due to place-specific conditions, the same agent may have different preferences depending on the sector of the city considered as potential location, a phenomena known as spatial heterogeneity. Segmenting the city by defining zones where agents are supposed to behave similarly has been a common modelling solution, assigning different zonal preference parameters in the estimation process. This has been usually done with two-step methods, where spatial segmentation is done independently of the location choice process, something that could bias estimation results. We propose and test a one-step model for simultaneous estimation of location preference parameters and spatial segmentation, therefore accounting for heterogeneity across agents and space. The model is based on Ellickson’s bid-auction approach for location choice and latent class models. We test our model with a case study in Santiago, Chile and compare it with other models for spatial segmentation. In terms of predictive power, our approach outperforms a model with no zones, a model with zones defined exogenously, and a clustering-based two-step model. This novel approach allows for a better conceptual ground for urban predictive models with spatial segmentation.
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    Measuring 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.; CEDEUS (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.
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    Preferencias de localización y segmentación de agentes logísticos en la Región Metropolitana de Santiago, Chile
    (2023) Ureta, Gerardo; Hurtubia, Ricardo; Giesen, Ricardo
    Los agentes de logística urbana toman decisiones de localización respecto de sus instalaciones logísticas (IL), lo que tiene un impacto relevante en la forma urbana. A partir de datos públicos y privados, se analiza el fenómeno de expansión de las IL en Santiago de Chile y se estiman modelos de elección de localización para distintos tipos de IL, según segmentaciones exógenas basadas en superficie construida y calidad de la construcción. Los resultados muestran que durante los últimos 20 años se ha producido una expansión logística considerable en Santiago, tanto en términos del destino de uso de suelo predial, como en superficie construida. Los resultados de los modelos de localización muestran que los distintos segmentos de IL tienen patrones de localización claramente diferentes.