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Abstract The spatio-temporal variability of temperatures in cities impacts human well-being, particularly in a large metropolis. Low-cost sensors now allow the observation of urban temperatures at a much finer resolution, and, in recent years, there has been a proliferation of fixed and mobile monitoring networks. However, how to design such networks to maximize the information content of collected data remains an open challenge. In this study, we investigate the performance of different measurement networks and strategies by deploying virtual sensors to sample the temperature data set in high-resolution weather simulations in four American cities. Results show that, with proper designs and a sufficient number of sensors, fixed networks can capture the spatio-temporal variations of temperatures within the cities reasonably well. Based on the simulation study, the key to optimizing fixed sensor location is to capture the whole range of impervious fractions. Randomly moving mobile systems consistently outperform optimized fixed systems in measuring the trend of monthly mean temperatures, but they underperform in detecting mean daily maximum temperatures with errors up to 5 °C. For both networks, the grand challenge is to capture anomalous temperatures under extreme events of short duration, such as heat waves. Here, we show that hybrid networks are more robust systems under extreme events, reducing errors by more than 50%, because the time span of extreme events detected by fixed sensors and the spatial information measured by mobile sensors can complement each other. The main conclusion of this study concerns the importance of optimizing network design for enhancing the effectiveness of urban measurements. 

Abstract Extreme heat events can lead to increased risk of heat-related deaths. Furthermore, urban areas are often hotter than their rural surroundings, exacerbating heat waves. Unfortunately, validation is difficult; to our knowledge, most validations, even if they control for temperatures, really only validate a social vulnerability index instead of a heat vulnerability index. Here we investigate how to construct and validate a heat vulnerability index given uncertainty ranges in data for the city of Rio de Janeiro. First, we compare excess deaths of certain types of circulatory diseases during heat waves. Second, we use demographic and environmental data and factor analysis to construct a set of unobserved factors and respective weightings related to heat vulnerability, including a Monte Carlo analysis to represent the uncertainty ranges assigned to the input data. Finally, we use distance to hospital and clinics and their health record data as an instrumental variable to validate our factors. We find that we can validate the Rio de Janeiro heat vulnerability index against excess deaths during heat waves; specifically, we use three types of regressions coupled with difference in difference calculations to show this is indeed a heat vulnerability index as opposed to a social vulnerability index. The factor analysis identifies two factors that contribute to >70% of the variability in the data; one socio-economic factor and one urban form factor. This suggests it is necessary to add a step to existing methods for validation of heat vulnerability indices, that of the difference-in-difference calculation.