Urban Heat Islands: Beating the Heat with Multi-Modal Spatial Analysis

Yong, Marcus; Lim, Kwan Hui

doi:10.1109/bigdata50022.2020.9378391

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…With increasing urbanization in the world, it is important for urban planners to understand crowd levels for better urban planning [1]. Many users have made their social media posts publicly accessible and this can potentially be a source of useful insights for urban analytics, such as identifying events, detecting crowd levels and other environmental phenomenon [2], [3]. While another possible solution is to employ the use of physical sensors, such sensors are potentially costly and time consuming to implement on a large scale.…”

Section: Introductionmentioning

confidence: 99%

Urban Crowdsensing using Social Media: An Empirical Study on Transformer and Recurrent Neural Networks

Heng

Liu

Lim

2020

2020 IEEE International Conference on Big Data (Big Data)

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An important aspect of urban planning is understanding crowd levels at various locations, which typically require the use of physical sensors. Such sensors are potentially costly and time consuming to implement on a large scale. To address this issue, we utilize publicly available social media datasets and use them as the basis for two urban sensing problems, namely event detection and crowd level prediction. One main contribution of this work is our collected dataset from Twitter and Flickr, alongside ground truth events. We demonstrate the usefulness of this dataset with two preliminary supervised learning approaches: firstly, a series of neural network models to determine if a social media post is related to an event and secondly a regression model using social media post counts to predict actual crowd levels. We discuss preliminary results from these tasks and highlight some challenges.

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Section: Introductionmentioning

confidence: 99%