MM-UrbanFAC: Urban Functional Area Classification Model Based on Multimodal Machine Learning

Xu, Xiujuan; Bai, Yulin; Liu, Yu; Zhao, Xiaowei; Sun, Yuzhi

doi:10.1109/tits.2021.3083486

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…Compared to a single model, ensemble methods can effectively improve the performance of prediction by averaging the results of different models [72], so we also utilize Random Forest (RF), Gradient Boosting Machine (GBM), XGBoosting (XGBoost), and Categorical Boosting (CatBoost). The ensemble methods suppress the dispersion of the prediction results and improves the generalization and robustness of the multiple classification models by aggregating the results of the different models [73].…”

Section: B Training Classification Models To Predict New Technology C...mentioning

confidence: 99%

A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

2022

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Section: B Training Classification Models To Predict New Technology C...mentioning

confidence: 99%

A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

2022

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“…In parallel to the functional zones that urban space with specific functions constraining human being activities [11,13,14], human activity zone (HAZ) refers to a clustered area consisting of a combination of geospatial units exhibiting a certain similarity characterised by the human activity patterns [15]. As the representative of human activity dynamic in the urban areas, HAZ has been associated with amounts of identified urban functions to reveal the urban movements and structures [16][17][18], such as the intensity and evolution of urban space [19], the discrimination in the centre or sub-centre of urban areas [20] and identification and classification of function zones and land use areas [11,12,21,22].…”

Section: Introductionmentioning

confidence: 99%

Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic

et al. 2023

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Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.

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Multi-attribute diagnosis of urban flood-bearing bodies based on integrated learning with Stacking–GPR–QPSO coupling

Lv,

Wu,

Zheng

et al. 2024

Journal of Hydrology

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MM-UrbanFAC: Urban Functional Area Classification Model Based on Multimodal Machine Learning

Cited by 7 publications

References 22 publications

A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic

Multi-attribute diagnosis of urban flood-bearing bodies based on integrated learning with Stacking–GPR–QPSO coupling

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