Haifeng Niu 🌍
Haifeng Niu

Assistant Professor

Renmin University of China

I am an Assistant Professor at the School of Public Administration and Policy, Renmin University of China (RUC), and an affiliated member of the Lab of Interdisciplinary Spatial Analysis at the University of Cambridge. Prior to joining RUC, I served as a Research Associate on the European Union’s Horizon 2020-funded project Emotional Cities (WP4) on urban health and wellbeing. I obtained my PhD from the Department of Land Economy, University of Cambridge.

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Recent Publication
Using decision-tree methodologies to explore determinants of health and wellbeing outcomes at the local authority scale: the case study of London
The Road to Recovery: sensing public opinion towards reopening measures with social media data in post-lockdown cities
Delineating urban functional use from points of interest data with neural network embedding: a case study in Greater London
Neural Network

Delineating urban functional use from points of interest data with neural network embedding: a case study in Greater London

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. A neural network embedding model is employed in delineating urban functional use from POI (Points of Interest) data. Doc2Vec model directly trains vector representations for spatial areas while considering the spatial distribution of POIs. This paper explores the functional similarity among 574 POI classes and 4836 LSOAs (Lower Layer Super Output Areas) in Greater London. Doc2Vec model outperforms other semantic models (Word2Vec, LDA and TF-IDF) in urban functional areas identification. Similarity of POI classes trained by Doc2Vec model Create your slides in Markdown - click the Slides button to check out the example. Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.

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Publications
Haifeng Niu, Xinmeng Tu, Elisabete A. Silva (2023). Using decision-tree methodologies to explore determinants of health and wellbeing outcomes at the local authority scale: the case study of London. Built Environment.
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Haifeng Niu, Elisabete A. Silva (2023). Understanding temporal and spatial patterns of urban activities across demographic groups through geotagged social media data. _Computers, Environment and Urban Systems_.
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Yiqiao Chen, Haifeng Niu, Elisabete A. Silva (2021). The Road to Recovery: sensing public opinion towards reopening measures with social media data in post-lockdown cities. Cities.
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Haifeng Niu, Elisabete A. Silva (2021). Delineating urban functional use from points of interest data with neural network embedding: a case study in Greater London. Computers, Environment and Urban Systems.
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Haifeng Niu, Elisabete A. Silva (2020). Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications and Methods. Journal of Urban Planning and Development.
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Projects
eMOTIONAL Cities
Embedding of POIs in Greater London
Neural Word Embedding

Embedding of POIs in Greater London

This project use the neural word embedding technique to explore Points of Interest (POIs) data especially in the relationship between POI Classes. Doc2Vec model, a common neural word embedding model developed by Quoc V. Le and Tomas Mikolov from Google, is adopted in this project. The input data for this two-layer neural network is the POI sequences based on geographical distribution of 0.4 million in Greater London. The specific method for constructing POI sequences can be found in the UKGISR 2020 paper. Doc2Vec model returns fixed-length vectors (20-dimensional) for 574 POI classes with fix length. The similarity matrix between all pairs of POI classes can be calculated by the cosine distance between the vectors. To illustrate the high-dimensional matrix, we use TensorFlow embedding projector to visualise.

Centre for Digital Built Britain 2018 Mini Project