London POI Classes Embedding

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.

For a better interactive exploration, you can access the visualised POIs embedding via TensorFlow.

Haifeng Niu
Haifeng Niu
Research Associate

I am a Research Associate at the Lab of Interdisciplinary Spatial Analysis, Department of Land Economy, University of Cambridge, currently leading spatial analysis work in the European Union’s Horizon 2020-funded project Emotional Cities(WP4). My expertise includes urban big data mining, spatial data science, geo-visualisation, and urban sensing and modelling. I have a strong interest in how the intersection of machine learning & AI and urban big data better supports urban planning, policy-making and smart management.