H4M

In this repository we provide code of the paper:

H4M: Heterogeneous, Multi-source, Multi-modal, Multi-view and Multi-distributional Dataset for Socioeconomic Analytics in Case of Beijing

Yaping Zhao, Shuhui Shi, Ramgopal Ravi, Zhongrui Wang, Edmund Y. Lam, Jichang Zhao

arxiv link: https://arxiv.org/abs/2208.12542

The H4M Dataset is released at: https://indigopurple.github.io/H4M/index.html

Usage

0. For pre-requisites, run:

conda env create -f environment.yml
conda activate h4m

1. Visit the website of H4M Dataset. Download the Original H4M Dataset into this project folder, where the directory structure should be:

• H4M/
  • data/
    • dsaa_dataset_order_rename.csv
    • traffic.txt
    • points_of_interest.json
    • geo_tweets/
      • 20130914.txt
      • ...

2. To reproduce the results and figures in the paper, run:

python h4m.py

3. For further research, visit the website of H4M Dataset.

Related Work

Title	Paper	Code
House Price Prediction: A Multi-Source Data Fusion Perspective	Paper	Code
A Large-Scale Spatio-Temporal Multimodal Fusion Framework for Traffic Prediction	Paper	-
Large-Scale Traffic Congestion Prediction based on Multimodal Fusion and Representation Mapping	Paper	Code
PATE: Property, Amenities, Traffic and Emotions Coming Together for Real Estate Price Prediction	Paper	Code
H4M: Heterogeneous, Multi-source, Multi-modal, Multi-view and Multi-distributional Dataset for Socioeconomic Analytics in Case of Beijing	Paper	Code

Citation

Cite our paper if you find it interesting!

@ARTICLE{zhao2024,
  author={Zhao, Yaping and Zhao, Jichang and Lam, Edmund Y.},
  journal={Big Data Mining and Analytics}, 
  title={House Price Prediction: A Multi-Source Data Fusion Perspective}, 
  year={2024},
keywords={price prediction;real estate;data mining;machine learning},
  doi={10.26599/BDMA.2024.9020019}}


@inproceedings{zhao2022h4m,
  title={{H4M}: Heterogeneous, Multi-source, Multi-modal, Multi-view and Multi-distributional Dataset for Socioeconomic Analytics in Case of Beijing},
  author={Zhao, Yaping and Shi, Shuhui and Ravi, Ramgopal  and Wang, Zhongrui and Lam, Edmund Y and Zhao, Jichang},
  booktitle={IEEE International Conference on Data Science and Advanced Analytics},
  year={2022},
  organization={IEEE}
}

@inproceedings{zhao2022pate,
  title={{PATE}: Property, Amenities, Traffic and Emotions Coming Together for Real Estate Price Prediction},
  author={Zhao, Yaping and Ravi, Ramgopal and Shi, Shuhui and Wang, Zhongrui and Lam, Edmund Y and Zhao, Jichang},
  booktitle={IEEE International Conference on Data Science and Advanced Analytics},
  year={2022},
  organization={IEEE}
}