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

TL;DR

This paper introduces H4M, a comprehensive dataset combining diverse data sources like real estate, traffic, and social media from Beijing, designed to facilitate socioeconomic research and urban planning applications.

Contribution

The paper presents a novel, multi-source, multi-modal dataset for socioeconomic analysis, enabling new research opportunities in urban studies and social sentiment analysis.

Findings

H4M dataset covers diverse data types from Beijing.

The dataset supports various socioeconomic and urban planning studies.

H4M is publicly available for research use.

Abstract

The study of socioeconomic status has been reformed by the availability of digital records containing data on real estate, points of interest, traffic and social media trends such as micro-blogging. In this paper, we describe a heterogeneous, multi-source, multi-modal, multi-view and multi-distributional dataset named "H4M". The mixed dataset contains data on real estate transactions, points of interest, traffic patterns and micro-blogging trends from Beijing, China. The unique composition of H4M makes it an ideal test bed for methodologies and approaches aimed at studying and solving problems related to real estate, traffic, urban mobility planning, social sentiment analysis etc. The dataset is available at: https://indigopurple.github.io/H4M/index.html