The Geography of Cultural Ties and Human Mobility: Big Data in Urban Contexts

Abstract

A largely unexplored big data application in urban contexts is how cultural ties affect human mobility patterns. This article explores China's intercity human mobility patterns from social media data to contribute to our understanding of this question. Exposure to human mobility patterns is measured by big data computational strategy for identifying hundreds of millions of individuals' space–time footprint trajectories. Linguistic data are coded as a proxy for cultural ties from a unique geographically coded atlas of dialect distributions. We find that cultural ties are associated with human mobility flows between city pairs, contingent on commuting costs and geographical distances. Such effects are not distributed evenly over time and space, however. These findings present useful insights in support of the cultural mechanism that can account for the rise, decline, and dynamics of human mobility between regions.

在城市脉络中, 一个尚未被探索的大数据运用, 便是文化连结如何影响人类的能动性模式。本文从社交媒体数据探讨中国的城际人类能动性模式, 以此对上述问题的理解作出贡献。揭露人类的能动性模式, 是透过指认亿万人的时空足迹轨道的大数据电脑化策略进行估计。本研究从一个方言分佈的特殊地理编码地图集中, 将语言数据编码成文化连结的代理。我们发现, 文化连结与成对的城市组合之间的人类能动性流动有关, 并视通勤成本和地理距离而定。但此般效应却不具有均等的时间与空间分佈。这些研究发现呈现出有用的洞见, 支持能够解释区域间的人类能动性的兴起、衰退与动态的文化机制。

Una aplicación de los big data en gran medida inexplorada en los contextos urbanos concierne al modo como los patrones de movilidad humana son afectados por las ataduras culturales. Este artículo explora los patrones de movilidad humana interurbanos de China, con datos de los medios sociales, para contribuir a nuestro entendimiento de tal cuestión. La exposición a los patrones de movilidad humana es medida por medio de una estrategia computacional de big data para identificar cientos de millones de trayectorias espacio–tiempo de pisadas de individuos. Los datos lingüísticos son codificados como un proxy de ataduras culturales desde un atlas único de las distribuciones de dialectos geográficamente codificado. Descubrimos que las ataduras culturales están asociadas con flujos de movilidad humana entre pares de ciudades, contingentes con los costos del viaje pendular y la distancia geográfica. Sin embargo, tales efectos no están uniformemente distribuidos a través del tiempo y del espacio. En estos hallazgos se encuentran útiles percepciones en apoyo del mecanismo cultural que puede explicar la aparición, declive y dinámica de la movilidad humana entre regiones.

Key Words:

• big data
• cultural diversity
• geographical proximity
• human mobility

关键词：:

• 关键词：大数据
• 文化多样性
• 地理邻近性
• 人类能动性。

Palabras clave:

• big data
• diversidad cultural
• proximidad geográfica
• movilidad humana

Acknowledgments

We are grateful to Mei-Po Kwan and the anonymous reviewers for insightful comments. We thank Steve Gibbons, Stephan Heblich, Henry Overman, Paul Cheshire, Max Nathan, Sabine D'Costa, Yu Liu, Yanwei Chai, Siqi Zheng, Jie Chen, Huiping Li, and other seminar participants at the London School of Economics and Political Science, Tsinghua University, Peking University, Shanghai Jiaotong University, and Shanghai University of Finance and Economics for helpful suggestions and comments. We thank Weiyang Zhang for providing railway traffic flow data. We are indebted to Jiaoe Wang and Jingjuan Jiao for providing the minimum railroads travel time origin–destination matrix data.

Funding

Jianghao Wang acknowledges financial support from the National Natural Science Foundation of China (Project No. 41421001), Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University), and Key Technologies Research and Development Program of China (No. 2012BAI32B06). Tianshi Dai thanks the Natural Science Foundation of Guangdong Province, China (Project No. S2013040015623). Wenjie Wu would like to thank the National Natural Science Foundation of China (Project No. 41230632 and Project No. 71473105). The authors also acknowledge financial support from the Program for New Century Excellent Talents in University (NCET-110856) and National Social Science Foundation (14ZDB144).

Notes

1. Note that we look at the aggregate impact of cultural diversity on human mobility between cities, rather than analyzing the individual mobility within a city.

2. Note that in the preliminary work, we did the data cleaning for reducing the noise and outliers of the data set. To reduce the data computational burdens, we have filtered out Web advertising and gaming activities on Weibo. These messages generated substantial data volumes but did not reflect human physical mobility activities over time and space. Further, we have excluded the posted mobility information with unrealistic mobility patterns. Following the convention, a mobility pattern is considered to be unrealistic (Hawelka et al. 2014) if a social media user traveled from one city to another city with unusual speed (i.e., speed over 1,000 km/hour).

3. There is a large literature in using remote sensing data for reflecting land uses and urban development (e.g., Platt and Rapoza 2008; Wu et al. 2009; Hu and Wang 2013), although this is not the direct concern in our work.

4. This framework is drawn based on Anderson, De Palma, and Thisse (1992), Murata (2003), and Falck et al. (2012).

5. We define tourism cities as places that have at least one 5A tourism attraction point as awarded by the China National Tourism Administration.

6. See the detailed background information available at http://en.wikipedia.org/wiki/Chunyun.

7. Note that we selected weekday and weekend travel flow samples in January, April, July, and October as the corresponding representative month for each season.

8. Following the convention, core cities include the provincial-level municipalities (Beijing, Shanghai, Chongqing, Tianjin), vice-provincial cities, and provincial capital cities; other cities are defined as the peripheral metropolitan areas.

Additional information

Notes on contributors

Wenjie Wu

WENJIE WU is an Associate Professor in the School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, EH14 4AS, UK. E-mail: [email protected]. His research interests include urban transformations in China and the use of big data and GIS in urban analysis.

Jianghao Wang

JIANGHAO WANG is an Assistant Professor in the State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Chaoyang 100101, Beijing, China. E-mail: [email protected]. His research interests include geospatial analysis and modeling, spatiotemporal data mining, and remote sensing of environment.

Tianshi Dai

TIANSHI DAI is an Assistant Professor in the College of Economics at Jinan University, Guangzhou, 510632, China. E-mail: [email protected]. His research interests include development economics and public economics.