Abstract
Understanding human mobility patterns provides knowledge about impacts of a socio-economic transformation in a rapidly urbanizing environment. This study assesses a long-term mobility data which uses a face-to-face questionnaire and GPS logger-based method of data collection for extracting socio-economic impacts from the rapid transformation. Conversion of mobility related information such as travel distance, direction, and time from the questionnaire survey into spatiotemporal information was carried out by developing an algorithm. To illustrate the proposed approach, a case study in Dawei Special Economic Zone, Myanmar was conducted. The results show that the questionnaire-based mobility data can be associated with GPS-based mobility data and diverse mobility patterns are found for different social groups in the stage of urban formation. The results enabled an understanding of the human dynamics in interactions, which can be used for monitoring rural sustainability and its challenges in the future with the background of the accelerated project development in the area.
Highlights
Rapid socio-economic transformations induced by urbanization and industrialization have a significant impact on local human mobility patterns [1,2]
After the mobility data is obtained with Global Positioning System (GPS) loggers, spatiotemporal data such as time, latitude and longitude are extracted from the devices
Even though traditional mobility data such as questionnaire survey and diary can only be displayed at the individual level [35], this method enables a large volume of mobility data to be visualized simultaneously and compare mobility patterns in time-series
Summary
Rapid socio-economic transformations induced by urbanization and industrialization have a significant impact on local human mobility patterns [1,2]. Understanding human mobility patterns can help in the exploration of the underlying driving factors and its diverse impacts to local villagers It can provide a significant insight in understanding the urban formation, and to monitor rural sustainability at a community level in the background of future accelerated project development. Assessment of a long-term human mobility pattern by merging both questionnaire and GPS survey data may be a key to understanding how local communities interact with a rapid socio-economic development and achieve rural sustainability. This study is expected to contribute to indexing the conventional human mobility data in order to handle further spatial data analysis associated with socio-economic data with use of remote sensing and GIS data
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