Jiaxiang Liu
PhD Researcher,
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Biography
I am a human geographer with specific interests in historical and urban geographies. My PhD study, titled "Anshan Steel Metropolis: Analysing Colonial Urban Construction and Industrial Heritage during the Manchukuo Period", is mainly focused on reconstructing the historical geography of Anshan city in China using both GIS technology and archival research methods.
I got my bachelor's degree in Geographical Information Science at the Nanjing Normal University, China in 2022. My master's degree was obtained from the University of Edinburgh in Earth Observation and Geoinformation Management in 2023. Over the past six years, I've been working with GIS and remote sensing to solve various geographical issues related to urban and health geography.
Research Summary
This project plans to focus on a unique city in China and its colonial past, Anshan, from the initial gathering place of 13 villages with the South Manchurian Railway Zone (Simplified Chinese:… read more
Current Research
This project plans to focus on a unique city in China and its colonial past, Anshan, from the initial gathering place of 13 villages with the South Manchurian Railway Zone (Simplified Chinese: 南满洲铁道附属地) to an important and famous industrial metropolis for more than a hundred years, which was entirely designed and built by Japanese colonizers at first. As the only example of Asian leadership in modern colonialism from the late 19th century, Japanese colonialism has not received much academic interest. This project tries to combine archival research and GIS technology to reconstruct the historical geography of this colonial industrial city from the 1932 to the end year of the First Five-Year Plan of the Chinese Communist Party in 1957.
Past Research
This is the past reseach of my master dissertation at the Univsersity of Edinburgh. It investigates the spatial and temporal patterns of Covid-19 infection rates in England during the second wave from 9th August 2020 to 17th April 2021. The Space-Time cubes with 3D visualisations are also employed to determine the distribution and changing patterns of hot and cold spots, and spatial clustering. The primary findings are as follows: (a) the majority of cold spots patterns locate in the southern England; (b) the distribution of cold and hot spots varies greatly between rural and urban areas. Additionally, using Spatial Panel data models, we examine the relations between Covid-19 diagnostic rate and population mobility. It accordingly determines the direct effects and spatial spillovers of reduced mobility and infection rates with a better-fitting spatial autocorrelation model (SAC). This research will help inform targeted policy to be implemented for those vulnerable districts and populations in the future.