Zhu Wei

Zhu Wei

Geographic information Science

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portfolio

publications

Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data

Published in sustainability, 2020

Location-based social media have facilitated us to bridge the gap between virtual and physical worlds through the exploration of human online dynamics from a geographic perspective. This study uses a large collection of geotagged photos from Flickr to investigate the complexity of spatial interactions at the country level. We adopted three levels of administrative divisions in mainland China—province, city, and county—as basic geographic units and established three types of topology—province–province network, city–city network, and county–county network—from the extracted user movement trajectories. We conducted the scaling analysis based on heavy-tailed distribution statistics including power law exponents, goodness of fit index, and ht-index, by which we characterized a great complexity of the trajectory lengths, spatial distribution of geotagged photos, and the related metrics of built networks. The great complexity indicates the highly imbalanced ratio of populated-to-unpopulated areas or large-to-small flows between areas. More interestingly, all power law exponents were around 2 for the networks at various spatial and temporal scales. Such a recurrence of scaling statistics at multiple resolutions can be regarded a statistical self-similarity and could thus help us to reveal the fractal nature of human mobility patterns.

Recommended citation: Zhu, W.; Ma, D.; Zhao, Z.; Guo, R. Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data. Sustainability 2020, 12, 9778.https://doi.org/10.3390/su12229778 https://zhuwgiser.github.io/files/Paper/Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data.pdf

The topological structure of urban roads and its relation with human activities at the street-based community level

Published in Frontiers in Earth Science, 2022

The topological structure of the underlying streets can help us better understand urban space and human activities therein. As human urban movements are inherently heterogenous in space and statistics, whether or not the network of streets holds a similar degree of heterogeneity worth being investigated.Relying on the graph theory and complex-network thinking, we adopted the street segment analysis-based methods and computed segmentbased topological metrics in the downtown of two major cities in China: Beijing and Shanghai. More specifically, we used Flickr photo location data as a proxy of human urban activities and counted the movement flow at levels of both streetbased communities and street segments. We measured the heterogeneity of each segment-based metric via the extent of being long-tailed in the rank-size distribution (long-tailedness). We found that segment-based betweenness was most long-tailed and was the best metric for capturing human activities within each community and that neither segment-based degree nor can closeness show a similar extent of long-tailedness and can have a good correlation with the segment-based flow. These findings point to the insight that the positive relationship between street structure and human activities is significantly shaped by their shared heterogeneous nature.

Recommended citation: Zhang Y, Chen Y, Zhu W, Wang W and Zhang Q (2022), The topological structure of urban roads and its relation with human activities at the streetbased community level. Front. Earth Sci. 10:966907. doi: 10.3389/feart.2022.966907 https://zhuwgiser.github.io/files/Paper/The topological structure of urban roads and its relation with human activities at the street-based community level.pdf

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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