This repository contains the data and code for my project.
The COVID-19 virus has caused unprecedented impacts on people's day-to-day life, where cities around the world are still very much suffering from the pandemic. In order to combat the pandemic, different countries have implemented a series of stringent policies to mitigate the transmission of the virus. Accompanying these preventive measures are the potential changes in human movement trajectories. What does this mean to the urban environment and social activities that we know before the pandemic? How does human footprints change over time under the impacts of the pandemic? With the new emerging big data and crowd sourcing techniques, a wealth of geospatial data, such as location-based services (LBS), volunteered geographic information (VGI) data, is becoming more and more available. This provides the potentials for analyzing human mobility patterns at a much granular scale. In this project, I aim to analyze human mobility patterns over time to identify the potential changes of social diversity by leveraging mobile location data. The social diversity here refers to the diversity of home locations of visitors in an urban space. In other words, instead of focusing on the physical environment, the diversity is measured from the perspective of the social dimension of urban spaces based on the activity profiles of their visitors. Taking Auckland city as a case study, I try to answer three specific questions as listed below:
- How does social diversity change over time under the COVID-19 pandemic?
- Where are the most diverse neighbourhoods?
- Are there any similar character(s) of neighbourhoods with a relatively high value of diversity?
This repository contains all the data and code needed to reproduce the results and figures in this project. The data can be found in data folder. The steps taken to reproduce the results and figures can be found in:
- index.Rmd: Data analysis and visualization.
Text + figures and data: CC-BY-4.0
Code: See the DESCRIPTION file