About this Visualization
This tool is designed to help visualize a decade in the flows of immigration from China to Canada from 1912 to the imposition of the Chinese Exclusion Act in 1923, broken down by month. The data used is from the Head Tax Database, a record of all the Chinese immigrants who paid the tax imposed between 1885 and 1923 by the Canadian government in an attempt to control immigration and generate revenues. Although hundreds of locations in Canada are represented in the total dataset, and the number of entries is just under 100,000, in order to represent the data meaningfully, a subset of 5,000+ entries to 9 urban destinations with interesting patterns was selected. These destinations were Montreal, Toronto, Winnipeg, Nanaimo, Ottawa, Calgary, Hamilton, Windsor, and Edmonton. The top two urban destinations—Vancouver and Victoria, B.C., were not used in order to focus on destinations in other parts of Canada. Although these destinations suggest that much Chinese immigration to Canada during this period was to the big cities or industrial towns like Nanaimo, it should be emphasised that this is only a fraction of the entire dataset and there were in fact thousands of Chinese who moved to small towns in rural areas as some of the other visualizations of Chinese migration to Saskatchewan show. The four districts in Guangdong Province from which a majority of Chinese migrants to North America came--commonly known as the “Four Counties” (Siyup) region--were selected for this visualization. The tool visualises the flows between these four counties to each of the selected cities in Canada every month over the 1912 to 1923 period. Scrolling through the visualization should reveal some of the changes at higher and lower levels that took place during the 1912 to 1923 period – a notable one since it was punctuated by the First World War, which Canada was involved in as a dominion of the British Empire. In the process of preparing the data for the visualization, we uncovered some intriguing trends that we sought to capture and dictated which dimensions in the rich dataset we chose to present. In the end, district of origin proved to be the dimension most conducive to allowing you, the user, to explore the data. We hope the visualization serves as a tool that lets you discover some of these trends for yourself.