School of Humanities and Social Science Division of Social Science 197 Involution and Intergenerational Education Investment Supervisor: WANG, Wen / SOSC Student: HU, Zhongying / ECOF Course: UROP1100, Spring This project serves the purpose of investigating higher education resources distribution and labor market changes to explain the reasons for the involution underlying China's economic development. Through literature reviewing, some possible parameters to proxy the quality of higher education and its data sources were found. After data collection and cleaning, I obtained the dataset of higher education resources distribution of China's provincial and city level from 1992 to 2020. Meanwhile, I also cleaned the census data to obtain the city level labor market dataset of China. After that, data quality checking through plotting was conducted and fixed effect model was used to analyze the influencing factors of education resources allocation and employment in China. Finally, this report shows the relationship between different parameters and educational resources as well as employment. Using Street View Imagery Data to Study City and Society Supervisor: ZHANG, Han / SOSC Student: JIAN, Yu Kei / COMP Course: UROP1100, Summer The Undergraduate Research Opportunities Program (UROP) project titled "Using Street View Imagery Data to Study City and Society" aims to investigate the relationship between Chinese citizens' perception with regards to surveillance cameras and the number of surveillance cameras present in their residential zones. To figure out the number of surveillance cameras deployed on the streets, we use Python to crawl street view images from Baidu map and feed them into an artificial intelligence (AI) model to detect and identify surveillance cameras within them. This progress report will provide a clear and detailed demonstration of the image crawler and the problems I encountered during this project period. Using Street View Imagery Data to Study City and Society Supervisor: ZHANG, Han / SOSC Student: WANG, Jiadi / COMP Course: UROP1100, Summer In the contemporary world of rapid urbanization, comprehending the correlation between cities and society is of utmost importance. While social science research has traditionally relied on survey data, modern online providers such as Google and Baidu have presented novel opportunities to investigate cities and their social dynamics using street view imagery data. The objective of this research initiative is to explore the potential of employing street view imagery data to analyze and quantify social phenomena pertaining to urbanization, such as gentrification, urban expansion, and surveillance. By leveraging machine learning and statistical analysis techniques, we endeavor to acquire fresh insights into these phenomena and their social implications.
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