School of Humanities and Social Science Division of Social Science 213 Ridership and Public Transit Expansion Supervisor: WANG Wen / SOSC Student: WANG Yiwei / QSA Course: UROP1100, Summer This report is a progress report of the project on ridership and public transit expansion. Specifically, this project investigates the effects of public transit expansion in the U.S. and how it may affect ridership and commuting patterns of residents. The main content of this semester includes data downloading and data cleaning. The source of the data includes Census Transportation Planning Products Program (CTPP) and American Community Survey (ACS) 5-year estimates. I finished downloading and cleaning the tract-level transportation data and census data using Stata in UROP. This report will review the process of downloading and cleaning data. Identifying Protest Events in China with Social Media Data Supervisor: ZHANG Han / SOSC Student: FANG Zihang / GCS Course: UROP1100, Fall UROP2100, Spring The report describes the progress of the tasks that has been done in the Spring semester of 2022 and plans to be tried out in the incoming Summer. Integration of the advanced digital information and algorithmic means with modern governance has been a fuzz and one of the tasks of modernization of modern governments in terms of their endeavours in bolstering governance and state capacity. For mainland China (referred to as China thereafter) as a party state, an increasingly firm and responsive grip of the societal affairs and more importantly, a pre-emptive approach to forestall the protest potentials have been adopted with the assistance of pervasive implementation of digital supervision facilities like closed-circuit television (CCTV) and manual complements entrenched in the grassroot governance system like Community Grid-style Management (CGSM), (shequ) wanggehua guanli. The paper in the first part will recapitulate the related literature regarding the CGSM framed against the backdrop of overall supervision and governance in China, and then the text analysis as a growingly prominent tool in analysing the text data an incorporate it into the regression and big data. With the Quanteda package in R language, the paper shows preliminarily the summary statistics of the text dataset and generate word cloud for visual demonstration to represent the baseline landscape.
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