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This paper analyzes the geography of super-gentrification in US cities, the further intensification of class upgrading after a neighborhood has already gentrified. Building a national longitudinal tract database of gentrification intensity indicators, we analyze where this process has occurred across the 45 most populous metropolitan regions. We develop a method for quantifying metro-specific gentrification indices, then compare the class and racial demographics of super-gentrified tracts against other kinds of affluent places. We also interpret these national patterns with a case study of gentrification’s broader geographies in greater New York City. While super-gentrification is most commonly researched in global mega-cities, we found a wider geography including substantial suburban and smaller city patterns. We also found that super-gentrified neighborhoods are less racially diverse than other gentrified neighborhoods, and are more demographically similar to historically affluent (but not recently gentrified) neighborhoods. The study contributes a national comparative analysis of gentrification intensity patterns, and a longitudinal analysis of what happens after a neighborhood has already gentrified. 

This study provides preliminary insights into the linguistic features that contribute to Internet censorship in mainland China. We collected a corpus of 344 censored and uncensored microblog posts thatwere published onSinaWeibo and built a Naive Bayes classifier based on the linguistic, topic-independent, features. The classifier achieves a 79.34%accuracy in predicting whether a blog post would be censored on Sina Weibo.