scholarly journals Sexual orientation and gender identity victimization among young adults in the New York City metropolitan area: The P18 cohort study.

2019 ◽  
Vol 6 (4) ◽  
pp. 399-407
Author(s):  
Caleb LoSchiavo ◽  
Perry N. Halkitis ◽  
Farzana Kapadia
Keyword(s):  
New York ◽  
New York City ◽  
Young Adults ◽  
Cohort Study ◽  
Sexual Orientation ◽  
Gender Identity ◽  
Metropolitan Area ◽  
York City ◽  
And Gender

Club Drug Using Young Adults in New York City

2007 ◽  
Author(s):  
Jeffrey Parsons ◽  
Brian Kelly ◽  
Jonathan Weiser ◽  
Brooke Wells
Keyword(s):  
New York ◽  
New York City ◽  
Young Adults ◽  
York City ◽  
Club Drug

scholarly journals Early assessment of cancer outcomes in New York City firefighters after the 9/11 attacks: an observational cohort study

The Lancet ◽  
2011 ◽  
Vol 378 (9794) ◽  
pp. 898-905 ◽  
Author(s):  
Rachel Zeig-Owens ◽  
Mayris P Webber ◽  
Charles B Hall ◽  
Theresa Schwartz ◽  
Nadia Jaber ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Cohort Study ◽  
York City ◽  
Observational Cohort Study ◽  
Cancer Outcomes ◽  
Early Assessment ◽  
Observational Cohort

scholarly journals Canine and human infection with Borrelia burgdorferi in the New York City metropolitan area

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Brian H. Herrin ◽  
Melissa J. Beall ◽  
Xiao Feng ◽  
Monica Papeş ◽  
Susan E. Little
Keyword(s):  
New York ◽  
New York City ◽  
Borrelia Burgdorferi ◽  
Metropolitan Area ◽  
York City ◽  
Human Infection

Examination of New York City Transit’s Bus and Subway Ridership Trends During the COVID-19 Pandemic

2021 ◽  
pp. 036119812110288
Author(s):  
Anne Halvorsen ◽  
Daniel Wood ◽  
Darian Jefferson ◽  
Timon Stasko ◽  
Jack Hui ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Metropolitan Area ◽  
Relative Intensity ◽  
York City ◽  
Race And Ethnicity ◽  
New Technology ◽  
Time Of Day ◽  
Data Availability ◽  
Partial Data

The New York City metropolitan area was hard hit by COVID-19, and the pandemic brought with it unprecedented challenges for New York City Transit. This paper addresses the techniques used to estimate dramatically changing ridership, at a time when previously dependable sources suddenly became unavailable (e.g., local bus payment data, manual field checks). The paper describes alterations to ridership models, as well as the expanding use of automated passenger counters, including validation of new technology and scaling to account for partial data availability. The paper then examines the trends in subway and bus ridership. Peak periods shifted by both time of day and relative intensity compared with the rest of the day, but not in the same way on weekdays and weekends. On average, trip distances became longer for subway and local bus routes, but overall average bus trip distances decreased owing to a drop in express bus usage. Subway ridership changes were compared with neighborhood demographic statistics and numerous correlations were identified, including with employment, income, and race and ethnicity. Other factors, such as the presence of hospitals, were not found to be significant.

Sign in / Sign up

Export Citation Format

Share Document