Cities reshaped by Airbnb: A case study in New York City, Chicago, and Los Angeles.

In the last 10 years, Airbnb has rapidly grown from a simple, online bed and breakfast operation to a leading global hospitality service provider. Scholars have been using different spatial analysis tools to study its potential impacts on cities. To better understand Airbnb's impact this featured graphic applied a cartogram processing tool to reshape census tracts based on Airbnb listing intensity in three major US cities (New York City, Chicago, and Los Angeles). Results showed that different cities have different patterns of Airbnb listings. Census tracts in New York City became completely unrecognizable after the analysis, which indicted a highly skewed Airbnb distribution in the city. Compared with New York City, we saw less and least deformation in Chicago and Los Angeles, respectively, where Airbnb was more evenly distributed. The results showed that Airbnb listings were very evenly distributed in the large US cities. Airbnb would impose completely different impacts on different neighborhoods based on their locations.

Understanding the Shared E-scooter Travels in Austin, TX.

This paper investigated the travel patterns of 1.7 million shared E-scooter trips from April 2018 to February 2019 in Austin, TX. There were more than 6000 active E-scooters in operation each month, generating over 150,000 trips and covered approximately 117,000 miles. During this period, the average travel distance and operation time of E-scooter trips were 0.77 miles and 7.55 min, respectively. We further identified two E-scooter usage hotspots in the city (Downtown Austin and the University of Texas campus). The spatial analysis showed that more trips originated from Downtown Austin than were completed, while the opposite was true for the UT campus. We also investigated the relationship between the number of E-scooter trips and the surrounding environments. The results show that areas with higher population density and more residents with higher education were correlated with more E-scooter trips. A shorter distance to the city center, the presence of transit stations, better street connectivity, and more compact land use were also associated with increased E scooter usage in Austin, TX. Surprisingly, the proportion of young residents within a neighbourhood was negatively correlated with E-scooter usage.

An empirical analysis of Airbnb listings in forty American cities.

In the past ten years, Airbnb has rapidly grown from a small, online bed and breakfast product to a leading peer-to-peer hospitality magnate which operates in eighty thousand cities globally. It now offers rooms-for-rent, entire houses for rent, and even allows people to book 'experiences' through the platform. Consequently, cities, researchers, and the concerned public are focusing more on its impacts and exploring viable ways to regulate and facilitate the business while minimizing its potentially negative effects. To better understand Airbnb's operation in US cities, this paper explored how demographics, socioeconomics, and transportation might affect Airbnb listings in forty US cities. The results showed that Airbnb rentals were more likely to locate in neighborhoods with good transit service, short distances to the city center, and high median house value and household income. This study indicated the possible social inequality risk in the shared economy.

Reduction in population exposure to PM2.5 and cancer risk due to PM2.5-bound PAHs exposure in Beijing, China during the APEC meeting.

Radical measures for controlling ambient air pollution sources were employed by the Chinese government during the Asia-Pacific Economic Cooperation (APEC) meeting in 2014, providing a unique case to evaluate the health effect benefits from such measures. To examine the cancer risk reduction from the source control measures during the APEC meeting, we estimated the reduction in population exposure to PM2.5 and PAHs and the reduction in PAHs-associated cancer risk if the control measures were sustained over time. We determined the population exposure to PM2.5 and PM2.5-bound PAHs for the 21.52 million Beijing residents using a Land Use Regression model to determine the spatial distribution of PM2.5 and a Monte Carlo approach to revise indoor/outdoor infiltration factor and time activity patterns. Into the model and approach, we incorporated the spatial variance and indoor/outdoor differences in the PM2.5 and PM2.5-bound PAHs concentrations, based on measurements. We then estimated lung cancer risk using the population attributable fraction (PAF), assuming the control measures were sustained over time. The mean PM2.5 exposure concentration decreased from 37.5 µg/m3 (CI:17.1-74.9 µg/m3) to 24.0 µg/m3 (CI:10.2-47.7 µg/m3), whereas the mean PM2.5-bound equivalent benzo[a]pyrene (BaPeq) exposure concentration decreased from 7.1 ng/m3 (CI:3.3-14.2 ng/m3) to 4.2 ng/m3 (CI:1.8-7.7 ng/m3), resulting in a reduction in the lung cancer PAF from 0.75% to 0.45%, if the measures were sustained over time.