Differing Views of Lodging Reality: Airdna, STR, and Airbnb

2018 ◽  
Vol 60 (3) ◽  
pp. 193-199 ◽  
Author(s):  
Vinod Agarwal ◽  
James V. Koch ◽  
Robert M. McNab
Keyword(s):  
At Risk ◽  
Performance Metrics ◽  
Short Term ◽  
Virginia Beach ◽  
Independent Firm ◽  
Using Data ◽  
Occupancy Rates ◽  
Upward Biases

Airbnb is an Internet-based firm that connects potential short-term renters with hosts who own or control rental properties. Its rapidly expanding activities are tracked by Airdna, an independent firm that generates seemingly conventional performance metrics describing Airbnb. These metrics include occupancy rates, average daily rates, and revenue per available room. However, Airdna does not adhere to long-established STR definitions for these variables. Using data from Virginia Beach, Virginia, we demonstrate that Airdna’s performance metrics exhibit notable upward biases vis-á-vis STR’s metrics. Potential rental hosts, hoteliers, tax collectors, and investors are at risk if they act on the assumption that Airdna’s metrics are comparable with widely understood measures used by STR and tourism experts.

scholarly journals Transmission Dynamics and Short-Term Forecasts of COVID-19: Nepal 2020/2021

Epidemiologia ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 639-659
Author(s):  
Sushma Dahal ◽  
Ruiyan Luo ◽  
Raj Kumar Subedi ◽  
Meghnath Dhimal ◽  
Gerardo Chowell
Keyword(s):  
Performance Metrics ◽  
Growth Models ◽  
National Level ◽  
Growth Parameter ◽  
Transmission Dynamics ◽  
Short Term ◽  
Linear Pattern ◽  
Mobility Data ◽  
Using Data ◽  
Second Wave

Nepal was hard hit by a second wave of COVID-19 from April–May 2021. We investigated the transmission dynamics of COVID-19 at the national and provincial levels by using data on laboratory-confirmed RT-PCR positive cases from the official national situation reports. We performed 8 week-to-week sequential forecasts of 10-days and 20-days at national level using three dynamic phenomenological growth models from 5 March 2021–22 May 2021. We also estimated effective and instantaneous reproduction numbers at national and provincial levels using established methods and evaluated the mobility trends using Google’s mobility data. Our forecast estimates indicated a declining trend of COVID-19 cases in Nepal as of June 2021. Sub-epidemic and Richards models provided reasonable short-term projections of COVID-19 cases based on standard performance metrics. There was a linear pattern in the trajectory of COVID-19 incidence during the first wave (deceleration of growth parameter (p) = 0.41–0.43, reproduction number (Rt) at 1.1 (95% CI: 1.1, 1.2)), and a sub-exponential growth pattern in the second wave (p = 0.61 (95% CI: 0.58, 0.64)) and Rt at 1.3 (95% CI: 1.3, 1.3)). Across provinces, Rt ranged from 1.2 to 1.5 during the early growth phase of the second wave. The instantaneous Rt fluctuated around 1.0 since January 2021 indicating well sustained transmission. The peak in mobility across different areas coincided with an increasing incidence trend of COVID-19. In conclusion, we found that the sub-epidemic and Richards models yielded reasonable short-terms projections of the COVID-19 trajectory in Nepal, which are useful for healthcare utilization planning.

scholarly journals A Skellam regression model for quantifying positional value in soccer

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Konstantinos Pelechrinis ◽  
Wayne Winston
Keyword(s):  
Regression Model ◽  
Video Game ◽  
Soccer Player ◽  
Performance Metrics ◽  
Optical Tracking ◽  
General Managers ◽  
Annual Salary ◽  
Using Data ◽  
English Premier League ◽  
Positional Value

Abstract Soccer is undeniably the most popular sport world-wide and everyone from general managers and coaching staff to fans and media are interested in evaluating players’ performance. Metrics applied successfully in other sports, such as the (adjusted) +/− that allows for division of credit among a basketball team’s players, exhibit several challenges when applied to soccer due to severe co-linearities. Recently, a number of player evaluation metrics have been developed utilizing optical tracking data, but they are based on proprietary data. In this work, our objective is to develop an open framework that can estimate the expected contribution of a soccer player to his team’s winning chances using publicly available data. In particular, using data from (i) approximately 20,000 games from 11 European leagues over eight seasons, and, (ii) player ratings from the FIFA video game, we estimate through a Skellam regression model the importance of every line (attackers, midfielders, defenders and goalkeeping) in winning a soccer game. We consequently translate the model to expected league points added above a replacement player (eLPAR). This model can further be used as a guide for allocating a team’s salary budget to players based on their expected contributions on the pitch. We showcase similar applications using annual salary data from the English Premier League and identify evidence that in our dataset the market appears to under-value defensive line players relative to goalkeepers.

scholarly journals Higher Handgrip Strength Is Linked to Better Cognitive Performance in Chinese Adults with Hypertension

2021 ◽  
Vol 11 (8) ◽  
pp. 985
Author(s):  
Shenghua Lu ◽  
Fabian Herold ◽  
Yanjie Zhang ◽  
Yuruo Lei ◽  
Arthur F. Kramer ◽  
...  
Keyword(s):  
Cognitive Performance ◽  
Short Term Memory ◽  
Handgrip Strength ◽  
Adult Health ◽  
Linear Regression Models ◽  
Chinese Adults ◽  
Short Term ◽  
Term Memory ◽  
Using Data ◽  
The Relationship

Objective: There is growing evidence that in adults, higher levels of handgrip strength (HGS) are linked to better cognitive performance. However, the relationship between HGS and cognitive performance has not been sufficiently investigated in special cohorts, such as individuals with hypertension who have an intrinsically higher risk of cognitive decline. Thus, the purpose of this study was to examine the relationship between HGS and cognitive performance in adults with hypertension using data from the Global Ageing and Adult Health Survey (SAGE). Methods: A total of 4486 Chinese adults with hypertension from the SAGE were included in this study. Absolute handgrip strength (aHGS in kilograms) was measured using a handheld electronic dynamometer, and cognitive performance was assessed in the domains of short-term memory, delayed memory, and language ability. Multiple linear regression models were fitted to examine the association between relative handgrip strength (rHGS; aHGS divided by body mass index) and measures of cognitive performance. Results: Overall, higher levels of rHGS were associated with higher scores in short-term memory (β = 0.20) and language (β = 0.63) compared with the lowest tertiles of rHGS. In male participants, higher HGS was associated with higher scores in short-term memory (β = 0.31), language (β = 0.64), and delayed memory (β = 0.22). There were no associations between rHGS and cognitive performance measures in females. Conclusion: We observed that a higher level of rHGS was associated with better cognitive performance among hypertensive male individuals. Further studies are needed to investigate the neurobiological mechanisms, including sex-specific differences driving the relationship between measures of HGS and cognitive performance in individuals with hypertension.

scholarly journals Long-Term Development of Urban Agriculture: Resilience and Sustainability of Farmers Facing the Covid-19 Pandemic in Japan

2021 ◽  
Vol 13 (8) ◽  
pp. 4316
Author(s):  
Shingo Yoshida ◽  
Hironori Yagi
Keyword(s):  
Urban Agriculture ◽  
Food Systems ◽  
Direct Marketing ◽  
Farming System ◽  
Long Term Effects ◽  
Short Term ◽  
Short Supply ◽  
Using Data ◽  
Urban People

The coronavirus disease 2019 (Covid-19) pandemic has forced global food systems to face unprecedented uncertain shocks even in terms of human health. Urban agriculture is expected to be more resilient because of its short supply chain for urban people and diversified farming activities. However, the short-and long-term effects of the Covid-19 pandemic on urban farms remain unclear. This study aims to reveal the conditions for farm resilience to the Covid-19 pandemic in 2020 and the relationship between short-term farm resilience and long-term farm development using data from a survey of 74 farms located in Tokyo. The results are as follows. First, more than half of the sample farms increased their farm sales during this period. This resilience can be called the “persistence” approach. Second, short-term farm resilience and other sustainable farm activities contributed to improving farmers’ intentions for long-term farm development and farmland preservation. Third, the most important resilience attributes were the direct marketing, entrepreneurship, and social networks of farmers. We discussed the necessity of building farmers’ transformative capabilities for a more resilient urban farming system. These results imply that support to enhance the short-term resilience of urban farms is worth more than the short-term profit of the farms.

Determinants of Treasury-LIBOR Swap Spreads

2005 ◽  
Vol 08 (04) ◽  
pp. 687-705 ◽  
Author(s):  
D. K. Malhotra ◽  
Vivek Bhargava ◽  
Mukesh Chaudhry
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Default Risk ◽  
Yield Curve ◽  
Short Term ◽  
Long Run ◽  
Interest Rate Volatility ◽  
Short Run ◽  
Using Data ◽  
Swap Spreads

Using data from the Treasury versus London Interbank Offer Swap Rates (LIBOR) for October 1987 to June 1998, this paper examines the determinants of swap spreads in the Treasury-LIBOR interest rate swap market. This study hypothesizes Treasury-LIBOR swap spreads as a function of the Treasury rate of comparable maturity, the slope of the yield curve, the volatility of short-term interest rates, a proxy for default risk, and liquidity in the swap market. The study finds that, in the long-run, swap spreads are negatively related to the yield curve slope and liquidity in the swap market. We also find that swap spreads are positively related to the short-term interest rate volatility. In the short-run, swap market's response to higher default risk seems to be higher spread between the bid and offer rates.

Effects of early intervention in children at risk: Short-term and long-term findings from an attachment-based intervention program

2015 ◽  
Vol 3 (3) ◽  
pp. 98-102 ◽  
Author(s):  
Annabel Zwönitzer ◽  
Ute Ziegenhain ◽  
Ina Bovenschen ◽  
Kristina Bressem ◽  
Melanie Pillhofer ◽  
...  
Keyword(s):  
At Risk ◽  
Early Intervention ◽  
Intervention Program ◽  
Children At Risk ◽  
Short Term

Children in Custody: 20-Year Trends in Juvenile Detention, Correctional, and Shelter Facilities

1998 ◽  
Vol 44 (4) ◽  
pp. 526-543 ◽  
Author(s):  
Bradford Smith
Keyword(s):  
At Risk ◽  
Violent Offenders ◽  
Juvenile Detention ◽  
Correctional Facilities ◽  
Residential Placement ◽  
Statistical Series ◽  
Juvenile Population ◽  
Juvenile Correctional Facilities ◽  
Using Data

Using data drawn from the Children in Custody (CIC) statistical series, this article presents 20-year trends (1) in the number and characteristics of juvenile correctional facilities, (2) in the number and characteristics of youth held in juvenile correctional facilities, and (3) in the costs of confinement of youth held in juvenile correctional facilities. A brief history and some of the limitations of the CIC data are presented. The new Census of Juveniles in Residential Placement that replaced CIC in 1997 is briefly described. The article concludes that, even after controlling for the size of the at-risk juvenile population and inflation, there were more juveniles, more males, more minorities, and more violent offenders in more crowded, secure, and costly juvenile correctional facilities in 1995 than there were in the preceding years.

On-Demand Public Transit: A Markovian Continuous Approximation Model

2021 ◽  
Author(s):  
Daniel F. Silva ◽  
Alexander Vinel ◽  
Bekircan Kirkici
Keyword(s):  
System Performance ◽  
Performance Metrics ◽  
Public Transit ◽  
Continuous Approximation ◽  
Efficient Operation ◽  
Fleet Sizing ◽  
On Demand ◽  
Single Vehicle ◽  
Transit Agencies ◽  
Using Data

With recent advances in mobile technology, public transit agencies around the world have started actively experimenting with new transportation modes, many of which can be characterized as on-demand public transit. Design and efficient operation of such systems can be particularly challenging, because they often need to carefully balance demand volume with resource availability. We propose a family of models for on-demand public transit that combine a continuous approximation methodology with a Markov process. Our goal is to develop a tractable method to evaluate and predict system performance, specifically focusing on obtaining the probability distribution of performance metrics. This information can then be used in capital planning, such as fleet sizing, contracting, and driver scheduling, among other things. We present the analytical solution for a stylized single-vehicle model of first-mile operation. Then, we describe several extensions to the base model, including two approaches for the multivehicle case. We use computational experiments to illustrate the effects of the inputs on the performance metrics and to compare different modes of transit. Finally, we include a case study, using data collected from a real-world pilot on-demand public transit project in a major U.S. metropolitan area, to showcase how the proposed model can be used to predict system performance and support decision making.

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