Rental Housing Spot Markets: How Online Information Exchanges Can Supplement Transacted-Rents Data

Traditional US rental housing data sources such as the American Community Survey and the American Housing Survey report on the transacted market—what existing renters pay each month. They do not explicitly tell us about the spot market—i.e., the asking rents that current homeseekers must pay to acquire housing—though they are routinely used as a proxy. This study compares governmental data to millions of contemporaneous rental listings and finds that asking rents diverge substantially from these most recent estimates. Conventional housing data understate current market conditions and affordability challenges, especially in cities with tight and expensive rental markets

Using Open Source Data to Identify Transit Deserts in Four Major Chinese Cities

You are viewing an article from the International Journal of Geo-Information that was in the Good Systems Network Digest in February 2020.Office of the VP for Researc

Pricing Strategy and Quick Response Adoption System with Strategic Customers

This study determined the competitive advantage of a quick response (QR) system when a firm faces forward-looking customers with heterogeneous and uncertain valuations for a product, uncertain demand, and two selling periods. We identify two classes of pricing strategies, namely, no-price commitment strategy and price commitment strategy. Interestingly, the unique equilibrium is proven to exist if and only if most customers have high tastes on a product’s value. We also prove that when customers possess beliefs about the markdown in the second period being smaller enough, a firm obtains a high profit with price commitment; otherwise he obtains a high profit without price commitment. Moreover, we distinguish the competitive advantage of a QR system from two strategies. When a firm uses no-price commitment strategy, the value of QR system in the first period decreases and in the second period increases with customer’s strategic behavior. When a firm provides price commitment, the value of QR system in the first period may increase, decrease, or decrease first and then increase with customer’s strategic behavior. And the value of QR in the second period under price commitment strategy decreases or rises first and then decreases with customer’s strategic behavior

Mapping the Intercounty Transmission Risk of COVID-19 in New York State

Social Science Research Network published a piece over COVID-19 and includes three UT Affiliates that is published on May 1, 2020.Office of the VP for Researc

Exploring the Spatial Distribution of Air Pollutants and COVID-19 Death Rate: A Case Study for Los Angeles County, California

Objective Since March 2020, COVID-19 has rapidly spread across the world with over 240 million cases and over 5 million deaths as of November 2021. It has been unclear what role air pollutants may play in exacerbating respiratory illnesses such as COVID-19 due to their interaction with the respiratory system. The association with air pollutants and COVID-19 severity has been explored at the regional and metropolitan area, however it is unclear if such an association is consistent at the neighborhood level. Methods Weekly death rates from COVID-19 from March 2020 to November 2021 were compared using one-sided unpaired t-tests across 11 neighborhoods located in Los Angeles County using data collected by the Los Angeles County Public Health department. Air pollutant information was collected from Environmental Protection Agency (EPA) sensors located in the 11 neighborhoods and were also analyzed using a one-sided unpaired t-test between neighborhoods that had a significant difference in COVID-19 death rates. Results Out of 23 significant comparisons for COVID-19 weekly death rate, 18 comparisons confirmed that NO2 levels were higher in neighborhoods that had higher COVID-19 weekly death rates, similarly, 12 out of 19 comparisons confirmed the same relationship with CO levels, 14 out of 23 comparisons confirmed the same relationship with ozone levels, and 6 out of 6 comparisons confirmed the same relationship with PM 10. Implications Our study found a positive association with air pollutants and COVID-19 deaths as seen in the literature on a smaller area within Los Angeles County. This association along with biological plausibility suggests a potential causal link, which may serve as an important public health consideration for urban planners and policy makers in terms of reducing urban air pollution

Ride-hailing services can make travel easier for disadvantaged communities in low-density transit deserts

Ride-hailing services are often seen as benefiting the middle classes and other well-off Americans. But can they also serve people living in low-density areas, especially those who do not own vehicles? In new research, Shengxiao (Alex) Li, Wei Zhai, Junfeng Jiao, and Chao (Kenneth) Wang examined how ride-hailing services have reshaped transportation across neighborhoods in Austin, Texas. They find that while ride-hailing services mainly serve those who live downtown, where transit services are available, they also have the potential to help people living in low-income and low-density neighborhoods, and those without vehicles

Fire and Smoke Digital Twin -- A computational framework for modeling fire incident outcomes

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens.Comment: 8 pages, 8 figures, conferenc

Micro-Clearance Oil Film Temperature Field Characteristics of High Speed and Heavy Type Hydrostatic Thrust Bearing under Extreme Operating Conditions

To explore the micro-clearance oil film temperature field characteristics of hydrostatic thrust bearings under operating conditions of high speed and heavy load, a mathematical model of micro-clearance oil film is established. According to the principle of computational fluid dynamics, the relationship between load capacity and rotational speed is calculated, and the model is solved using the finite volume method. The micro-clearance oil film temperature field is also investigated and tested to verify the theoretical analysis. The results show that the rotational speed is coupled with the load-carrying capacity of hydrostatic thrust bearings. When the extreme operating conditions are between 0t-228.9r/min and 4t-214.9r/min, the oil film maximum temperature increases slowly with the load increase and rotational speed decrease, and the average temperature decreases slowly. On the other hand, when the extreme operating conditions are between 4t-214.9r/min and 32t-78.9r/min, the maximum temperature and the average temperature slowly decrease as the load increases and the rotational speed decreases; the influence of rotational speed is greater than that of load, and the temperature rise of the upstream side is sharper than that of the downstream side

AGGA: A Dataset of Academic Guidelines for Generative AIs

AGGA (Academic Guidelines for Generative AIs) is a dataset of 80 academic guidelines for the usage of generative AIs and large language models in academia, selected systematically and collected from official university websites across six continents. Comprising 181,225 words, the dataset supports natural language processing tasks such as language modeling, sentiment and semantic analysis, model synthesis, classification, and topic labeling. It can also serve as a benchmark for ambiguity detection and requirements categorization. This resource aims to facilitate research on AI governance in educational contexts, promoting a deeper understanding of the integration of AI technologies in academia