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The field of Walkable Neighborhood Systems is about neighborhoods as systems of land use, mobility, and transportation pricing, and how they can be understood as dense areas delineated by attractive walking distances. To date, four papers have been published in this field: Neighborhood density and travel mode (Lewis in Int J Sustain Dev World Ecol, 2018. https://doi.org/10.1080/13504509.2017.1321052), Walkable Neighborhood Systems (Lewis and Adhikari), San Francisco's neighborhoods and auto dependency (Lewis and Grande in Cities, 2019. https://doi.org/10.1016/j.cities.2018.12.017), and the Mismeasurement of Mobility for Walkable Neighborhood Systems (Lewis et al., Mismeasurement of Mobility for Walkable Neighborhood Systems, Mineta Transportation Institute, 2020). This paper summarizes the concepts and definitions of the field and replicates for Boston the methodology used in the San Francisco case study. We found 54 Boston neighborhoods using maps in a GIS and guidelines to determine neighborhood boundaries based on walking distances and land use. We establish much higher accuracy than using census blocks. We analyze neighborhood density as our independent variable and three dependent variables: sustainable modes (mode split), food sources, and walk score. The data is presented in tables, correlations, and choropleth-based graphics. Density correlated strongly with the dependent variables. The paper concludes with ideas about how to improve the performance of Walkable Neighborhood Systems for affordability, sustainability, mobility, health and safety, design, and community. [ABSTRACT FROM AUTHOR]

Water quality extremes, which water quality models often struggle to predict, are a grave concern to water supply facilities. Most existing water quality models use mean error functions to maximize the predictability of water quality mean value. This paper describes a composite quantile regression neural network (CQRNN) model, which simultaneously estimates non-crossing regression quantiles by minimizing the composite quantile regression error function. This method can improve the prediction of extremes. This paper evaluates the performance of CQRNN for predicting extreme values of turbidity and total organic carbon (TOC) and compares with quantile regression (QR), linear regression (LR), and k-nearest neighbors (KNN) in an application to the Hetch Hetchy Regional Water System, which is the primary water supply for San Francisco, CA. CQRNN is superior to QR, LR, and KNN for predicting the mean trend and extremes of turbidity and TOC, especially for the non-Gaussian turbidity data. The performance of CQRNN is the most stable relative to other methods over different training sample sizes. [ABSTRACT FROM AUTHOR]

In this paper additive models with p-order autoregressive conditional symmetric errors based on penalized regression splines are proposed for modeling trend and seasonality in time series. The aim with this kind of approach is try to model the autocorrelation and seasonality properly to assess the existence of a significant trend. A backfitting iterative process jointly with a quasi-Newton algorithm are developed for estimating the additive components, the dispersion parameter and the autocorrelation coefficients. The effective degrees of freedom concerning the fitting are derived from an appropriate smoother. Inferential results and selection model procedures are proposed as well as some diagnostic methods, such as residual analysis based on the conditional quantile residual and sensitivity studies based on the local influence approach. Simulations studies are performed to assess the large sample behavior of the maximum penalized likelihood estimators. Finally, the methodology is applied for modeling the daily average temperature of San Francisco city from January 1995 to April 2020. [ABSTRACT FROM AUTHOR]

The Bubonic Plague outbreak that wormed its way through San Francisco's Chinatown in 1900 tells a story of prejudice guiding health policy, resulting in enormous suffering for much of its Chinese population. This article seeks to discuss the potential for hidden "prejudice" should Artificial Intelligence (AI) gain a dominant foothold in healthcare systems. Using a toy model, this piece explores potential future outcomes, should AI continue to develop without bound. Where potential dangers may lurk will be discussed, so that the full benefits AI has to offer can be reaped whilst avoiding the pitfalls. The model is produced using the computer programming language MATLAB and offers visual representations of potential outcomes. Interwoven with these potential outcomes are numerous historical models for problems caused by prejudice and recent issues in AI systems, from police prediction and facial recognition software to recruitment tools. Therefore, this research's novel angle, of using historical precedents to model and discuss potential futures, offers a unique contribution. [ABSTRACT FROM AUTHOR]

The quality of the urban environment is crucial for societal well-being. Yet, measuring and tracking the quality of urban environment, their evolution, and spatial disparities is difficult due to the amount of on-the-ground data needed to capture these patterns. The growing availability of street view images presents new prospects in identifying urban features. However, the reliability and consistency of these methods across different locations and time remains largely unexplored. We aim to develop a comprehensive index of urban quality and change at the street segment level using Google Street View (GSV) imagery. We focus on eight object classes that indicate urban decay or contribute to an unsightly urban space, such as potholes, graffiti, garbage, tents, barred or broken windows, discolored or dilapidated façades, weeds, and utility markings. We train an object detection model on a dataset of images from different cities and assess the performance of these urban indices. We evaluate the effectiveness of this method in various urban contexts over time and discuss its potential for urban planning and public policy. We demonstrate the use of these indices in three applications: the Tenderloin in San Francisco, the Doctores and Historic Center neighborhoods in Mexico City, and South Bend, Indiana. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic inspired calls for rapid decarceration of prisons and jails to slow the spread of disease in a high-risk congregate setting. Due to the rarity of intentionally-decarcerative policies, little is known about the effects of rapid decarceration on individuals with serious mental illness (SMI) substance use disorder (SUD), a population who receive many services via the criminal legal system (CLS). We conducted interviews with 13 key informants involved in CLS in San Francisco, CA to better understand the implication of the decarcerative policies put into practice in early 2020. Participants described a tension between the desire to have fewer people incarcerated and the challenges of accessing services and support – especially during the lockdown period of the pandemic – outside of the CLS given the number of services that are only accessible to those who have been arrested, incarcerated, or sentenced. These findings emphasize the need for investing in community social services rather than further expanding the CLS to achieve the goal of supporting individuals with SMI and SUD shrinking the US system of mass incarceration. [ABSTRACT FROM AUTHOR]

In this study, we consider the patient, provider, and public health repercussions of San Francisco's (SF) COVID-related response to homelessness using tourist hotels to house people experiencing homelessness (PEH). We describe the demographics, medical comorbidities, and healthcare utilization patterns of a subset of PEH who accessed the shelter-in-place (SIP) hotel sites during the 2020–2021 pandemic. We focus on how SIP hotels impacted connection to outpatient care and higher-cost emergency utilization. Our mixed methods study integrates qualitative and quantitative data to consider the impact of this temporary housing initiative among a medically complex cohort in a time of increased morbidity and mortality related to substance use. We found that temporary SIP housing increased outpatient care and reduced higher-cost hospital utilization. Our results can inform the future design and implementation of integrated supportive housing models to reduce mortality and promote wellness for PEH. [ABSTRACT FROM AUTHOR]

Mobile bicycle transects were conducted in Golden Gate Park and surrounding residential neighborhoods of San Francisco, CA, during the period July–October 2012. Measurements included air temperature at six heights from 0.15 to 2.5 m, as well as relative humidity, solar radiation, and surface radiometric temperature. Park temperatures were lower than surrounding neighborhoods at all measurement levels, and mean Park Cool Island (PCI) values ranged from 0.9 °C for grass playing fields to 1.9 °C for tree groves during typical cool (~ 16 °C) summer conditions. These increased to 2.6 °C and 3.7 °C, respectively, during periodic warm events (~ 30 °C). Grass fields produced strong temperature lapse profiles, similar to asphalt surfaces, while dense trees produced a near-isothermal profile. Shade enhanced cooling by 1–8 °C throughout the 2.5-m layer, peaking near the surface, and frequently producing inversions below 0.5 m. Complexity in the lower surface layer temperature profile suggests measurements of air temperature at one height may be insufficient to characterize the PCI. The park also produced a consistent humidity enhancement, with differences in vapor pressure between urban and park surfaces of up to 10 hPa. The humidity difference was positively correlated with the PCI and also doubled between cool and warm periods. This, along with assessment of the role of shade, and the daytime peak, suggests evaporative cooling and shade cooling are the main drivers of the PCI in Golden Gate Park. The advection of cool air from the park into leeward neighborhoods was limited below 2.5 m, and was only observable within the first few hundred meters. Implications for urban planning suggest urban green spaces that provide abundant vegetation, including dense tree canopies, and are widely accessible to inhabitants can be most effective for in situ thermal comfort, particularly temperature relief during heat events. [ABSTRACT FROM AUTHOR]

As SARS-CoV-2 continues to spread and vaccines are rolled-out, the "double burden" of disparities in exposure and vaccination intersect to determine patterns of infection, immunity, and mortality. Serology provides a unique opportunity to measure prior infection and vaccination simultaneously. Leveraging algorithmically-selected residual sera from two hospital networks in the city of San Francisco, cross-sectional samples from 1,014 individuals from February 4–17, 2021 were each tested on two assays (Ortho Clinical Diagnostics VITROS Anti-SARS-CoV-2 and Roche Elecsys Anti-SARS-CoV-2), capturing the first year of the epidemic and early roll-out of vaccination. We estimated, using Bayesian estimation of infection and vaccination, that infection risk of Hispanic/Latinx residents was five times greater than of White residents aged 18–64 (95% Credible Interval (CrI): 3.2–10.3), and that White residents over 65 were twice as likely to be vaccinated as Black/African American residents (95% CrI: 1.1–4.6). We found that socioeconomically-deprived zipcodes had higher infection probabilities and lower vaccination coverage than wealthier zipcodes. While vaccination has created a 'light at the end of the tunnel' for this pandemic, ongoing challenges in achieving and maintaining equity must also be considered. Continued monitoring of SARS-CoV-2 at the population level is important for identifying at-risk groups. Here the authors analyse data from a serological surveillance platform in San Francisco and find considerable variation in infection and vaccination history by race/ethnicity and socioeconomic status. [ABSTRACT FROM AUTHOR]

Background: Science, technology, engineering, and mathematics (STEM) influence almost every aspect of our daily lives. However, despite the high demand for STEM occupational talent, the STEM pipeline continues leaking, with less than one-sixth of high school students pursuing STEM majors and only 50% of entering STEM college majors matriculating into STEM fields. Science identity has been identified as the most powerful predictor of high school students pursuing an undergraduate STEM major as reported by Chang (Machine learning approach to predicting STEM college major choice, American Educational Research Association (AERA), San Francisco, 2020). Though the construct is gaining lots of attention, it remains largely ill-defined, not operationalized at the high school level, and not based upon traditional identity theory. The purpose of this study was to develop a valid and reliable instrument that measures high school students' science identity, the Science Identity (SciID) Scale. Results: Subject experts and a small group of high school students provided content validation for the proposed scale. Exploratory factor analysis revealed an optimal two-factor solution, reflecting the traditional two-dimensions of identity theory: Exploration and Commitment. Cronbach's alpha revealed good internal consistency for both factors. Finally, structural equation modeling confirmed the convergent validity of the instrument with the external variables of science achievement and science career interest. Furthermore, the divergent validity between science identity and science self-concept was also confirmed. Conclusions: Initial results indicate that the SciID Scale is a valid and reliable instrument that accurately measures a high school student's standing on this construct. The soundness of this instrument will enable policy makers and practitioners to design more effective intervention programs aimed at cultivating high school students' science identity. [ABSTRACT FROM AUTHOR]

There is an urgent need for innovative methods to reduce transmission of bloodborne pathogens like HIV and HCV among people who inject drugs (PWID). We investigate if PWID who acquire non-pathogenic bloodborne viruses like anelloviruses and pegiviruses might be at greater risk of acquiring a bloodborne pathogen. PWID who later acquire HCV accumulate more non-pathogenic viruses in plasma than matched controls who do not acquire HCV infection. Additionally, phylogenetic analysis of those non-pathogenic virus sequences reveals drug use networks. Here we find first in Baltimore and confirm in San Francisco that the accumulation of non-pathogenic viruses in PWID is a harbinger for subsequent acquisition of pathogenic viruses, knowledge that may guide the prioritization of the public health resources to combat HIV and HCV. Spread of bloodborne infections, such as HCV and HIV, is a problem, particularly amongst people who inject drugs (PWID). Here, the authors describe and then confirm in observational PWID cohorts that those with more non-pathogenic viruses in plasma were more likely later to acquire HCV than PWID who had fewer of these non-pathogenic viruses. [ABSTRACT FROM AUTHOR]