Dallas-Fort Worth Research Articles

Shame Buffers and Spatial Risk-Mitigation: Exploring the effects of social stigma on the locational strategies of illicit massage businesses and their customers

Traditional locational analysis in urban retail/business geography begins with the assumption that customers will visit retail locations that are most convenient, and that businesses that locate close to their customers’ residences or workplaces would be preferred to locations farther from those places. This assumption is problematic when applied to economic activities for which social stigma exists. In this article, we examine the influence of social stigma on the locational choices made by customers of illicit massage businesses in the Dallas-Fort Worth, Texas metropolitan area. We introduce the concept of shame buffers – zones around the places to which each customer is most deeply socially embedded, their home, workplace, and other community spaces, where the customer would not engage in stigmatized behaviors for fear of being observed and facing social shaming. We then examine the validity of the shame-buffer concept by analyzing customers' mobility patterns for both legal and illicit massage businesses in the Dallas-Fort Worth Metropolitan Area (DFW metro).

Travel Time Variability in Urban Mobility: Exploring Transportation System Reliability Performance Using Ridesharing Data

Travel time variability (TTV) is a crucial indicator of transportation network performance, assessing travel time reliability and delays. This study investigates TTV metrics within the context of shared mobility using probe data from transportation network companies (TNCs) in Chicago, Los Angeles, and Dallas–Fort Worth. Eight reliability metrics are analyzed and compared for each origin–destination (OD) pair in the network, including standard deviation (SD), the Planning Time Index (PTI), the Travel Time Index (TTI), the Buffer Index (BI), On-time Measures PR (alpha), and the Misery Index (MI), to evaluate their effectiveness in clustering OD pairs using K-means clustering. The findings confirm that SD, PTI, and MI are particularly effective in measuring travel time reliability and clustering within urban systems. This study identifies the most unbalanced supply–demand OD pairs/regions in each city, noting that low/medium-SD clusters around metropolitan airports indicate stable travel times even in high-demand zones, while high-SD clusters in downtown areas reveal significant traffic demands and unreliability. These patterns become more pronounced in study areas with multiple city centers. This study highlights the need for targeted strategies to enhance travel time reliability, particularly in regions like Dallas–Fort Worth, where public transportation alternatives are limited.

Multi-crowdsourced data fusion for modeling link-level traffic resilience to adverse weather events

Climate change leads to more frequent and intense weather events, posing escalating risks to road traffic. Crowdsourced data present new opportunities to monitor and investigate road traffic changes during adverse weather. This study utilizes two types of crowdsourced data: passively collected location data (PCLD) from mobile devices and actively collected user reports (ACUR) from the WAZE app, to examine the impact of floods, winter storms, and fog on road traffic. Three metrics derived from PCLD—speed change, event duration, and area under the curve (AUC)—along with an additional metric from ACUR, user-perceived severity, are utilized to assess link-level traffic resilience. All metrics are correlated with road characteristics such as functional class, number of lanes, divider types, road geometry, and historical traffic, to analyze underlying relationships and make forecasting. Using the Dallas–Fort Worth (DFW) metroplex in 2022 as a case, the study finds that overall, winter storms have the most substantial impact on road traffic, followed by floods and fog. Travel demand metrics, such as POI visits and traffic volume, experience a more significant decrease than traffic speed. The impact of weather also varies by road characteristics, with higher-class roads with greater volume and higher speed experiencing more substantial changes. Road geometry, such as minimal altitude and slopes, also significantly influences road traffic resilience under floods but not during winter storms. Moreover, the study reveals that ACUR may not accurately reflect the actual impact during extreme weather events, as few users are able to travel outside to actively act as “sensors”. In sum, this study evaluates a set of crowdsourced data for assessing link-level traffic resilience, designs various resilience metrics, and examines how they vary across road attributes and weather events, which are essential for guiding disaster preparedness, response, and recovery in road transportation systems.

Impact of Zoning Regulations on Transportation Equity of Job Accessibility: A Spatial Autoregressive Approach for the Dallas–Fort Worth and Houston-Galveston Area Council Areas

Impact of Zoning Regulations on Transportation Equity of Job Accessibility: A Spatial Autoregressive Approach for the Dallas–Fort Worth and Houston-Galveston Area Council Areas

Deep-Learning Framework for Terminal Airspace Trajectory Prediction and In-Time Prognostics

Terminal airspace around an airport is the biggest bottleneck for commercial operations in the National Airspace System. To prognosticate the safety status of the terminal airspace, effective prediction of the airspace evolution is necessary. While there are fixed procedural structures for managing operations at an airport, the confluence of a large number of aircraft and the complex interactions between the pilots and air traffic controllers make it challenging to predict its evolution. Modeling the high-dimensional spatiotemporal interactions in the airspace given different environmental and infrastructural constraints is necessary for effective predictions of future aircraft trajectories that characterize the airspace state at any given moment. A novel deep-learning architecture using Graph Neural Networks is proposed to predict trajectories of aircraft 10 min into the future and estimate prognostic metrics for the airspace. The uncertainty in the future is quantified by predicting distributions of future trajectories instead of point estimates. The framework’s viability for trajectory prediction and prognosis is demonstrated with terminal airspace data from Dallas Fort Worth International Airport.

Precision Irrigation Soil Moisture Mapper: A Thermal Inertia Approach to Estimating Volumetric Soil Water Content Using Unmanned Aerial Vehicles and Multispectral Imagery

To address the issue of estimating soil moisture at a hyper-resolution scale, a methodology referred to as Precision Irrigation Soil Moisture Mapper (PrISMM), that includes three key components, is developed: high-resolution remotely sensed optical and thermal data, surface energy balance modeling, and site-specific soil analysis. An Unmanned Aerial Vehicle/System (UAV or UAS) collects high-resolution multispectral imagery in the Dallas–Fort Worth metropolitan study area. Orthomosaics are converted to thermal inertia estimates in a spatially distributed format using the remotely sensed data combined with a set of surface energy balance modeling equations. Using thermal and physical properties of soil gained from site-specific soil analysis, thermal inertia estimates were further converted from thermal inertia to daily volumetric soil water content (VSWC) with a horizonal resolution of 8.6 cm. A ground truthing dataset of measured VSWC values taken from a Time Domain Reflectometer was compared with model results, producing a reasonable correlation with an average coefficient of determination of (R2) = 0.79, an average root mean square error (RMSE) = 0.0408, and mean absolute error (MAE) = 0.0308. This study highlights a practical approach of estimating VSWC for irrigation purposes while providing superior spatio-temporal coverage over in situ methods. The authors envision that PrISMM can be implemented in water usage management by relating VSWC with weather forecasts and evapotranspiration rates to develop time-based spatially distributed irrigation management plans.

Effects of Heavy Alcohol Use on Fasting Glucose and Insulin Resistance in Mid-Life Adults

Introduction: As our population ages, type 2 diabetes has become more prevalent in the United States and one in five mid-life adults (50-64 years) have type 2 diabetes. Unhealthy and heavy alcohol use (that exceeds recommended drinking limits) causes dysfunction of the liver and pancreas that can impact glucose control and thereby increase risks of type 2 diabetes. In young adults, we previously found that fasting glucose and insulin resistance were not associated with heavy alcohol use. However, it remains unclear if heavy alcohol use contributes to the development of type 2 diabetes in an aged population. Therefore, the purpose of this study is to determine the effect of heavy alcohol use on fasting glucose and insulin resistance in apparently healthy mid-life adults. Methods: Mid-life men (n=21) and postmenopausal women (n=28), non-smokers, free of obesity and major clinical diseases, were recruited from the community around the Dallas—Fort Worth metroplex. All participants underwent physical exam and fasting blood sampling. Alcohol use was assessed by the US Alcohol Use Disorders Identification Test (USAUDIT) and a dried blood spot phosphatidylethanol (PEth) test. A PEth cutoff of 20 ng/mL was used to define heavy drinkers. Insulin resistance was assessed by calculating HOMA-IR (Homeostatic model assessment for insulin resistance) as insulin (μU/L) x glucose (nmol/L)/22.5. Results: A total of 24 participants had PEth levels of <20 ng/mL (non-heavy drinkers; PEth ranged from <limit of quantification to 13 ng/mL; mean ± SEM for age: 58±1 years and body mass index: 25.2±0.6 kg/m2) and the other 25 participants had PEth levels ≥20 ng/mL (heavy drinkers; PEth ranged from 20 to 665 ng/mL; age: 57±1 years and body mass index: 25.7±0.5 kg/m2). Heavy drinkers had higher USAUDIT scores than non-heavy drinkers (11.3±1.4 vs. 3.3±0.5, p<0.001). No significant difference between these two groups were found in lipid values (P≥0.1) as well as renal (P≥0.3) and liver function values (P≥0.08). Compared to non-heavy drinkers, heavy drinkers had a higher level of fasting blood glucose (96±2 vs. 91±1 mg/dL, P=0.02), but similar blood insulin (9.2±2.5 vs. 5.5±0.5 μIU/L, P=0.2) and HOMA-IR (2.23±0.64 vs. 1.23±0.11, P=0.1). The results for stepwise linear regression model indicate that only USAUDIT score was an independent factor of fasting glucose (B=0.59, p<0.001). Conclusion: Our results indicate that compared to mid-life adult non-heavy drinkers, heavy drinkers had higher fasting blood glucose levels, despite similar renal and liver function. Our results also indicate that higher risks of alcohol use disorder were associated with higher levels of fasting glucose. These data suggest that heavy alcohol use may have negative effects on glucose regulation in aged populations and increase risks of type 2 diabetes. This work was supported by NIAAA AA028537 to CLH. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Model predictive control for optimal dispatch of chillers and thermal energy storage tank in airports

Cost of energy consumption is one of the biggest operational cost for airports, and it is increasing from time to time as airports expand to support growing number of passengers. Various factors affect the energy consumption including efficiency of airport Heating Ventilation and Air conditioning (HVAC) systems, which in turn depends on the efficiency of individual subsystems. In this paper, we present an optimal scheduling method for the central plant system at Dallas Fort Worth airport, involving chillers, pumps, and a thermal energy storage (TES) system. A model predictive control (MPC) problem is formulated to minimize both energy and demand charge costs while satisfying the cooling needs of the airport. The proposed Mixed-Integer Nonlinear Programming (MINLP) formulation includes performance curve based models for chillers and pumps and a simplified state of charge model for TES. The formulation also includes predictions of cooling load and chilled water return temperature. Simulation results for a month in summer show savings around 10% compared to the baseline. Initial recommendations based on insights from simulation results to the manual operation procedures resulted in significant savings. Field test results show a 7% chiller efficiency improvement.

Analyzing Public School Education Inequalities in the Dallas-Fort Worth Metroplex

Purpose: This paper investigates the influences of racial and socioeconomic factors on public schools' performance in the Dallas-Fort Worth (DFW) Metroplex. Research Methods: I obtained data from the Texas Education Agency (TEA) and the American Community Survey (ACS), representing 366,568 students attending 239 public high schools in the DFW Metroplex, which included public schools located in Colin County, Dallas County, Tarrant County, and Denton County. I measured public school performance based on graduation rate metrics and average SAT scores from graduating students. Findings: I found a significant correlation between racial and socioeconomic factors on public school performance. I found that the percent composition of socioeconomically disadvantaged students was the highest correlator to decreased academic performance. Further, I found a positive correlation between the percentage of socioeconomically disadvantaged students attending a given high school in relation to the percentage of African-American and Hispanic students attending a given high school. Notably, I found that public schools in the DFW Metroplex were highly segregated by racial and socioeconomic factors. Implications: This study highlights the necessity for policy pushes to diversify public school districts. Many public schools in the DFW Metroplex remain extensively segregated by racial and socioeconomic factors, and our findings underscore the importance of ensuring equitable resource distribution amongst public schools, specifically in school systems with large percentages of African-American, Hispanic, and socioeconomically disadvantaged students.

Background Influence of PM2.5 in Dallas–Fort Worth Area and Recommendations for Source Apportionment

Source apportionment of observed PM2.5 concentrations is of growing interest as communities seek ways to improve their air quality. We evaluated publicly available PM2.5 data from the USEPA in the Dallas–Fort Worth metropolitan area to determine the contributions from various PM2.5 sources to the total PM2.5 observed. The approach combines interpolation and fixed effect regression models to disentangle background from local PM2.5 contributions. These models found that January had the lowest total PM2.5 mean concentrations, ranging from 5.0 µg/m3 to 6.4 µg/m3, depending on monitoring location. July had the highest total PM2.5 mean concentrations, ranging from 8.7 µg/m3 to 11.1 µg/m3, depending on the location. January also had the lowest mean local PM2.5 concentrations, ranging from 2.6 µg/m3 to 3.6 µg/m3, depending on the location. Despite having the lowest local PM2.5 concentrations, January had the highest local attributions [51–57%]. July had the highest mean local PM2.5 concentrations, ranging from 2.9 µg/m3 to 4.1 µg/m3, depending on the location. Despite having the highest local PM2.5 concentrations, July had the lowest local attributions [33–37%]. These results suggest that local contributions have a limited effect on total PM2.5 concentrations and that the observed seasonal changes are likely the result of background influence, as opposed to modest changes in local contributions. Overall, the results demonstrate that in the Dallas–Fort Worth metropolitan area, approximately half of the observed total PM2.5 is from background PM2.5 sources and half is from local PM2.5 sources. Among the local PM2.5 source contributions in the Dallas–Fort Worth metropolitan area, our analysis shows that the vast majority is from non-point sources, such as from the transportation sector. While local point sources may have some incremental site-specific local contribution, such contributions are not clearly distinguishable in the data evaluated. We present this approach as a roadmap for disentangling PM2.5 concentrations at different spatial levels (i.e., the local, regional, or state level) and from various sectors (i.e., residential, industrial, transport, etc.). This roadmap can help decision-makers to optimize mitigatory, regulatory, and/or community efforts towards reducing total community PM2.5 exposure.

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning.

An examination of sUAS operations in proximity to a major U.S. Airport

The use of small, Unmanned Aircraft Systems (sUAS) has proliferated in the past several years. While operators are supposed to avoid operations in the immediate vicinity of airports, there are sometimes violations. In extreme cases, these violations can result in the complete grounding of flights at major airports around the world. These disruptions can lead to thousands of flights canceled and hundreds of thousands of passengers displaced. A compounding problem is the lack of empirical data to understand the amount of, and types of, sUAS operations around major airports. The purpose of this study was to assess the characteristics of sUAS operations near the Dallas-Fort Worth International Airport (DFW) by objectively analyzing data collected over a 36-month timeframe. The findings provide empirical data on the types of sUAS operations around DFW, yearly variance in operations, levels of UAS facility map compliance, types and times of sUAS flown, and sUAS launching points. The data indicates a growing trend in operations between 2018 and 2020, with a possible plateau in 2021 and beyond. sUAS operations in the airport vicinity are high, with approximately 70 % operating in the 400-foot grid levels or at points farther away from critical airport operations. Flights appear to follow seasonality and daily operation patterns, and growth seems to be in smaller vehicles with weights less than 250 g. In addition to the results, the authors discuss practical implications for National Airspace System (NAS) safety and the implications of integrating new operations into communities, such as drone deliveries and Advanced Air Mobility operations.

67 Are these familiar words? Analyzing the utility of a new Spanish verbal memory test for children in North Texas

Objective:The American Academy of Clinical Neuropsychology’s (AACN) Relevance 2050 Initiative goals highlight the need for new assessment methods that are inclusive of the rising heterogeneous population in the US. In 2022, the Texas Education Agency reported that approximately 20% of the student population in Texas public schools are English learners (ELs), and approximately 90% of them are Spanish-speaking. In an effort to address the need for more Spanish cognitive measures normed in the US, a pediatric neuropsychology research team in North Dallas developed the Spanish Verbal Memory Test for Children (SVMT-C). To assist with establishing its validity, this study aims to corroborate that the words chosen for the list are familiar to children of different cultural backgrounds in North Texas.Participants and Methods:Enrollment of healthy, Spanish-speaking children between 6.0 and 17.11 years old within the Dallas Fort-Worth (DFW) metroplex began in January 2022 and continues to date. Study participation entails completing an in-person testing session with the child, while the parent/legal guardian completes a word-related survey along with other forms. The parent survey asks parents to indicate their child’s knowledge of 45 words (15 are target words). The testing session with the child includes completion of several cognitive tests (e.g., SVMT-C, EOWPVT-4:SBE) and a posttest survey that measures the child’s knowledge of the target words on the SVMT-C. The EOWPVT-4:SBE was used to estimate vocabulary level in Spanish to support proficiency determinations.Results:To date, 23 parent-child dyads have participated in the study, and 7 different countries of origin are represented in the overall sample. Data of children who earned SS<85 in Spanish on the vocabulary test were omitted, leaving the pediatric sample at n=20. Ages ranged between 6.2 and 15.2 years old. Eighteen children were bilingual, one was monolingual, and one was multilingual. Only Spanish-speaking parents completed the Spanish Verbal Memory Test Survey, leaving the parent sample at n=21. The child survey revealed that 95% of the children (19 of 20) knew all 15 target words; only one 6-year-old child did not know a word, which contradicted their parent’s report. The parent survey revealed that 90% of parents reported their child knew all 15 target words and 100% of parents reported their child knew 14 of 15 words; only two parents (19 of 21) were unsure if their child knew one of the words but the child then earned a score of 100% on their survey.Conclusions:Creating a verbal memory measure in Spanish for use in the US presents a set of unique challenges because of the variability in terminology that exists in the language. Lack of familiarity with terminology may influence performance and invalidate results. In this endeavor, the goal was to recognize these nuances and create a relevant measure that uses common words for Spanish-speaking children regardless of cultural background. Thus far, the data supports the appropriateness of the words listed in the SVMT-C with a 100% familiarity rate among children ages 7 to 15 years old.

38 ¿Gustaría participar? - Recruitment of Spanish-speaking families for a pediatric neuropsychology study in North Texas

Objective:Hispanics account for approximately 19% of the US population and are the second largest ethnic group in the United States, yet they remain underrepresented in neuropsychology research. Common recruitment barriers include language, fear/mistrust, and unfamiliarity with neuropsychology. These recruitment challenges then interfere with the development of measures normed on Spanish-speaking Hispanics. The research team for a Spanish-based neuropsychological study at a pediatric medical setting in North Texas utilized several methods to maximize recruitment of Hispanics and identify the most successful strategies. It was hypothesized that internal recruitment efforts would have the best outcome.Participants and Methods:Recruitment of healthy Spanish-speaking children between 6.0 and 17.11 years old began in October 2021 and continues to date. Participants have been recruited within the Dallas Fort-Worth (DFW) metroplex using internal efforts within the pediatric medical center and external efforts in the community-at-large. Internal recruitment efforts have included: 1) setting up flyers at 19 different ambulatory clinics, 2) emailing study flyer to several internal groups, and 3) sharing information during a Hispanic workgroup meeting. Community-based efforts have included collaborating with: 1) a Spanishimmersion private elementary school (i.e., shared information with parents via email and sent flyers home with students), 2) three mental health colleagues (i.e., displayed study flyers within their clinic space and promoted study through word-of-mouth), 3) a local city council (i.e., featured flyer in electronic newsletter), and 4) a non-profit community organization (i.e., shared information and flyer through mass-text messages, social media post, and mass email to subscribers).Results:To date, 74 parent-child pairs have made one-time contact with the research team to inquire about the study and 55 have completed a second contact with initial screener by phone (19 lost to follow up). Of the screened families, 58% heard of the study through the non-profit organization, 31% through the Spanish-immersion private school, and 11% from internal recruitment efforts.Conclusions:Although we hypothesized that internal -based recruitment within the medical institution would be most fruitful, our findings did not support this hypothesis. A possible explanation could be that children recruited from medical clinics may not meet criteria for participation in our study (i.e., healthy children). Another possible reason may be that flyer-based recruitment in a medical clinic is too passive or impersonal. Recruitment through community organizations with sources known and trusted by participants was found to be the most successful method to recruit potential participants. Considering these findings, our approach to recruitment will move away from passive and indirect methods of recruitment (i.e., flyers in clinics) and emphasize alliance with community-based organizations to promote trust building and collaborative relationships between researchers, community organizations, and Hispanic research participants.

The pricing and quality effects of network structure choice: Evidence from American airlines’ international route cancellations

American Airlines (AA) terminated two non-stop services from Chicago to Beijing and Shanghai, respectively, in October 2018. Using the DB1B database, I study the pricing and quality effects of AA’s network structure switch for the Chicago-China route. In particular, I examine how average fares and on-time performances (OTP) in AA’s three hubs, namely, Chicago (ORD), Dallas/Fort Worth (DFW), and Los Angeles (LAX), would be affected following the route cancellations. The estimation results indicate higher fares and worsening OTP for nonstop flights connecting DFW and ORD but not those connecting LAX. Coupled with AA’s reconstruction plans, these findings may be partly caused by the fact that most international passengers who would depart from ORD are now diverted through DFW instead, given its proximity, which disproportionally drives up the demand and traffic at DFW. My analysis adds to the literature by investigating the impact of network choice of an international market on price and quality provision in the domestic market.

Post-acute sequelae of COVID-19 and longitudinal antibody levels in a community-based cohort.

Coronavirus disease 2019 (COVID-19) infection invokes variable immune responses and poses a risk of post-acute sequelae SARS-CoV-2 infection (PASC) symptoms; however, most data on natural history are derived from patients with severe infection. Further data are needed among patients with mild infection, who comprise most cases. The Dallas Fort-Worth (DFW) COVID-19 Prevalence Study included 21,597 community-dwelling adults (ages 18-89) who underwent COVID-19 PCR and anti-nucleocapsid antibody testing between July 2020 and March 2021. We invited participants with positive COVID-19 results (cases) and a subset with negative results (controls), matched on age, sex, race/ethnicity, and ZIP code, to complete a follow-up questionnaire for PASC symptoms and repeat anti-nucleocapsid testing, and anti-spike antibody testing between July and December 2021. Of 3,917 adults invited to participate, 2260 (57.7%) completed the questionnaire- 1150 cases and 1110 controls. Persistent symptoms were reported in 21.1% of cases, with the most common being shortness of breath, fatigue, and loss of taste or smell. Among 292 cases with asymptomatic infection, >15% reported new fatigue and 8-10% reported new loss of taste/smell, myalgias, or headache. Median anti-nucleocapsid levels in cases decreased from 3.5U to 0.7U over a median follow-up of 8.6 months. Anti-spike antibody levels at 6-7 months post-vaccination in cases were similar to that of controls. More than 1 in 5 patients with COVID-19 infection, including those with mild infection, reported persistent symptoms during follow-up. Both nucleocapsid and spike protein antibody levels decreased within six months following a COVID-19 infection and vaccination.

How Dallas Fort Worth International Airport is using modular construction to reduce development costs and timelines

Construction is one of the biggest challenges facing airports. Not only can construction negatively affect the airport experience for millions of passengers, it also can severely hamper airport operations and revenue. Yet, until recently, nearly allairport construction was performed in a similar fashion with whole sections of airports being shut down for months — often years — to build and/or renovate space from scratch. Modular construction represents a radical departure from this conventional building process. By constructing whole sections of a building off-site or landside, modular construction can dramatically minimise disruptions to airport operations and drive costs down by opening labour markets to expanded pools of talent. This paper will examine how modular construction has evolved, how it is currently being used in the renovation of Dallas Fort Worth International Airport’s Pier A and C expansion and how it could revolutionise amenity design and concession leasing.

Integrating spatial statistics and machine learning to identify relationships between e-commerce and distribution facilities in Texas, US

This paper proposes a novel analytical framework that integrates spatial statistics and machine learning techniques to identify relationships between e-commerce and distribution facilities. The framework includes centrographic analysis, global and local spatial association measurements, and a recently popularized interpretable machine learning approach – gradient boosting decision trees (GBDT) – to analyze warehousing location choices. We apply this framework to ZIP Codes Business Patterns data from 2003 to 2016 in three large metropolitan areas in Texas, US (i.e., Dallas-Fort Worth, Austin, and Houston). The thematic maps reveal the spatial clustering of areas with higher e-commerce activity but lower logistics facility coverage. It is worth noting that we do not observe logistics sprawl in the study region. The GBDT results show that industrial activities and transportation network accessibility are key factors influencing warehousing location choices. We also find that the relationship between warehouses and e-commerce establishments is weaker in Houston, a major maritime gateway for goods entering and leaving, as compared to Dallas-Fort Worth and Austin. Implications for local freight transportation planners and decision-makers are discussed.

Positive Changes in Safety Perception Among Blacks with HIV and Comorbidities: Assessment of Social Determinants of Health During COVID-19

PurposeThis study aimed to examine the impact of the COVID-19 lockdown on social determinants of health (SDOH) among Blacks with HIV and a comorbid diagnosis of hypertension or type 2 diabetes mellitus (T2DM).MethodsThis was a longitudinal survey study. The inclusion criteria were adults ≥ 18 years and the presence of hypertension and/or diabetes, along with a positive HIV diagnosis. This study enrolled patients in the HIV clinics and chain specialty pharmacies in the Dallas-Fort Worth (DFW) area. A survey of ten questions examining SDOH was conducted before, during, and after the lockdown. A proportional odds mixed effects logistic regression model was applied to assess differences between time points.ResultsA total of 27 participants were included. Respondents felt significantly safer in their living place post-lockdown than in the pre-lockdown period (odds ratio = 6.39, 95% CI [1.08–37.73]). No other statistically significant differences in the responses were found over the study timeframe. However, borderline p values indicated better SDOH status post-lockdown as compared to pre-lockdown.ConclusionStudy participants feel safer one year after lockdown compared to pre-lockdown. The CARES Act and the moratorium on rent and mortgage are among the factors that may explain this increase. Future research should include designing and evaluating interventions for social equity enhancement.

Delivery methods, risk sharing, standards and performance for construction, operations, and management: The TEXpress managed lanes system, Dallas-Ft. Worth, Texas

Dynamically tolled managed lanes and public–private partnership (P3) infrastructure delivery approaches can provide decision makers with innovative solutions for addressing traffic congestion challenges. These diverse delivery methods entail very different allocations of risks, costs, and benefits. Thus, evaluating managed toll road performance under different delivery methods can offer valuable insights. To that end, this study evaluates primary documents from the Dallas-Fort Worth, Texas, region’s TEXpress managed lanes system to explore its risk allocations, construction standards, and operations and maintenance standards. Texas employed P3 delivery approaches for 5 of the system’s 6 major development projects, with the more complex, multi-stage contracts sharing and/or transferring more risk with/to the private sector. The three DBFOM contracts were more than three times larger (average size $2.08 billion) than the DBB, DBM and DBOM projects (average size $656 million). The state also imposed more varied and complex construction requirements via the P3 contracts, including penalties for poor performance. The three DBFOM P3 projects delivered their facilities early; the other three projects delivered their facilities late. One DBFOM P3 project had no change orders; the other four projects had 207, 28, 13 and 8 change orders valued at 6.6%, 4.1%, −1.2%, and 0.35% of project cost, respectively. Despite differing delivery methods, few substantial differences appeared among the projects’ operations and maintenance standards.