BACKGROUND: Motor vehicle collisions are a leading source of mortality and injury on urban highways. From a temporal perspective, the determination of a road segment as being collision-prone over time can fluctuate dramatically, making it difficult for transportation agencies to propose traffic interventions. However, there has been limited research to identify and characterize collision-prone road segments with varying collision density patterns over time.
METHODS: This study proposes an identification and characterization framework that profiles collision-prone roads with various collision density variations. We first employ the spatio-temporal network kernel density estimation (STNKDE) method and time-series clustering to identify road segments with different collision density patterns. Next, we characterize collision-prone road segments based on spatio-temporal information, consequences, vehicle types, and contributing factors to collisions. The proposed method is applied to two-year motor vehicle collision records for New York City.
RESULTS: Seven clusters of road segments with different collision density patterns were identified. Road segments frequently determined as collision-prone were primarily found in Lower Manhattan and the center of the Bronx borough. Furthermore, collisions near road segments that exhibit greater collision densities over time result in more fatalities and injuries, many of which are caused by both human and vehicle factors.
CONCLUSIONS: Collision-prone road segments with various collision density patterns over time have distinct differences in the spatio-temporal domain and the collisions that occur on them.
CONCLUSIONS: The proposed method can help policymakers understand how collision-prone road segments change over time, and can serve as a reference for more targeted traffic treatment.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents\' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model\'s dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

Traffic accidents have emerged as one of the most public health safety matters, raising concerns from both the public and urban administrators. The ability to accurately predict traffic accident not only supports the governmental decision-making in advance but also enhances public confidence in safety measures. However, the efficacy of traditional spatio-temporal prediction models are compromised by the skewed distributions and sparse labeling of accident data. To this end, we propose a Sparse Spatio-Temporal Dynamic Hypergraph Learning (SST-DHL) framework that captures higher-order dependencies in sparse traffic accidents by combining hypergraph learning and self-supervised learning. The SST-DHL model incorporates a multi-view spatiotemporal convolution block to capture local correlations and semantics of traffic accidents, a cross-regional dynamic hypergraph learning model to identify global spatiotemporal dependencies, and a two-supervised self-learning paradigm to capture both local and global spatiotemporal patterns. Through experimentation on New York City and London accident datasets, we demonstrate that our proposed SST-DHL exhibits significant improvements compared to optimal baseline models at different sparsity levels. Additionally, it offers enhanced interpretability of results by elucidating complex spatio-temporal dependencies among various traffic accident instances. Our study demonstrates the effectiveness of the SST-DHL framework in accurately predicting traffic accidents, thereby enhancing public safety and trust.

Working from home (WFH) has been adopted as a key mitigation strategy in the COVID-19 pandemic; yet few research has studied its impact on pandemic outcomes. Using multiple sources of data including cellphone data and online survey during the pandemic, this study investigates the effect of WFH on intra-city health disparities during the COVID-19 pandemic in American cities. Pandemic data for zip code tabulation areas and cellphone mobility data for census block groups in New York City (NYC), Chicago, and Philadelphia are converted to census tract level, which are then merged with 2019 census data. WFH is measured with the proportion of workers who potentially can telework based on employment composition in census tracts and percentages of jobs in each industry that actually WFH during the pandemic. Results show that while infection and death rates are higher in NYC, intra-city disparities in pandemic outcomes are more pronounced in Philadelphia. Poisson regressions show a negative association between WFH and COVID-19 infection and death rates in NYC and Chicago, which is weakened by increased time spent at home during the pandemic and in minority neighborhoods (in NYC). In Philadelphia, WFH is barely relevant for infection rates but has a marginally positive association with death rates, which is also moderated by the time spent at home. This study demonstrates the relative effectiveness of WFH in mitigating pandemic outcomes and underscores the intersectionality between WFH and race/ethnicity and resident behaviors. It provides important policy implications for future pandemic mitigation.

Central Park is an iconic feature of New York City, which was the first and one of the hardest hit cities in the United States by the Coronavirus. State-level stay-at-home order, raising COVID-19 cases, as well as the public\'s personal concerns regarding exposure to the virus, led to a significant reduction of Central Park visitation. We utilized extensive cellphone tracking data to conduct one of the pioneering empirical studies assessing the economic impact of COVID-19 on urban parks. We integrated the difference-in-difference (DID) design with the recreation-demand model. The DID design aids in identifying the causal impacts, controlling for unobservable factors that might confound the treatment effects of interest. Concurrently, the recreational demand model examines the driving factors of visitation changes and enables us to estimate the welfare changes experienced by New York City\'s residents. Our findings shine a light on the substantial, yet often overlooked, welfare loss triggered by the pandemic. The analysis indicates that the pandemic resulted in a 94% reduction in visitation, corresponding to an annual consumer surplus loss of $450 million. We noted a rebound in visitation following the initial outbreak, influenced by shifts in government policy, weather conditions, holiday periods, and personal characteristics.

This study aimed to determine whether condom use varied between adolescents and young women using long-acting reversible contraception (LARC) vs non-LARC hormonal methods and assess if the initiation of LARC was associated with lower condom use.
This study used data from a large longitudinal study of sexually active females aged 13-25 years. Questionnaires assessed contraception, condom use, sexual history, and partner characteristics at the baseline visit and every 6 months. Log-binomial regression analyses examined associations between hormonal contraceptive methods and condom use, and the moderating effects of age and number of sexual partners. Exploratory analyses compared condom use based on partner characteristics.
Of 1512 participants, 1116 reported LARC or non-LARC hormonal method use during any study visit. Among baseline and new LARC users, 75.7% and 84.7% reported intrauterine device (IUD) use, respectively. Condom use at baseline among hormonal non-LARC users (37.5%) was significantly higher (p < 0.01) than LARC users (23.5%). Condom use among LARC vs non-LARC users was moderated by age in that LARC was associated with lower condom use among participants aged 13-18 years, but not those aged 19-25 years. Number of sexual partners was not a significant moderator. Among participants with increased sexually transmitted infection (STI) risk based on partner characteristics, LARC users had lower condom use compared to non-LARC users.
Condom discontinuation was common following initiation of LARC and hormonal non-LARC methods. However, condom use was lower in LARC users at baseline, among younger adolescents, and if partners had risk factors for STIs.
Condom discontinuation following initiation of highly effective contraception increases the risk of STI. Young women using LARC may be at greater risk than non-LARC users given lower condom use despite having partners with risk factors for STIs. Condom use counseling for STI protection is critical for adolescents.

OBJECTIVE: We examine the association between high ambient temperature and acute mental health-related healthcare encounters in New York City for children, adolescents and young adults.
METHODS: This case-crossover study included emergency department (ED) visits and hospital encounters with a primary diagnosis of any mental health disorder during warm-season months (June-August) in New York City from 2005 to 2011 from patients of three age groups (6-11, 12-17 and 18-25 years). Using a distributed lag non-linear model over 0-5 lag days, by fitting a conditional logistic regression for each age group, we calculated the cumulative odds ratios of mental health encounters associated with an elevated temperature. Analyses were stratified by race/ethnicity, payment source and mental health categories to elucidate vulnerable subpopulations.
RESULTS: In New York City, there were 82,982 mental health-related encounters for young people aged 6 to 25 years during our study period months. Elevated temperature days were associated with higher risk of mental health-related ED and hospital encounters for the 6- to 11-year-olds (odds ratio [OR]: 1.28, 95% confidence interval [CI]: 1.13-1.46), for the 12- to 17-year-olds (OR: 1.17, 95% CI: 1.09-1.25) and for the 18- to 25-year-olds (OR: 1.09, 95% CI: 1.04-1.15). Children with reaction disorders, adolescents with anxiety and bipolar disorders, young adults with psychosis and reaction disorders and Black and non-Hispanic children and adolescents showed vulnerability to elevated temperature.
CONCLUSIONS: We found that elevated ambient temperatures were associated with acute mental health ED or hospital encounters across childhood, adolescence and young adulthood.

Long Island Sound (LIS) frequently experiences ozone (O3) exceedance events that surpass national ambient air quality standards (NAAQS) due to complex driving factors. The underlying mechanisms governing summertime O3 pollution are investigated through collaborative observations from lidar remote sensing and ground samplers during the 2018 LIS Tropospheric O3 Study (LISTOS). Regional transport and local chemical reactions are identified as the two key driving factors behind the observed O3 episodes in LIS. An enhanced laminar structure is observed in the O3 vertical structure in the atmospheric boundary layer (i.e., 0-2 km layer) for the case dominated by regional transport. An O3 formation regime shift is found in ozone-precursor sensitivity (OPS) for the O3 exceedance event dominated by regional transport with NOx-enriched air mass transport from the New York City (NYC) urban area to LIS. Furthermore, the Integrated Process Rate (IPR) analysis demonstrates that transport from the NYC urban area contributed 40% and 27.1% of surface O3 enhancement to the cases dominated by regional transport and local production, respectively. This study provides scientific evidence to uncovers two key processes that govern summertime O3 pollution over LIS and can help to improve emission control strategies to meet the attainment standards for ambient O3 levels over LIS and other similar coastal areas.

Individual tree structure mapping in cities is important for urban environmental studies. Despite mapping products for tree canopy cover and biomass are reported at multiple spatial scales using various approaches, spatially explicit mapping of individual trees and their three-dimensional structure is sparse. Here we produced an individual tree dataset including tree locations, height, crown area, crown volume, and biomass over the entire New York City, USA for 6,005,690 trees. Individual trees were detected and mapped from remotely sensed datasets along with their height and crown size information. Tree biomass in 296 field plots was measured and modelled using i-Tree Eco. Wall-to-wall tree biomass was mapped using relationships between field measurements and remotely sensed datasets and downscaled to individual trees. Validation using field-plot measurements indicated that our mapping products overestimated tree number, mean tree height and maximum tree height by 11.1%, 8.6%, and 5.3%, respectively. These overestimations were mainly due to the spatial and temporal mis-match between field measurements and remote sensing observations and uncertainties in tree segmentation algorithms. This dataset enables the evaluation of urban forest ecosystem services including regulating urban heat and promoting urban health, which can provide valuable insights for urban forest management and policy making.

Low fruit and vegetable (FV) intake and high sugar-sweetened beverage (SSB) consumption are independently associated with an increased risk of developing cardiovascular disease (CVD). Many people in New York City (NYC) have low FV intake and high SSB consumption, partly due to high cost of fresh FVs and low cost of and easy access to SSBs. A potential implementation of an SSB tax and an FV subsidy program could result in substantial public health and economic benefits. We used a validated microsimulation model for predicting CVD events to estimate the health impact and cost-effectiveness of SSB taxes, FV subsidies, and funding FV subsidies with an SSB tax in NYC. Population demographics and health profiles were estimated using data from the NYC Health and Nutrition Examination Survey. Policy effects and price elasticity were derived from recent meta-analyses. We found that funding FV subsidies with an SSB tax was projected to be the most cost-effective policy from the healthcare sector perspective. From the societal perspective, the most cost-effective policy was SSB taxes. All policy scenarios could prevent more CVD events and save more healthcare costs among men compared to women, and among Black vs. White adults. Public health practitioners and policymakers may want to consider adopting this combination of policy actions, while weighing feasibility considerations and other unintended consequences.