Spatio-temporal Estimation Research Articles

Urban Traffic Simulation with Shared Mobility Services: An Approach Using Spatiotemporal Network Kernel Density Estimation and MATSim

The popularity of smartphones and 4G/5G network have enabled various novel transportation modes based on shared mobility, such as app-based ride-hailing services (e.g., Uber and Lyft) and shared micromobility services (e.g., Veo and Gotcha). However, little is known about to what degree their operations impact urban traffic, which is important for transportation planning and policy making. These companies seldom share their ride data due to business and user privacy reasons, and they are young and still exploring and growing their market to new locations. Recently, transportation engineering researchers began to collect data in large cities trying to understand the transportation impacts of shared mobility services, but (1) there does not exist a general analytics framework applicable to any city, and (2) the studies were based on historical data and cannot project the future easily to catch up with the rapid development of shared mobility services. In this paper, we introduce a general framework for multi-modal urban traffic analytics. The goal is to build a digital twin of the transportation in a city, i.e., an accurate agent-based transportation simulation model, based on a medium-sized dataset of the interested transportation modes collected by the research group combined with other open data sources such as US Census Bureau. With this digital twin, transportation engineering researchers can flexibly analyze the impact of shared mobility services under different scenarios, such as “if the number of Uber drivers doubles” or “if the number of deployed e-scooters doubles”. The digital twin may also enable new opportunities, such as being an environment for learning policies with reinforcement learning. Our framework consists of three steps: (1) fitting the spatiotemporal distribution of the shared mobility rides on the underlying road network, (2) generating the travel day-plans of the entire urban population based on the learned spatiotemporal ride distribution and user-specified parameters, and (3) configuring an agent-based simulation software such as MATSim to execute the generated realistic day-plans, which provides detailed transportation data for analytics. At the core of our approach is a new spatiotemporal network kernel density estimation (KDE) method that fixes the flaw of prior methods where the contributions of different data samples are not equal. We also propose a crowdsourcing method to collect app-based ride data that is easy to carry out in any city. Using Birmingham, AL as an example, we demonstrate how our framework can be applied to help transportation engineering researchers analyze the impacts of Uber ride-hailing and Veo e-scooter services.

Spatiotemporal estimates and health risks of atmospheric trace metals across Hong Kong during 2016–2020

Spatiotemporal estimates and health risks of atmospheric trace metals across Hong Kong during 2016–2020

Independent effects of long and short-term exposures to non-optimal increased temperature on mortality

Although the short-term heat effects are well-established, longer-term effects, beyond those, have recently received attention, in the context of climate change. Our study aims to investigate the potential effects of long-term exposure to non-optimal warm period temperatures on all-cause mortality in four large regions in the UK, Norway, Italy, and Greece. Daily all-cause mortality counts from 1996 to 2018 for four European NUTS2 regions including 52-662 small areas were collected and associated with spatiotemporal temperature estimates. A NUTS2 region–specific mixed quasi-Poisson over-dispersed model, with a random intercept per small area within NUTS2 regions, was applied to investigate the association between long-term temperature exposure and mortality during the warm period (May to September), adjusting for short-term temperature, seasonality, long-term trends, and small-area population characteristics. As long-term temperature exposure indices per small area, we considered: 1) the warm period annual average temperature, 2) the annual standard deviation (SD) of warm period temperature, and 3) the coefficient of variation of warm period temperature (CV). We found consistent results following short-term temperature exposure on mortality, with higher effects in southern areas. Results on the shape of the long-term association between average temperature and mortality differed by country, while the different temperature metrics produced inconsistent findings. Increased mortality was associated with increased annual warm season temperature, lower SD and increased CV in Greece, with higher SD and decreased CV in Italy and with decreased annual temperature and CV in Norway. Effects in the UK did not reach the nominal level of statistical significance. Although our study implies an impact on mortality resulting from longer-term temperature exposure, its direction varied across areas and on the temperature metric used. Further research is warranted, applying non-ecological study designs and covering various geographical areas to capture the impact of individual and area-specific characteristics.

Spatiotemporal estimation of surface NO2 concentrations in the Pearl River Delta region based on TROPOMI data and machine learning

Nitrogen dioxide (NO2) is a major air pollutant, and its concentration data are crucial for the study of air pollution and its impact on the environment. Although satellite data provide an effective method for estimating surface concentrations on a large scale through integrated modeling, the estimation of surface NO2 concentrations is hampered by the substantial amount of missing satellite data. This restricts in-depth studies of surface NO2 pollution. This study aims to reconstruct the missing data on tropospheric NO2 vertical column density from the TROPOspheric Monitoring Instrument (TROPOMI NO2). Subsequently, the reconstructed TROPOMI NO2 data and other predictor variables were utilized to estimate the daily surface NO2 concentrations at a 1 km resolution for the Pearl River Delta (PRD) region. The TROPOMI NO2 reconstruction models and the surface NO2 estimation model were both developed using the XGBoost algorithm. Additionally, comparative experiments were conducted between the XGBoost model and other traditional machine learning models, and the performances of the XGBoost model were evaluated through 10-fold cross-validation (CV) sample-based and site-based evaluations. The results indicate that the sample-based and site-based CV R2 values were 0.873 and 0.709, respectively. The feature importance scores indicate that TROPOMI NO2 was the most significant variable contributing to the estimation model. This indicates that the reconstruction of TROPOMI NO2 data and the development of an Extreme Gradient Boosting (XGBoost) model are suitable for the spatiotemporal estimation of surface NO2 concentrations in the PRD region, effectively reflecting the spatiotemporal distribution and evolution of surface NO2 concentrations in the area.

Data-driven spatio-temporal estimation of soil moisture and temperature based on Lipschitz interpolation

This study estimates agricultural soil variables using a non-parametric machine learning technique based on Lipschitz interpolation. This method is adapted for the first time to learn spatio-temporal dynamics, accounting for two-dimensional spatial and one temporal coordinate inputs separately. The estimator is validated on real agricultural data, addressing challenges like measurement noise and quantization. The experimental setup, including an edge layer with measurement devices and a cloud layer for data storage and processing, is detailed. Despite its simplicity, the method presents a compelling alternative to Gaussian processes and neural networks.

Statistical Relationship between Satellite observed Land Surface Temperature and in-situ measured Surface Air Temperature over the Indian Region: An Exploratory Study

This paper presents an investigation of spatio-temporal estimation of daily minimum (Tmin) and maximum (Tmax) surface air temperature using satellite (INSAT and MODIS) derived Land surface temperature and in-situ observational data over the Indian region. Least Absolute Shrinkage and Selection Operator (LASSO) regression technique is used to identify influencing neighboring stations. To capture spatial and temporal variability of surface air temperature of a particular IMD station, Linear Regression and Auto-Regressive Integrated Moving Average, which is known as ARIMA, are used respectively. These models are statistically ensembled using stack generalization. Obtained station-by-station relationship is validated on an independent test data using Root Mean Squared Error (RMSE) to check the validity of the model under consideration. Results show ensembled model outperform (has lowest RMSE) the traditional methods for prediction of Tmin and Tmax with RMSE within a range of [1, 2] range for most of the regions and seasons.

Autonomous data sampling for high-resolution spatiotemporal fish biomass estimates

Many key ecological dynamics such as biomass distributions are only detectable on a fine spatiotemporal scale. Autonomous data collection with Unmanned Surface Vehicles (USV) creates new possibilities for cost efficient and high-resolution aquatic data sampling. However, the spatial coverage and sampling resolution remain uncertain due to the novelty of the technology. Further, there is no established method for analysing such fine-scale autocorrelated data without aggregation, potentially compromising data resolution. We here used a USV with an echosounder, a conductivity-temperature sensor and a flourometer to collect data from April–July 2019–2023 in a 60x80km area in the central Baltic Sea. The USV covered a total distance of 8000 nmi, over 42–81 days per year, with an average speed of 0.5 m/s. We combined the hydroacoustic data with publicly available oceanographic data from Copernicus Marine Service Information (CMSI) to describe seasonal distribution dynamics of a small pelagic fish community. Key oceanographic variables collected by the USV were correlated with CMSI estimates at daily/monthly resolution, respectively, to test for suitability to scale (Temperature 0.99/0.97; Salinity −0.77/−0.26; Chlorophyll-a 0.12/0.28). We investigated two approaches of Species Distribution Models (SDMs): generalized additive models (GAM) versus spatiotemporal generalized linear mixed effect models (GLMM). The GLMMs explained the observed data better than the GAMs (R2 0.31 and 0.20, respectively). The addition of environmental variables increased the explanatory capability of GAM and GLMM by 25 % and ∼ 3 %, respectively. Due to the high data resolution, we found significant amounts of positive autocorrelation (R: 0.05–0.30) across more than 50 sequential observations (>6 hours). However, we found that diel patterns in fish detection strongly affected the abundance estimates due to vertically migrating species hiding in the ‘acoustic dead zone’ near the seabed. Such dynamics could only be estimated and corrected for in predictions on the high-resolution data, complicating the trade-off between autocorrelation and high-resolution for SDMs. We compared estimates and effect sizes/directions in identical SDMs on 2x2km/month aggregated (i.e non-autocorrelated) observations and non-aggregated (i.e. autocorrelated) observations, and found relatively little difference in spatiotemporal estimates (r = 0.80). For the first time, we predicted the distribution of a small pelagic fish community at a high spatial resolution, in an area essential to breeding top predators, opening up for new applications in ecological studies locally and globally.

Data Reconstruction Using Smart Sensor Placement.

This paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. In particular, the approach uses the Kriged Kalman Filter (KKF) for uncertainty-aware field reconstruction. Unlike other reconstruction methods, the positional uncertainty originating from the data acquisition platform is integrated into the KKF estimator. Numerical results are presented to show the efficacy of the proposed dynamic sensor placement strategy together with the KKF field estimator.

Correction to: A statistical significance test for spatio-temporal receptive field estimates obtained using spike-triggered averaging of binary pseudo-random sequences

Correction to: A statistical significance test for spatio-temporal receptive field estimates obtained using spike-triggered averaging of binary pseudo-random sequences

Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model

Carbon dioxide (CO2) serves as a crucial greenhouse gas that traps heat and regulates the Earth's temperature. High spatiotemporal resolution CO2 estimation can provide valuable information to understand the characteristics of fine-scale climate change trends and to formulate more effective emission reduction strategies. This study presents a spatiotemporal ResNet model (ST-ResNet) specifically developed to estimate the highest resolution (1 km × 1 km) daily column-averaged dry-air mole fraction of CO2 (XCO2) in China from 2015 to 2020. The ST-ResNet model excels in estimating XCO2 by comprehensively considering the complex relationships between XCO2 and its various influencing factors, while efficiently capturing both temporal and spatial correlations, thereby demonstrating remarkable generalization capability. The results show that the ST-ResNet generates a highly accurate XCO2 dataset, outperforming the traditional ResNet. Ground-based validation results further confirm the high accuracy and spatiotemporal resolution of our estimated data product. Using this dataset, the spatial and temporal characteristics of XCO2 across the entire China and several urban agglomerations have been analyzed. The high spatiotemporal resolution estimated XCO2 dataset for China is made publicly available at [https://doi.org/10.6084/m9.figshare.25272868], offering substantial potential for fine-scale carbon research.

Monthly electricity consumption data at 1 km × 1 km grid for 280 cities in China from 2012 to 2019

High spatio-temporal resolution estimates of electricity consumption are essential for formulating effective energy transition strategies. However, the data availability is limited by complex spatio-temporal heterogeneity and insufficient multi-source feature fusion. To address these issues, this study introduces an innovative downscaling method that combines multi-source data with machine learning and spatial interpolation techniques. The method’s accuracy showed significant improvements, with determination coefficients (R2) increasing by 30.1% and 33.4% over the baseline model in two evaluation datasets. With this advanced model, we estimated monthly electricity consumption across 1 km x 1 km grid for 280 Chinese cities from 2012 to 2019. Our dataset is highly consistent with officially released electricity consumption of different industries (Pearson correlation coefficients within 0.83 - 0.91). Moreover, our data can reflect the electricity consumption patterns of different urban land uses compared to other datasets. This study bridges a significant gap in fine-grained electricity consumption data, providing a robust foundation for the development of sustainable energy policies.

Extending our understanding on the retrievals of surface energy fluxes and surface soil moisture from the “triangle” technique

The present study demonstrates the capability of an inversion modelling scheme so-called the “triangle” to retrieve spatiotemporal estimates of surface energy fluxes and soil surface moisture (SSM) at high resolution using ASTER satellite imagery synergistically with SimSphere land biosphere model. In addition, as a further objective of this study is to examine the use of the technique for retrieving the Evaporative (EF) and the Non-Evaporative (NEF) Fractions as representations of the daytime average fluxes. The applicability of the investigated technique, is demonstrated for sixteen calendar days of year 2011 using in-situ data acquired from nine CarboEurope sites representing a variety of climatic, topographic and environmental conditions. Results indicated a close agreement between all the inverted parameters and the corresponding in-situ data. SSM predicted maps showed a small bias of 0.08 vol vol−1, a scatter of 0.18 vol vol−1 and a RMSD of 0.19 vol vol−1. The predicted LE fluxes showed a relatively low overall agreement (RMSD = 65.10 Wm-2), whereas for H flux reported RMSD was 85.02 Wm-2. The results also confirmed the ability of the investigated technique to provide meaningful estimates of the NEF and EF. All in all, the present study findings were at least comparable, or of higher accuracy, to those reported in other similar verification studies of the “triangle” using both high resolution (airborne) and low resolution (satellite) data. To our knowledge, this study represents the first comprehensive evaluation of the performance of this particular methodological implementation at a European setting combining the SimSphere SVAT model and ASTER EO datasets.

Spatiotemporal fault estimation for switched nonlinear reaction–diffusion systems via adaptive iterative learning

SummaryIn this paper, an iterative learning‐based spatiotemporal fault estimation issue in switched reaction–diffusion systems is investigated. Initially, average dwell‐time switching rules are utilized to describe a class of switched reaction–diffusion systems characterized by mode jumps. Then, different from the existing fault estimation methods, a fault estimator is designed for spatiotemporal faults to realize an accurate estimation of faults by using the iterative learning strategy. Subsequently, to improve the speed of fault estimation, an adaptive iterative learning‐based fault estimation law is proposed, which can achieve faster fault estimation by continuously adjusting the iterative learning gain. Moreover, sufficient conditions for the convergence of the fault estimation error are obtained by using the ‐norm and the mathematical induction methods. Finally, an illustrative example is presented to check the practicality and superiority of the proposed fault estimation scheme.

Advancing the Local Pulse Wave Velocity Measurement-Wave Confluence Decomposition Using a Double Gaussian Propagation Model.

Background Pulse wave velocity (PWV) is a marker of arterial stiffness and local measurements could facilitate its widescale clinical use. However, confluence of incident and early reflected waves leads to biased spatiotemporal PWV estimates. Objective We introduce the Double Gaussian Propagation Model (DGPM) to measure local PWV in consideration of wave confluence (PWV[Formula: see text]) and compare it against conventional spatiotemporal PWV (PWV[Formula: see text]), with Bramwell-Hill PWV (PWV[Formula: see text]) and blood pressure (BP) as reference measures. Methods Ten subjects ranging from normotension to hypertension were repeatedly measured at rest and with induced PWV changes. Carotid distension waveforms over a 19 mm wide segment were acquired from ultrasonography, simultaneously with noninvasive continuous BP. Per cardiac cycle, the 8-parameter DGPM (amplitude, centroid, width, and velocity, respectively of forward and backward propagating wave) was fitted to the distension waveforms' systolic foot and dicrotic notch complexes. Corresponding PWV[Formula: see text] was computed from linear fittings of respective feature timings and distances. Regression analyses were conducted with PWV[Formula: see text] and PWV[Formula: see text] as predictors, and various PWV and BP measures as response variables. Results Whereas PWV[Formula: see text] correlations were insignificant, PWV[Formula: see text] estimated the reference PWV[Formula: see text] with a significant reduction in errors (P < 0.001), explained up to 65% PWV[Formula: see text] variability at rest, demonstrated higher intra-method consistency and correlated significantly with all BP measures (P < 0.001). Conclusion The proposed DGPM measures local carotid PWV in consideration of wave confluence, showing significant correlations with Bramwell-Hill PWV and BP at two distinct waveform complexes. Thereby PWV[Formula: see text] outperforms the conventional PWV[Formula: see text] in all investigated respects, potentially enabling PWV assessment in routine clinical practice.

Bayesian hierarchical model for bias-correcting climate models

Abstract. Climate models, derived from process understanding, are essential tools in the study of climate change and its wide-ranging impacts. Hindcast and future simulations provide comprehensive spatiotemporal estimates of climatology that are frequently employed within the environmental sciences community, although the output can be afflicted with bias that impedes direct interpretation. Post-processing bias correction approaches utilise observational data to address this challenge, although they are typically criticised for not being physically justified and not considering uncertainty in the correction. This paper proposes a novel Bayesian bias correction framework that robustly propagates uncertainty and models underlying spatial covariance patterns. Shared latent Gaussian processes are assumed between the in situ observations and climate model output, with the aim of partially preserving the covariance structure from the climate model after bias correction, which is based on well-established physical laws. Results demonstrate added value in modelling shared generating processes under several simulated scenarios, with the most value added for the case of sparse in situ observations and smooth underlying bias. Additionally, the propagation of uncertainty to a simulated final bias-corrected time series is illustrated, which is of key importance to a range of stakeholders, such as climate scientists engaged in impact studies, decision-makers trying to understand the likelihood of particular scenarios and individuals involved in climate change adaption strategies where accurate risk assessment is required for optimal resource allocation. This paper focuses on one-dimensional simulated examples for clarity, although the code implementation is developed to also work on multi-dimensional input data, encouraging follow-on real-world application studies that will further validate performance and remaining limitations. The Bayesian framework supports uncertainty propagation under model adaptations required for specific applications, providing a flexible approach that increases the scope of data assimilation tasks more generally.

A Bayesian deep learning approach for video-based estimation and uncertainty quantification of urban rainfall intensity

Accurate, high-resolution spatiotemporal estimates of rainfall intensity (RI) are essential for effective prevention and control of urban flooding. Traditional methods are often costly and provide inadequate coverage. Meanwhile, the associated uncertainty of RI estimates is often overlooked, potentially leading to poor decisions in management of urban floods. Here we examine the potential of video imagery recorded by surveillance cameras in urban areas, for providing real-time estimates of RI. Specifically, we propose the use of Bayesian deep learning (DL) to estimate RI and its related uncertainty in a real-time manner. Our DL approach combines the strengths of convolutional neural network (CNN) and long short-term memory (LSTM) to construct a suitable model for this task, and uses variational inference to quantify the uncertainty of the CNN-LSTM model. The proposed approach is tested using video imagery captured under various light-intensity conditions, including daytime, nighttime, early morning, and early evening, and evaluated via experiments using both random samples and independent rainfall events. Further, we show that the temporal processing provided by the LSTM network is important to achieve good performance of RI estimation. The results indicate the strong potential for leveraging urban camera networks to obtain high-precision spatiotemporal RI estimates at low cost, which can be extremely valuable for implementing effective flood control measures and planning emergency responses in the urban areas.

The effect of spatio-temporal sample imbalance in epidemiologic surveillance using opportunistic samples: An ecological study using real and simulated self-reported COVID-19 symptom data

Open surveys complementing surveillance programs often yield opportunistically sampled data characterised by spatio-temporal imbalance. We set up our study to understand to what extent spatio-temporal statistical models using such data achieve in describing epidemiological trends. We used self-reported symptomatic COVID-19 data from two Belgian regions, Flanders and the Brussels-Capital Region. These data were collected in a large-scale open survey with spatio-temporally imbalanced participation rates. We compared incidence estimates of both self-reported symptoms and test-confirmed COVID-19 cases obtained through generalised linear mixed models correcting for spatio-temporal correlation. We additionally simulated symptom incidences under different sampling strategies to explore the impact of sample imbalance, sample size and disease incidence, on trend detection. Our study shows that spatio-temporal sample imbalance generally does not lead to bad model performances in spatio-temporal trend estimation and high-risk area detection. Except for low-incidence diseases, collecting large samples will often be more essential than ensuring spatio-temporally sample balance.

Air pollution exposure during pregnancy and lung function in childhood: The LUIS study.

The adverse effects of high air pollution levels on childhood lung function are well-known. Limited evidence exists on the effects of moderate exposure levels during early life on childhood lung function. We investigated the association of exposure to moderate air pollution during pregnancy, infancy, and preschool time with lung function at school age in a Swiss population-based study. Fine-scale spatiotemporal model estimates of particulate matter with a diameter <2.5 µm (PM2.5) and nitrogen dioxide (NO2) were linked with residential address histories. We compared air pollution exposures within different time windows (whole pregnancy, first, second, and third trimester of pregnancy, first year of life, preschool age) with forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) measured cross-sectionally using linear regression models adjusted for potential confounders. We included 2182 children, ages 6-17 years. Prenatal air pollution exposure was associated with reduced lung function at school age. In children aged 12 years, per 10 µg·m-3 increase in PM2.5 during pregnancy, FEV1 was 55 mL lower (95% CI -84 to -25 mL) and FVC 62 mL lower (95% CI -96 to -28 mL). Associations were age-dependent since they were stronger in younger and weaker in older children. PM2.5 exposure after birth was not associated with reduced lung function. There was no association between NO2 exposure and lung function. In utero lung development is most sensitive to air pollution exposure, since even modest PM2.5 exposure during the prenatal time was associated with reduced lung function, most prominent in younger children.

High resolution assessment of commercial fisheries activity along the US West Coast using Vessel Monitoring System data with a case study using California groundfish fisheries

Commercial fisheries along the US West Coast are important components of local and regional economies. They use various fishing gear, target a high diversity of species, and are highly spatially heterogeneous, making it challenging to generate a synoptic picture of fisheries activity in the region. Still, understanding the spatial and temporal dynamics of US West Coast fisheries is critical to meet the US legal mandate to manage fisheries sustainably and to better coordinate activities among a growing number of users of ocean space, including offshore renewable energy, aquaculture, shipping, and interactions with habitats and key non-fishery species such as seabirds and marine mammals. We analyzed vessel tracking data from Vessel Monitoring System (VMS) from 2010 to 2017 to generate high-resolution spatio-temporal estimates of contemporary fishing effort across a wide range of commercial fisheries along the entire US West Coast. We identified over 247,000 fishing trips across the entire VMS data, covering over 25 different fisheries. We validated the spatial accuracy of our analyses using independent estimates of spatial groundfish fisheries effort generated through the NOAA’s National Marine Fisheries Service Observer Program. Additionally, for commercial groundfish fisheries operating in federal waters in California, we combined the VMS data with landings and ex-vessel value data from California commercial fisheries landings receipts to generate highly resolved estimates of landings and ex-vessel value, matching over 38,000 fish tickets with VMS data that included 87% of the landings and 76% of the ex-vessel value for groundfish. We highlight fisheries-specific and spatially-resolved patterns of effort, landings, and ex-vessel value, a bimodal distribution of fishing effort with respect to depth, and variable and generally declining effort over eight years. The information generated by our study can help inform future sustainable spatial fisheries management and other activities in the marine environment including offshore renewable energy planning.

Complete spatiotemporal quantification of cardiac motion in mice through enhanced acquisition and super-resolution reconstruction.

The quantification of cardiac motion using cardiac magnetic resonance imaging (CMR) has shown promise as an early-stage marker for cardiovascular diseases. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive. Complete spatiotemporal strain calculations are primarily hampered by poor spatial resolution, with the rapid motion of the cardiac wall also challenging the reproducibility of such strains. We hypothesize that a super-resolution reconstruction (SRR) framework that leverages combined image acquisitions at multiple orientations will enhance the reproducibility of complete spatiotemporal strain estimation. Two sets of CMR acquisitions were obtained for five wild-type mice, combining short-axis scans with radial and orthogonal long-axis scans. Super-resolution reconstruction, integrated with tissue classification, was performed to generate full four-dimensional (4D) images. The resulting enhanced and full 4D images enabled complete quantification of the motion in terms of 4D myocardial strains. Additionally, the effects of SRR in improving accurate strain measurements were evaluated using an in-silico heart phantom. The SRR framework revealed near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain accuracy. In essence, a comprehensive methodology was generated to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain calculations.