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1
Gonçalves, Bruno, Diogo Coutinho, Bruno Travassos, João Brito, and Pedro Figueiredo. "Match Analysis of Soccer Refereeing Using Spatiotemporal Data: A Case Study." Sensors 21, no. 7 (April 5, 2021): 2541. http://dx.doi.org/10.3390/s21072541.
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This case study explored how spatiotemporal data can develop key metrics to evaluate and understand elite soccer referees’ performance during one elite soccer match. The dynamic position of players from both teams, the ball and three elite referees allowed to capture the following performance metrics: (i) assistant referees: alignment with the second last defender; (ii) referee: referee diagonal movement—a position density was computed and a principal component analysis was carried to identify the directions of greatest variability; and (iii) referee: assessing the distance from the referee to the ball. All computations were processed when the ball was in-play and separated by 1st and 2nd halves. The first metric showed an alignment lower than 1 m between the assistant referee and the second last defender. The second metric showed that in the 1st half, the referee position ellipsis area was 548 m2, which increased during the 2nd half (671 m2). The third metric showed an increase in the distance from the referee to the ball and >80% of the distance between 5–30 m during the 2nd half. The findings may be used as a starting point to elaborate normative behavior models from the referee’s movement performance in soccer.
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Hacker, Kathryn P., Andrew J. Greenlee, Alison L. Hill, Daniel Schneider, and Michael Z. Levy. "Spatiotemporal trends in bed bug metrics: New York City." PLOS ONE 17, no. 5 (May 26, 2022): e0268798. http://dx.doi.org/10.1371/journal.pone.0268798.
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Bed bug outbreaks pose a major challenge in urban environments and cause significant strain on public resources. Few studies have systematically analyzed this insect epidemic or the potential effects of policies to combat bed bugs. Here we use three sources of administrative data to characterize the spatial-temporal trends of bed bug inquiries, complaints, and reports in New York City. Bed bug complaints have significantly decreased (p < 0.01) from 2014–2020, the absolute number of complaints per month dropping by half (875 average complaints per month to 440 average complaints per month); conversely, complaints for other insects including cockroaches and flies did not decrease over the same period. Despite the decrease of bed bug complaints, areas with reported high bed bug infestation tend to remain infested, highlighting the persistence of these pests. There are limitations to the datasets; still the evidence available suggests that interventions employed by New York City residents and lawmakers are stemming the bed bug epidemic and may serve as a model for other large cities.
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Shiu, Janice, Sarah Fletcher, and Dara Entekhabi. "Spatiotemporal monsoon characteristics and maize yields in West Africa." Environmental Research Communications 3, no. 12 (December 1, 2021): 125007. http://dx.doi.org/10.1088/2515-7620/ac3776.
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Abstract To assess the vulnerability of rainfed agriculture in West Africa (WA) to climate change, a detailed understanding of the relationship between food crop yields and seasonal rainfall characteristics is required. The highly seasonal rainfall in the region is expected to change characteristics such as seasonal timing, duration, intensity, and intermittency. The food crop yield response to changes in these characteristics needs greater understanding. We follow a data-driven approach based on historical yield and climate data. Such an approach complements model-based approaches. Previous data-driven studies use spatially and temporally averaged precipitation measures, which do not describe the high degree of spatial and temporal variability of the West African Monsoon (WAM), the primary source of water for agriculture in the region. This has led previous studies to find small or insignificant dependence of crop yields on precipitation amount. Here, we develop metrics that characterize important temporal features and variability in growing season precipitation, including total precipitation, onset and duration of the WAM, and number of non-precipitating days. For each temporal precipitation metric, we apply several unique spatial aggregation functions that allow us to assess how different patterns of high-resolution spatial variability are related to country-level maize yields. We develop correlation analyses between spatiotemporal precipitation metrics and detrended country-level maize yields based on findings that non-climatic factors, such as agricultural policy reform and increased investment, have driven the region’s long-term increase in maize yields. Results show that that the variability in the number of days without rain during the monsoon season and the lower bounds to the spatial rain pattern and end to the monsoon season are most strongly associated with maize yields. Our findings highlight the importance of considering spatial and temporal variability in precipitation when evaluating impacts on crop yields, providing a possible explanation for weak connections found in previous studies.
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Mandal, Somnath, Sanjit Kundu, Subrata Haldar, Subhasis Bhattacharya, and Suman Paul. "Monitoring and Measuring the Urban Forms Using Spatial Metrics of Howrah City, India." Remote Sensing of Land 4, no. 1-2 (November 22, 2020): 19–39. http://dx.doi.org/10.21523/gcj1.20040103.
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Monitoring and measurement of urban growth pattern with the help of urban-rural gradient and spatial metrics are gaining significant importance in recent times. Rapid and unplanned urban growth has a great impact on natural resources, local ecology, forestry and infrastructure. Temporal satellite data, gradient analysis and landscape metrics of urban landscapes will help to evolve appropriate strategies for integrated planning and sustainable management of natural resources. This communication focuses on spatiotemporal patterns of land use dynamics of Howrah Municipal Corporation (HMC), India and its surroundings with six buffer zones of 2kms. Analysis has been carried out on HMC using temporal remote sensing data. HMC has been used to identify the changes in the gradient of urban to peri-urban and rural regions. Further, the entire study area has been divided into eight zones radiated from city center based on directions. Different landscape metrics have been computed for each zone which helps to understand the spatiotemporal patterns and associated dynamics of the landscape at local levels.
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Paley, Derek A., and Artur Wolek. "Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 91–114. http://dx.doi.org/10.1146/annurev-control-073119-090634.
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The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.
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Parrott, Lael, Raphaël Proulx, and Xavier Thibert-Plante. "Three-dimensional metrics for the analysis of spatiotemporal data in ecology." Ecological Informatics 3, no. 6 (December 2008): 343–53. http://dx.doi.org/10.1016/j.ecoinf.2008.07.001.
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Zhao, Jie, Wenfu Yang, Junhuan Peng, Cheng Li, Zhen Li, and Xiaosong Liu. "ANALYZING AND MODELING THE SPATIOTEMPORAL DYNAMICS OF URBAN EXPANSION: A CASE STUDY OF HANGZHOU CITY, CHINA." Journal of Environmental Engineering and Landscape Management 27, no. 4 (November 28, 2019): 228–41. http://dx.doi.org/10.3846/jeelm.2019.11561.
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Understanding the spatiotemporal characteristics of urban expansion is increasingly important for assisting the decision making related to sustainable urban development. By integrating remote sensing (RS), spatial metrics, and the cellular automata (CA) model, this study explored the spatiotemporal dynamics of urban expansion and simulated future scenarios for Hangzhou City, China. The land cover maps (2002, 2008, and 2013) were derived from Landsat images. Moreover, the spatial metrics were applied to characterize the spatial pattern of urban land. The CA model was developed to simulate three scenarios (Business-As-Usual (BAU), Environmental Protection (EP), and Coordination Development (CD)) based on the various strategies. In addition, the scenarios were further evaluated and compared. The results indicated that Hangzhou City has experienced significant urban expansion, and the urban area has increased by 698.59 km2. Meanwhile, the spatial pattern of urban land has become more fragmented and complex. Hangzhou City will face unprecedented pressure on land use efficiency and coordination development if this historical trend continues. The CD scenario was regarded as the optimized scenario for achieving sustainable development. The findings revealed the spatiotemporal characteristics of urban expansion and provide a support for future urban development.
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Hornberger, Zachary, Bruce Cox, and Raymond R. Hill. "Analysis of the effects of spatiotemporal demand data aggregation methods on distance and volume errors." Journal of Defense Analytics and Logistics 5, no. 1 (May 10, 2021): 29–45. http://dx.doi.org/10.1108/jdal-03-2020-0003.
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Purpose Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors. Design/methodology/approach This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering. Findings As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands. Originality/value This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.
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Nouri, Milad, and Mehdi Homaee. "Spatiotemporal changes of snow metrics in mountainous data-scarce areas using reanalyses." Journal of Hydrology 603 (December 2021): 126858. http://dx.doi.org/10.1016/j.jhydrol.2021.126858.
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Adams, David K., Henrique M. J. Barbosa, and Karen Patricia Gaitán De Los Ríos. "A Spatiotemporal Water Vapor–Deep Convection Correlation Metric Derived from the Amazon Dense GNSS Meteorological Network." Monthly Weather Review 145, no. 1 (January 1, 2017): 279–88. http://dx.doi.org/10.1175/mwr-d-16-0140.1.
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Abstract Deep atmospheric convection, which covers a large range of spatial scales during its evolution, continues to be a challenge for models to replicate, particularly over land in the tropics. Specifically, the shallow-to-deep convective transition and organization on the mesoscale are often not properly represented in coarse-resolution models. High-resolution models offer insights on physical mechanisms responsible for the shallow-to-deep transition. Model verification, however, at both coarse and high resolution requires validation and, hence, observational metrics, which are lacking in the tropics. Here a straightforward metric derived from the Amazon Dense GNSS Meteorological Network (~100 km × 100 km) is presented based on a spatial correlation decay time scale during convective evolution on the mesoscale. For the shallow-to-deep transition, the correlation decay time scale is shown to be around 3.5 h. This novel result provides a much needed metric from the deep tropics for numerical models to replicate.
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Li, Fangzheng, Wei Zheng, Yu Wang, Junhui Liang, Shuang Xie, Shiyi Guo, Xiong Li, and Changming Yu. "Urban Green Space Fragmentation and Urbanization: A Spatiotemporal Perspective." Forests 10, no. 4 (April 13, 2019): 333. http://dx.doi.org/10.3390/f10040333.
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Urbanization leads to the occupation of green areas, directly contributing to a high level of fragmentation of urban green spaces, which, in turn, results in numerous socioeconomic and environmental problems. Consequently, an understanding of the relationships between patterns of urban green spaces and urbanization processes is essential. Although previous quantitative studies have examined this relationship, they have not included an exploration of spatial heterogeneities in the effects of urbanization on the spatial patterns of urban green areas. We therefore applied a spatiotemporal perspective to examine the above relationship, while considering the wider planning context. First, we quantified the extent of fragmentation of urban green spaces using landscape metrics comprising the largest patch index (LPI) and landscape shape index (LSI). Next, using the calculated spatial metrics and nighttime light data (NTL) for central Beijing for the period 1992–2016, we applied a geographically weighted regression model to assess variations in the spatiotemporal effects of urbanization on the fragmentation of urban green spaces. The results showed that urbanization initially occurred mainly in the northern parts of Beijing, whereas urbanization of southern urban fringe areas occurred after 2008. The reduction in green spaces along with increasing fragmentation and complex spatial patterns are indicative of issues relating to Beijing’s rapid urbanization and planning policies. This study contributes to an understanding of how urbanization influences fragmentation of urban green spaces and offers insights for the planning of urban green spaces from the perspective of promoting sustainability.
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Yun, Sooin, Jason E. Smerdon, Bo Li, and Xianyang Zhang. "A pseudoproxy assessment of why climate field reconstruction methods perform the way they do in time and space." Climate of the Past 17, no. 6 (December 17, 2021): 2583–605. http://dx.doi.org/10.5194/cp-17-2583-2021.
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Abstract. Spatiotemporal paleoclimate reconstructions that seek to estimate climate conditions over the last several millennia are derived from multiple climate proxy records (e.g., tree rings, ice cores, corals, and cave formations) that are heterogeneously distributed across land and marine environments. Assessing the skill of the methods used for these reconstructions is critical as a means of understanding the spatiotemporal uncertainties in the derived reconstruction products. Traditional statistical measures of skill have been applied in past applications, but they often lack formal null hypotheses that incorporate the spatiotemporal characteristics of the fields and allow for formal significance testing. More recent attempts have developed assessment metrics to evaluate the difference of the characteristics between two spatiotemporal fields. We apply these assessment metrics to results from synthetic reconstruction experiments based on multiple climate model simulations to assess the skill of four reconstruction methods. We further interpret the comparisons using analysis of empirical orthogonal functions (EOFs) that represent the noise-filtered climate field. We demonstrate that the underlying features of a targeted temperature field that can affect the performance of CFRs include the following: (i) the characteristics of the eigenvalue spectrum, namely the amount of variance captured in the leading EOFs; (ii) the temporal stability of the leading EOFs; (iii) the representation of the climate over the sampling network with respect to the global climate; and (iv) the strength of spatial covariance, i.e., the dominance of teleconnections, in the targeted temperature field. The features of climate models and reconstruction methods identified in this paper demonstrate more detailed assessments of reconstruction methods and point to important areas of testing and improving real-world reconstruction methods.
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Petrov, Andrey N., and Ramanathan Sugumaran. "Quantifying spatiotemporal dynamics of agricultural landscapes using remotely sensed data and landscape metrics." Geocarto International 24, no. 3 (June 2009): 223–40. http://dx.doi.org/10.1080/10106040802547784.
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Zhou, Xudong, Jan Polcher, Tao Yang, and Ching-Sheng Huang. "A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products." Hydrology and Earth System Sciences 24, no. 4 (April 23, 2020): 2061–81. http://dx.doi.org/10.5194/hess-24-2061-2020.
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Abstract. Ensemble estimates based on multiple datasets are frequently applied once many datasets are available for the same climatic variable. An uncertainty estimate based on the difference between the ensemble datasets is always provided along with the ensemble mean estimate to show to what extent the ensemble members are consistent with each other. However, one fundamental flaw of classic uncertainty estimates is that only the uncertainty in one dimension (either the temporal variability or the spatial heterogeneity) can be considered, whereas the variation along the other dimension is dismissed due to limitations in algorithms for classic uncertainty estimates, resulting in an incomplete assessment of the uncertainties. This study introduces a three-dimensional variance partitioning approach and proposes a new uncertainty estimation (Ue) that includes the data uncertainties in both spatiotemporal scales. The new approach avoids pre-averaging in either of the spatiotemporal dimensions and, as a result, the Ue estimate is around 20 % higher than the classic uncertainty metrics. The deviation of Ue from the classic metrics is apparent for regions with strong spatial heterogeneity and where the variations significantly differ in temporal and spatial scales. This shows that classic metrics underestimate the uncertainty through averaging, which means a loss of information in the variations across spatiotemporal scales. Decomposing the formula for Ue shows that Ue has integrated four different variations across the ensemble dataset members, while only two of the components are represented in the classic uncertainty estimates. This analysis of the decomposition explains the correlation as well as the differences between the newly proposed Ue and the two classic uncertainty metrics. The new approach is implemented and analysed with multiple precipitation products of different types (e.g. gauge-based products, merged products and GCMs) which contain different sources of uncertainties with different magnitudes. Ue of the gauge-based precipitation products is the smallest, while Ue of the other products is generally larger because other uncertainty sources are included and the constraints of the observations are not as strong as in gauge-based products. This new three-dimensional approach is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over large regions within any given period.
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Chappelier, J. C., and A. Grumbach. "RST: A Connectionist Architecture to Deal with Spatiotemporal Relationships." Neural Computation 10, no. 4 (May 1, 1998): 883–902. http://dx.doi.org/10.1162/089976698300017548.
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In the past decade, connectionism has proved its efficiency in the field of static pattern recognition. The next challenge is to deal with spatiotemporal problems. This article presents a new connectionist architecture, RST (ŕeseau spatio temporel [spatio temporal network]), with such spatiotemporal capacities. It aims at taking into account at the architecture level both spatial relationships (e.g., as between neighboring pixels in an image) and temporal relationships (e.g., as between consecutive images in a video sequence). Concerning the spatial aspect, the network is embedded in actual space (two-or three-dimensional), the metrics of which directly influence its structure through a connection distribution function. For the temporal aspect, we looked toward biology and used a leaky-integrator neuron model with a refractory period and postsynaptic potentials. The propagation of activity by spatiotemporal synchronized waves enables RST to perform motion detection and localization in sequences of video images.
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Gómez, Jairo A., Jorge E. Patiño, Juan C. Duque, and Santiago Passos. "Spatiotemporal Modeling of Urban Growth Using Machine Learning." Remote Sensing 12, no. 1 (December 28, 2019): 109. http://dx.doi.org/10.3390/rs12010109.
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This paper presents a general framework for modeling the growth of three important variables for cities: population distribution, binary urban footprint, and urban footprint in color. The framework models the population distribution as a spatiotemporal regression problem using machine learning, and it obtains the binary urban footprint from the population distribution through a binary classifier plus a temporal correction for existing urban regions. The framework estimates the urban footprint in color from its previous value, as well as from past and current values of the binary urban footprint using a semantic inpainting algorithm. By combining this framework with free data from the Landsat archive and the Global Human Settlement Layer framework, interested users can get approximate growth predictions of any city in the world. These predictions can be improved with the inclusion in the framework of additional spatially distributed input variables over time subject to availability. Unlike widely used growth models based on cellular automata, there are two main advantages of using the proposed machine learning-based framework. Firstly, it does not require to define rules a priori because the model learns the dynamics of growth directly from the historical data. Secondly, it is very easy to train new machine learning models using different explanatory input variables to assess their impact. As a proof of concept, we tested the framework in Valledupar and Rionegro, two Latin American cities located in Colombia with different geomorphological characteristics, and found that the model predictions were in close agreement with the ground-truth based on performance metrics, such as the root-mean-square error, zero-mean normalized cross-correlation, Pearson’s correlation coefficient for continuous variables, and a few others for discrete variables such as the intersection over union, accuracy, and the f 1 metric. In summary, our framework for modeling urban growth is flexible, allows sensitivity analyses, and can help policymakers worldwide to assess different what-if scenarios during the planning cycle of sustainable and resilient cities.
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Yan, An, and Bill Howe. "Fairness-Aware Demand Prediction for New Mobility." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1079–87. http://dx.doi.org/10.1609/aaai.v34i01.5458.
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Emerging transportation modes, including car-sharing, bike-sharing, and ride-hailing, are transforming urban mobility yet have been shown to reinforce socioeconomic inequity. These services rely on accurate demand prediction, but the demand data on which these models are trained reflect biases around demographics, socioeconomic conditions, and entrenched geographic patterns. To address these biases and improve fairness, we present FairST, a fairness-aware demand prediction model for spatiotemporal urban applications, with emphasis on new mobility. We use 1D (time-varying, space-constant), 2D (space-varying, time-constant) and 3D (both time- and space-varying) convolutional branches to integrate heterogeneous features, while including fairness metrics as a form of regularization to improve equity across demographic groups. We propose two spatiotemporal fairness metrics, region-based fairness gap (RFG), applicable when demographic information is provided as a constant for a region, and individual-based fairness gap (IFG), applicable when a continuous distribution of demographic information is available. Experimental results on bike share and ride share datasets show that FairST can reduce inequity in demand prediction for multiple sensitive attributes (i.e. race, age, and education level), while achieving better accuracy than even state-of-the-art fairness-oblivious methods.
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Li, Mujie, Zezhong Zheng, Mingcang Zhu, Yue He, Jun Xia, Xueye Chen, Qingjun Peng, Yong He, Xiang Zhang, and Pengshan Li. "The Spatiotemporal Evolution of Urban Impervious Surface for Chengdu, China." Photogrammetric Engineering & Remote Sensing 87, no. 7 (July 1, 2021): 491–502. http://dx.doi.org/10.14358/pers.87.7.491.
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The spatiotemporal evolution of an impervious surface (IS) is significant for urban planning. In this paper, the IS was extracted and its spatiotemporal evolution for the Chengdu urban area was analyzed based on Landsat imagery. Our experimental results indicated that convolutional neural networks achieved the better performance with an overall accuracy of 98.32%, Kappa coefficient of 0.98, and Macro F1 of 98.28%, and the farmland was replaced by IS from 2001 to 2017, and the IS area (ISA) increased by 51.24 km2; that is, the growth rate was up to 13.8% in sixteen years. According to the landscape metrics, the IS expanded and agglomerated into large patches from small fragmented ones. In addition, the gross domestic product change of the secondary industry was similar to the change of ISA between 2001 and 2017. Thus, the spatiotemporal evolution of IS was associated with the economic development of the Chengdu urban area in the past sixteen years.
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Scherler, Dirk, and Wolfgang Schwanghart. "Drainage divide networks – Part 2: Response to perturbations." Earth Surface Dynamics 8, no. 2 (April 20, 2020): 261–74. http://dx.doi.org/10.5194/esurf-8-261-2020.
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Abstract. Drainage divides are organized into tree-like networks that may record information about drainage divide mobility. However, views diverge about how to best assess divide mobility. Here, we apply a new approach of automatically extracting and ordering drainage divide networks from digital elevation models to results from landscape evolution model experiments. We compared landscapes perturbed by strike-slip faulting and spatiotemporal variations in erodibility to a reference model to assess which topographic metrics (hillslope relief, flow distance, and χ) are diagnostic of divide mobility. Results show that divide segments that are a minimum distance of ∼5 km from river confluences strive to attain constant values of hillslope relief and flow distance to the nearest stream. Disruptions of such patterns can be related to mobile divides that are lower than stable divides, closer to streams, and often asymmetric in shape. In general, we observe that drainage divides high up in the network, i.e., at great distances from river confluences, are more susceptible to disruptions than divides closer to these confluences and are thus more likely to record disturbance for a longer time period. We found that across-divide differences in hillslope relief proved more useful for assessing divide migration than other tested metrics. However, even stable drainage divide networks exhibit across-divide differences in any of the studied topographic metrics. Finally, we propose a new metric to quantify the connectivity of divide junctions.
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Haberlie, Alex M., and Walker S. Ashley. "A Method for Identifying Midlatitude Mesoscale Convective Systems in Radar Mosaics. Part II: Tracking." Journal of Applied Meteorology and Climatology 57, no. 7 (July 2018): 1599–621. http://dx.doi.org/10.1175/jamc-d-17-0294.1.
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AbstractThis research is Part II of a two-part study that evaluates the ability of image-processing and select machine-learning algorithms to detect, classify, and track midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. This paper focuses on the tracking portion of this framework. Tracking is completed through a two-step process using slice (snapshots of instantaneous MCS intensity) data generated in Part I. The first step is to perform spatiotemporal matching, which associates slices through temporally adjacent radar-reflectivity images to generate swaths, or storm tracks. When multiple slices are found to be matches, a difference-minimization procedure is used to associate the most similar slice with the existing swath. Once this step is completed, a second step combines swaths that are spatiotemporally close. Tracking performance is assessed by calculating select metrics for all available swath-building perturbations to determine the optimal approach in tracking. Frequency maps and time series generated from the swaths suggest that the spatiotemporal occurrence of these swaths is reasonable as determined from previous work. Further, these events exhibit a diurnal cycle that is distinct from that of overall convection for the conterminous United States. Last, machine-learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains.
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Lu, Chiahao, Kenneth H. Louie, Emily L. Twedell, Jerrold L. Vitek, Colum D. MacKinnon, and Scott E. Cooper. "Overground versus treadmill walking in Parkinson’s disease: Relationship between speed and spatiotemporal gait metrics." Gait & Posture 93 (March 2022): 96–101. http://dx.doi.org/10.1016/j.gaitpost.2022.01.020.
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Thapa, Rajesh, and Yuji Murayama. "Examining Spatiotemporal Urbanization Patterns in Kathmandu Valley, Nepal: Remote Sensing and Spatial Metrics Approaches." Remote Sensing 1, no. 3 (September 3, 2009): 534–56. http://dx.doi.org/10.3390/rs1030534.
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Alqurashi, Abdullah F. "Quantification of Urban Patterns and Processes through Space and Time Using Remote Sensing Data: A Comparative Study between Three Saudi Arabian Cities." Sustainability 13, no. 22 (November 15, 2021): 12615. http://dx.doi.org/10.3390/su132212615.
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Saudi Arabia has developed rapidly over the last five decades in the wake of an extensive development programme implemented by the government throughout the entire country. Several previous studies have measured the extent and rate of urbanization in Saudi Arabian cities, but most of this research used only remote sensing data or a single index to explain urban growth patterns. This study used satellite data and a set of landscape metrics to quantify the spatiotemporal urban growth patterns and processes in three Saudi Arabian cities–Riyadh, Jeddah and Makkah. First, Landsat images were collected and classified for the years 1985, 1990, 2000, 2007, 2014 and 2020. Classification was carried out through an object-based image analysis (OBIA) to map the extent of urbanization. The classified maps were then used to compute seven landscape metrics to determine the spatial configuration of urban areas. The spatial metrics were calculated for the entire landscape and across buffer zones that were delineated from the urban core centre of each city. The overall accuracies were >94% for all the classified maps. The spatiotemporal results indicated that all three cities have experienced significant urban growth during the last four decades. Urban patterns in Jeddah were more dispersed than in Riyadh, which showed aggregated patterns (especially in recent years), while urban growth in Makkah tended to be more fragmented. The urban form in Riyadh was relatively simple, while a complex form was associated with Makkah and Jeddah. Understanding the rates, patterns, processes and trajectories of changes to urban land use is essential for various decision-making processes.
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Kümmerer, Matthias, Thomas S. A. Wallis, and Matthias Bethge. "Information-theoretic model comparison unifies saliency metrics." Proceedings of the National Academy of Sciences 112, no. 52 (December 10, 2015): 16054–59. http://dx.doi.org/10.1073/pnas.1510393112.
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Learning the properties of an image associated with human gaze placement is important both for understanding how biological systems explore the environment and for computer vision applications. There is a large literature on quantitative eye movement models that seeks to predict fixations from images (sometimes termed “saliency” prediction). A major problem known to the field is that existing model comparison metrics give inconsistent results, causing confusion. We argue that the primary reason for these inconsistencies is because different metrics and models use different definitions of what a “saliency map” entails. For example, some metrics expect a model to account for image-independent central fixation bias whereas others will penalize a model that does. Here we bring saliency evaluation into the domain of information by framing fixation prediction models probabilistically and calculating information gain. We jointly optimize the scale, the center bias, and spatial blurring of all models within this framework. Evaluating existing metrics on these rephrased models produces almost perfect agreement in model rankings across the metrics. Model performance is separated from center bias and spatial blurring, avoiding the confounding of these factors in model comparison. We additionally provide a method to show where and how models fail to capture information in the fixations on the pixel level. These methods are readily extended to spatiotemporal models of fixation scanpaths, and we provide a software package to facilitate their use.
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Guarnier, Letícia, Fabricia Benda Oliveira, Carlos Henrique Rodrigues de Oliveira, and Vicente Sombra da Fonseca. "MULTI-SPATIOTEMPORAL SIMULATION OF EDGE EFFECT ON FOREST PATCHES IN THE BARRA SECA RIVER BASIN, ES." FLORESTA 50, no. 4 (September 29, 2020): 1864. http://dx.doi.org/10.5380/rf.v50i4.66577.
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The Atlantic Forest is intensely fragmented and this fragmentation process has caused an expressive increase of forest remnants and, consequently, increased edge effect with different physical-biological intensities in the transition areas between the patch and the matrix. This study used landscape metrics to understand and analyze how different edge effect distances affect the structure of the forest landscape in the Barra Seca River basin (ES), in 1985, 1996, 2006 and 2016. Remote sensing images were processed and using the Bhattacharya algorithm with supervised classification, the forest patches of the study area were classified and isolated. Landscape ecology metrics were computed with Patch Analyst and V-Late 2 Beta extensions. The forest patches were divided into four size classes as follows smaller than 5 ha (C1); between 5 and 10 ha (C2); between 10 and 100 ha (C3); and over 100 ha (C4). The edge effect simulation using landscape metrics was performed using the edge effect distances of 20, 40, 60, 80, 100, 140, and 200 m. Forest fragmentation increased between 1985 and 2016 while the number of patches greater than 100 ha decreased. Currently, the basin landscape consists mainly of small patches, which have larger relative areas affected by edge effect while many patches smaller than 10 ha are completely dominated by edge effect for distances greater than 60 meters. The edge effect simulation for different distances allowed verifying the intensification of the edge effect on the forest patches of the Barra Seca River basin.
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Yao, Yuan, Yiling Lu, Fang Zhang, Lulu Liu, and Min Liao. "Analysis of Seasonal Daytime Urban Thermal Environment Dynamics in a Tropical Coastal City Based on the Spatiotemporal Fusion Model." Advances in Meteorology 2020 (November 23, 2020): 1–16. http://dx.doi.org/10.1155/2020/8182676.
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This study investigated the seasonal variations of daytime urban thermal environment (UTE) based on land surface temperature (LST) in Shenzhen of 2015. The spatial and temporal adaptive reflectance fusion model (STARFM) was used for retrieving seasonal daytime LST at high spatiotemporal resolution by combining MODIS and HJ-1B LST data. Next, the relationship between the land cover and daytime in each season was examined. Finally, daytime LST patterns were classified, and the effects of seasonal variations of high-grade daytime LSTs were analyzed with landscape metrics. The results showed that (1) the STARFM is capable of generating seasonal daytime LST data at high spatiotemporal resolution. (2) Daytime LSTs were generally higher in the western parts of Shenzhen in spring and summer. (3) Daytime LST in each land cover type showed an increasing trend form winter to summer and decreased from summer to autumn. The highest and lowest daytime LSTs in each season were observed in ISAs and water bodies. (4) Landscape metrics provided a quantitative method for describing seasonal variations in daytime LSTs, and it was found that seasons influenced the intensity and extent of daytime LSTs in Shenzhen. These findings may be helpful for urban planners developing regional urban strategies to improve daytime urban thermal comfort conditions.
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Dietzel, Charles, Hakan Oguz, Jeffery J. Hemphill, Keith C. Clarke, and Nicholas Gazulis. "Diffusion and Coalescence of the Houston Metropolitan Area: Evidence Supporting a New Urban Theory." Environment and Planning B: Planning and Design 32, no. 2 (April 2005): 231–46. http://dx.doi.org/10.1068/b31148.
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The authors build on a recent development in urban geographic theory, providing evidence of an oscillatory behavior in spatiotemporal patterns of urban growth. With the aid of remotely sensed data, the spatial extent of urban areas in the Houston (USA) metropolitan region from 1974 to 2002 was analyzed by spatial metrics. Regularities in the spatial urban growth pattern were identified with temporal periods as short as thirty years by means of spatial metric values, including mean nearest-neighbor distance, mean patch area, total number of urban patches, and mean patch fractal dimension. Through changes in these values, a distinct oscillation between phases of diffusion and coalescence in urban growth was revealed. The results suggest that the hypothesized process of diffusion and coalescence may occur over shorter time periods than previously thought, and that the patterns are readily observable in real-world systems.
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Silva, B. L. C., F. C. Souza, K. R. Ferreira, G. R. Queiroz, and L. A. Santos. "SPATIOTEMPORAL SEGMENTATION OF SATELLITE IMAGE TIME SERIES USING SELF-ORGANIZING MAP." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 255–61. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-255-2022.
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Abstract. Nowadays, researchers have free access to an unprecedentedly large amount of remote sensing images collected by satellites and sensors with different spatial, temporal, and spectral resolutions. This scenario has promoted the use of satellite image time series for spatiotemporal analysis. This paper presents a methodology for spatiotemporal segmentation of satellite image time series. Spatiotemporal segmentation finds homogeneous regions in space and time from remote sensing images based on spectral features. The proposed approach is unsupervised based on the self-organizing map (SOM) neural network and hierarchical clustering algorithm. It was implemented and applied to a region in the Mato Grosso state, Brazil. The results were evaluated using qualitative and quantitative approaches. In the qualitative approach, visual analysis was performed based on the land use and land cover map of the TerrraClass Cerrado project. In the quantitative approach, supervised and geometric metrics were used to analyze the quality of the produced segments. The results obtained are promising since the segments produced were homogeneous and with a low occurrence of over-segmentation.
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Eastman, Michael, Simon Parry, Catherine Sefton, Juhyun Park, and Judy England. "Reconstructing Spatiotemporal Dynamics in Hydrological State Along Intermittent Rivers." Water 13, no. 4 (February 14, 2021): 493. http://dx.doi.org/10.3390/w13040493.
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Despite the impact of flow cessation on aquatic ecology, the hydrology of intermittent rivers has been largely overlooked. This has resulted in a lack of monitoring projects, and consequently, datasets spanning a period of sufficient duration to characterise both hydrological extremes. This report documents an investigation into the potential for statistical modelling to simulate the spatiotemporal dynamics of flowing, ponded and dry hydrological states in an internationally rare hydrological state dataset. The models presented predict unrecorded hydrological state data with performance metrics exceeding 95%, providing insights into the relationship between ponding prevalence and the performance of statistical simulation of this ecologically important intermediate state between drying and flowing conditions. This work demonstrates the potential for hydrological intermittence to be simulated in areas where hydrological state data are often sparse, providing opportunities for quality control and data infilling. This further understanding of the processes driving intermittence will inform future water resource assessments and the influence of climate change on hydrological intermittence.
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Monnin, P., A. Viry, J. Damet, M. Nowak, V. Vitzthum, and D. Racine. "A novel method to assess the spatiotemporal image quality in fluoroscopy." Physics in Medicine & Biology 66, no. 24 (December 6, 2021): 245001. http://dx.doi.org/10.1088/1361-6560/ac3c15.
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Abstract Objectives. The planar formulation of the noise equivalent quanta (NEQ) and detective quantum efficiency (DQE) used to assess the image quality of projection images does not deal with the influence of temporal resolution on signal blurring and image noise. These metrics require correction factors based on temporal resolution when used for dynamic imaging systems such as fluoroscopy. Additionally, the standard NEQ and detector DQE are determined on pre-processed images in scatter-free conditions for effective energies produced by additional aluminium or copper filters that are not representative of clinical fluoroscopic procedures. In this work, we developed a method to measure ‘frame NEQ’ and ‘frame system DQE’ which include the temporal frequency bandwidth and consider the anti-scatter grid, the detector and the image processing procedures for beam qualities with scatter fractions representative of clinical use. Approach. We used a solid water phantom to simulate a patient and a thin copper disc to measure the spatial resolution. The copper disc, set in uniform rectilinear motion in the image plane, assessed the temporal resolution. These new metrics were tested on two fluoroscopy systems, a C-arm and a floor-mounted cardiology, for multiple parameters: phantom thicknesses from 5 to 20 cm, frame rates from 3 to 30 fps, spatial and temporal image processing of different weights. Main results. The frame NEQ correctly described the image quality for different scatter conditions, temporal resolutions and image processing techniques. The frame system DQE varied between 0.38 and 0.65 within the different beam and scatter conditions, and correctly mitigated the influence of spatial and temporal image processing. Significance. This study introduces and validates an unbiased formulation of in-plane NEQ and system DQE to assess the spatiotemporal image quality of fluoroscopy systems.
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Su, Jianbin, Haishen Lü, Wade T. Crow, Yonghua Zhu, and Yifan Cui. "The Effect of Spatiotemporal Resolution Degradation on the Accuracy of IMERG Products over the Huai River Basin." Journal of Hydrometeorology 21, no. 5 (May 2020): 1073–88. http://dx.doi.org/10.1175/jhm-d-19-0158.1.
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AbstractThe rapid development of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) precipitation product provides new opportunities for a wide range of Earth system and natural hazard applications. Spatiotemporal averaging is a common method for IMERG users to acquire suitable resolutions specific to their research or application purpose and has a direct impact on the overall quality of IMERG precipitation estimates. Here, three different IMERG, version 06 (V06), latency run products (i.e., early, late, and final) are assessed against a ground-based benchmark along a continuous series of spatiotemporal resolutions over the Huai River basin (HuaiRB) between June 2014 and May 2017. In general, IMERG products better capture the spatial pattern of precipitation, and demonstrate better reliability, in the southern portion of the HuaiRB relative to its northern region. Furthermore, the degradation of spatiotemporal resolution is associated with better rain/no-rain determination and the consistent improvement of rainfall product performance metrics. This improvement is more pronounced for IMERG products at fine spatiotemporal resolution. However, due to the presence of autocorrelated errors, the performance improvement associated with the degradation of spatiotemporal resolution is less than theoretical expectations assuming purely uncorrelated errors. Component analysis indicates that while both temporal and spatial aggregation do not mitigate temporally autocorrelated errors, temporal averaging can remove spatially autocorrelated error. Hence, temporal averaging is found to be more effective than spatial averaging for improving the quality of IMERG products. These results will inform users of the reliability of IMERG products at different spatiotemporal scales and assist in unifying former disparate validation assessments applied at different scales within the literature.
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Mugiraneza, T., J. Haas, and Y. Ban. "SPATIOTEMPORAL ANALYSIS OF URBAN LAND COVER CHANGES IN KIGALI, RWANDA USING MULTITEMPORAL LANDSAT DATA AND LANDSCAPE METRICS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W2 (November 16, 2017): 137–44. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w2-137-2017.
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Mapping urbanization and ensuing environmental impacts using satellite data combined with landscape metrics has become a hot research topic. The objectives of the study are to analyze the spatio-temporal evolution of urbanization patterns of Kigali, Rwanda over the last three decades (from 1984 to 2015) using multitemporal Landsat data and to assess the associated environmental impact using landscape metrics. Landsat images, Normalized Difference Vegetation Index (NDVI), Grey Level Co-occurrence Matrix (GLCM) variance texture and digital elevation model (DEM) data were classified using a support vector machine (SVM). Eight landscape indices were derived from classified images for urbanization environment impact assessment. Seven land cover classes were derived with an overall accuracy exceeding 88&thinsp;% with Kappa Coefficients around 0.8. As most prominent changes, cropland was reduced considerably in favour of built-up areas that increased from 2,349&thinsp;ha to 11,579&thinsp;ha between 1984 and 2015. During those 31 years, the increased number of patches in most land cover classes illustrated landscape fragmentation, especially for forest. The landscape configuration indices demonstrate that in general the land cover pattern remained stable for cropland but it was highly changed in built-up areas. Satellite-based analysis and quantification of urbanization and its effects using landscape metrics are found to be interesting for grassroots and provide a cost-effective method for urban information production. This information can be used for e.g. potential design and implementation of early warning systems that cater for urbanization effects.
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Liu, Song, Xinsu Zhang, Yongjiu Feng, Huan Xie, Li Jiang, and Zhenkun Lei. "Spatiotemporal Dynamics of Urban Green Space Influenced by Rapid Urbanization and Land Use Policies in Shanghai." Forests 12, no. 4 (April 14, 2021): 476. http://dx.doi.org/10.3390/f12040476.
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Urbanization has led to the continuous expansion of built-up areas and the ever-growing urban population, threatening the quantity and quality of urban green space (UGS). Exploring the spatiotemporal variations of UGS is substantially conducive to the formulation of land-use policies to protect the ecosystems. As one of the largest megacities all around the world, Shanghai has experienced rapid urbanization in the past three decades. Insights into how UGS changes in response to urbanization and greening policies are essential for guiding sustainable urban development. This paper employed integrated approaches to characterize the changing patterns and intensities of green space in Shanghai, China from 1990 to 2015. The spatiotemporal dynamics of the UGS pattern were derived through four main methods: green space ratio, dynamic change degree (DCD), transition matrix and landscape metrics. The results showed that Shanghai’s green space decreased from 84.8% in 1990 to 61.9% in 2015 while the built-up areas increased from 15.0% to 36.5%. Among the green space sub-types, farmland was largely encroached and fragmented by urban sprawl, especially in the Outer Ring Expressway and Suburban Ring Expressway belts of the city. About 1522 km2 of the green space has transferred into built-up areas, followed by farmland, waterbody, forest, and grassland in descending order. The 2000–2010 period witnessed the strong urban expansion and dramatic changes in UGS, but then the change around 2015 turned down and stable. The landscape pattern metrics showed that the entire green space in Shanghai was growingly fragmented and isolated during the past 25 years. Combined with the green space-related planning and policies issued in 1990–2015, the results revealed that both rapid urbanization and greening policies accounted for the spatiotemporal dynamics of UGS. Based on the results, some implicants to new urban planning and policies of Shanghai were highlighted.
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Shema-Shiratzky, Shirley, Yiftah Beer, Amit Mor, and Avi Elbaz. "Smartphone-based inertial sensors technology – Validation of a new application to measure spatiotemporal gait metrics." Gait & Posture 93 (March 2022): 102–6. http://dx.doi.org/10.1016/j.gaitpost.2022.01.024.
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Buttle, J. M., W. J. Greenwood, and R. E. Gerber. "Spatiotemporal patterns of baseflow metrics for basins draining the Oak Ridges Moraine, southern Ontario, Canada." Canadian Water Resources Journal / Revue canadienne des ressources hydriques 40, no. 1 (January 2, 2015): 3–22. http://dx.doi.org/10.1080/07011784.2014.985511.
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Debbage, Neil, Bradley Bereitschaft, and J. Marshall Shepherd. "Quantifying the Spatiotemporal Trends of Urban Sprawl Among Large U.S. Metropolitan Areas Via Spatial Metrics." Applied Spatial Analysis and Policy 10, no. 3 (May 23, 2016): 317–45. http://dx.doi.org/10.1007/s12061-016-9190-6.
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Buitre, Mary, Hongsheng Zhang, and Hui Lin. "The Mangrove Forests Change and Impacts from Tropical Cyclones in the Philippines Using Time Series Satellite Imagery." Remote Sensing 11, no. 6 (March 22, 2019): 688. http://dx.doi.org/10.3390/rs11060688.
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The Philippines is rich in mangrove forests, containing 50% of the total mangrove species of the world. However, the vast mangrove areas of the country have declined to about half of its cover in the past century. In the 1970s, action was taken to protect the remaining mangrove forests under a government initiative, recognizing the ecological benefits mangrove forests can bring. Here, we examine two mangrove areas in the Philippines—Coron in Palawan and Balangiga-Lawaan in Eastern Samar over a 30-year period. Sets of Landsat images from 1987 to 2016 were classified and spatially analyzed using four landscape metrics. Additional analyses of the mangrove areas’ spatiotemporal dynamics were conducted. The impact of typhoon landfall on the mangrove areas was also analyzed in a qualitative manner. Spatiotemporal changes indicate that both the Coron and Balangiga-Lawaan mangrove forests, though declared as protected areas, are still suffering from mangrove area loss. Mangrove areal shrinkage and expansion can be attributed to both typhoon occurrence and management practices. Overall, our study reveals which mangrove forests need more responsive action, and provides a different perspective in understanding the spatiotemporal dynamics of these mangrove areas.
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Li, Sijia, Chao Wu, Yu Lin, Zhengyang Li, and Qingyun Du. "Urban Morphology Promotes Urban Vibrancy from the Spatiotemporal and Synergetic Perspectives: A Case Study Using Multisource Data in Shenzhen, China." Sustainability 12, no. 12 (June 12, 2020): 4829. http://dx.doi.org/10.3390/su12124829.
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Urban vibrancy is the key and the foundation for monitoring the status of urban spatial development, assisting in data-driven urban development planning and realizing sustainable urban development. Based on a dataset of multisource geographical big data, the understanding and analysis of urban vibrancy can be deepened with fine granularity. The working framework in this study focuses on the comprehensive perspective of urban morphology, which is decomposed into two dimensions (formality and functionality) and four elements (road, block, building, point of interest). The geographically and temporally weighted regression model was first applied to determine the spatiotemporal effect of the morphological metrics on vibrancy, and then, the geographical detector was employed from the perspective of spatially stratified heterogeneity to reveal the synergetic impacts. The following findings were revealed. (1) Dense street networks, small and medium-sized blocks, and the diversification and intensification of building and land use are beneficial to urban vibrancy. (2) Under the premise of adapting to local conditions, urban spaces combine multiple morphological metrics for the accomplishment of a full-region and all-time vibrancy. (3) The mixture of urban functions is worthy of attention for vibrancy growth because of its extraordinary synergy, not its capacity. Morphological metrics serve to foster and prolong urban vibrancy, adapt to urban sustainability, and contend against inefficient, disorderly urban sprawl. These findings provide significant implications for urban planners/designers and policymakers to optimize urban morphology, improve the vibrancy in large cities, and implement high-quality city planning.
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Li, Yun, Moming Li, Megan Rice, Haoyuan Zhang, Dexuan Sha, Mei Li, Yanfang Su, and Chaowei Yang. "The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective." International Journal of Environmental Research and Public Health 18, no. 3 (January 23, 2021): 996. http://dx.doi.org/10.3390/ijerph18030996.
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Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.
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Ma, Yunru, Kumar Mithraratne, Nichola Wilson, Yanxin Zhang, and Xiangbin Wang. "Kinect V2-Based Gait Analysis for Children with Cerebral Palsy: Validity and Reliability of Spatial Margin of Stability and Spatiotemporal Variables." Sensors 21, no. 6 (March 17, 2021): 2104. http://dx.doi.org/10.3390/s21062104.
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Children with cerebral palsy (CP) have high risks of falling. It is necessary to evaluate gait stability for children with CP. In comparison to traditional motion capture techniques, the Kinect has the potential to be utilised as a cost-effective gait stability assessment tool, ensuring frequent and uninterrupted gait monitoring. To evaluate the validity and reliability of this measurement, in this study, ten children with CP performed two testing sessions, of which gait data were recorded by a Kinect V2 sensor and a referential Motion Analysis system. The margin of stability (MOS) and gait spatiotemporal metrics were examined. For the spatiotemporal parameters, intraclass correlation coefficient (ICC2,k) values were from 0.83 to 0.99 between two devices and from 0.78 to 0.88 between two testing sessions. For the MOS outcomes, ICC2,k values ranged from 0.42 to 0.99 between two devices and 0.28 to 0.69 between two test sessions. The Kinect V2 was able to provide valid and reliable spatiotemporal gait parameters, and it could also offer accurate outcome measures for the minimum MOS. The reliability of the Kinect V2 when assessing time-specific MOS variables was limited. The Kinect V2 shows the potential to be used as a cost-effective tool for CP gait stability assessment.
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Liu, Chunyang, Jiping Liu, Jian Wang, Shenghua Xu, Houzeng Han, and Yang Chen. "An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation." ISPRS International Journal of Geo-Information 8, no. 8 (August 13, 2019): 355. http://dx.doi.org/10.3390/ijgi8080355.
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Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it is difficult to capture dynamic user’s preferences using CF based methods. MC based methods suffer from strong independence assumptions. RNN based methods are still in the early stage of incorporating spatiotemporal context information, and the user’s main behavioral intention in the current sequence is not emphasized. To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. We first designed a novel variant of GRU, which acquired the user’s sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into the GRU network in each time step. Then, we integrated an attention model into our network, which is a personalized process and can capture the user’s main behavioral intention in the user’s check-in history. Moreover, we conducted an extensive performance evaluation on two real-world datasets: Foursquare and Gowalla. The experimental results demonstrated that the proposed ATST-GRU network outperforms the existing state-of-the-art POI recommendation methods significantly regarding two commonly-used evaluation metrics.
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Coats, S., J. E. Smerdon, S. Stevenson, J. T. Fasullo, B. Otto-Bliesner, and T. R. Ault. "Paleoclimate Constraints on the Spatiotemporal Character of Past and Future Droughts." Journal of Climate 33, no. 22 (November 15, 2020): 9883–903. http://dx.doi.org/10.1175/jcli-d-20-0004.1.
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AbstractMachine-learning-based methods that identify drought in three-dimensional space–time are applied to climate model simulations and tree-ring-based reconstructions of hydroclimate over the Northern Hemisphere extratropics for the past 1000 years, as well as twenty-first-century projections. Analyzing reconstructed and simulated drought in this context provides a paleoclimate constraint on the spatiotemporal characteristics of simulated droughts. Climate models project that there will be large increases in the persistence and severity of droughts over the coming century, but with little change in their spatial extent. Nevertheless, climate models exhibit biases in the spatiotemporal characteristics of persistent and severe droughts over parts of the Northern Hemisphere. We use the paleoclimate record and results from a linear inverse modeling-based framework to conclude that climate models underestimate the range of potential future hydroclimate states. Complicating this picture, however, are divergent changes in the characteristics of persistent and severe droughts when quantified using different hydroclimate metrics. Collectively our results imply that these divergent responses and the aforementioned biases must be better understood if we are to increase confidence in future hydroclimate projections. Importantly, the novel framework presented herein can be applied to other climate features to robustly describe their spatiotemporal characteristics and provide constraints on future changes to those characteristics.
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Da, Longchao, and Hua Wei. "CrowdGAIL: A spatiotemporal aware method for agent navigation." Electronic Research Archive 31, no. 2 (2022): 1134–46. http://dx.doi.org/10.3934/era.2023057.
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<abstract><p>Agent navigation has been a crucial task in today's service and automated factories. Many efforts are to set specific rules for agents in a certain scenario to regulate the agent's behaviors. However, not all situations could be in advance considered, which might lead to terrible performance in a real-world application. In this paper, we propose CrowdGAIL, a method to learn from expert behaviors as an instructing policy, can train most 'human-like' agents in navigation problems without manually setting any reward function or beforehand regulations. First, the proposed model structure is based on generative adversarial imitation learning (GAIL), which imitates how humans take actions and move toward the target to a maximum extent, and by comparison, we prove the advantage of proximal policy optimization (PPO) to trust region policy optimization, thus, GAIL-PPO is what we base. Second, we design a special Sequential DemoBuffer compatible with the inner long short-term memory structure to apply spatiotemporal instruction on the agent's next step. Third, the paper demonstrates the potential of the model with an integrated social manner in a multi-agent scenario by considering human collision avoidance as well as social comfort distance. At last, experiments on the generated dataset from CrowdNav verify how close our model would act like a human being in the trajectory aspect and also how it could guide the multi-agents by avoiding any collision. Under the same evaluation metrics, CrowdGAIL shows better results compared with classic Social-GAN.</p></abstract>
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Frimenko, Rebecca, Charles Goodyear, and Dustin Bruening. "Interactions of sex and aging on spatiotemporal metrics in non-pathological gait: a descriptive meta-analysis." Physiotherapy 101, no. 3 (September 2015): 266–72. http://dx.doi.org/10.1016/j.physio.2015.01.003.
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Cao, Hui, Jian Liu, Jianglong Chen, Jinlong Gao, Guizhou Wang, and Wanfeng Zhang. "Spatiotemporal Patterns of Urban Land Use Change in Typical Cities in the Greater Mekong Subregion (GMS)." Remote Sensing 11, no. 7 (April 3, 2019): 801. http://dx.doi.org/10.3390/rs11070801.
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The Greater Mekong Subregion (GMS) has experienced rapid economic growth and urbanization. However, few studies have paid attention to urban land use dynamics, especially spatiotemporal patterns of urban expansion and land use change, in this region. This research aimed to conduct a comprehensive study of urban land use change in Xishuangbanna, Yangon, Vientiane, Phnom Penh, Bangkok, and Ho Chi Minh City, from 1990 to 2015. The analysis was based on land use maps derived from Landsat satellite products and employed urban expansion intensity, sector analysis, gradient-direction analysis, and landscape metrics. The results show Xishuangbanna, Yangon, Vientiane, Phnom Penh, Bangkok, and Ho Chi Minh City all experienced dramatic urban expansion and land use change since 1990, with urban expansion intensities of 15.01, 5.26, 9.15, 1.56, 11.88 and 11.91, respectively. The landscape metrics analysis indicated that urban areas were always aggregated and self-connected, while other land use types showed trends of disaggregation and fragmentation. In the process of urban expansion, paddy and natural land use types were commonly transformed to built up area. The results further reveal several common issues in urban land use, e.g. land fragmentation and loss of natural land use types. Finally, the discussion on the relationship between government policy and land use change for these cities shows land reform and attitude toward foreign direct investments played important roles in urban land use change in GMS.
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Tuckwell, Henry C. "Cortical Potential Distributions and Information Processing." Neural Computation 12, no. 12 (December 1, 2000): 2777–95. http://dx.doi.org/10.1162/089976600300014719.
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The use of sets of spatiotemporal cortical potential distributions (CPDs) as the basis for cognitive information processing results in a very large space of cognitive elements with natural metrics. Results obtained from current source density (CSD) analysis suggest that in the CPD picture, action potentials may make only a relatively minor contribution to the brain's code. In order to establish if two CPDs are close, we consider standard metrics in spaces of continuous functions, and these may be employed to ascertain if two stimuli will be identified as the same. The correspondence between the electrical activity within brain regions, including not only action potentials but all postsynaptic potentials (PSPs), and CPDs is considered. We examine the possibility of using the CSD approach to find potential distributions using the descriptive approach in which precise sets of times are ascribed to the occurrence of action potentials and PSPs. Using metrics in the multidimensional space of paths of collections of point processes, we show that closeness of CPDs is implied by closeness of sets of spike times and PSP times if a certain metric is used but not others. We also set forth a dynamical model consisting of a system of reaction-diffusion equations for ionic concentrations coupled with nerve membrane potential equations and active transport systems. Making the approximation of a descriptive approach, the correspondence between sets of spike times and PSP times and CPDs is obtained as with the CSD method. However, since it is not possible to ascribe precise times to the occurrence of PSPs and action potentials, the descriptive approach cannot be used to describe the configuration of electrical activity in cortical regions accurately. We also discuss how the CPD framework relates to the binding problem and submillisecond timing.
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Rifat, Shaikh Abdullah Al, and Weibo Liu. "Quantifying Spatiotemporal Patterns and Major Explanatory Factors of Urban Expansion in Miami Metropolitan Area During 1992–2016." Remote Sensing 11, no. 21 (October 25, 2019): 2493. http://dx.doi.org/10.3390/rs11212493.
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Urban expansion is one of the most dramatic forms of land transformation in the world and it is one of the greatest challenges in achieving sustainable development in the 21st century. Previous studies analyzed urbanization patterns in areas with rapid urban expansion while urban areas with low to moderate expansion have been overlooked, especially in developed countries. In this study, we examined the spatiotemporal dynamics of urban expansion patterns in South Florida, United States (US) over the last 25 years (1992–2016) using Remote Sensing and GIS techniques. The main goal of this paper was to investigate the degree and spatiotemporal patterns of urban expansion at different administrative level in the study area and how spatiotemporal variance in different explanatory factors influence urban expansion in this region. More specifically, this research quantifies the rates, types, intensity, and landscape metrics of urban expansion in Miami-Fort Lauderdale-Palm Beach, Florida Metropolitan Statistical Area (Miami MSA) which is the 7th largest MSA and 4th largest urbanized area in the US using remote sensing (satellite imageries) data from National Land Cover Datasets (NLCD) and Coastal Change Analysis Program (C-CAP) at 30 m spatial resolution. We further investigated the urban growth patterns at the county and city areas that are located within this MSA to portray the local ‘picture’ of urban growth in this region. Urban expansion in this region can be divided into two time periods: pre-2001 and post-2001 where the former experienced rapid urban expansion and the later had comparatively slow urban expansion. Results suggest that infilling was the dominant type of urban expansion followed by edge-expansion and outlying. Results from landscape metrics represent that newly developed urban lands became more aggregated and simplified in form as the time progressed in the study region. Also, new urban lands were generated away from the east coast and historic cities which eventually created new urban cores. We also used correlation analysis and multiple linear stepwise regression to address major explanatory factors of spatiotemporal change in urban expansion during the study period. Although the influence of factors on urban expansion varied temporally, Population and Distance to Coast were the strongest variables followed by Distance to Roads and Median Income that influence overall urban expansion in the study area.
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Asam, Sarah, Mattia Callegari, Michael Matiu, Giuseppe Fiore, Ludovica De Gregorio, Alexander Jacob, Annette Menzel, Marc Zebisch, and Claudia Notarnicola. "Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps—An Earth Observation-Based Analysis." Remote Sensing 10, no. 11 (November 7, 2018): 1757. http://dx.doi.org/10.3390/rs10111757.
Full textAbstract:
Alpine ecosystems are particularly sensitive to climate change, and therefore it is of significant interest to understand the relationships between phenology and its seasonal drivers in mountain areas. However, no alpine-wide assessment on the relationship between land surface phenology (LSP) patterns and its climatic drivers including snow exists. Here, an assessment of the influence of snow cover variations on vegetation phenology is presented, which is based on a 17-year time-series of MODIS data. From this data snow cover duration (SCD) and phenology metrics based on the Normalized Difference Vegetation Index (NDVI) have been extracted at 250 m resolution for the entire European Alps. The combined influence of additional climate drivers on phenology are shown on a regional scale for the Italian province of South Tyrol using reanalyzed climate data. The relationship between vegetation and snow metrics strongly depended on altitude. Temporal trends towards an earlier onset of vegetation growth, increasing monthly mean NDVI in spring and late summer, as well as shorter SCD were observed, but they were mostly non-significant and the magnitude of these tendencies differed by altitude. Significant negative correlations between monthly mean NDVI and SCD were observed for 15–55% of all vegetated pixels, especially from December to April and in altitudes from 1000–2000 m. On the regional scale of South Tyrol, the seasonality of NDVI and SCD achieved the highest share of correlating pixels above 1500 m, while at lower elevations mean temperature correlated best. Examining the combined effect of climate variables, for average altitude and exposition, SCD had the highest effect on NDVI, followed by mean temperature and radiation. The presented analysis allows to assess the spatiotemporal patterns of earth-observation based snow and vegetation metrics over the Alps, as well as to understand the relative importance of snow as phenological driver with respect to other climate variables.
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Ying, Hanchi, Yee Leung, Feilong Cao, Tung Fung, and Jie Xue. "Sparsity-Based Spatiotemporal Fusion via Adaptive Multi-Band Constraints." Remote Sensing 10, no. 10 (October 16, 2018): 1646. http://dx.doi.org/10.3390/rs10101646.
Full textAbstract:
Remote sensing is an important means to monitor the dynamics of the earth surface. It is still challenging for single-sensor systems to provide spatially high resolution images with high revisit frequency because of the technological limitations. Spatiotemporal fusion is an effective approach to obtain remote sensing images high in both spatial and temporal resolutions. Though dictionary learning fusion methods appear to be promising for spatiotemporal fusion, they do not consider the structure similarity between spectral bands in the fusion task. To capitalize on the significance of this feature, a novel fusion model, named the adaptive multi-band constraints fusion model (AMCFM), is formulated to produce better fusion images in this paper. This model considers structure similarity between spectral bands and uses the edge information to improve the fusion results by adopting adaptive multi-band constraints. Moreover, to address the shortcomings of the ℓ 1 norm which only considers the sparsity structure of dictionaries, our model uses the nuclear norm which balances sparsity and correlation by producing an appropriate coefficient in the reconstruction step. We perform experiments on real-life images to substantiate our conceptual augments. In the empirical study, the near-infrared (NIR), red and green bands of Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) are fused and the prediction accuracy is assessed by both metrics and visual effects. The experiments show that our proposed method performs better than state-of-the-art methods. It also sheds light on future research.
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Murillo, Elisa M., and Cameron R. Homeyer. "Severe Hail Fall and Hailstorm Detection Using Remote Sensing Observations." Journal of Applied Meteorology and Climatology 58, no. 5 (May 2019): 947–70. http://dx.doi.org/10.1175/jamc-d-18-0247.1.
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AbstractSevere hail days account for the vast majority of severe weather–induced property losses in the United States each year. In the United States, real-time detection of severe storms is largely conducted using ground-based radar observations, mostly using the operational Next Generation Weather Radar network (NEXRAD), which provides three-dimensional information on the physics and dynamics of storms at ~5-min time intervals. Recent NEXRAD upgrades to higher resolution and to dual-polarization capabilities have provided improved hydrometeor discrimination in real time. New geostationary satellite platforms have also led to significant changes in observing capabilities over the United States beginning in 2016, with spatiotemporal resolution that is comparable to that of NEXRAD. Given these recent improvements, a thorough assessment of their ability to identify hailstorms and hail occurrence and to discriminate between hail sizes is needed. This study provides a comprehensive comparative analysis of existing observational radar and satellite products from more than 10 000 storms objectively identified via radar echo-top tracking and nearly 6000 hail reports during 30 recent severe weather days (2013–present). It is found that radar observations provide the most skillful discrimination between severe and nonsevere hailstorms and identification of individual hail occurrence. Single-polarization and dual-polarization radar observations perform similarly at these tasks, with the greatest skill found from combining both single- and dual-polarization metrics. In addition, revisions to the “maximum expected size of hail” (MESH) metric are proposed and are shown to improve spatiotemporal comparisons between reported hail sizes and radar-based estimates for the cases studied.