Dynamic Spatial Information Research Articles

A new dynamic spatial information design framework for AR-HUD to evoke drivers’ instinctive responses and improve accident prevention

Driver’s instinctive responses and skill-based behaviors enable them to react faster and better control their vehicle in dangerous situations. This study incorporated dynamic spatial information design (DSID) in an augmented reality head-up display (AR-HUD) under manual driving conditions. By integrating the skill, rule, and knowledge (SRK) taxonomy and situation awareness (SA) theory, our AR-HUD successfully evoked drivers’ instinctive responses and improved driving safety. First, we converted symbol and sign information processed at the knowledge-based and rule-based levels, respectively, into signal information processed at the skill-based level. Then we developed four AR-HUD interfaces with different dynamic designs for use in a hazardous scenario at an intersection. Finally, we investigated each design’s impact on drivers’ SA and driving performance. Experimental results demonstrated that our DSID enhanced drivers’ SA and accident-avoidance capabilities while reducing their cognitive workload. Among the four AR-HUD interfaces, the one that incorporated all three information elements under study (i.e., lateral warning, dynamic driving space, and speedometer) performed the best. This indicates that our proposed framework has potential applications in other similar dangerous driving scenarios, thus contributing to the development of safer and more efficient driving environments.

Evidence for the adaptive parsing of non-communicative eye movements during joint attention interactions.

During social interactions, the ability to detect and respond to gaze-based joint attention bids often involves the evaluation of non-communicative eye movements. However, very little is known about how much humans are able to track and parse spatial information from these non-communicative eye movements over time, and the extent to which this influences joint attention outcomes. This was investigated in the current study using an interactive computer-based joint attention game. Using a fully within-subjects design, we specifically examined whether participants were quicker to respond to communicative joint attention bids that followed predictive, as opposed to random or no, non-communicative gaze behaviour. Our results suggest that in complex, dynamic tasks, people adaptively use and dismiss non-communicative gaze information depending on whether it informs the locus of an upcoming joint attention bid. We also went further to examine the extent to which this ability to track dynamic spatial information was specific to processing gaze information. This was achieved by comparing performance to a closely matched non-social task where eye gaze cues were replaced with dynamic arrow stimuli. Whilst we found that people are also able to track and use dynamic non-social information from arrows, there was clear evidence for a relative advantage for tracking gaze cues during social interactions. The implications of these findings for social neuroscience and autism research are discussed.

Multi-node wind speed forecasting based on a novel dynamic spatial–temporal graph network

Providing reliable and accurate wind speed predictions for the power system ensures a stable power output. However, the high wind speed uncertainty and the complex spatial–temporal correlation between wind turbines still make wind speed prediction a significant challenge. This paper proposes a novel dynamic spatial–temporal graph network (DSTGN) to make wind speed predictions by accurately capturing the dynamic dependencies of multi-turbine on an arbitrary graph structure. DSTGN adopts an extensible and serialized structure consisting of stacked dynamic spatial–temporal blocks to model dependencies. In spatial dependency extraction, a graph generation module is used in the spatial block to produce static and dynamic spatial information. A parameterized adjacency matrix expands the spatial hypothesis space rather than being limited by the distance between turbines. In temporal dependency extraction, the multi-head attention mechanism models temporal associations of diverse time horizons. Based on publicly available wind datasets, extensive experimental results showed that the proposed model significantly improves performance. Compared to recent state-of-the-art models, MAE, RMSE and MAPE are improved over 4.57%, 4.25% and 8.31%, respectively. Furthermore, through ablation studies, we verified the benefits of each component of the proposed model, especially the positive role of graph sampling in improving performance.

Cortical temporal integration can account for limits of temporal perception: investigations in the binaural system

The auditory system has exquisite temporal coding in the periphery which is transformed into a rate-based code in central auditory structures, like auditory cortex. However, the cortex is still able to synchronize, albeit at lower modulation rates, to acoustic fluctuations. The perceptual significance of this cortical synchronization is unknown. We estimated physiological synchronization limits of cortex (in humans with electroencephalography) and brainstem neurons (in chinchillas) to dynamic binaural cues using a novel system-identification technique, along with parallel perceptual measurements. We find that cortex can synchronize to dynamic binaural cues up to approximately 10 Hz, which aligns well with our measured limits of perceiving dynamic spatial information and utilizing dynamic binaural cues for spatial unmasking, i.e. measures of binaural sluggishness. We also find that the tracking limit for frequency modulation (FM) is similar to the limit for spatial tracking, demonstrating that this sluggish tracking is a more general perceptual limit that can be accounted for by cortical temporal integration limits.

Dynamic Spatio-Temporal Graph Fusion Convolutional Network for Urban Traffic Prediction

Urban traffic prediction is essential for intelligent transportation systems. However, traffic data often exhibit highly complex spatio-temporal correlations, posing challenges for accurate forecasting. Graph neural networks have demonstrated an outstanding ability in capturing spatial correlations and are now extensively applied to traffic prediction. However, many graph-based methods neglect the dynamic spatial features between road segments and the continuity of spatial features across adjacent time steps, leading to subpar predictive performance. This paper proposes a Dynamic Spatio-Temporal Graph Fusion Convolutional Network (DSTGFCN) to enhance the accuracy of traffic prediction. Specifically, we designed a dynamic graph fusion module without prior road spatial information, which extracts dynamic spatial information among roads from observed data. Subsequently, we fused the dynamic spatial features of the current time step and adjacent time steps to generate a dynamic graph for each time step. The graph convolutional gated recurrent network was employed to model the spatio-temporal correlations jointly. Additionally, residual connections were added to the model to enhance the ability to extract long-term temporal relationships. Finally, we conducted experiments on six publicly available traffic datasets, and the results demonstrated that DSTGFCN outperforms the baseline models with state-of-the-art predictive performance.

Machines as mapmakers and map users: key questions to ponder upon?

Abstract. This article explores the convergence of cartography and robotic mapping, addressing key challenges and opportunities that arise as machines increasingly serve as both mapmakers and map users. The author investigates three critical questions: (1) how to best represent geographical data and maps for machines, (2) what dynamic information about our environment should be made readily available to machines, and (3) which ethical, religious, and cultural norms should be considered for autonomous entities. By addressing these questions, the author aims to facilitate the development of geospatial data representation, management, and analysis for autonomous systems, while ensuring harmonious coexistence with humans. In the scope of this paper, author tries to propose an approach to bridge the gap between traditional cartography and the emerging needs of machines as user and makers by building common ground through cross-disciplinary collaboration, joint research groups, and the development of common standards and frameworks. In this proposal, particularly by using design thinking approach, an event-mapping principle, as an approach that represents spatial information as events, is highlighted as a promising common framework for integrating static and dynamic spatial information. Since, event-based mapping and models can improve the representation of geographical data for machines, enabling them to better understand the environment and make informed decisions in complex and dynamic contexts. This convergence will ultimately transform the way we think about maps and geographical information systems in the age of machines.

Multi-Level Dynamic Analysis of Landscape Patterns of Chinese Megacities during the Period of 2016–2021 Based on a Spatiotemporal Land-Cover Classification Model Using High-Resolution Satellite Imagery: A Case Study of Beijing, China

In today’s accelerating urbanization process, timely and effective monitoring of land-cover dynamics, landscape pattern analysis, and evaluation of built-up urban areas (BUAs) have important research significance and practical value for the sustainable development, planning and management, and ecological protection of cities. High-spatial-resolution remote sensing (HRRS) images have the advantages of high-accuracy Earth observations, covering a large area, and having a short playback period, and they can objectively and accurately provide fine dynamic spatial information about the land cover in urban built-up areas. However, the complexity and comprehensiveness of the urban structure have led to a single-scale analysis method, which makes it difficult to accurately and comprehensively reflect the characteristics of the BUA landscape pattern. Therefore, in this study, a joint evaluation method for an urban land-cover spatiotemporal-mapping chain and multi-scale landscape pattern using high-resolution remote sensing imagery was developed. First, a pixel–object–knowledge model with temporal and spatial classifications was proposed for the spatiotemporal mapping of urban land cover. Based on this, a multi-scale district–BUA–city block–land cover type map of the city was established and a joint multi-scale evaluation index was constructed for the multi-scale dynamic analysis of the urban landscape pattern. The accuracies of the land cover in 2016 and 2021 were 91.9% and 90.4%, respectively, and the kappa coefficients were 0.90 and 0.88, respectively, indicating that the method can provide effective and reliable information for spatial mapping and landscape pattern analysis. In addition, the multi-scale analysis of the urban landscape pattern revealed that, during the period of 2016–2021, Beijing maintained the same high urbanization rate in the inner part of the city, while the outer part of the city kept expanding, which also reflects the validity and comprehensiveness of the analysis method developed in this study.

Motion events in early child Mandarin: How preschoolers communicate dynamic spatial information in toy-play context?

This study explored Chinese preschoolers’ expression of motion events in a toy-play context, focusing on developmental patterns and family predictors. A total of 1683 utterances produced by 192 preschoolers (aged 2, aged 3, aged 4, aged 5) about motion events were identified from the Corpus (oLi & Tse, Li & Tse, 2011). The descriptive and regression results revealed that: (1) there were significant age differences in the motion events production, utterance density, and lexical diversity (path verbs types), within the same half-hour play session; (2) the age 4.5 was identified as a critical period for developing caused motion events; (3) fathers’ daily time with children and the frequency of parents’ television watching with children were positive predictors for early motion events production.

DuroNet

Urban crime is an ongoing problem in metropolitan development and attracts general concern from the international community. As an effective means of defending urban safety, crime prediction plays a crucial role in patrol force allocation and public safety. However, urban crime data is a macro result of crime patterns overlapped by various irrelevant factors that cause inhomogeneous noises—local outliers and irregular waves. These noises might obstruct the learning process of crime prediction models and result in a deviation of performance. To tackle the problem, we propose a novel paradigm of <underline>Du</underline>al-<underline>ro</underline>bust Enhanced Spatial-temporal Learning <underline>Net</underline>work (DuroNet), an encoder-decoder architecture that possesses an adaptive robustness for reducing the effect of outliers and waves. The robustness is mainly reflected on two aspects. One is a locality enhanced module that employs local temporal context information to smooth the deviation of outliers and dynamic spatial information to assist in understanding normal points. The other is a self-attention-based pattern representation module to weaken the effect of irregular waves by learning attentive weights. Finally, extensive experiments are conducted on two real-world crime datasets before and after adding Gaussian noises. The results demonstrate the superior performance of our DuroNet over the state-of-the-art methods.

Developing and Testing a Bayesian Analysis of Fluorescence Lifetime Measurements.

FRET measurements can provide dynamic spatial information on length scales smaller than the diffraction limit of light. Several methods exist to measure FRET between fluorophores, including Fluorescence Lifetime Imaging Microscopy (FLIM), which relies on the reduction of fluorescence lifetime when a fluorophore is undergoing FRET. FLIM measurements take the form of histograms of photon arrival times, containing contributions from a mixed population of fluorophores both undergoing and not undergoing FRET, with the measured distribution being a mixture of exponentials of different lifetimes. Here, we present an analysis method based on Bayesian inference that rigorously takes into account several experimental complications. We test the precision and accuracy of our analysis on controlled experimental data and verify that we can faithfully extract model parameters, both in the low-photon and low-fraction regimes.

Development of a web geoservices platform for School of Environmental Sciences, Mahatma Gandhi University, Kerala, India

Abstract. Web geoservices is the obvious graduation of Geographic Information System in a distributed environment through a simple browser. It enables organizations to share domain-specific rich and dynamic spatial information over the web. The present study attempted to design and develop a web enabled GIS application for the School of Environmental Sciences, Mahatma Gandhi University, Kottayam, Kerala, India to publish various geographical databases to the public through its website. The development of this project is based upon the open source tools and techniques. The output portal site is platform independent. The premier webgis frame work ‘Geomoose’ is utilized. Apache server is used as the Web Server and the UMN Map Server is used as the map server for this project. It provides various customised tools to query the geographical database in different ways and search for various facilities in the geographical area like banks, attractive places, hospitals, hotels etc. The portal site was tested with the output geographical database of 2 projects of the School such as 1) the Tourism Information System for the Malabar region of Kerala State consisting of 5 northern districts 2) the geoenvironmental appraisal of the Athirappilly Hydroelectric Project covering the entire Chalakkudy river basin.

DETEKSI KONDISI KETAHANAN PANGAN BERAS MENGGUNAKAN PEMODELAN SPASIAL KERENTANAN PANGAN

In 2005 and 2009, BKP and WFP has provided food security conditions in Indonesia on Food Insecurity Map which were developed using food availability, food accessibility, food absorption and food vulnerability. There are 100 out of 265 districts in Indonesia or about 37,7%, which fall into the vulnerable to very vulnerable categories, where 11 districts were found in Java. The main objective of this research is to develope a spatial model of the rice production vulnerability (KPB) based on Remote Sensing and GIS technologies for estimating the food insecurity condition. Several criteria used to obtain food vulnerability information are percentage level of green vegetation (PV), rainfall anomaly (ACH), land degradation due to erosion (Deg), and paddy harvest failure due to drought and flood in paddy field (BK). Dynamic spatial information on the greenness level of land cover can be obtained from multitemporal EVI (Enhanced vegetation Index) of MODIS (Moderate Resolution Imaging Spectroradiometer) data. Spatial information of paddy harvest failure caused by drought and flood was estimated by using vegetation index, land surface temperature, rainfall and moisture parameters with advance image processing of multitemporal EVI MODIS data. The GIS technology were used to perform spatial modelling based on weighted overlay index (multicriteria analysis). The method for computing weight of factors in the vulnerability model was AHP (Analytical Hierarchy Process). The spatial model of production vulnerability (KPB) developed in this study is as follows: KPB = 0,102 PV + 0,179 Deg + 0,276 ACH + 0,443 BK. In this study, level of production vulnerability can be categorized into six classes, i.e.: (1) invulnerable; (2) very low vulnerability; (3) low vulnerability; (4) moderately vulnerable; (5) highly vulnerable; and (6) extremely vulnerable. The result of spatial modelling then was used to evaluate progress production vulnerability condition at several sub-districts in Indramayu Regency. According to the investigation results of WFP in 2005, this area fall into moderately vulnerable category. Only few sub-districts that fall into highly and extremely vulnerable during the period of May ~ August 2008, namely: Kandanghaur, Losarang, part of Lohbener, and Arahan.Keywords: remote sensing, GIS, food vulnerability, vegetation index, AHP

DETEKSI KONDISI KETAHANAN PANGAN BERAS MENGGUNAKAN PEMODELAN SPASIAL KERENTANAN PANGAN

In 2005 and 2009, BKP and WFP has provided food security conditions in Indonesia on Food Insecurity Map which were developed using food availability, food accessibility, food absorption and food vulnerability. There are 100 out of 265 districts in Indonesia or about 37,7%, which fall into the vulnerable to very vulnerable categories, where 11 districts were found in Java. The main objective of this research is to develope a spatial model of the rice production vulnerability (KPB) based on Remote Sensing and GIS technologies for estimating the food insecurity condition. Several criteria used to obtain food vulnerability information are percentage level of green vegetation (PV), rainfall anomaly (ACH), land degradation due to erosion (Deg), and paddy harvest failure due to drought and flood in paddy field (BK). Dynamic spatial information on the greenness level of land cover can be obtained from multitemporal EVI (Enhanced vegetation Index) of MODIS (Moderate Resolution Imaging Spectroradiometer) data. Spatial information of paddy harvest failure caused by drought and flood was estimated by using vegetation index, land surface temperature, rainfall and moisture parameters with advance image processing of multitemporal EVI MODIS data. The GIS technology were used to perform spatial modelling based on weighted overlay index (multicriteria analysis). The method for computing weight of factors in the vulnerability model was AHP (Analytical Hierarchy Process). The spatial model of production vulnerability (KPB) developed in this study is as follows: KPB = 0,102 PV + 0,179 Deg + 0,276 ACH + 0,443 BK. In this study, level of production vulnerability can be categorized into six classes, i.e.: (1) invulnerable; (2) very low vulnerability; (3) low vulnerability; (4) moderately vulnerable; (5) highly vulnerable; and (6) extremely vulnerable. The result of spatial modelling then was used to evaluate progress production vulnerability condition at several sub-districts in Indramayu Regency. According to the investigation results of WFP in 2005, this area fall into moderately vulnerable category. Only few sub-districts that fall into highly and extremely vulnerable during the period of May ~ August 2008, namely: Kandanghaur, Losarang, part of Lohbener, and Arahan. Keywords: remote sensing, GIS, food vulnerability, vegetation index, AHP

A REMOTE SENSING AND GIS BASED DYNAMIC SOIL EROSION MONITORING SYSTEM IN THE MAJOR SOIL EROSION AREAS IN UPPER JIALINGJIANG WATERSHED

Jialingjiang river is one of major_soil erosion watershed of the Yangtze river, its basin area and average annual runoff account respectively for 15.8% and 15.6% of the total amount of the Yangtze river upstream the Yichang hydrologic station, but its sediment production accounts, for 25.5%. Severe soil erosion is not only a big threat to the downstream ecological environment but also a critical factor in the future operation of the Three Gorge reservoir. Therefore, the government has already been developing a number of soil and water conservation projects in this watershed since 1989. Its two main upstream tributaries Bailongjiang and Xihanshui are the most severe soil erosion prone areas, which were selected in this study. Two time series of Landsat TM images in 1992 and 1996 were used to investigate the dynamic variation of soil erosion in the area. An Arc/Info GIS based dynamic spatial information system was developed for data storage and manipulation with its user interface compiled with GeoMedia and Visual Basic.

Electroanalytical approaches to study signaling mechanisms in the gastrointestinal tract

Electroanalytical techniques over the past few years have been applied to study real-time release of various signaling molecules in the GI tract. These approaches have become highly attractive as they provide dynamic spatial information on the amount of signaling molecules released. Although these approaches are relatively new to the field, the studies to date have provided useful insights into the alterations in signaling mechanisms during maturation, obesity and in a model of colitis. New methods and techniques have also allowed the possibility to obtain information on the signaling process and future developments will provide a wide diverse array of information that will be of benefit to all researchers in the field of gastroenterology. This review focuses on the types of techniques utilized, the information they can provide, their potential advantages and disadvantages in monitoring signaling processes in the gastrointestinal tract, the existing scientific studies that have utilized electroanalytical methods to date and the future potential impact of such approaches.

Monitoring qualitative spatiotemporal change for geosensor networks

Recent technological advances in geosensor networks demand new models of distributed computation with dynamic spatial information. This paper presents a computational model of spatial change in dynamic regions (such as may be derived from discretizations of continuous fields) founded on embeddings of graphs in orientable surfaces. Continuous change, connectedness and regularity of dynamic regions are defined and local transition rules are used to constrain region evolution and enable more efficient inference of a region's state. The model provides a framework for the detection of global high‐level events based on local low‐level ‘snapshot’ spatiotemporal data. The approach has particular relevance to environmental monitoring with geosensor networks, where technological constraints make the detection of global behaviour from local conditions highly advantageous.

Vestibulo-Ocular Reflex Modification after Virtual Environment Exposure

Immersion in an illusory world is possible by means of virtual reality (VR), where environmental perception is modified by artificial sensorial stimulation. The application of VR for the assessment and rehabilitation of pathologies affecting the vestibular system, in terms of both diagnosis and care, could represent an interesting new line of research. Our perception of reality is in fact based on static and dynamic spatial information perceived by our senses. During head movements in a virtual environment the images on the display and the labyrinthine information relative to the head angular accelerations differ and therefore a visuo-vestibular conflict is present. It is known that mismatches between visual and labyrinthine information may modify the vestibulo-oculomotor reflex (VOR) gain. We studied the post-immersion modifications in 20 healthy subjects (mean age 25 years) exposed to a virtual environment for 20 min by wearing a head-mounted display. VOR gain and phase were measured by means of harmonic sinusoidal stimulation in the dark before, at the end of and 30 min after VR exposure. A VOR gain reduction was observed in all subjects at the end of VR exposure which disappeared after 30 min. Our data show that exposure to a virtual environment can induce a temporary modification of the VOR gain. This finding can be employed to enable an artificial, instrumental modification of the VOR gain and therefore opens up new perspectives in the assessment and rehabilitation of vestibular diseases.

Individual Differences in Real-Time Information Coordination: Relating Dynamic Spatial and Verbal Information

Past research has suggested that a coordination ability may critically contribute in the capacity to control attention in situations of increased mental load. Evidence for such an ability was sought by analyzing individual differences in performance in a complex, real-time task. The task required the integration of information obtained from visual and verbal processing channels to produce a single response. Subjects saw two objects moving across a visual display and had to determine the veracity of a verbal statement about the objects, e.g., “object A is not moving faster than object B.” Verbal statements varied in syntactic complexity and truth value and visual displays varied in the relative speed ratio of the moving objects. Male and female subjects (N = 60) performed the verbal and spatial tasks alone and in combination across testing sessions. Gender differences were obtained for the speed judgment task with males demonstrating greater sensitivity in detecting speed differences. Support for a general coordination ability was weak in the full sample. However, when male and female data were analyzed separately, evidence was obtained among females for the contribution of a coordination ability in performing the combined task. The latter finding is attributed to male-female differences in the resource load associated with processing dynamic spatial information.