Space-time Cube Research Articles

Research on Improved Visualization Method of Space Time Cube

Research on Improved Visualization Method of Space Time Cube

Spatio-Temporal Visualization of Peatlands Changes

Increased deforestation and draining of peatlands with the started of the drainage occurs quickly makes the availability of peatlands as carbon storage is even more alarming. The presence of peat land conversion greatly affect the hydrological balance of the ecosystem. The conversion of peatland have the massive effect with the increase of drainage constructions for land cultivation needs. This makes the neighborhood unity peat hydrology (KHG) which is wet into dry so prone to burning and experiencing land subsidence. Information of peatland degradation is presented in the spatio - temporal visualization using Space Time Cube (STC). The usability in order to determine the extent of the spatio - temporal visualization can be used by users to obtaining information peatland degradation in terms of effectiveness, efficiency and satisfaction for users. Drainage density built on KHG getting bigger every year with the index of medium density classification. Increasing the number of long-drainage occurs at 119.4934 km per year from 2000 to 2008 and 148.6954 km per year from 2008 to 2012. Based on observations in 12 years has increased the number of distribution Hotspot significantly in February. In May and September the spread of hotspots increased by the average number of hotspots at 15 points per month. The drainage development’s and distribution of hotspot has dominant occurs at the HTI in the KHG Peatland degradation is presented in Visualization spatio-temporal, which visualization is very effective, fairly efficient, and give satisfaction to the users in displaying information peatland degradation in time series.

Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams

Accurate information on the number and distribution of pedestrians in space and time helps urban planners maintain current city infrastructure and design better public spaces for local residents and visitors. Previous studies have demonstrated that using webcams together with crowdsourcing platforms to locate pedestrians in the captured images is a promising technique for analyzing pedestrian activity. However, it is challenging to efficiently transform the time series of pedestrian locations in the images to information suitable for geospatial analytics, as well as visualize data in a meaningful way to inform urban design or decision making. In this study, we propose to use a space-time cube (STC) representation of pedestrian data to analyze the spatio-temporal patterns of pedestrians in public spaces. We take advantage of AMOS (The Archive of Many Outdoor Scenes), a large database of images captured by thousands of publicly available, outdoor webcams. We developed a method to obtain georeferenced spatio-temporal data from webcams and to transform them into high-resolution continuous representation of pedestrian densities by combining bivariate kernel density estimation with trivariate, spatio-temporal spline interpolation. We demonstrate our method on two case studies analyzing pedestrian activity of two city plazas. The first case study explores daily and weekly spatio-temporal patterns of pedestrian activity while the second one highlights the differences in pattern before and after plaza’s redevelopment. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time series of pedestrian occurrences captured by webcam images.

Analysis of spatio-temporal rainfall trends across southern African biomes between 1981 and 2016

Southern African biomes experience significant changes in the distribution of rainfall that are linked to El Niño–Southern Oscillation. As such, an understanding of the spatio-temporal rainfall trends is key in predicting rainfall patterns as well as validation of climate change projections. Currently, the available information on rainfall trends in southern Africa is scanty with most studies focusing either on the spatial or the temporal dimension at localised levels. The novelty of this study is its regional aspect (i.e. all of southern African arid and semi-arid biomes) and the simultaneous integration of space and time in rainfall trend analysis through the use of space time rainfall cube. In this study, we simultaneously examined spatial and temporal rainfall trends based on the space-time rainfall cube derived from 1981 to 2016 CHIRPS satellite rainfall data. The space time rainfall trend analysis revealed a significant (P < 0.05) decrease of rainfall across most biomes particularly in the northern parts of the savanna biome and southwestern biomes (i.e. karoo, desert and fynbos). Statistically significant (P < 0.05) rainfall increase was observed in the central parts of the region mostly within the savanna biome. In terms of the magnitude of rainfall change, some of the areas experienced as much as 12 mm rainfall decrease in the mean annual rainfall while others recorded an increase of 14 mm. Our results provide baseline information for climate change adaptation and ecosystem conservation.

Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration.

A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions.

SPATIO-TEMPORAL CHANGES IN WHEAT CROP CULTIVATION IN INDIA

Abstract. This study was conducted to understand the changes in spatiotemporal characteristics of wheat crop production including the changes in area and yield. We employed the emerging hot and cold spot analysis along with space time cube and space-time cluster density analysis to study the spatial changes in wheat crop production, area and yield, and understand the changes in spatiotemporal features. We made a comprehensive analysis of the changes in wheat crop production, area and yield on pan India basis for the period from 1999 to 2015. The major findings were: (a) During the study period significant increase in wheat yield occurred within the North Indian states of Punjab and Haryana and intensifying hot spots appeared within the Indo-Gangetic plains. (b) The Analysis of the area under wheat cultivation showed a persistent hot spot in the Northern states of Uttarakhand and Uttar Pradesh, Punjab and Haryana, with new hot spots observed in the regions of Central India during the years 2014 and 2015. (d) The analysis of the wheat crop production showed significant new cold spots in Rajasthan and Gujarat, with intensifying hotspots emanating into the lower delta regions of Ganges. Present study also revealed the potential of GIS based data models when related with additional background information, to segregate the most significant clusters of changes (increase / decrease) happening over active wheat crop cultivation. We expect the results from this study to help in increasing the wheat crop yield and production in the future.

Geovisualizing space and time in a science-art exhibit

Geovisualizing space and time in a science-art exhibit

Space-time mapping of historical plague epidemics in modern Osaka, Japan

Abstract. In modern times Japan, large-scale epidemics of infectious diseases such as cholera and plague were repeatedly introduced from major cities with ports. Osaka, along with Kobe, is the earliest city in the country to have plague epidemics at that age. To consider the counter measures of such epidemics, epidemic reports were often edited to record the details of the epidemic trends with individual records of infected persons. In case of the plague epidemics in Osaka city, the three-volume set of the Second Osaka Prefectural Report of Plague Epidemics (hereafter, the Plague Epidemic Report) was compiled and published in 1909. According to this report, the plague epidemics prevailed in the city between 1899 and 1900 and between 1905 and 1907. Especially in November 1907, the number of cases exceeded 220 which was the largest number of monthly incidences in the city.This study aims to digitally reconstruct the spatio-temporal sequences of the city-wide historical plague epidemics at the individual level from September 1906 to the end of December 1907, recorded in the Plague Epidemic Report, by using a geographic information system (GIS). We examine the possibilities of visual understanding about the geographical processes of the plague epidemics in the city through space-time mapping of the historical materials of the disease in a GIS environment. The database and visualized space-time features of the epidemics would be useful for theoretical studies of infectious disease epidemiology with spatial dimension, since it is not easy to obtain such detailed individual data of infected persons in a city-wide extent at present days due to privacy protection policies. In addition, this study may highlight the old city structure of Osaka in modern times through the epidemic sequences. This may contribute to the studies of historical geography as well as those of Digital Humanities in a wider context.We developed the GIS-based space-time database of plague cases as follows. Firstly, we digitally archived the Plague Epidemic Report by scanning the entire pages including Figures and Tables. Secondly, we built a database of cases from the scanned list of plague case lists with their attributes including id number, the date of onset, the reason and date of identification, the disease type, address, location of discovery, occupation, sex, age and others. Thirdly, geographical coordinates of the occurrence location were identified as the points drawn in the 1 : 20,000 scale maps included in the Plague Epidemic Report with the aid of GIS-based geo-referencing. At the result, the records of 661 cases with the geographical coordinates were established. In addition, we construct other geographical information in the Plague Epidemic Report, such as the aggregated numbers of caught rats carrying the plague by police box regions over the city. It would be interesting to note that the distribution of plague cases is almost consistent with the distribution of the number of infected rats.Using the spatiotemporal database developed here, we conduct the mapping of plague cases in a space-time cube setting (Figure 1). Each black dot in the figure represents one infected case with the geographical locations and timing (temporal locations) defined by his/her onset day-counts after 13 Sep 1906 when the first case in the list appeared. We enhanced the visualization of space-time point distribution by using space-time kernel density estimation as shown in Figure 2 including two iso-surfaces of high (coloured in red) and middle (coloured in blue) density domains. We developed a tool for ArcGIS Pro (ESRI Inc.) combined with R (R Core Team) to generate the space-time iso-surfaces of densities. The tool enables us to create a web-based interactive digital content of the historical epidemic.It is noteworthy that the case attribute in the database included relationships with other recorded cases. Figure 2 has the lines showing the relationships between the cases. While most of them are short and almost vertically stand meaning that infections were occurred in a limited geographical extent, there are also long-length lines indicating that local outbreaks at different locations were connected by the person-to-person contacts, possibly reflecting possibly commuting networks at that age. We intend to argue how the plague epidemics spread in the city by associating the space-time sequences of infection with the demographic and socioeconomic attributes of the cases in the context of modern Osaka.

Space-Time Cube and Mixed Reality – New Approaches to Support Understanding Historical Landscape Changes

Abstract. Mixed reality is a rather new technology but came to its nowadays success through the availability of devices like Microsoft HoloLens which easily support the users and developers to use such devices. Therefore, visualization specialists like cartographers paid attention due to interaction possibilities such devices provide. In particular, to utilize the huge amount of opportunities such device gave. The applicability within the cartographic domain needs to be further investigated.The main goal of this contribution is to evaluate the applicability of a mixed reality device in the domain of spatio-temporal representations on the example of the space-time cube to show cultural landscape changes. The hologram of the space-time cube provides the changes of the Royal Castle in Warsaw and their surrounding elements. The hologram therefore incorporated the different buildings of the castle, space-time prisms and space-time links to connect building elements over the years. The visual variables colour hue, colour value and transparency are mainly used to feature distinguishable space-time prisms and to show the space-time links. Different colour schemes are developed which features the characteristics of a mixed reality device. The possibilities of input actions are ranging from gaze/head movement, to gesture and voice.The usability evaluation of the mixed reality hologram showed the overall comfort of interactions, perception of the visual components of the space-time cube and determines advantageous features and limitations of the technology. Most of the found limitations are connected to current devices, like e.g. resolution or field of view. An important aspect which came out is, that the experience the user has which such devices/technology plays an important role in successfully use and knowledge discovery from such applications.

Volume-based large dynamic graph analysis supported by evolution provenance

We present an approach for the visualization and interactive analysis of dynamic graphs that contain a large number of time steps. A specific focus is put on the support of analyzing temporal aspects in the data. Central to our approach is a static, volumetric representation of the dynamic graph based on the concept of space-time cubes that we create by stacking the adjacency matrices of all time steps. The use of GPU-accelerated volume rendering techniques allows us to render this representation interactively. We identified four classes of analytics methods as being important for the analysis of large and complex graph data, which we discuss in detail: data views, aggregation and filtering, comparison, and evolution provenance. Implementations of the respective methods are presented in an integrated application, enabling interactive exploration and analysis of large graphs. We demonstrate the applicability, usefulness, and scalability of our approach by presenting two examples for analyzing dynamic graphs. Furthermore, we let visualization experts evaluate our analytics approach.

Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space-time cube-based approach.

ObjectiveHemorrhagic fever with renal syndrome (HFRS), a natural–focal infectious disease caused by hantaviruses, resulted in 37 deaths between 2011 and 2015 in Hubei Province, China. HFRS outbreaks are seasonally distributed, exhibiting heterogeneity in space and time. We aimed to identify the spatial and temporal characteristics of HFRS epidemics and their probable influencing factors.MethodsWe used the space–time cube (STC) method to investigate HFRS epidemics in different space–time locations. STC can be used to visualize the trajectories of moving objects (or changing tendencies) in space and time in three dimensions. We applied space–time statistical methods, including space–time hot spot and space–time local outlier analyses, based on a calculated STC model of HFRS cases, to identify spatial and temporal hotspots and outlier distributions. We used the space–time gravity center method to reveal associations between possible factors and HFRS epidemics.ResultsIn this research, HFRS cases for each space–time location were defined by the STC model, which can present the dynamic characteristics of HFRS epidemics. The STC model delivered accurate and detailed results for the spatiotemporal patterns of HFRS epidemics.ConclusionThe methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns.

Cartographic Symbol Design Considerations for the Space–Time Cube

ABSTRACTThe cartographic representation of geographic phenomena in the space–time cube comes with special challenges and opportunities when compared with two-dimensional maps. While the added dimension allows the display of attributes that vary with time, it is difficult to display rapidly varying temporal data given the limited display height. In this study, we adapt 2D cyclic point symbols to construct 3D surfaces designed along a helical path for the space–time cube. We demonstrate how these complex 3D helical surfaces can display detailed data, including data reported daily over 100 years and data reported in four-hour intervals over a year. To create the point symbols, each value is plotted along the curve of a helix, with each turn of the helix representing one year or week, respectively. The model is modified by varying the radii from the time axis to all points using the attribute value, in these cases maximum daily temperature and four-hourly ridership, and then creating a triangulated surface from the resulting points. Using techniques common to terrain representation, we apply hue and saturation to the surface based on attribute values, and lightness based on relief shading. Multiple surfaces can be displayed in a space–time cube with a consistent time interval facing the viewer, and the surfaces or viewer perspective can be rotated to display synchronized variations. We see this method as one example of how cartographic design can refine or enhance operations in the space–time cube.

An interactive method for identifying the stay points of the trajectory of moving objects

The stay points of trajectory include important semantic information. Identifying the stay points is an indispensable step for mining and analyzing the trajectory data. Current methods for identifying stay points generally require pre-setting thresholds, which have a major impact on the identification results. Meanwhile, the setting of these thresholds is heavily reliant on empirical experience. There are many types of moving objects, such as vehicles, ships, airplanes, people and animals. These moving objects have a large variation in their trajectory data due to their different geometric/physical properties and navigation environments. Therefore, setting the appropriate thresholds for the identification process is by no means easy for non-professionals. To cope with this challenge, an interactive visualization method is suggested in this paper in order to help users to set the thresholds. In the proposed method, the Space-Time Cube (STC) is used to visualize the trajectory as the first step. In the next step, users interactively choose the typical stay points of the trajectory. The threshold is atomically determined using the geometric characteristics of the 3-dimensional bounding box of the space where the moving object stays on. Finally, the sliding window method is used to extract the stay points. Experimental results demonstrate that the proposed method is an efficient method for determining the thresholds in an intuitive and fast manner. As a result, it can significantly improve the efficiency of identifying the stay points of the trajectory for non-professionals.

The space-time cube as an effective way of representing and analysing the streetscape along a pedestrian route in an urban environment

. The graphical representation of a complex polygonal object, such as an isovist field (i.e. a 2D isovist in its evolution over time along a pedestrian path in an urban environment) is a hard research issue that was stated in the late 1970s by Benedikt [1]. The generalized space-time cube from Bach and al. [2] is a conceptual framework allowing to propose innovative implementations for the analysis and the display of this complex object. The purpose of this article is to situate, within this formal framework, all the graphical representation solutions that have already been developed for isovist fields. It also aims to identify the problems raised by each of these solutions (spatial anchoring, arbitrary sampling, etc.) and to propose related solutions.

Trends in the Alaskan Bottom-Trawl Fishery from 1993 to 2015: A GIS-based Spatiotemporal Analysis

Using fishery-dependent observer data from National Marine Fisheries (NMFS) provides insight into the location and intensity of bottom-trawl fishing effort, and allows those areas most exposed to fishing pressure to be identified. In this study, the spatial and temporal extent of Alaskan bottom-trawl fishing effort in the Bering Sea, Aleutian Islands and Gulf of Alaska between 1993 and 2015 is explored within a space-time cube in ArcGIS Pro. The variables analysed were number of hauls per area and total catch per area. Statistical techniques were used to examine spatiotemporal clustering within the data. Results indicate that fishing was significantly clustered over space and time. A three-dimensional hotspot analysis shows which areas were most intensely fished and illustrates the trends over the relatively long study period. The data were then compared with sea ice concentration to determine the effect of changing climate on fishing activity. Sea ice had a limited effect on the spatial patterns of fishing effort, but certain areas in the Bering Sea exhibited increased fishing effort in years with less sea ice.

Exploring Customer Data Using Spatio-Temporal Analysis: Case Study of Fixed Broadband Provider

ABSTRACT Spatio-temporal analysis refers to the analytical technique of geographical data based on the spatial and temporal distribution of geographical objects. In business practices, implementation of spatio-temporal analysis has opportunities in providing support for strategic decision making process in resource and asset management, investment planning, customer relationship management, and marketing strategy development. Capturing the opportunities to provide support for company decision making process and strategy development, this research was conducted using spatio-temporal analysis in one of biggest fixed broadband provider in Indonesia which offers Gigabit Passive Optical Network (GPON) configuration. In this study, the analysis was conducted on the overall customer dataset and divided dataset of customers which differed based on type of customer and location environment characteristics by exploring spatially the patterns, temporal trends, and emerging hot spots of customer distribution through fishnet grid and space time cube created from GIS-based dataset of customer order. The results obtained from this study provide an overview of customer area mapping in the form of thematic map.

Scale-Space Splatting: Reforming Spacetime for Cross-Scale Exploration of Integral Measures in Molecular Dynamics.

Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space/time localization of the studied phenomena. This leads to degenerate cases where the measures fail to capture distinct behaviour. In order to drill into these aggregate values, we propose a multi-scale visual exploration technique. Our novel representation, based on partial domain aggregation, enables the construction of a continuous scale-space for discrete datasets and the simultaneous exploration of scales in both space and time. We link these two scale-spaces in a scale-space space-time cube and model linked views as orthogonal slices through this cube, thus enabling the rapid identification of spatio-temporal patterns at multiple scales. To demonstrate the effectiveness of our approach, we showcase an advanced exploration of a protein-ligand simulation.

Event-Based Dynamic Graph Visualisation.

Dynamic graph drawing algorithms take as input a series of timeslices that standard, force-directed algorithms can exploit to compute a layout. However, often dynamic graphs are expressed as a series of events where the nodes and edges have real coordinates along the time dimension that are not confined to discrete timeslices. Current techniques for dynamic graph drawing impose a set of timeslices on this event-based data in order to draw the dynamic graph, but it is unclear how many timeslices should be selected: too many timeslices slows the computation of the layout, while too few timeslices obscures important temporal features, such as causality. To address these limitations, we introduce a novel model for drawing event-based dynamic graphs and the first dynamic graph drawing algorithm, DynNoSlice, that is capable of drawing dynamic graphs in this model. DynNoSlice is an offline, force-directed algorithm that draws event-based, dynamic graphs in the space-time cube (2D+time). We also present a method to extract representative small multiples from the space-time cube. To demonstrate the advantages of our approach, DynNoSlice is compared with state-of-the-art timeslicing methods using a metrics-based experiment. Finally, we present case studies of event-based dynamic data visualised with the new model and algorithm.

Measuring and visualizing place-based space-time job accessibility

Place-based accessibility measures, such as the gravity-based model, are widely applied to study the spatial accessibility of workers to job opportunities in cities. However, gravity-based measures often suffer from three main limitations: (1) they are sensitive to the spatial configuration and scale of the units of analysis, which are not specifically designed for capturing job accessibility patterns and are often too coarse; (2) they omit the temporal dynamics of job opportunities and workers in the calculation, instead assuming that they remain stable over time; and (3) they do not lend themselves to dynamic geovisualization techniques. In this paper, a new methodological framework for measuring and visualizing place-based job accessibility in space and time is presented that overcomes these three limitations. First, discretization and dasymetric mapping approaches are used to disaggregate counts of jobs and workers over specific time intervals to a fine-scale grid. Second, Shen's (1998) gravity-based accessibility measure is modified to account for temporal fluctuations in the spatial distributions of the supply of jobs and the demand of workers and is used to estimate hourly job accessibility at each cell. Third, a four-dimensional volumetric rendering approach is employed to integrate the hourly job access estimates into a space-time cube environment, which enables the users to interactively visualize the space-time job accessibility patterns. The integrated framework is demonstrated in the context of a case study of the Tampa Bay region of Florida. The findings demonstrate the value of the proposed methodology in job accessibility analysis and the policy-making process.

Cartographic Design and the Space–Time Cube

ABSTRACTThis paper reports on the results of an empirical evaluation that aimed to define the effectiveness and efficiency of different visual variables in depicting the Space–Time Cube’s (STC) content. Existing STC applications demonstrate that the most used visual variables are size and colour hue. Less is known, however, about their usability metrics. The research sets design criteria for STC contents, such as space–time paths, based on the cartographic design theory. The visual variables colour hue, colour value, colour saturation, size and orientation have been applied in two different use case studies. Besides, to support the three-dimensional visual environment, depth cues such as shading and transparency were considered too. User tests have been executed based on real-world problems with particular attention for the visualization strategy and data complexity. The outcomes revealed the most efficient and effective visual variables to represent data of various complexities in the STC.