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A complete tour of metro users consists of their journeys inside the carriage and various activities outside the carriage, in particular, those in or around metro station areas (MSAs). To fathom out the spatiality and magnitude of those activities, which involve substantial interactions among people or with urban spaces, we assume that (a) metro users who spent 30 min or more together in or around the same MSA would physically interact with at least another person there; (b) the more an MSA sees metro riders co-presenting there the higher social interaction potential (SIP) there is; (c) SIP of an MSA is positively correlated with the number of distinct riders co-presenting in that MSA. By exploiting two-day metro smartcard data of Wuhan, China, we use the number of distinct riders co-presenting in that MSA to measure and visualize the MSA-level SIP in that city. Our visuals show the SIP varies across MSA and time of the day. Some MSAs have higher SIP in the daytime whereas other MSAs have higher SIP at the nighttime. Few MSAs continuously have high SIP. These results inform us where and when SIP would be the highest and the lowest across MSAs, which can facilitate metro operators’ monitoring and management of MSAs on the one hand and help businessmen and officials decide where and when to provide services and/or sell products across MSAs.

Drought is a common natural disaster that greatly affects the crop yield and water supply in China. However, the spatiotemporal characteristics of drought in China are not well understood. This paper explores the spatial and temporal distributions of droughts in China over the past 40 years using multiscale standardized precipitation evapotranspiration index (SPEI) values calculated by monthly precipitation and temperature data from 612 meteorological stations in China from 1980 to 2019 and combines the space–time cube (STC), Mann–Kendall test, emerging spatiotemporal hot-spot analysis, spatiotemporal clustering, and local outliers for the analysis. The results were as follows: 1) the drought frequency and STC show that there is a significant difference in the spatiotemporal distribution of drought in China, with the most severe drought in Northwest China, followed by the western part of Southwest China and the northern part of North China. 2) The emerging spatiotemporal hot-spot analysis of SPEI6 over the past 40 years reveals two cold spots in subregion 4, indicating that future droughts in the region will be more severe. 3) A local outlier analysis of the multiscale SPEI yields a low–low outlier in western North China, indicating relatively more severe year-round drought in this area than in other areas. The low–high outlier in central China indicates that this region was not dry in the past and that drought will become more severe in this region in the future.

The problem of urban crashes brings huge challenges and threats to local police and governments, especially in many cities in developing countries such as China. To reduce the frequency and severit...

The elderly may have unique, daily travel behaviour characteristics compared to other age groups, associated with age and physical ability. Defining these characteristics can inform urban infrastructure construction and planning. In this study, 20 elders aged between 60 and 70 years, living in the city centre of Tianjin, were selected to complete the survey. A total of 2232 hours of participant travel behaviour were collected via GPS equipment from July to August 2019. Data were used to create a space–time cube. Based on a statistical analysis of the GPS data, results indicated that the elderly mainly had six kinds of daily travel behaviours: visiting, shopping, outdoor exercise, eating out, going to the hospital and picking up and dropping off grandchildren. The main activity time was from 10:00 a.m. to 9:00 p.m. Their travel mode was mostly pedestrian-based, with an average single travel distance of about 1.01 km, and an average single travel time of about 0.5 hours. Using the space–time cube, characteristics of elderly daily travel behaviour were visualised. In addition, a typical space–time cube was summarised and presented. Data and methods from this study can provide reference and support for future-related research.

The space-time cube is not a cube of course, but the idea of one is useful. Its base is a spatial domain, $$D_t$$ , and the “cube” is traced out by a process of spatial domains, $$\{D_t:t\ge 0\}$$ . Now fill the cube with a spatio-temporal stochastic process $$\{Y_t(\mathbf{s} ):\mathbf{s} \in D_t,t\ge 0\}$$ . Assume that $$\{D_t\}$$ is fixed and known (but clearly it too could be stochastic). Slicing the cube laterally for a fixed $$t_0$$ generates a spatial stochastic process $$\{Y_{t_0}(\mathbf{s} ):\mathbf{s} \in D_{t_0}\}$$ . Slicing the cube longitudinally for a fixed $$\mathbf{s} _0$$ generates a temporal process $$\{Y_t(\mathbf{s} _0):t\ge 0\}$$ that, after dicing, yields a time series, $$\{Y_0(\mathbf{s} _0),Y_1(\mathbf{s} _0),\ldots \}$$ . These are the main highways that traverse the cube but other, less-traveled paths, can be taken. In this paper, we discuss spatio-temporal data and processes whose domain is the space-time cube, and we incorporate them into hierarchical statistical models for spatio-temporal data.

The article contains the findings of geoarchaeological research conducted by means of geoinformatics, related to various aspects of territory occupied in early Middle Ages by the so-called Cherven Towns. The issues presented in the publication concern the complex aspect of the borderland between Poland and the Rurik Rus during the formation of these two monarchies. The main objectives include: presenting the diverse time of strongholds’ use over the centuries within the early medieval borderland of south-eastern Poland, demonstrating the relationships between them, as well as visualizing the research findings by means of geoinformatics tools, based on geoarchaeological data. The article presents maps and visualizations that can help understand how medieval societies functioned. These maps and visualizations have been developed by means of GIS and graphical software. Data was verified by means of field research carried out in 2021 directly on the selected fortified settlements. The result comprises a variety of maps and models. The main outcome of the research is a mapped diversification of the termination of existence of the discussed defensive structures, for example, in the Wiar river catchment, which took place from the 11th century, near Sanok, until the 13th/14th century. This indicates a change in the defense strategy of these areas within the quoted territory of influence of medieval Poland and Rus, including within the range of the Carpathian arch. CZASOPRZESTRZENNA ANALIZA FUNKCJONOWANIA GRODÓW NA POŁUDNIOWYM ODCINKU POLSKO-RUSKIEGO POGRANICZA, ZE SZCZEGÓLNYM UWZGLĘDNIENIEM OŚRODKÓW KARPACKICH, BADANIA Z WYKORZYSTANIEM NARZĘDZI GIS Artykuł zawiera rezultaty badań geoarcheologicznych prowadzonych metodami geoinformatycznymi, dotyczących szeroko rozumianego terytorium zajmowanego przez tzw. Grody Czerwieńskie we wczesnym średniowieczu. Publikacja dotyczy złożonego aspektu pogranicza Polski i Rusi Rurykowiczów w trakcie tworzenia się tych dwóch monarchii. Głównymi celami opracowania są: ukazanie zróżnicowania czasu korzystania z ośrodków grodowych na przestrzeni wieków na obszarze wczesnośredniowiecznego pogranicza południowo-wschodniej Polski, przedstawienie relacji jakie zachodziły pomiędzy nimi oraz zwizualizowanie otrzymanych wyników badań za pomocą narzędzi geoinformatycznych, na podstawie danych geoarcheologicznych. W opracowaniu przedstawiono mapy i wizualizacje, które mogą pomóc zrozumieć, jak funkcjonowały społeczności wieków średnich. Do ich opracowania zastosowano narzędzia GIS i programy graficzne. W celu weryfikacji danych, w 2021 r. przeprowadzono badania terenowe bezpośrednio na wybranych grodziskach. W rezultacie opracowano szereg map i modeli. Głównym wynikiem przeprowadzonych badań jest wskazanie zróżnicowania czasu zakończenia istnienia omawianych obiektów obronnych – przykładowo w zlewni rzeki Wiar miało to miejsce do XI wieku, nieopodal Sanoka do XIII/XIV wieku. Świadczy to o zmianie strategii obrony danych terenów w obrębie przytoczonego terytorium wpływu średniowiecznej Polski i Rusi, w tym w zasięgu łuku Karpat.

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.

In this research, we analyzed COVID-19 distribution patterns based on hotspots and space–time cubes (STC) in East Java, Indonesia. The data were collected based on the East Java COVID-19 Radar report results from a four-month period, namely March, April, May, and June 2020. Hour, day, and date information were used as the basis of the analysis. We used two spatial analysis models: the emerging hotspot analysis and STC. Both techniques allow us to identify the hotspot cluster temporally. Three-dimensional visualizations can be used to determine the direction of spread of COVID-19 hotspots. The results showed that the spread of COVID-19 throughout East Java was centered in Surabaya, then mostly spread towards suburban areas and other cities. An emerging hotspot analysis was carried out to identify the patterns of COVID-19 hotspots in each bin. Both cities featured oscillating patterns and sporadic hotspots that accumulated over four months. This pattern indicates that newly infected patients always follow the recovery of previous COVID-19 patients and that the increase in the number of positive patients is higher when compared to patients who recover. The monthly hotspot analysis results yielded detailed COVID-19 spatiotemporal information and facilitated more in-depth analysis of events and policies in each location/time bin. The COVID-19 hotspot pattern in East Java, visually speaking, has an amoeba-like pattern. Many positive cases tend to be close to the city, in places with high road density, near trade and business facilities, financial storage, transportation, entertainment, and food venues. Determining the spatial and temporal resolution for the STC model is crucial because it affects the level of detail for the information of endemic disease distribution and is important for the emerging hotspot analysis results. We believe that similar research is still rare in Indonesia, although it has been done elsewhere, in different contexts and focuses.

In general, neighbourhoods are susceptible to changes such as economic expansion or decline, new developments and infrastructure, new business and industry, gentrification or super gentrification, decline and abandonment. In this paper, we assess the ability of Airbnb data to identify locations prone to neighbourhood change using data from the Airbnb platform in Dublin, Ireland. Emerging Hotspot Analysis was utilized to identify areas where change is potentially occurring. The results of the analysis were validated by analysing literature about different types of neighbourhood change occurring in Dublin. The results show patterns of change which are occurring in many neighbourhoods in Dublin can be captured by changes in the Airbnb data. The city centre appears to have reached saturation point in the volume of Airbnb lettings, while other areas which are undergoing different forms of Airbnb change are emerging as changing neighbourhoods. This paper shows that Airbnb data has a high potential to reveal underlying socioeconomic processes in the city and also highlights the importance of open access to data for urban studies and monitoring.

This study seeks to examine and analyze the spatial and temporal patterns of 2019 novel coronavirus disease (COVID-19) outbreaks and identify the spatiotemporal distribution characteristics and changing trends of cases. Hence, local outlier analysis and emerging spatiotemporal hot spot analysis were performed to analyze the spatiotemporal clustering pattern and cold/hot spot trends of COVID-19 cases based on space-time cube during the period from 23 January 2020 to 24 February 2020. The main findings are as follows: (1) The outbreak had spread rapidly throughout the country within a short time and the current totality incidence rate has decreased. (2) The spatiotemporal distribution of cases was uneven. In terms of the spatiotemporal clustering pattern, Wuhan and Shiyan city were the center as both cities had high-high clustering pattern with a surrounding unstable multiple-type pattern in partial areas of Henan, Anhui, Jiangxi, and Hunan provinces, and Chongqing city. Those regions are continuously in the hot spot on the spatiotemporal tendency. (3) The spatiotemporal analysis technology based on the space-time cube can analyze comprehensively the spatiotemporal pattern of epidemiological data and produce a visual output of the consequences, which can reflect intuitively the distribution and trend of data in space-time. Therefore, the Chinese government should strengthen the prevention and control efforts in a targeted manner to cope with a highly changeable situation.

The idea of a smart city seeks to improve the quality of life, which environmental sustainability is one of its main challenges, and low air quality is an important obstacle to achieving the sustainability. Accordingly, we investigated the relationships between Urmia Lake drying-up and PM10 concentration in Tabriz by 3D visualization of space-time cube in ArcGIS pro 2.7. So, Satellite images of the lake and PM10 data were collected. Next, a space-time cube was created for each season of the lake and PM10 during 2004 to 2019. Then Time series analysis, Emerging hot spot analysis and 3D visualization of these cubes were performed. The results showed that there is a clear relationship between drying-up of the lake and increasing of PM10 in Tabriz and we recognized that the drying-up has a significant impact on increasing of PM10 concentration in Tabriz.

Die zweidimensionale Darstellung der Topographie, eine der grundlegenden Aufgaben der Kartographie, wurde um Visualisierungswerkzeuge erweitert, die sowohl die Darstellung drei- als auch vierdimensionaler (3D + Zeit) Geoobjekte ermoglichen. Als Anwendungsbeispiele fur raumzeitliche Darstellungen konnen Trajektorien von sich bewegenden Objekten oder das Auftreten von Ereignissen an verschiedenen Orten zu unterschiedlichen Zeiten genannt werden. Zur letzteren Kategorie zahlen auch nicht mobile Objekte wie die Landschaft und Gebaude, die einem zeitlichen Verlauf unterliegen wie Bau, Veranderung und Zerstorung. Mithilfe des Space-Time Cube STC) werden die zeitlichen Veranderungen von Gebauden und umgebender Landschaft am Beispiel des koniglichen Warschauer Schlosses aufgezeigt und somit dem Besucher neue Moglichleiten der virtuellen Erkundung und der Analyse raumzeitlicher Zusammenhange geboten. Mapping landscape is the fundamental mission of cartography. The two-dimensional representation of the earth surface has been extended by different visualization tools for three- and four-dimensional (3D + time) objects. Examples of use for advanced space-time visualization methods are trajectories of moving objects as well as events, which are occurring at different places and at different times. Even buildings and their surrounding landscape are following a chronological sequence (construction, modification, destruction) and can be visualized in space and time. Using the space-time cube (STC) approach, the temporal changes over last centuries of the Royal Castle in Warsaw are visualized as a case study to feature virtual exploration and the of analysis space-time relationships.

Objective Hemorrhagic 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. Methods We 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. Results In 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. Conclusion The methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns.

Throughout history California has been subjected to large catastrophic wildfires and the trend seems to be accelerating in recent years. We analysed and mapped the spatial–temporal patterns of predicted wildfire occurrence across California from 2000 until the end of the century. We identified areas that are extremely vulnerable to wildfires and analysed the threat to the wildland–urban interface and across California’s ecosystems. Mapping statewide projections of wildfire occurrence through space and time, and identifying different types of wildfire hot spots, is essential in identifying locations that will be increasingly threatened in the near and distant future. This newfound knowledge enhances our ability to conceptualise wildfire risk and make informed decisions.

Visual display is a technique that utilizes tools on the computer to explore and to present information. There are some variation of visual display methods that have been developed and widely used in the needs of geovisualization and cartography such us Parallel Coordinate Plot (PCP), Space Time Cube (STC), Self-Organize Map (SOM) and Cartogram. This research uses these methods includes definitions and functions, recommendations for using an application and their respective advantages and disadvantages. Applying the right visualization is based on the needs or goals of geovisualization because each visual display has unique characteristics.

The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an...

An analysis of the anthrax epidemic situation among livestock animals in the Republic of Kazakhstan over the period 1933-2016 is presented. During this time, 4,064 anthrax outbreaks (mainly in cattle, small ruminants, pigs and horses) were recorded. They fall into five historical periods of increase and decrease in the annual anthrax incidence (1933-1953; 1954-1968; 1969-1983; 1984- 2001; and 2002-2016), which has been associated with changes in economic activity and veterinary surveillance. To evaluate the temporal trends of incidence variation for each of these time periods, the following methods were applied: i) spatio-temporal analysis using a space-time cube to assess the presence of hotspots (i.e., areas of outbreak clustering) and the trends of their emergence over time; and ii) a linear regression model that was used to evaluate the annual numbers of outbreaks as a function of time. The results show increasing trends during the first two periods followed by a decreasing trend up to now. The peak years of anthrax outbreaks occurred in 1965-1968 but outbreaks still continue with an average annual number of outbreaks of 1.2 (95% confidence interval: 0.6-1.8). The space-time analysis approach enabled visualisation of areas with statistically significant increasing or decreasing trends of outbreak clustering providing a practical opportunity to inform decision-makers and allowing the veterinary services to concentrate their efforts on monitoring the possible risk factors in the identified locations.

Visualization combining space and time in a single display called “space time cube (STC)” is used for visualizing spatio-temporal movement data. An STC enables us to explore not only shapes and positions of vehicle trajectories but also their temporal distributions. However, it is difficult for users to manually find optimal viewpoints for understanding such characteristics of trajectories. In this paper, we propose an optimal viewpoints selection method for visualizing the spatio-temporal characteristics of vehicle trajectories on a large set of crossroads using an STC. For this purpose, we provide an algorithm based on viewpoint entropy weighted by angles of trajectories with a horizontal line as a measure of viewpoint quality on a projected 2D image. We then argue that our method can be adapted to crossroads with different trajectory shapes.

Freight transport has received increasing attention in recent years as regions and cities across Europe are struggling with congestion issues while the industry demands more efficient freight transport. Consequently, there is a need for knowledge about how fast freight moves on the roads and significantly about the location of the bottlenecks. This paper analyses whether such knowledge can be generated using tracking data from logistics companies. The chosen case region is North Jutland in Denmark, and the method deployed is emerging hot spot analysis on tracking data from approximately 1,500 trailers provided by one large Danish logistics company for the period 2012-2016. This methodology is relatively new in the transport field and has never been used in a Danish context before, and the paper shows that by using the tracking data and this methodology it is possible to identify where freight moves fast and slow in the region.

The popularity of tracking devices continues to contribute to increasing volumes of spatio-temporal data about moving objects. Current approaches in analysing these data are unable to capture collective behaviour and correlations among moving objects. An example of these types of patterns is moving flocks. This article develops an improved algorithm for mining such patterns following a frequent pattern discovery approach, a well-known task in traditional data mining. It uses transaction-based data representation of trajectories to generate a database that facilitates the application of scalable and efficient frequent pattern mining algorithms. Results were compared with an existing method (Basic Flock Evaluation or BFE) and are demonstrated for both synthetic and real data sets with a large number of trajectories. The results illustrate a significant performance increase. Furthermore, the improved algorithm has been embedded into a visual environment that allows manipulation of input parameters and interactive recomputation of the resulting flocks. To illustrate the visual environment a data set containing 30 years of tropical cyclone tracks with 6 hourly observations is used. The example illustrates how the visual environment facilitates exploration and verification of flocks by changing the input parameters and instantly showing the spatio-temporal distribution of the resulting flocks in the Space-Time Cube

Analysis of eye movements has been used for decades as a method for assessing the performance of visual stimuli. Until recently, this has mainly been applied to static and non-cartographic stimuli, but due to technological developments and reduced cost of equipment, interactive and cartographic applications are now feasible. suggest a new analysis method which applies Hagerstrand’s Space-Time-Cube (STC; ) to eye movement data. However, in an interactive three-dimensional STC, identifying and exploring key behaviours can be difficult. In order to ameliorate these difficulties, we propose a variation of the STC method, which uses two-dimensional projections of the STC onto the XT and YT planes. These two-dimensional projections are found to facilitate rapid identification of significant patterns in the data set. A prototype implementing this and other dynamical methods has been developed, and is presented with examples illustrating the benefits of working with two-dimensional projections of the STC.

In this era of abundant space–time geographic information systems (GIS) data, one challenge in GIScience is to adapt and improve current GIS environments to facilitate new ways of space–time thinki...

This paper introduces a novel four-stage methodology for real-estate valuation. This research shows that space, property, economic, neighbourhood and time features are all contributing factors in producing a house price predictor in which validation shows a 96.6% accuracy on Gaussian Process Regression beating regression-kriging, random forests and an M5P-decision-tree. The output is integrated into a commercial real estate decision engine.

PurposeThe purpose of this paper is to provide a method to examine the differences in behaviour during a post‐quake period.Design/methodology/approachFieldwork and questionnaires were used to collect the households’ members’ movement behaviours after the 2004 Chuetsu Earthquake. In total, three study areas were selected in Kawaguchi town (Niigata Prefecture) in order to enhance how the visualisation process can provide support in better understanding the behaviour during evacuation and recovery process. For this purpose the Space‐Time‐Cube (STC) was used to represent and analyse residents’ movement paths over time.FindingsDifferences appear in the spatio‐temporal paths of the three study areas, implying a connection between the geographical location and movement patterns. The city centre shows disorganized Spatio‐Temporal‐Patterns (STPs) during the first week of the recovery process, eventually becoming organized after the rescuers’ arrival. Moving towards the isolated areas of the town, a progressive STP organisation can be observed, explaining the faster response after the seismic event.Research limitations/implicationsSpatio‐temporal data are difficult and costly to collect, especially if a long period of time passes between the seismic event and the survey.Practical implicationsThe STC can be used as tool to enhance the disaster management techniques and provide support in crisis situations.Originality/valueThe paper provides a practical approach to investigate the reactions after a seismic event and can be used in larger study areas to develop better strategies in disaster management.

The Jacquez k nearest neighbor test, originally developed to improve upon shortcomings of existing tests for space–time interaction, has been shown to be a robust and powerful method of detecting interaction. Despite its flexibility and power, however, the test has three main shortcomings: (i) it discards important information regarding the spatial and temporal scales at which the detected interaction takes place; (ii) the results of the test have not been visualized; and (iii) recent research demonstrates the test to be susceptible to population shift bias. This study presents enhancements to the Jacquez k nearest neighbors test with the goal of addressing each of these three shortcomings and of improving the utility of the test. Data on Burkitt's lymphoma cases in Uganda between 1961 and 1975 are used to illustrate the modifications and enhanced visual output of the test. Output from the enhanced test is compared with that provided by alternative tests of space–time interaction. Results show the enhancements presented in this study transform the Jacquez test into a complete, descriptive, and informative metric that can be used as a stand-alone measure of global space–time interaction. Copyright © 2012 John Wiley & Sons, Ltd.

This study adopts a near real-time space-time cube approach to portray a dynamic urban air pollution scenario across space and time. Originating from time geography, space-time cubes provide an approach to integrate spatial and temporal air pollution information into a 3D space. The base of the cube represents the variation of air pollution in a 2D geographical space while the height represents time. This way, the changes of pollution over time can be described by the different component layers of the cube from the base up. The diurnal ambient ozone (O3) pollution in Houston, Texas is modeled in this study using the space-time air pollution cube. Two methods, land use regression (LUR) modeling and spatial interpolation, were applied to build the hourly component layers for the air pollution cube. It was found that the LUR modeling performed better than the spatial interpolation in predicting air pollution level. With the availability of real-time air pollution data, this approach can be extended to produce real-time air pollution cube is for more accurate air pollution measurement across space and time, which can provide important support to studies in epidemiology, health geography, and environmental regulation.

The purpose of this study is to analyze how the spatiotemporal characteristics of traffic accidents involving the elderly population in Seoul are changing by time period. We applied kernel density estimation and hotspot analyses to analyze the spatial characteristics of elderly people’s traffic accidents, and the space-time cube, emerging hotspot, and space-time kernel density estimation analyses to analyze the spatiotemporal characteristics. In addition, we analyzed elderly people’s traffic accidents by dividing cases into those in which the drivers were elderly people and those in which elderly people were victims of traffic accidents, and used the traffic accidents data in Seoul for 2013 for analysis. The main findings were as follows: (1) the hotspots for elderly people’s traffic accidents differed according to whether they were drivers or victims. (2) The hourly analysis showed that the hotspots for elderly drivers’ traffic accidents are in specific areas north of the Han River during the period from morning to afternoon, whereas the hotspots for elderly victims are distributed over a wide area from daytime to evening. (3) Monthly analysis showed that the hotspots are weak during winter and summer, whereas they are strong in the hiking and climbing areas in Seoul during spring and fall. Further, elderly victims’ hotspots are more sporadic than elderly drivers’ hotspots. (4) The analysis for the entire period of 2013 indicates that traffic accidents involving elderly people are increasing in specific areas on the north side of the Han River. We expect the results of this study to aid in reducing the number of traffic accidents involving elderly people in the future.

Over the past several decades, archaeologists have been developing and improving methods for collection, analysis, visualization, and modeling of site information. Despite the increasing capabilities of GIScience tools in recent years, archaeology is an application domain where these capabilities have not been fully utilized. This paper presents preliminary results on the development of geovisual analytics functions for archaeological investigation by extending Space–Time Cube (STC) software currently under development. Graph-theoretical techniques are implemented to uncover and visualize spatio-temporal relationships between artefacts from different historical periods excavated at different locations. These techniques are implemented and tested on a database of archaeological sites in Puerto Rico. The contribution of this research to visual analytics is the integration of space, time, and relationships within a single interactive environment. The extended STC facilitates insight into potential interactio...

Two dimensional static maps and three dimensional space-time cubes are among the most studied techniques to visualize human movement data. Previous research suggests that both techniques are useful in different types of tasks. However, the analysis of trajectory data may not be focused in just one type of task, motivating further studies to quantify the advantages of combining both types of techniques. This paper describes our work-in-progress addressing this issue, proposing the combination of 2D maps and 3D space-time cubes for human trajectory visualization, and overviewing possible metrics for its evaluation.

This article is a revision of the previous edition article by D. Dransch, volume 6, pp. 3908–3911, © 2001, Elsevier Ltd.

This paper reports the results of an empirical usability experiment on the performance of the space-time cube in a GeoVisual analytics environment. It was developed to explore movement data based on the requirements of human geographers. The interactive environment consists of multiple coordinated views incorporating three graphical representations. For the experiment, two groups of the user, domain experts and non-domain experts, had to execute several map-use tasks to answers specific question. The data collected during the experiment were analysis resulting in a set of usability metrics related to the effectiveness, efficiency and user satisfaction of developed application. The comparison of both groups showed that domain experts were able to operate the visual analytical environment more effectively and efficiently due to their interest to explore their data. The user feedback derived from the analysis of both experiments was further processed for the improvement of the application.

This paper describes a framework for a visual analysis of lightning data described by 3D coordinates and the precise occurrence time. First lightning cells are detected and tracked. After that we developed a GUI (interactive graphic user interface) in order to enable the visual exploration of movement patterns and other characteristics of lightning cells. In particular we present different visual concepts for the dynamic lightning cells and tracks within a Space–Time-Cube and a 3D view. Furthermore a statistical analysis is presented. The developed GUI which aims to support decision making includes the visual and statistical representation of cell features as centroid, extension, density, size etc., within a specific temporal and spatial range of interest.

Considering space and time which are the basic reference in people's ordinary are still not widely used in the current social networking services, the paper envisioned the uses of the Space-Time-Cube in this area. Specifically, the study represented two main components of social networking services (social networks and person profiles) in a spatial-temporal manner. For the social networks, Space-Time Cube provides different views for both group-level or individual-level social networks; for the personal profiles, Space-Time-Cube can organically organize and represent them by using the Annotated-Space-Time-Path. It can be concluded that the spatial-temporal information is an important type of factor of the social networking services and the Space-Time-Cube can make social networking information more convenient and understandable for each participant.

Eye movement recordings produce large quantities of spatiotemporal data, and are more and more frequently used as an aid to gain further insight into human thinking in usability studies in GIScience domain among others. After reviewing some common visualization methods for eye movement data, the limitations of these methods are discussed. This paper proposes an approach that enables the use of the Space-Time-Cube (STC) for representation of eye movement recordings. Via interactive functions in the STC, spatio-temporal patterns in eye movement data could be analyzed. A case study is presented according to proposed solutions for eye movement data analysis. Finally, the advantages and limitations of using the STC to visually analyze eye movement recordings are summarized and discussed.