Figure 9 - uploaded by Gennady Andrienko
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One-minute aggregates for the area near the stadium. The color band in the bottom shows the dynamics of the calls.
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Events that happened in the past are important for understanding the ongoing processes, predicting future developments, and making informed decisions. Significant and/or interesting events tend to attract many people. Some people leave traces of their attendance in the form of computer-processable data, such as records in the databases of mobile ph...
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... a detailed analysis, we aggregated the calls in the stadium area with one minute temporal resolution for the time intervals when the peaks occurred. Figure 9 shows the counts of the calls in the stadium area on Sunday with one minute resolution. The major peaks occurred at 19:50, 21:15, and around 22:30 (both before and after that). ...
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Identifying and characterizing variations of human activity ? specifically changes in intensity and similarity ? in urban environments provide insights into the social component of those eminently complex systems. Using large volumes of user-generated mobile phone data, we derive mobile communication profiles that we use as a proxy for the collecti...
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... Mobility data collected by GPS are usually inaccurate owing to measurement errors and the low sampling rate. Map-matching [81,82] is the Errors and uncertainty of trajectories [12][13][14][15] Mobility patterns [14,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] Origin-destination patterns [25,[32][33][34][35][36][37][38][39][40] Human co-occurrence [13, 41- Traffic situation understanding [22,56] Traffic zone division [19,58,59] Congestion Congestion monitoring [60] Congestion discovery [16,29,[61][62][63] Congestion propagation [64] Congestion events' cascades [65] Congestion causal inference [29,56,61] Congestion prediction [60] Public transportation Network accessibility [66] Bus schedule analysis [67] Interchange behavior [55] System usage [35] System efficiency [68][69][70] Bus network optimization [48, 69,70] Shuttle bus planning [47,71] Traffic safety ...
... In addition to GPS-based data, Chen et al. [14] worked on the uncertainty of trajectories inferred from sparse geo-tagged posts. Given clean mobility data, various visual analytics approaches have been developed for studying mobility patterns [14,[19][20][21][22][23][24][25][26][27][28][29][30][31], origin-destination (OD) patterns [25,[32][33][34][35][36][37][38][39][40], co-occurrences [13,[41][42][43], and mobility semantics [15,[44][45][46][47]. These analyses support in-depth understanding of how citizens move within the urban space. ...
... Voronoi diagrams [148] are also areas on a map. In Andrienko et al.'s study [26] (see Fig. 2(B3)), urban space is divided based on significant locations using a Voronoi diagram. Each region of a polygon is covered by a significant location. ...
Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.
... Research questions that can be answered with geospatial analysis are multidisciplinary in (Hyvärinen and Saltikoff, 2010), studying structure, dynamics, and rhythms of natural cities (Jiang and Miao, 2015;Morales et al., 2017), making observations about street networks in cities (Boeing, 2017), tracking infectious diseases (Padmanabhan et al., 2013), managing crisis situations (MacEachren et al., 2011a), capturing human movement patterns across political borders (Blanford et al., 2015), discovering significant events and patterns (Andrienko et al., 2010), understanding protest movements (Gleason, 2013), finding geographic patterns (Conover et al., 2013) and correlations in communication networks and languages (Mocanu et al., 2013), fine-tuning communication or marketing strategies (Bhattacharya et al., 2019), and answering many other questions related to human movements, dynamics, and communication. Researchers use maps to 1) report their findings, 2) verify whether social media is more reliable than other techniques for finding statistical relationships, 3) discover new patterns and insights about phenomena, 4) generate hypotheses about phenomena, and 5) understand laws that make generalizations about movements. ...
This chapter presents major issues with retrieving, sampling, geocoding and analyzing geospatial and temporal patterns in social media data. The chapter takes an interdisciplinary approach that includes perspectives from different knowledge domains, including information science, geographic information science, geovisualization, information visualization, visual analytics, complex systems, and data science, presenting rich illustrative examples and case studies. It also discusses the benefits and shortcomings of geospatial methods, gives numerous suggestions on how to: collect geospatial data, avoid biases, aggregate data for protecting the privacy of social media contributors during the investigation, and what research questions to ask about people's locations in space or social phenomena. We complete with an overview of the advantages geospatial methods add to the analysis of social media. We carry readers to a conclusion that such techniques allow researchers to perceive the behaviors of social media contributors from a different perspective and discover static and dynamic patterns of users' spatial collective behaviors that are hard to detect to the unaided senses.
... In particular, we discovered 4 out of 15 cases in which bar charts and line charts likely share the same coordinate systems, but they actually have different y-coordinates, which would be easily overlooked (e.g., Fig. 7-A4). There are examples of accompanied visualizations (A3 [53], A4 [54], A5 [55], A6 [56], & A7 [57]) and coordinated visualizations (B4 [38], B5 [58], B6 [59], B7 [60], B8 [61], & B9 [62]). ...
Composite visualization is a popular design strategy that represents complex datasets by integrating multiple visualizations in a meaningful and aesthetic layout, such as juxtaposition, overlay, and nesting. With this strategy, numerous novel designs have been proposed in visualization publications to accomplish various visual analytic tasks. These well-crafted composite visualizations have formed a valuable collection for designers and researchers to address real-world problems and inspire new research topics and designs. However, there is a lack of understanding of design patterns of composite visualization, thus failing to provide holistic design space and concrete examples for practical use. In this paper, we opted to revisit the composite visualizations in VIS publications and answered what and how visualizations of different types are composed together. To achieve this, we first constructed a corpus of composite visualizations from IEEE VIS publications and decomposed them into a series of basic visualization types (e.g., bar chart, map, and matrix). With this corpus, we studied the spatial (e.g., separated or overlaying) and semantic relationships (e.g., with same types or shared axis) between visualizations and proposed a taxonomy consisting of eight different design patterns (e.g., repeated, stacked, accompanied, and nested). Furthermore, we analyzed and discussed common practices of composite visualizations, such as the distribution of different patterns and correlations between visualization types. From the analysis and examples, we obtained insights into different design patterns on the utilities, advantages, and disadvantages. Finally, we developed an interactive system to help visualization developers and researchers conveniently explore collected examples and design patterns.
... The data generated by social networks may contain geo-tags that add to the information the geographical position from which it was generated or when it was generated. This temporal data allows a better understanding of the user's mobility pattern [5,16,147]. However, this data is very dispersed and has a lot of uncertainty [168]. ...
... Finally, data from mobile devices can register calls and messages and collect data through telecommunication operators' cell stations. With such information, it is possible to generate user traces to provide services in the VEC [5,16,147]. ...
Vehicular Edge Computing (VEC), based on the Edge Computing motivation and fundamentals, is a promising technology supporting Intelligent Transport Systems services, smart city applications, and urban computing. VEC can provide and manage computational resources closer to vehicles and end-users, providing access to
services at lower latency and meeting the minimum execution requirements for each service type. This survey describes VEC’s concepts and technologies; we also present an overview of existing VEC architectures, discussing them and exemplifying them through layered designs. Besides, we describe the underlying vehicular communication in supporting resource allocation mechanisms. With the intent to overview the risks,
breaches, and measures in VEC, we review related security approaches and methods. Finally, we conclude this survey work with an overview and study of VEC’s main challenges. Unlike other surveys in which they are focused on content caching and data offloading, this work proposes a taxonomy based on the architectures in which VEC serves as the central element. VEC supports such architectures in capturing and disseminating
data and resources to offer services aimed at a smart city through their aggregation and the allocation in a secure manner.
... Second, based on these visualization theories and techniques, researchers also used visual analytics to examine taxi GPS data [47], [48], geo-tagged social media data [49], and telco data [50] to improve their understanding of the data and for pattern discovery [51]. In particular, researchers leveraged interactive visual analytics to comprehend the transportation system [52], [53], [54], [55], [56], to solve urban problems [57] including air pollution [58], [59], traffic congestion [60], and bus route planning [61]; to explore route diversity [62], significant places [63], [64], [65], and urban network centralities [66]; and to find ideal home locations [67] and billboard locations [68] for commercial use. These visual analytics studies design new visualizations and integrate them in a novel way to deliver a comprehensive exploration for enhancing analysts' perception. ...
This paper has been accepted by IEEE Transactions on Visualization and Computer Graphics (TVCG).
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The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have taken various measures and implemented policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the rapid spread of this epidemic. The common intention of these countermeasures is to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policy makers have expressed the urgency of being able to effectively evaluate the effects of human restriction policies with the aid of big data and information technology. Thus, in this study, based on big human mobility data and city POI data, we designed an interactive visual analytics system named EpiMob (Epidemic Mobility).
The system interactively simulates the changes in human mobility and the number of infected people in response to the implementation of a certain restriction policy or combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies, and the result reflecting the infection situation under different policies is dynamically displayed and can be flexibly compared.
We completed multiple case studies of the largest metropolitan area in Japan (i.e., Greater Tokyo Area) and conducted interviews with domain experts to demonstrate that our system can provide illustrative insight by measuring and comparing the effects of different human mobility restriction policies for epidemic control.
... The visualization of location properties can be categorized into the point-, region-, and line-based techniques [79]. Point-based techniques [7,23,67] represent the locations with the points in their spatial contexts. Region-based techniques [2,72] render the aggregated location data based on certain spatial divisions. ...
Bus routes are typically updated every 3-5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of the automated approaches cannot produce satisfactory results in real-world settings without laborious inspection and evaluation of the candidates. The limitations observed in these approaches motivate us to collaborate with domain experts and propose a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network. Developing such a solution involves three major challenges, namely, a) the in-depth analysis of complex bus route networks, b) the interactive generation of improved route candidates, and c) the effective evaluation of alternative bus routes. For challenge a, we employ an overview-to-detail approach by dividing the analysis of a complex bus network into three levels to facilitate the efficient identification of deficient routes. For challenge b, we improve a route generation model and interpret the performance of the generation with tailored visualizations. For challenge c, we incorporate a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one. The proposed system is evaluated with two usage scenarios based on real-world data and received positive feedback from the experts.
... The visualization of location properties can be categorized into the point-, region-, and line-based techniques [81]. Point-based techniques [7,67,84] represent the locations with the points in their spatial contexts. Region-based techniques [2,72] render the aggregated location data based on certain spatial divisions. ...
Bus routes are typically updated every 3-5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of the automated approaches cannot produce satisfactory results in real-world settings without laborious inspection and evaluation of the candidates. The limitations observed in these approaches motivate us to collaborate with domain experts and propose a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network. Developing such a solution involves three major challenges, namely, a) the in-depth analysis of complex bus route networks, b) the interactive generation of improved route candidates, and c) the effective evaluation of alternative bus routes. For challenge a, we employ an overview-to-detail approach by dividing the analysis of a complex bus network into three levels to facilitate the efficient identification of deficient routes. For challenge b, we improve a route generation model and interpret the performance of the generation with tailored visualizations. For challenge c, we incorporate a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one. The proposed system is evaluated with two usage scenarios based on real-world data and received positive feedback from the experts.
... In order to explore the potential features of interest, a large amount of improvements have been conducted to enhance the visual expression of line charts (Zhao et al. 2018b;Muelder et al. 2016;Shi et al. 2012). For example, Andrienko et al. (2010) designed two graphs ( Fig. 1d) to indicate that the calling behavior on Saturday and Sunday differs from that in the working days. The upper graph is a traditional line chart, in which the temporal records of 238 areas are depicted as lines, overlapping each other. ...
... Standard line chart and its enhancements. a A traditional line chart(Munzner 2014); b a line chart with color and shape encoding(Pagot et al. 2011); c comparison through small multiples(Chang et al. 2007); d statistical aggregation(Andrienko et al. 2010); e Focus?Context approach(Kincaid 2010). f Interactive lenses on line chart(Zhao et al. 2011) ...
Conventional statistical charts are widely used in visual analysis. With the development of digital techniques, statistical charts are confronted with problems when data grow in scale and complexity. Accordingly, a huge amount of effort has been paid on the enhancement of standard charts, making the design space dramatically increased. It is cumbersome for naive users to choose appropriate design in a specific analysis scenario. In this paper, we survey the enhancement techniques for a compact set of statistical charts, and identify the types and usage scenarios. Motivated by the new problems, such as data volume and complexity, we present a challenge-and-task-driven framework to guide the understanding of the design space and the decision-making process.
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... Based on geotagged Flickr photos, Rattenbury and Naaman (2009) identified point clusters using K-means clustering, and detected representative textual tags for each cluster using an algorithm called TagMaps. Andrienko et al. (2010b) proposed a visual analytics framework which detects special place zones containing periodic or irregular events. Hu et al. (2015) used geotagged Flickr photos to extract urban areas of interest (AOI) by performing DBSCAN clustering and chi-shape algorithm (Figure 6(a)). ...
... With the grouped data, we can use the texts associated with each group to examine its semantics. The work of Andrienko et al. (2010b) is an example of the geo-first approach. In text-first, we begin with the text part by extracting information from it, and then investigate the spatial or spatiotemporal patterns of the extracted information. ...
Many datasets nowadays contain links between geographic locations and natural language texts. These links can be geotags, such as geotagged tweets or geotagged Wikipedia pages, in which location coordinates are explicitly attached to texts. These links can also be place mentions, such as those in news articles, travel blogs, or historical archives, in which texts are implicitly connected to the mentioned places. This kind of data is referred to as geo‐text data. The availability of large amounts of geo‐text data brings both challenges and opportunities. On the one hand, it is challenging to automatically process this kind of data due to the unstructured texts and the complex spatial footprints of some places. On the other hand, geo‐text data offers unique research opportunities through the rich information contained in texts and the special links between texts and geography. As a result, geo‐text data facilitates various studies especially those in data‐driven geospatial semantics. This paper discusses geo‐text data and related concepts. With a focus on data‐driven research, this paper systematically reviews a large number of studies that have discovered multiple types of knowledge from geo‐text data. Based on the literature review, a generalized workflow is extracted and key challenges for future work are discussed.
... Buono et al. [Buono et al. 2005] proposed pattern search approach on time-series analysis to find similar occurrences, and also used dynamic query to investigate results. There are other works automatically detecting special subsequences using query techniques, for example to find the most unusual time series subsequences based on periodicity [Keogh et al. 2005], or to detect periodicity and peak/pit [Andrienko et al. 2010]. Besides, Hao et al. [Hao et al. 2011] conduct prediction on time-series with peak preserved. ...
In seismic research, a hypothesis is that ionosphere disturbances are related to lithosphere activities such as earthquakes. Domain scientists are urgent to discover disturbance patterns of electromagnetic attributes in ionosphere around earthquakes, and to propose related hypotheses. However, the workflow of seismic researchers usually only supports pattern extraction from a few earthquakes. To explore the pattern-based hypotheses on a large spatiotemporal scale meets challenges, due to the limitation of their analysis tools. To tackle the problem, we develop a visual analytics system which not only supports pattern extraction of the original workflow in a way of dynamic query, but also extends the work with hypotheses exploration on a global scale. Domain scientists can easily utilize our system to explore the heterogeneous dataset, and to extract patterns and explore related hypotheses visually and interactively. We conduct several case studies to demonstrate the usage and effectiveness of our system in the research of relationships between ionosphere disturbances and earthquakes.
