Casual Information Visualization on Exploring Spatiotemporal Data

The goal of this thesis is to study how the diverse data on the Web which are familiar to everyone can be visualized, and with a special consideration on their spatial and temporal information. We introduce novel approaches and visualization techniques dealing with different types of data contents: interactively browsing large amount of tags linking with geospace and time, navigating and locating spatiotemporal photos or videos in collections, and especially, providing visual supports for the exploration of diverse Web contents on arbitrary webpages in terms of augmented Web browsing

A Multi-Scale Correlative Approach for Crowd-Sourced Multi-Variate Spatiotemporal Data

With the increase in community-contributed data availability, citizens and analysts are interested in identifying patterns, trends and correlation within these datasets. Various levels of aggregation are often applied to interpret such large data schemes. Identifying the proper scales of aggregation is a non-trivial task in this exploratory data analysis process. In this paper, we present an integrated visual analytics environment that facilitates the exploration of multivariate categorical spatiotemporal data at multiple spatial scales of aggregation, focusing on citizen-contributed data. We propose a compact visual correlation representation by embedding various statistical measures across different spatial regions to enable users to explore correlations between multiple data categories across different spatial scales. The system provides several scale-sensitive spatial partitioning strategies to examine the sensitivity of correlations at varying spatial extents. To demonstrate the capabilities of our system, we provide several usage scenarios from various domains including citizen-contributed social media (soundscape ecology) data

Delineating Intra-Urban Spatial Connectivity Patterns by Travel-Activities: A Case Study of Beijing, China

Travel activities have been widely applied to quantify spatial interactions between places, regions and nations. In this paper, we model the spatial connectivities between 652 Traffic Analysis Zones (TAZs) in Beijing by a taxi OD dataset. First, we unveil the gravitational structure of intra-urban spatial connectivities of Beijing. On overall, the inter-TAZ interactions are well governed by the Gravity Model $G_{ij} = {\lambda}p_{i}p_{j}/d_{ij}$, where $p_{i}$, $p_{j}$ are degrees of TAZ $i$, $j$ and $d_{ij}$ the distance between them, with a goodness-of-fit around 0.8. Second, the network based analysis well reveals the polycentric form of Beijing. Last, we detect the semantics of inter-TAZ connectivities based on their spatiotemporal patterns. We further find that inter-TAZ connections deviating from the Gravity Model can be well explained by link semantics.Comment: 6 pages, 4 figure

Visual Event Cueing in Linked Spatiotemporal Data

abstract: The media disperses a large amount of information daily pertaining to political events social movements, and societal conflicts. Media pertaining to these topics, no matter the format of publication used, are framed a particular way. Framing is used not for just guiding audiences to desired beliefs, but also to fuel societal change or legitimize/delegitimize social movements. For this reason, tools that can help to clarify when changes in social discourse occur and identify their causes are of great use. This thesis presents a visual analytics framework that allows for the exploration and visualization of changes that occur in social climate with respect to space and time. Focusing on the links between data from the Armed Conflict Location and Event Data Project (ACLED) and a streaming RSS news data set, users can be cued into interesting events enabling them to form and explore hypothesis. This visual analytics framework also focuses on improving intervention detection, allowing users to hypothesize about correlations between events and happiness levels, and supports collaborative analysis.Dissertation/ThesisMasters Thesis Computer Science 201

A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we have developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and the multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, which combined COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from LGA-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding of analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors against the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios

Visitor bikeshare usage: tracking visitor spatiotemporal behavior using big data

Bikeshare programs are a popular, convenient, and sustainable mode of transportation that provide a range of benefits to urban communities such as reduction in carbon emissions, decreased travel times, financial savings, and heightened physical activity. Although, tourists are especially inclined to use bikeshare to explore a destination as the programs are a convenient, cheap, flexible, and an active alternative to vehicles and mass transit little research or attention has focused on visitor usage. As such the current study investigated the spatial-temporal usage patterns of bikeshare by visitors to an urban community using GPS based big data (N = 353,733). The results revealed differential usage patterns between visitors and local residents based on user provided ZIP Codes using a 50 mile geometric circular buffer around the urban destination. The visitors and residents significantly varied on numerous trip behaviors including route selection, time of rental, checkout/check-in locations, distance, speed, duration, and physical activity intensity. The user patterns uncovered suggest visitors primarily use bikeshare for leisure based urban exploration, compared to residents’ primary use of bikeshare to be public transportation related. Implications for bikeshare, urban planning, and tourism management are provided aimed at delivering a more sustainable and richer visitor experience

Using Bikeshare Datasets to Improve Urban Cycling Experience and Research Urban Cycling Behaviour

With access to public and shared transport systems becoming increasingly digitized, transaction datasets of unprecedented size as well as temporal and spatial precision are automatically generated (Blythe and Bryan 2007; Bagchi and White 2005; Pelletier et al. 2011). Data collected through smartcard payment methods are perhaps the largest and most obvious example. Although introduced for the purpose of improving payment processes, such data provide a detailed view of demand on a transport system, the potential for service improvements to be suggested (Ferrari et al. 2014) and an opportunity for studying individual traveller behaviour (Agard et al. 2006; Morency et al. 2006; Lathia et al. 2013). A substantial benefit of such data over more traditional data collection methods is that a complete and total record of usage for every smartcard customer is automatically generated (Bagchi and White 2005). Problems associated with sampling and recall bias, which make actively collected travel surveys somewhat difficult to administer, are avoided. The two most obvious disadvantages, at least for travel behaviour research, are that those individuals using smartcard technology may not be representative of the total population using that system or navigating a city more generally; and that variables such as individual trip purpose can only be inferred since they are not recorded directly