Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work on network reconstruction with point-process models, which has often focused on exclusively temporal information, our approach uses both temporal and spatial information and does not assume a specific parametric form of network dynamics. This leads to an effective way of recovering an underlying network. We illustrate our approach using both synthetic networks and networks constructed from real-world data sets (a location-based social media network, a narrative of crime events, and violent gang crimes). Our results demonstrate that, in comparison to using only temporal data, our spatiotemporal approach yields improved network reconstruction, providing a basis for meaningful subsequent analysis --- such as community structure and motif analysis --- of the reconstructed networks

Passive Visual Analytics of Social Media Data for Detection of Unusual Events

Now that social media sites have gained substantial traction, huge amounts of un-analyzed valuable data are being generated. Posts containing images and text have spatiotemporal data attached as well, having immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. However, the large volume of unstructured social media data hinders exploration and examination. To analyze such social media data, the S.M.A.R.T system provides the analyst with an interactive visual spatiotemporal analysis and spatial decision support environment that assists in evacuation planning and disaster management. S.M.A.R.T fetches data from various social media sources and arranges them in a perceivable manner, which is visually appealing. This in turn is a huge aid in finding and understanding abnormal events. Introducing a passive mode makes the tool more efficient, where it automatically detects idle time and gives a summary of all the anomalies encountered in the inactive period as soon as the analyst resumes monitoring. Using the tool, the analyst can first extract major topics from a set of selected messages and rank them probabilistically. The case studies in the past show improved situational awareness by using the methods mentioned before

SOCIAL MEDIA FOOTPRINTS OF PUBLIC PERCEPTION ON ENERGY ISSUES IN THE CONTERMINOUS UNITED STATES

Energy has been at the top of the national and global political agenda along with other concomitant challenges, such as poverty, disaster and climate change. Social perception on various energy issues, such as its availability, development and consumption deeply affect our energy future. This type of information is traditionally collected through structured energy surveys. However, these surveys are often subject to formidable costs and intensive labor, as well as a lack of temporal dimensions. Social media can provide a more cost-effective solution to collect massive amount of data on public opinions in a timely manner that may complement the survey. The purpose of this study is to use machine learning algorithms and social media conversations to characterize the spatiotemporal topics and social perception on different energy in terms of spatial and temporal dimensions. Text analysis algorithms, such as sentiment analysis and topic analysis, were employed to offer insights into the public attitudes and those prominent issues related to energy. The results show that the energy related public perceptions exhibited spatiotemporal dynamics. The study is expected to help inform decision making, formulate national energy policies, and update entrepreneurial energy development decisions

Comparing the Spatial and Temporal Activity Patterns between Snapchat, Twitter and Flickr in Florida

Social media services generate enormous amounts of spatiotemporal data that can be used to characterize and analyse user activities and social behaviour. Although crowdsourced data have the advantage of comprehensive spatial and temporal coverage compared to data collected in more traditional ways, the various social media platforms target different user groups, which leads to user selection bias. Since data from social media platforms are used for a variety of geospatial applications, understanding such differences and their implications for analysis results is important for geoscientists. Therefore, this research analyses differences in spatial and temporal contribution patterns to three online platforms, namely Flickr, Twitter and Snapchat, over a six-week period in Florida. For the comparison of spatial contribution patterns, a set of negative binomial regression models are estimated to identify which socio-economic factors and characteristics of the built and natural environments are associated with contribution activities. The contribution differences observed are discussed in light of the targeted user groups and different purposes of the three platforms

Can Social Media Help Us Understand The Impact of Climate Change on Forests in The US?

While social media data are increasingly being used in the study of pressing environmental problems, their ability to monitor environmental changes has scarcely been assessed. Understanding this viability is highly important as climate change increasingly impacts public health, and behavior. We examine social media photographs associated with wildfires in Yellowstone National Park to assess if images and content can adequately capture environmental change associated with large-scale landscape impacts - wildfires - using computer vision, natural language processing and spatiotemporal analysis. We find that social media posts associated with wildfire events rarely capture the fires themselves, while landscape impacts including burnt trees and early succession are more frequently the topic of photography. Furthermore, we find that computer vision has challenges with capturing these phenomena. While capturing wildfires proved difficult, developing multimodal analysis including natural language processing, spatial, trend and computer vision analysis at scale may open opportunities for more general understanding of social media’s efficacy for monitoring environmental change

Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

Temporal and Spatiotemporal Investigation of Tourist Attraction Visit Sentiment on Twitter

In this paper, we propose a sentiment-based approach to investigate the temporal and spatiotemporal effects on tourists\u27 emotions when visiting a city\u27s tourist destinations. Our approach consists of four steps: data collection and preprocessing from social media; visitor origin identification; visit sentiment identification; and temporal and spatiotemporal analysis. The temporal and spatiotemporal dimensions include day of the year, season of the year, day of the week, location sentiment progression, enjoyment measure, and multi-location sentiment progression. We apply this approach to the city of Chicago using over eight million tweets. Results show that seasonal weather, as well as special days and activities like concerts, impact tourists\u27 emotions. In addition, our analysis suggests that tourists experience greater levels of enjoyment in places such as observatories rather than zoos. Finally, we find that local and international visitors tend to convey negative sentiment when visiting more than one attraction in a day whereas the opposite holds for out of state visitors

Encounter and its configurational logic: Understanding spatiotemporal co-presence with road network and social media check-in data

Public space facilitates the social interaction between people. It is widely accepted that the connection between spaces creates the possibility of the mutual visibility between people. The relationship between spatial configuration and the spatiotemporal encounters, however, has rarely been investigated explicitly in empirical cases. The focus of this study is two folded: firstly, it examines the way to measure spatiotemporal encounters between different groups of people based on their mobility records; secondly, it investigates how the design of the built environment contributes to physical co-presence on spatial and temporal dimensions. Using ubiquitous individual social media check-in data in Central Shanghai, China, this study proposes a framework for quantifying physical face-to-face co-presence patterns between the defined local random walkers and the remote visitors across time in every street. In the introduced People-Space-Time (PST) model, social capital is conceptualised as an integration among social difference, spatial distance (metric and geometrical distance) and time distance. The reliability of the applied data and the effectiveness of the introduced methods are validated by the investigations of the scaling nature of the extracted mobility patterns and the correlation between the outputs and surveyed data. The produced spatiotemporal patterns of face-toface co-presence reveal that city centres and the large-scale urban complexes (e.g., transport hubs, shopping malls, stadiums, etc.) are ideal places for people to encounter. The results of the regression analyses demonstrate that spatial and functional centrality measures are significant variables for predicting spatiotemporal co-presence in streets, but in which the functional centrality structures maintain a higher standard of explanatory power than the spatial network. The temporal complexity of the co-presence is revealed by the temporally shifting performance of the integrated regression models across time. The findings in this study yield that it is the spatio-functional interaction influencing spatiotemporal variation of the physical encounter between people, and reclaim the necessity of adding fine-scale land-use patterns in the traditional configurational analysis for deeply understanding the social processes with urban big data in the contemporary digitalised cities