Tracing the German Centennial Flood in the Stream of Tweets: First Lessons Learned

Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results con rm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions

Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction

This paper presents a computational model of the processing of dynamic spatial relations occurring in an embodied robotic interaction setup. A complete system is introduced that allows autonomous robots to produce and interpret dynamic spatial phrases (in English) given an environment of moving objects. The model unites two separate research strands: computational cognitive semantics and on commonsense spatial representation and reasoning. The model for the first time demonstrates an integration of these different strands.Comment: in: Pham, D.-N. and Park, S.-B., editors, PRICAI 2014: Trends in Artificial Intelligence, volume 8862 of Lecture Notes in Computer Science, pages 958-971. Springe

Spatio-Temporal Modeling of Wildfire Risks in the U.S. Forest Sector

In the US forestry industry, wildfire has always been one of the leading causes of damage. This topic is of growing interest as wildfire has caused huge losses for landowners, residents and governments in recent years. While individual wildfire behavior is well studied (e.g. Butry 2009; Holmes 2010), a lot of new literature on broadscale wildfire risks (e.g. by county) is emerging (e.g. Butry et al. 2001; Prestemon et al. 2002). The papers of the latter category have provided useful suggestions for government wildfire management and policies. Although wildfire insurance for real estate owners is popular, the possibility to develop a forestry production insurance scheme accounting for wildfire risks has not yet been investigated. The purpose of our paper is to comprehensively evaluate broadscale wildfire risks in a spatio-temporal autoregressive scenario and to design an actuarially fair wildfire insurance scheme in the U.S. forest sector. Our research builds upon an extensive literature that has investigated crop insurance modeling. Wildfire risks are closely linked to environmental conditions. Weather, forestland size, aspects of human activity have been proved to be crucial causal factors for wildfire (Prestemon et al. 2002; Prestemon and Butry 2005; Mercer et al. 2007). In light of these factors, we carefully study wildfires ignited by different sources, such as by arson and lightning, and identify their underlying causes. We find that the decomposition of forestland ecosystem and socio-economic conditions have significant impacts on wildfire, as well as weather. Our models provide a good fit to data on frequency and propensity for fires to exist (e.g. R-square ranges from 0.4 to 0.8) and therefore provide important fundamental information on risks for the development of insurance contracts. A number of databases relevant to this topic are used. With the Florida wildfire frequency and loss size database, a complete survey of four measurements of annual wildfire risks is implemented. These four measurements are annual wildfire frequency, burned area, fire per acre and burned ratio at county level. In addition, the national forestry inventory and analysis (FIA) database, Regional Economic Information Systems (REIS) database and the national weather database have supplied forestland ecosystem, socioeconomic, and weather condition information respectively. With our spatio-temporal lattice models, impacts of environmental factors on wildfire and implications of wildfire management policies are assessed. Forestland size, private owners’ share of forestland, population and drought would positively contribute to wildfire risks significantly. Cold weather and high employment are found to be helpful in lessening wildfire risks. Among the forestland ecosystem, oak / pine & oak / hickory forestland would reduce wildfire risks while longleaf / slash & loblolly / shortleaf pine forestland would have a mixed impact. An interesting finding is that oak / gum / cypress forestland would reduce wildfire frequency, but would enhance wildfire propensity at the same time. Hurricanes could intensify wildfire risks in the same year, but would significantly decrease the next year’s wildfire risks. Meanwhile, cross sample validation verifies that our method can forecast wildfire risks adequately well. Since our approach does not incorporate any fixed-effect indicator or trend as in the panel data analysis (Prestemon et al. 2002), it offers a universal tool to evaluate and predict wildfire risks. Hence, given environmental information of a location, a corresponding actuarially fair insurance rate can be calculated.wildfires, forestry, weather, socio-economic, Spatio-Temporal autocorrelation, Risk and Uncertainty,

Hoodsquare: Modeling and Recommending Neighborhoods in Location-based Social Networks

Information garnered from activity on location-based social networks can be harnessed to characterize urban spaces and organize them into neighborhoods. In this work, we adopt a data-driven approach to the identification and modeling of urban neighborhoods using location-based social networks. We represent geographic points in the city using spatio-temporal information about Foursquare user check-ins and semantic information about places, with the goal of developing features to input into a novel neighborhood detection algorithm. The algorithm first employs a similarity metric that assesses the homogeneity of a geographic area, and then with a simple mechanism of geographic navigation, it detects the boundaries of a city's neighborhoods. The models and algorithms devised are subsequently integrated into a publicly available, map-based tool named Hoodsquare that allows users to explore activities and neighborhoods in cities around the world. Finally, we evaluate Hoodsquare in the context of a recommendation application where user profiles are matched to urban neighborhoods. By comparing with a number of baselines, we demonstrate how Hoodsquare can be used to accurately predict the home neighborhood of Twitter users. We also show that we are able to suggest neighborhoods geographically constrained in size, a desirable property in mobile recommendation scenarios for which geographical precision is key.Comment: ASE/IEEE SocialCom 201

Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction

With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied. However, much of the research has been conducted solely on voluntary checkin datasets collected from social apps such as Foursquare or Yelp. While these data contain rich information about recreational activities (e.g., restaurants, nightlife, and entertainment), information about more prosaic aspects of people's lives is sparse. This not only limits our understanding of users' daily routines, but more importantly the modeling assumptions developed based on characteristics of recreation-based data may not be suitable for richer check-in data. In this work, we present an analysis of education "check-in" data using WiFi access logs collected at Purdue University. We propose a heterogeneous graph-based method to encode the correlations between users, POIs, and activities, and then jointly learn embeddings for the vertices. We evaluate our method compared to previous state-of-the-art POI prediction methods, and show that the assumptions made by previous methods significantly degrade performance on our data with dense(r) activity signals. We also show how our learned embeddings could be used to identify similar students (e.g., for friend suggestions).Comment: published in KDD'1

A Local-Global LDA Model for Discovering Geographical Topics from Social Media

Micro-blogging services can track users' geo-locations when users check-in their places or use geo-tagging which implicitly reveals locations. This "geo tracking" can help to find topics triggered by some events in certain regions. However, discovering such topics is very challenging because of the large amount of noisy messages (e.g. daily conversations). This paper proposes a method to model geographical topics, which can filter out irrelevant words by different weights in the local and global contexts. Our method is based on the Latent Dirichlet Allocation (LDA) model but each word is generated from either a local or a global topic distribution by its generation probabilities. We evaluated our model with data collected from Weibo, which is currently the most popular micro-blogging service for Chinese. The evaluation results demonstrate that our method outperforms other baseline methods in several metrics such as model perplexity, two kinds of entropies and KL-divergence of discovered topics