Context-Preserving Visual Analytics of Multi-Scale Spatial Aggregation.

Spatial datasets (i.e., location-based social media, crime incident reports, and demographic data) often exhibit varied distribution patterns at multiple spatial scales. Examining these patterns across different scales enhances the understanding from global to local perspectives and offers new insights into the nature of various spatial phenomena. Conventional navigation techniques in such multi-scale data-rich spaces are often inefficient, require users to choose between an overview or detailed information, and do not support identifying spatial patterns at varying scales. In this work, we present a context-preserving visual analytics technique that aggregates spatial datasets into hierarchical clusters and visualizes the multi-scale aggregates in a single visual space. We design a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates and explore visual encoding strategies including color, transparency, shading, and shapes, in order to illustrate the hierarchical and statistical patterns of the multi-scale aggregates. We also propose a transparency-based technique that maintains a smooth visual transition as the users navigate across adjacent scales. To further support effective semantic exploration in the multi-scale space, we design a set of text-based encoding and layout methods that draw textual labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each scale, but also indicates the spatial coverage of the aggregates and their hierarchical relationships. We demonstrate the effectiveness of the proposed approaches through real-world application examples and user studies

Location Reference Recognition from Texts: A Survey and Comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

The role of geographic knowledge in sub-city level geolocation algorithms

Geolocation of microblog messages has been largely investigated in the lit- erature. Many solutions have been proposed that achieve good results at the city-level. Existing approaches are mainly data-driven (i.e., they rely on a training phase). However, the development of algorithms for geolocation at sub-city level is still an open problem also due to the absence of good training datasets. In this thesis, we investigate the role that external geographic know- ledge can play in geolocation approaches. We show how di)erent geographical data sources can be combined with a semantic layer to achieve reasonably accurate sub-city level geolocation. Moreover, we propose a knowledge-based method, called Sherloc, to accurately geolocate messages at sub-city level, by exploiting the presence in the message of toponyms possibly referring to the speci*c places in the target geographical area. Sherloc exploits the semantics associated with toponyms contained in gazetteers and embeds them into a metric space that captures the semantic distance among them. This allows toponyms to be represented as points and indexed by a spatial access method, allowing us to identify the semantically closest terms to a microblog message, that also form a cluster with respect to their spatial locations. In contrast to state-of-the-art methods, Sherloc requires no prior training, it is not limited to geolocating on a *xed spatial grid and it experimentally demonstrated its ability to infer the location at sub-city level with higher accuracy

A study on Analysis and Utilization of Crowd-sourced Spatio-temporal Contexts from Social Media

兵庫県立大学大学院201

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

On the Accuracy of Hyper-local Geotagging of Social Media Content

Social media users share billions of items per year, only a small fraction of which is geotagged. We present a data- driven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by modeling the location distributions of hyper-local n-grams that appear in the text. We explore the trade-off between accuracy, precision and coverage of this method. Further, we explore differences across content received from multiple platforms and devices, and show, for example, that content shared via different sources and applications produces significantly different geographic distributions, and that it is best to model and predict location for items according to their source. Our findings show the potential and the bounds of a data-driven approach to geotag short social media texts, and offer implications for all applications that use data-driven approaches to locate content.Comment: 10 page

Location reference recognition from texts: A survey and comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to the process of recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of the specific applications is still missing. Further, there lacks a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and a core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching-based, statistical learning-based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references across the world. Results from this thorough evaluation can help inform future methodological developments for location reference recognition, and can help guide the selection of proper approaches based on application needs