Papers by Scott Pezanowski
There has been much successful research to analyze the movement of people, wildlife, goods, and m... more There has been much successful research to analyze the movement of people, wildlife, goods, and more, where the data is precise movement trajectories. This research has mostly ignored geographic movement that comes in the form of text descriptions. Descriptions of things moving have an advantage over trajectory data. It often includes rich contextual information that describes what is moving, when it moves, why it moves, and how it moves. Challenges exist to utilizing this source, like ambiguities in the text's meaning and the author and reader's knowledge and background. Still, computational advances like Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning are well suited to uncover essential features in the movement descriptions. This research makes several contributions to improve the understanding of geographic movement described in text documents. It also guides further research to improved methods for utilizing this underused resource. First, I created a corpus of sentences labeled as describing geographic movement or not. Creating this corpus proved difficult without any comparable corpora to start with, high human labeling costs, and text ambiguities. To overcome these challenges, I developed an iterative process employing hand labeling, crowd voting for agreement, and machine learning to predict more labels. By merging advances in word embeddings with traditional machine learning models, and model ensembling, prediction accuracy is acceptable to produce a larger silver-standard corpus where a small amount of error in predictions is accepted. In addition to the detection of movement, my corpus will likely benefit computational processing of geography in text and spatial cognition. The corpus contains many geographic place mentions along with contextual information that computational processing can learn important linguistics features that are associated with the place mentions. Spatial cognition research can analyze differences in geographic understanding of the text both from the author's side and any reader's side. My process also provides a baseline method to detect statements that describe geographic movement. Second, I show how interpreting geographic movement described in text documents is challenging because of general spatial terms, linguistics that make the thing(s) movement unclear, and many temporal references and groupings. To overcome these challenges, I identified multiple essential characteristics of the movement described that humans use to differentiate descriptions. I also explore current computational text processing techniques to analyze the movement characteristics described in the text and show how these characteristics help people understand patterns in larger bodies of text describing movement. My findings contribute to an improved understanding of the critical characteristics of geographic movement in text descriptions. The third contribution in my research is an initial effort to derive meaningful information from geographic movement descriptions at a large and general scale. Geographic Information Retrieval (GIR) is a sub-domain of both IR and GIScience that has emphasized retrieval of documents that mention or are about places along with some focus on geographic feature extraction. GIR advances have created an as yet primarily unrealized potential to leverage text documents as sources for geographic-scale movement information. The geographic movement described in text documents can complement detailed movement data, provide an alternative when precise data does not exist, and provide the added benefit of rich context about the movement. As an initial large-scale effort to derive meaningful information from textual data describing geographic movement, we applied multiple computational techniques to hundreds of millions of statements. First, we identify and geolocate the geographic places mentioned. Next, we predict those that describe geographic movement. Finally, because the COVID-19 pandemic highlighted the importance of global movement disruptions, we predict if the statement describes not moving or restricted movement. Since the data is messy and complicated and the prediction techniques are not perfect, we designed and implemented a geovisual analytics system through which a visual interface enables humans to explore initial statement classifications, the places mentioned in them, and co-occurring place mentions to assess the validity of computational methods and provide direct feedback toward improving results. We include two user scenarios that show how a human can derive meaningful information about geographic movement through the geovisual analytics system. The user scenarios constitute systematic case studies to demonstrate the utility of the approach. Existing geographic movement research has improved analysis methods and shown how these methods enhance understanding of human movement, wildlife movement, and much…
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KN - Journal of Cartography and Geographic Information, 2022
Sensemaking using automatically extracted information from text is a challenging problem. In this... more Sensemaking using automatically extracted information from text is a challenging problem. In this paper, we address a specific type of information extraction, namely extracting information related to descriptions of movement. Aggregating and understanding information related to descriptions of movement and lack of movement specified in text can lead to an improved understanding and sensemaking of movement phenomena of various types, e.g., migration of people and animals, impediments to travel due to COVID-19, etc. We present GeoMovement, a system that is based on combining machine learning and rule-based extraction of movement-related information with state-of-the-art visualization techniques. Along with the depiction of movement, our tool can extract and present a lack of movement. Very little prior work exists on automatically extracting descriptions of movement, especially negation and movement. Apart from addressing these, GeoMovement also provides a novel integrated framework for combining these extraction modules with visualization. We include two systematic case studies of GeoMovement that show how humans can derive meaningful geographic movement information. GeoMovement can complement precise movement data, e.g., obtained using sensors, or be used by itself when precise data is unavailable.
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Geomatics Solutions for Disaster Management, 2007
Google Earth (GE) and related open geospatial technologies have changed both the accessibility of... more Google Earth (GE) and related open geospatial technologies have changed both the accessibility of and audience for geospatial information dramatically. Through data rich applications with easy to use interfaces, these technologies bring personalized geospatial information directly to the nonspecialist. When coupled with open geospatial data standards, such as Web Map Services (WMS), Web Features Services (WFS), and GeoRSS, the resulting web-based technologies have the potential to assimilate heterogeneous data from distributed sources rapidly enough to support time critical activities such as crisis response. Although the ability to view and interact with data in these environments is important, this functionality alone is not sufficient for the demands of crisis response activity. For example, GE’s standard version currently lacks geoanalysis capabilities such as geographic buffering and topology functions. In this paper, we present development of the “Google Earth Dashboard” (GED), a web-based interface powered by open geospatial standards and designed for supplementing and enhancing the geospatial capabilities of GE. The GED allows users to create custom maps through WMS layer addition to GE and perform traditional GIS analysis functions. Utility of the GED is presented in a usecase scenario where GIS operations implemented to work with GE are applied to support crisis management activities. The GED represents an important first step towards combining the ubiquity of GE and geospatial standards into an easy-to-use, data rich, geo-analytically powerful environment that can support crisis management activity.
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Cartography and Geographic Information Science, 2017
SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview +... more SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatio-temporal analytics using social media sources.
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Over the past two years, horrific disasters such as the Asian Tsunami, Hurricane Katrina, and the... more Over the past two years, horrific disasters such as the Asian Tsunami, Hurricane Katrina, and the Pakistan Earthquake have demonstrated the critical need for effective technological infrastructure that is scientifically grounded in geo-visual group interaction theory [1] and humanitarian knowledge management procedures [2] to quickly and effectively facilitate planning for predictable events and post-event response. In this demonstration, we address specific issues that negatively impact the effectiveness of geocollaborative process in disaster relief. These include lack of common group operating picture, lack of command structure understanding and blatant miscommunication and misunderstanding about where relief supplies needed to be delivered, who will deliver them, when they need to be delivered, and the relevancy of deliveries to stricken areas. Our approach improves on existing systems by using methods and technologies that meet the challenges of coordinating the efforts of dive...
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Geographically-grounded situational awareness (SA) is critical to crisis management and is essent... more Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and valid...
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a b s t r a c t This paper reports on the development and application of strategies and tools for... more a b s t r a c t This paper reports on the development and application of strategies and tools for geographic information seeking and knowledge building that leverages unstructured text resources found on the web. Geographic knowledge building from unstructured web sources starts with web document foraging during which the quantity, scope and diversity of web-based information create incredible cognitive burdens on an analyst's or researcher's ability to judge information relevancy. Determining information relevancy is ultimately a process of sensemaking. In this paper, we present our research on visually supporting web document foraging and sensemaking. In particular, we present the Sense-of-Place (SensePlace) analytic environment. The scientific goal of SensePlace is to visually and computationally support analyst sensemaking with text artifacts that have potential place, time, and thematic relevance to an analytical problem through identification and visual highlighting ...
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Transactions in GIS
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Google Earth (GE) and related open geospatial technologies have changed both the accessibility of... more Google Earth (GE) and related open geospatial technologies have changed both the accessibility of and audience for geospatial information dramatically. Through data rich applications with easy to use interfaces, these technologies bring personalized geospatial information directly to the non-specialist. When coupled with open geospatial data standards, such as Web Map Services (WMS), Web Features Services (WFS), and GeoRSS, the resulting web-based technologies have the potential to assimilate heterogeneous data from distributed sources rapidly enough to support time-critical activities such as crisis response. Although the ability to view and interact with data in these environments is important, this functionality alone is not sufficient for the demands of crisis response activity. For example, GE’s standard version currently lacks geoanalysis capabilities such as geographic buffering and topology functions. In this paper, we present development of the “Google Earth Dashboard ” (GE...
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Abstract: Disaster management is capturing increasing attention from researchers across many disc... more Abstract: Disaster management is capturing increasing attention from researchers across many disciplines (geography, sociology, operations research, and a range of other social, environmental, and information sciences). In geographic information science, important research efforts are targeting better collection and analysis of geospatial data on disasters, representation of risks and vulnerability, integration of physical processes and social models to enhance the prediction of hazard impact, and a range of information access, analysis, and problem-solving tools that support individual and joint work across the disaster management process. This chapter provides a framework for the design of geocollaborative environments. These environments are intended to support disaster management activities, through group interaction and collaboration that is enabled by access to relevant geographic information through geographic information technologies designed to support group as well as indi...
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Information Systems for Emergency Management
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: This report documents research in the GeoVISTA Center at The Pennsylvania State University (PSU... more : This report documents research in the GeoVISTA Center at The Pennsylvania State University (PSU) to develop visual analytics tools for leveraging data from microblogs, with a specific focus on Twitter as a primary data source. The tools are implemented within SensePlace2, a web-based application that provides a visual interface and display methods for query and exploration of large repositories of microblog data. Our emphasis is on revealing the where, when, what, and who components of microblog data to support situational awareness in natural disasters and other emergency events. This report summarizes project research to date and focuses specifically on outcomes for Task 4 in this research, which is directed to adding capabilities for message-focused queries and visual analysis of the query results. This augments the existing place-, tweeter-, and social-focused methods and tools completed for Tasks 1, 2 & 3, respectively. The report begins with a brief overview of work complete...
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In this paper, we introduce a web-enabled geovisual analytics approach to leveraging Twitter in s... more In this paper, we introduce a web-enabled geovisual analytics approach to leveraging Twitter in support of crisis management. The approach is implemented in a map-based, interactive web application that enables information foraging and sensemaking using “tweet” indexing and display based on place, time, and concept characteristics. In this paper, we outline the motivation for the research, review selected background briefly, describe the web application we have designed and implemented, and discuss our planned next steps.
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Crisis situations generate tens of millions of social media reports, many of which contain refere... more Crisis situations generate tens of millions of social media reports, many of which contain references to geographic features and locations. Contemporary systems are now capable of mining and visualizing these location references in social media reports, but we have yet to develop a deep understanding of what end-users will expect to do with this information when attempting to achieve situational awareness. To explore this problem, we have conducted a utility and usability analysis of SensePlace2, a geovisual analytics tool designed to explore geospatial information found in Tweets. Eight users completed a task analysis and survey study using SensePlace2. Our findings reveal user expectations and key paths for solving usability and utility issues to inform the design of future visual analytics systems that incorporate geographic information from social media.
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International Journal of Information Systems for Crisis Response and Management
Local social media users share and access critical information before, during, and after emergenc... more Local social media users share and access critical information before, during, and after emergencies. However, existing methods can identify local social media users only after an emergency has occurred, and only then discover a small proportion of users sharing information in a geographic area. To address these limitations, we introduce the method of Social Triangulation to identify local social media users who access community information before an emergency in order to develop emergency communications strategies that contribute to community resilience. Social Triangulation identifies local users vis-à-vis the community organizations they curate within their social networks and, as a result, helps reveal the information infrastructure of a community. Consequently, social triangulation can inform emergency communications planning by identifying “filter bubbles” among social media users loosely embedded in an information infrastructure, as well as community influencers who are well-...
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Transactions in GIS
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Papers by Scott Pezanowski