The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Knowing Your Population: Privacy-Sensitive Mining of Massive Data

Location and mobility patterns of individuals are important to environmental planning, societal resilience, public health, and a host of commercial applications. Mining telecommunication traffic and transactions data for such purposes is controversial, in particular raising issues of privacy. However, our hypothesis is that privacy-sensitive uses are possible and often beneficial enough to warrant considerable research and development efforts. Our work contends that peoples behavior can yield patterns of both significant commercial, and research, value. For such purposes, methods and algorithms for mining telecommunication data to extract commonly used routes and locations, articulated through time-geographical constructs, are described in a case study within the area of transportation planning and analysis. From the outset, these were designed to balance the privacy of subscribers and the added value of mobility patterns derived from their mobile communication traffic and transactions data. Our work directly contrasts the current, commonly held notion that value can only be added to services by directly monitoring the behavior of individuals, such as in current attempts at location-based services. We position our work within relevant legal frameworks for privacy and data protection, and show that our methods comply with such requirements and also follow best-practice

Combining Multi-Criteria Decision Making (MCDM) Methods with Building Information Modelling (BIM): A Review

Integrating building information to support decision-making has been a key challenge in the Architecture, Engineering, and Construction (AEC) industry. The synergy of Building Information Modelling (BIM) and Multi-Criteria Decision Making (MCDM) is expected to improve information integration and decision-making. The aim of this paper is to identify strategies to improve the synergy between MCDM and BIM. From the earliest literature (2009) to the present, this study examines 45 articles combining MCDM with BIM. We find that the five major application domains are sustainability, retrofit, supplier selection, safety, and constructability. Five established strategies for improving the synergy between MCDM and BIM were discussed and can be used as a benchmark for evaluating the application of decision techniques in practice. This study points out gaps of combining MCDM and BIM in the current literature. It also sheds new light into combining MCDM with BIM for practitioners, as to promote integrated decision-making

Map Room to Data and GIS Services: Five University Libraries Evolving to Meet Campus Needs and Changing Technologies

Programs for geospatial support at academic libraries have evolved over the past decade in response to changing campus needs and developing technologies. Geospatial applications have matured tremendously in this time, emerging from specialty tools to become broadly used across numerous disciplines. At many universities, the library has served as a central resource allowing students and faculty across academic departments access to GIS resources. Today, as many academic libraries evaluate their spaces and services, GIS and data services are central in discussions on how to further engage with patrons and meet increasingly diverse researcher needs. As library programs evolve to support increasingly technical data and GIS needs, many universities are faced with similar challenges and opportunities. To explore these themes, data and GIS services librarians and GIS specialists from five universities—the University of North Carolina at Chapel Hill, Texas A&M, New York University, North Carolina State University, and California Polytechnic State University—with different models of library geospatial and data support, describe their programs to help identify common services, as well as unique challenges, opportunities, and future plans

Development of a Methodology for Evaluating and Anticipating Improvised Explosive Device Threat Activity Using a Fault Tree Based Process

This document is a redacted version of the original dissertation titled \u27Development of a Methodology for Evaluating and Anticipating Improvised Explosive Device Threat Activity Using a Fault Tree Based Process.\u27 To allow for publication, information was removed which was considered sensitive in nature or which could be used by those who employ the Improvised Explosive Device, to negate any advantage gained by this research. The complete un-redacted dissertation is available (with proper vetting) to those whishing to further develop the concepts outlined in this document. Those interested in obtaining access to the complete document should contact the Joint IED Defeat Organization (JIEDDO). To date there is little published evidence to believe that a sufficient IED threat prediction capability has been developed. Most of the countermeasures seen on the battlefield today are reactive in nature designed to neutralize the effects of a device before it causes injury to military and civilian personnel. These countermeasures have meet with varying levels of success. An efficient threat prediction capability will significantly increase the ability of military forces to eliminate the threat associated with the IED. The lack of an accurate threat prediction capability is a possible result of not having identified all of the variables or the variable relationships associated with IED placement. This research analyzes the variables associated with an IED incident and develops an IED threat prediction process using the Fault Tree model. This dissertation also explores the use of visualization software to determine their suitability in C-IED operations. Furthermore, the application of a Fault Tree based process as a decision support tool for use by decision makers involved in C-IED operations is analyzed. This research is conducted in three phases with the first phase dedicated to the development of a Fault Tree diagram representing an IED incident. During this phase a complete Fault Tree is constructed identifying, sequencing, and establishing relationships between all variable associated with a successful IED attack against a military vehicle operating on a road. The second phase outlines the development of a complete process intended to serve as an operational guide for those attempting to employ the concepts addressed. To ensure a more precise understanding of the required procedures, a theoretical case study was used to articulate and demonstrate the requisite activities. Through this research, events were identified as required for an effective attack to take place. Through the integration of the Fault Tree, probability information and visualization assets a threat prediction capability is demonstrated. The ability to predict IED activity will provide military personnel a distinct advantage in defeating the IED threat and directly contribute to the increased safety of military and civilian personnel living and operating in an IED environment

BIM-based decision support for building condition assessment

Building condition assessment requires the integration of various types of data such as building characteristics, the properties of elements/systems and maintenance records. Previous research has focused on identifying these data and developing a building condition risk assessment model based on Bayesian networks (BN). However, due to interoperability issues, the process of transferring the data is performed manually, which requires considerable time and effort. To address this issue, this paper presents a data model to integrate the building condition risk assessment model into BIM. The proposed data model is implemented in existing software as a case study and tested and evaluated on three scenarios. Addressing interoperability will leverage the BIM tool as a data re- pository to automate the data transfer process and improve its consistency and reliability. It will also enable BIM to be a more effective tool for building condition and causality analysis visualization.This work was supported by Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) from Generalitat de Catalunya under Grant 2019 FI_B00064Postprint (published version

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