Logistics clusters, including inter-firm relations through community detection

This paper studies clusters in the logistics sector. Like traditional cluster research, indicators of concentration to detect co-location of employment are calculated. However, this approach is enhanced by including a quantitative analysis of the inter-firm relations between logistics companies through the use of a community detection algorithm on a microeconomic dataset of buyer-supplier relations. Combining both results in a typology of logistics clusters. Next to the big clusters characterized by employment concentration and many internal and external relations, spill-over and polycentric clusters are identified. This approach seems promising to detect in future research clusters in other sectors and place

City-Life No More? Young Adults' Disrupted Urban Experiences and Their Digital Mediation under Covid-19

The Covid-19 pandemic temporarily, yet significantly, reshuffled the position, functions and (mediated) constructions of cities and urban places. The national lockdown, implemented by Austria on 16 March 2020, turned cities overnight from centres of hybrid cultural, economic, social, political life and power to places where urban life(styles) were put on hold. This article begins by presenting first key results of a longitudinal study with young adults studying in educational institutions in the state of Tyrol through the harshest country-wide lockdown measures and their gradual withdrawal. We analyse how participants coped with the disruption of their urban lives and lifestyles and the strategies they employed to compensate. We highlight three main insights. First, participants who had originally migrated to the city from their (often rural) hometowns largely returned to join their families. From there, no longer being an object of physical experience, the city became a digitally imagined, constructed and communicated place, reiterating public discourses that condemned the city as a place where lockdown measures were breached, and the virus spread unchecked. Second, where possible and adapted to the affordances of digital media, students shifted their previous lifestyles to digital space as well as created innovative ways of socialising digitally—thus producing alternative digital forms of urban lifestyles and digitally-mediated urban experiences. Third, during the lockdown period, the importance, use intensity as well as a variety of digital media peaked tremendously. This trend, however, was short-lived as yearned-for offline sociability largely returned to the city once measures were relaxed, leaving those in rural homes detached from their urban peers

Geospatial data analysis in Russia’s geoweb

The chapter examines the role of geospatial data in Russia’s online ecosystem. Facilitated by the rise of geographic information systems and user-generated content, the distribution of geospatial data has blurred the line between physical spaces and their virtual representations. The chapter discusses different sources of these data available for Digital Russian Studies (e.g., social data and crowdsourced databases) together with the novel techniques for extracting geolocation from various data formats (e.g., textual documents and images). It also scrutinizes different ways of using these data, varying from mapping the spatial distribution of social and political phenomena to investigating the use of geotag data for cultural practices’ digitization to exploring the use of geoweb for narrating individual and collective identities online

Unfamiliar face recognition : Security, surveillance and smartphones

A person’s ability to recognize familiar faces across a wide range of viewing conditions is one of the most impressive facets of human cognition. As shown in Figure 1, it is easy to conclude, for a known individual, that each image in the set shows the same person (British Prime Minister David Cameron), despite a wide range of variation in viewing angle, physical appearance, camera and lighting. In fact, familiar face recognition performance is often at or near ceiling level, even when the images are of poor quality [1] or artificially distorted. [2] At first glance, the aptitude for familiar face recognition may suggest a similar level of expertise for the recognition of unfamiliar faces, thus the reliance on face-to-photo ID for identity verification. [3] This is not the case, as recent research shows people are surprisingly poor at recognizing new instances of an unfamiliar person. The poor recognition of unfamiliar faces is a concern for the United States. Many preliminary screenings involve facial recognition by security agents. In order for this method to be effective, more robust training for security agents needs to be established. The Department of Defense utilizes facial and iris recognition technologies in order to eliminate human error in identifying persons of interest during surveillance operations. [4] DoD guidelines should be implemented by security agent guidance programs to ensure best practices in identification of potential threats

Human dynamics in the age of big data: a theory-data-driven approach

The revolution of information and communication technology (ICT) in the past two decades have transformed the world and people’s lives with the ways that knowledge is produced. With the advancements in location-aware technologies, a large volume of data so-called “big data” is now available through various sources to explore the world. This dissertation examines the potential use of such data in understanding human dynamics by focusing on both theory- and data-driven approaches. Specifically, human dynamics represented by communication and activities is linked to geographic concepts of space and place through social media data to set a research platform for effective use of social media as an information system. Three case studies covering these conceptual linkages are presented to (1) identify communication patterns on social media; (2) identify spatial patterns of activities in urban areas and detect events; and (3) explore urban mobility patterns. The first case study examines the use of and communication dynamics on Twitter during Hurricane Sandy utilizing survey and data analytics techniques. Twitter was identified as a valuable source of disaster-related information. Additionally, the results shed lights on the most significant information that can be derived from Twitter during disasters and the need for establishing bi-directional communications during such events to achieve an effective communication. The second case study examines the potential of Twitter in identifying activities and events and exploring movements during Hurricane Sandy utilizing both time-geographic information and qualitative social media text data. The study provides insights for enhancing situational awareness during natural disasters. The third case study examines the potential of Twitter in modeling commuting trip distribution in New York City. By integrating both traditional and social media data and utilizing machine learning techniques, the study identified Twitter as a valuable source for transportation modeling. Despite the limitations of social media such as the accuracy issue, there is tremendous opportunity for geographers to enrich their understanding of human dynamics in the world. However, we will need new research frameworks, which integrate geographic concepts with information systems theories to theorize the process. Furthermore, integrating various data sources is the key to future research and will need new computational approaches. Addressing these computational challenges, therefore, will be a crucial step to extend the frontier of big data knowledge from a geographic perspective. KEYWORDS: Big data, social media, Twitter, human dynamics, VGI, natural disasters, Hurricane Sandy, transportation modeling, machine learning, situational awareness, NYC, GI

Public participation in the Geoweb era: Geosocial media use in local government

Advances in spatially enabled information and communication technologies (ICTs) have provided governments with the potential to enhance public participation and to collaborate with citizens. This dissertation critically assesses this potential and identifies the opportunities and challenges for local governments to embark on emerging geo-enabled practices. This dissertation first proposes a new typology for classifying geo-enabled practices related to public participation (termed here as geo-participation) and demonstrates the emerging opportunities presented by geo-participation to improve government-citizen collaboration and government operations. This dissertation then provides in-depth examinations of geosocial media as an exemplar geo-participation practice. The first empirical study assesses the potential of repurposing geosocial media data to gauge public opinions. The study suggests that geosocial media can help identify geographies of public perceptions concerning public facilities and services and have the potential to complement other methods of gauging public sentiment. The second empirical study assesses the usefulness of geosocial media for sharing non-emergency issues and identifies an important opportunity of enabling citizen collaboration for reporting and sharing non-emergency issues. Altogether, this dissertation makes several conceptual, empirical, and practical contributions to local government adoption of geo-participation. Conceptually, the proposed typology lays the foundation for researching and implementing geo-participation practices. Empirically, this dissertation tells a story of opportunities and challenges that sheds light on how local governments may adopt geosocial media to solicit citizen input and enable new forms of government-citizen interaction. Practically, this dissertation develops a tool for processing text-based citizen input and models of implementing geosocial media reporting that can help local government develop proper strategies of adopting geosocial media