Clemens Havas
Biographic Data
| ID | 6746183 |
|---|---|
| NAME | Clemens Havas |
| GIVEN NAMES | Clemens |
| FAMILY NAME | Havas |
| SIGNATURE | HAVAS C |
| AFFILIATIONS | University of Salzburg |
| ORCID | 0000-0003-0390-5094 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 6 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 1 |
Adapting mobile map application designs to map use context: A review and call for action on potential future research themes
The increased use of mobile maps in our highly mobile digital culture has resulted in a large variety of map users and map use situations. For mobile map applications that engage a broad user base and feature diverging map usage contexts, one-size-fits-all map interface designs might result in significant usability tradeoffs. To respond to this challenge, changing the map design based on map use context attributes, such as increasing icon sizes f…
Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data
Geo-social media data are widely used as a data source to model populations and processes in a variety of contexts. However, if the data do not adequately represent the population they are drawn from, analysis results will be biased. Unaddressed, these biases may lead to false interpretations and conclusions. In this paper, we propose a generic methodology for investigating the representativeness of geo-social media data for population groups of …
Modeling Patterns in Map Use Contexts and Mobile Map Design Usability
Mobile map applications are increasingly used in various aspects of our lives, leading to an increase in different map use situations and, therefore, map use contexts. Several empirical usability studies have identified how map design is associated with and impacted by selected map use context attributes. This research seeks to expand on these studies and analyzes combinations of map use contexts to identify relevant contextual factors that influ…
Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics
In 2015, within the timespan of only a few months, more than a million people made their way from Turkey to Central Europe in the wake of the Syrian civil war. At the time, public authorities and relief organisations struggled with the admission, transfer, care, and accommodation of refugees due to the information gap about ongoing refugee movements. Therefore, we propose an approach utilising machine learning methods and publicly available data …
Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning
Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven appr…
Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment
Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic informati…
Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment
Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic informati…
Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment
Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic informati…
Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning
Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven appr…
Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data
Geo-social media data are widely used as a data source to model populations and processes in a variety of contexts. However, if the data do not adequately represent the population they are drawn from, analysis results will be biased. Unaddressed, these biases may lead to false interpretations and conclusions. In this paper, we propose a generic methodology for investigating the representativeness of geo-social media data for population groups of …
Modeling Patterns in Map Use Contexts and Mobile Map Design Usability
Mobile map applications are increasingly used in various aspects of our lives, leading to an increase in different map use situations and, therefore, map use contexts. Several empirical usability studies have identified how map design is associated with and impacted by selected map use context attributes. This research seeks to expand on these studies and analyzes combinations of map use contexts to identify relevant contextual factors that influ…
Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics
In 2015, within the timespan of only a few months, more than a million people made their way from Turkey to Central Europe in the wake of the Syrian civil war. At the time, public authorities and relief organisations struggled with the admission, transfer, care, and accommodation of refugees due to the information gap about ongoing refugee movements. Therefore, we propose an approach utilising machine learning methods and publicly available data …
Adapting mobile map application designs to map use context: A review and call for action on potential future research themes
The increased use of mobile maps in our highly mobile digital culture has resulted in a large variety of map users and map use situations. For mobile map applications that engage a broad user base and feature diverging map usage contexts, one-size-fits-all map interface designs might result in significant usability tradeoffs. To respond to this challenge, changing the map design based on map use context attributes, such as increasing icon sizes f…
Computer Science (6 works) · Data science (5 works) · Geography (5 works) · Human Mobility and Location-Based Analysis (5 works) · Artificial Intelligence (4 works) · Geographic Information Systems Studies (4 works) · World Wide Web (4 works) · Social media (3 works) · Cartography (2 works) · Data mining (2 works)