Bernd Resch
Biographic Data
| ID | 1855447 |
|---|---|
| NAME | Bernd Resch |
| GIVEN NAMES | Bernd |
| FAMILY NAME | Resch |
| SIGNATURE | RESCH B |
| AFFILIATIONS | Harvard University |
| ORCID | 0000-0002-2233-6926 |
| VERIFIED | Yes |
| TOTAL WORKS | 30 |
| TOTAL CITATIONS | 37 |
| AUTHOR COUNT | 30 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 3 |
Understanding the influence of urban characteristics on cyclists’ stress measured through wearable sensors: A quantitative open data approach
The complexity of environmental factors experienced in active mobility presents unique challenges for the design of sustainable urban mobility environments. Particularly, active mobility modes are frequently associated with increased stress and unsafety. Most studies apply qualitative assessment methods for evaluating cyclists’ stress levels and subjective cycling experiences. Quantitative approaches are either limited in sample size, or conducte…
Bluesky as a social media data source for disaster management: Investigating Spatio-Temporal, Semantic and Emotional Patterns for Floods and Wildfires
Social media has become a key data source for near-real-time disaster monitoring and response, with Twitter playing a central role for over a decade. However, recent Application Programming Interface (API) changes on Twitter (now: X) have restricted academic data access, creating an urgent need to identify viable alternatives. This study investigates the suitability of the decentralised social media platform Bluesky for disaster-related geo-socia…
How politics affect pandemic forecasting: Spatio-temporal early warning capabilities of different geo-social media topics in the context of state-level political leaning
Objectives: Due to political polarization, adherence to public health measures varied across US states during the COVID-19 pandemic. Although social media posts have been shown effective in anticipating COVID-19 surges, the impact of political leaning on the effectiveness of different topics for early warning remains mostly unexplored. Our study examines the spatio-temporal early warning potential of different geo-social media topics across repub…
An Aspect-Based Emotion Analysis Approach on Wildfire-Related Geo-Social Media Data—A Case Study of the 2020 California Wildfires
Natural disasters like wildfires pose significant threats to communities, which necessitates timely and effective disaster response strategies. While Aspect-based Sentiment Analysis (ABSA) has been widely used to extract sentiment-related information at the sub-sentence level, the corresponding field of Aspect-based Emotion Analysis (ABEA) remains underexplored due to dataset limitations and the increased complexity of emotion classification. In …
Legal and ethical considerations for demand-driven data collection and AI-based analysis in flood response
During a disaster, the timely provision of customised and relevant data is of utmost importance. In the case of floods, data from remote sensing (satellite-based or airborne) is often used, but in recent years data from social media platforms has also been increasingly utilised. Focusing on these data sources, this study provides an in-depth assessment of requirements by emergency responders. Furthermore, the paper sheds light on the legal and et…
Long-term validation of inner-urban mobility metrics derived from Twitter/X
Urban mobility analysis using Twitter as a proxy has gained significant attention in various application fields; however, long-term validation studies are scarce. This paper addresses this gap by assessing the reliability of Twitter data for modeling inner-urban mobility dynamics over a 27-month period in the metropolitan area of Rio de Janeiro, Brazil. The evaluation involves the validation of Twitter-derived mobility estimates at both temporal …
Urban Aedes aegypti suitability indicators: A study in Rio de Janeiro, Brazil
More than just Tweets: The Potential of Alternative Geo-Social Media Data for Disaster Management
Natural disasters are increasingly prevalent worldwide, necessitating the utilisation of diverse datasets for effective disaster response. While geo-social media data represents a valuable resource in this context, the recent restrictions to Twitter data have significantly impacted its availability for disaster research. Alternative social media platforms to Twitter remain underexplored, leading to limited understanding of their potential. To add…
Contextual Enrichment of Crowds from Mobile Phone Data through Multimodal Geo-Social Media Analysis
The widespread use of mobile phones and social media platforms provides valuable information about users’ behavior and activities. Mobile phone data are rich on positional information, but lack semantic context. Conversely, geo-social media data reveal users’ opinions and activities, but are rather sparse in space and time. In the context of emergency management, both data types have been considered separately. To exploit their complementary natu…
Art in the city reduces the feeling of anxiety, stress, and negative mood: A field study examining the impact of artistic intervention in urban public space on well-being
Interaction with artistic intervention in urban street environment reduced subjective feelings of anxiety, stress, and negative mood. • However, this trend was not present in subjective positive mood as well as in physiological well-being measure (i.e., moment of stress). • Subjective experience in terms high restorativeness of the testing location, high aesthetic quality of the intervention (e.g., beauty, meaningfulness), and overall positive ex…
The Spatial Structures in the Austrian Covid-19 Protest Movement: A Virtual and Geospatial User Network Analysis
The emergence of the COVID-19 pandemic, followed by policy measures to combat the virus, evoked public protest movements world-wide. These movements were formed not only in the virtual world but also through local protest gatherings. In contrast to previous research that studied movements in the virtual world through digital network analysis, this study recognizes the importance of the spatial dimension of social movements through local interacti…
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…
AllforJan: How Twitter Users in Europe Reacted to the Murder of Ján Kuciak—Revealing Spatiotemporal Patterns through Sentiment Analysis and Topic Modeling
Social media platforms such as Twitter are considered a new mediator of collective action, in which various forms of civil movements unite around public posts, often using a common hashtag, thereby strengthening the movements. After 26 February 2018, the #AllforJan hashtag spread across the web when Ján Kuciak, a young journalist investigating corruption in Slovakia, and his fiancée were killed. The murder caused moral shock and mass protests in …
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…
Commuter Mobility Patterns in Social Media: Correlating Twitter and Lodes Data
The Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) are an important city planning resource in the USA. However, curating these statistics is resource-intensive, and their accuracy deteriorates when changes in population and urban structures lead to shifts in commuter patterns. Our study area is the San Francisco Bay area, and it has seen rapid population growth over the past years, which makes frequent u…
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 …
Applying Spatial Video Geonarratives and Physiological Measurements to Explore Perceived Safety in Baton Rouge, Louisiana
Spatial crime analysis, together with perceived (crime) safety analysis have tremendously benefitted from Geographic Information Science (GISc) and the application of geospatial technology. This research study discusses a novel methodological approach to document the use of emerging geospatial technologies to explore perceived urban safety from the lenses of fear of crime or crime perception in the city of Baton Rouge, USA. The mixed techniques i…
The role of user context in the design of mobile map applications
Mobile map applications are typically used by a broad range of users. Users can be diverse in their context attributes (e.g. map use experience, activities during map use), and several previous user experience (UX) studies have focused on understanding how some contextual factors influence the UX for designing maps that satisfy users' needs. A need for research remains to evaluate the relationship between user context, UX, and variants of mobile …
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…
Developing a Citizen Social Science approach to understand urban stress and promote wellbeing in urban communities
This paper sets out the future potential and challenges for developing an interdisciplinary, mixed-method Citizen Social Science approach to researching urban emotions. It focuses on urban stress, which is increasingly noted as a global mental health challenge facing both urbanised and rapidly urbanising societies. The paper reviews the existing use of mobile psychophysiological or biosensing within urban environments—as means of ‘capturing’ the …
Urban Emotion Sensing Beyond ‘Affective Capture’: Advancing Critical Interdisciplinary Methods
The use of mobile sensor methodologies in urban analytics to study 'urban emotions' is currently outpacing the science required to rigorously interpret the data generated. Interdisciplinary research on 'urban stress' could help inform urban wellbeing policies relating to healthier commuting and alleviation of work stress. The purpose of this paper is to address-through methodological experimentation-ethical, political and conceptual issues identi…
An Interdisciplinary Mixed-Methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability
Human-centered approaches are of particular importance when analyzing urban spaces in technology-driven fields, because understanding how people perceive and react to their environments depends on several dynamic and static factors, such as traffic volume, noise, safety, urban configuration, and greenness. Analyzing and interpreting emotions against the background of environmental information can provide insights into the spatial and temporal pro…
From Urban Stress to Neurourbanism: How Should We Research City Well-Being
Urbanicity has long been associated with stress, anxiety, and mental disorders. A new field of neurourbanism addresses these issues, applying neuroscience laboratory methods to tackle global urban problems and promote happier and healthier cities. Exploratory studies have trialed psychophysiological measurement beyond laboratories, capitalizing on the availability of biosensing technologies to capture geo-located physiological markers of emotiona…
Composition of place: Towards a compositional view of functional space
A long-standing question in GIScience is whether geographic information systems (GIS) facilitates an adequate quantifiable representation of the concept of place. Considering the difficulties of quantifying elusive concepts related to place, several researchers focus on more tangible dimensions of the human understanding of place. The most common approaches are semantic enrichment of spatial information and holistic conceptualization of the notio…
From Urban Stress to Neurourbanism: How Should We Research City Well-Being
Urbanicity has long been associated with stress, anxiety, and mental disorders. A new field of neurourbanism addresses these issues, applying neuroscience laboratory methods to tackle global urban problems and promote happier and healthier cities. Exploratory studies have trialed psychophysiological measurement beyond laboratories, capitalizing on the availability of biosensing technologies to capture geo-located physiological markers of emotiona…
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…
Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research
Inference attacks and protection measures are two sides of the same coin. Although the former aims to reveal information while the latter aims to hide it, they both increase awareness regarding the risks and threats from social media apps. On the one hand, inference attack studies explore the types of personal information that can be revealed and the methods used to extract it. An additional risk is that geosocial media data are collected massive…
A Geoprivacy by Design Guideline for Research Campaigns That Use Participatory Sensing Data
Participatory sensing applications collect personal data of monitored subjects along with their spatial or spatiotemporal stamps. The attributes of a monitored subject can be private, sensitive, or confidential information. Also, the spatial or spatiotemporal attributes are prone to inferential disclosure of private information. Although there is extensive problem-oriented literature on geoinformation disclosure, our work provides a clear guideli…
Web-based 4D visualization of marine geo-data using WebGL
For marine logistics and maintenance of extensive and expensive marine engineering projects in the coastal zone, it is essential that institutions provide the public with relevant information in an easily understandable yet comprehensive manner over the web. A perennial challenge, however, has been the development of spatio-temporal (four-dimensional (4D)) geo-visualization algorithms to enable the integration of time-varying geo-information in m…
More than just Tweets: The Potential of Alternative Geo-Social Media Data for Disaster Management
Natural disasters are increasingly prevalent worldwide, necessitating the utilisation of diverse datasets for effective disaster response. While geo-social media data represents a valuable resource in this context, the recent restrictions to Twitter data have significantly impacted its availability for disaster research. Alternative social media platforms to Twitter remain underexplored, leading to limited understanding of their potential. To add…
The role of user context in the design of mobile map applications
Mobile map applications are typically used by a broad range of users. Users can be diverse in their context attributes (e.g. map use experience, activities during map use), and several previous user experience (UX) studies have focused on understanding how some contextual factors influence the UX for designing maps that satisfy users' needs. A need for research remains to evaluate the relationship between user context, UX, and variants of mobile …
Performing Social Media: Artistic Approaches to Analyzing Big Data
Using several performances undertaken as part of a public art residency, this article discusses creative and playful approaches to social media data. Four pieces are discussed to demonstrate how social media posts can be reappropriated and represented. In this project, geotagged Flickr images were used as a foundation for arts performances to give new meanings to the spaces where the social media post was tagged. This demonstrates a way to bridge…
Web-based 4D visualization of marine geo-data using WebGL
For marine logistics and maintenance of extensive and expensive marine engineering projects in the coastal zone, it is essential that institutions provide the public with relevant information in an easily understandable yet comprehensive manner over the web. A perennial challenge, however, has been the development of spatio-temporal (four-dimensional (4D)) geo-visualization algorithms to enable the integration of time-varying geo-information in m…
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…
Performing Social Media: Artistic Approaches to Analyzing Big Data
Using several performances undertaken as part of a public art residency, this article discusses creative and playful approaches to social media data. Four pieces are discussed to demonstrate how social media posts can be reappropriated and represented. In this project, geotagged Flickr images were used as a foundation for arts performances to give new meanings to the spaces where the social media post was tagged. This demonstrates a way to bridge…
A Geoprivacy by Design Guideline for Research Campaigns That Use Participatory Sensing Data
Participatory sensing applications collect personal data of monitored subjects along with their spatial or spatiotemporal stamps. The attributes of a monitored subject can be private, sensitive, or confidential information. Also, the spatial or spatiotemporal attributes are prone to inferential disclosure of private information. Although there is extensive problem-oriented literature on geoinformation disclosure, our work provides a clear guideli…
Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research
Inference attacks and protection measures are two sides of the same coin. Although the former aims to reveal information while the latter aims to hide it, they both increase awareness regarding the risks and threats from social media apps. On the one hand, inference attack studies explore the types of personal information that can be revealed and the methods used to extract it. An additional risk is that geosocial media data are collected massive…
Composition of place: Towards a compositional view of functional space
A long-standing question in GIScience is whether geographic information systems (GIS) facilitates an adequate quantifiable representation of the concept of place. Considering the difficulties of quantifying elusive concepts related to place, several researchers focus on more tangible dimensions of the human understanding of place. The most common approaches are semantic enrichment of spatial information and holistic conceptualization of the notio…
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…
Developing a Citizen Social Science approach to understand urban stress and promote wellbeing in urban communities
This paper sets out the future potential and challenges for developing an interdisciplinary, mixed-method Citizen Social Science approach to researching urban emotions. It focuses on urban stress, which is increasingly noted as a global mental health challenge facing both urbanised and rapidly urbanising societies. The paper reviews the existing use of mobile psychophysiological or biosensing within urban environments—as means of ‘capturing’ the …
Urban Emotion Sensing Beyond ‘Affective Capture’: Advancing Critical Interdisciplinary Methods
The use of mobile sensor methodologies in urban analytics to study 'urban emotions' is currently outpacing the science required to rigorously interpret the data generated. Interdisciplinary research on 'urban stress' could help inform urban wellbeing policies relating to healthier commuting and alleviation of work stress. The purpose of this paper is to address-through methodological experimentation-ethical, political and conceptual issues identi…
An Interdisciplinary Mixed-Methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability
Human-centered approaches are of particular importance when analyzing urban spaces in technology-driven fields, because understanding how people perceive and react to their environments depends on several dynamic and static factors, such as traffic volume, noise, safety, urban configuration, and greenness. Analyzing and interpreting emotions against the background of environmental information can provide insights into the spatial and temporal pro…
From Urban Stress to Neurourbanism: How Should We Research City Well-Being
Urbanicity has long been associated with stress, anxiety, and mental disorders. A new field of neurourbanism addresses these issues, applying neuroscience laboratory methods to tackle global urban problems and promote happier and healthier cities. Exploratory studies have trialed psychophysiological measurement beyond laboratories, capitalizing on the availability of biosensing technologies to capture geo-located physiological markers of emotiona…
AllforJan: How Twitter Users in Europe Reacted to the Murder of Ján Kuciak—Revealing Spatiotemporal Patterns through Sentiment Analysis and Topic Modeling
Social media platforms such as Twitter are considered a new mediator of collective action, in which various forms of civil movements unite around public posts, often using a common hashtag, thereby strengthening the movements. After 26 February 2018, the #AllforJan hashtag spread across the web when Ján Kuciak, a young journalist investigating corruption in Slovakia, and his fiancée were killed. The murder caused moral shock and mass protests in …
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…
Commuter Mobility Patterns in Social Media: Correlating Twitter and Lodes Data
The Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) are an important city planning resource in the USA. However, curating these statistics is resource-intensive, and their accuracy deteriorates when changes in population and urban structures lead to shifts in commuter patterns. Our study area is the San Francisco Bay area, and it has seen rapid population growth over the past years, which makes frequent u…
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 …
Applying Spatial Video Geonarratives and Physiological Measurements to Explore Perceived Safety in Baton Rouge, Louisiana
Spatial crime analysis, together with perceived (crime) safety analysis have tremendously benefitted from Geographic Information Science (GISc) and the application of geospatial technology. This research study discusses a novel methodological approach to document the use of emerging geospatial technologies to explore perceived urban safety from the lenses of fear of crime or crime perception in the city of Baton Rouge, USA. The mixed techniques i…
The role of user context in the design of mobile map applications
Mobile map applications are typically used by a broad range of users. Users can be diverse in their context attributes (e.g. map use experience, activities during map use), and several previous user experience (UX) studies have focused on understanding how some contextual factors influence the UX for designing maps that satisfy users' needs. A need for research remains to evaluate the relationship between user context, UX, and variants of mobile …
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…
Contextual Enrichment of Crowds from Mobile Phone Data through Multimodal Geo-Social Media Analysis
The widespread use of mobile phones and social media platforms provides valuable information about users’ behavior and activities. Mobile phone data are rich on positional information, but lack semantic context. Conversely, geo-social media data reveal users’ opinions and activities, but are rather sparse in space and time. In the context of emergency management, both data types have been considered separately. To exploit their complementary natu…
Art in the city reduces the feeling of anxiety, stress, and negative mood: A field study examining the impact of artistic intervention in urban public space on well-being
Interaction with artistic intervention in urban street environment reduced subjective feelings of anxiety, stress, and negative mood. • However, this trend was not present in subjective positive mood as well as in physiological well-being measure (i.e., moment of stress). • Subjective experience in terms high restorativeness of the testing location, high aesthetic quality of the intervention (e.g., beauty, meaningfulness), and overall positive ex…
The Spatial Structures in the Austrian Covid-19 Protest Movement: A Virtual and Geospatial User Network Analysis
The emergence of the COVID-19 pandemic, followed by policy measures to combat the virus, evoked public protest movements world-wide. These movements were formed not only in the virtual world but also through local protest gatherings. In contrast to previous research that studied movements in the virtual world through digital network analysis, this study recognizes the importance of the spatial dimension of social movements through local interacti…
How politics affect pandemic forecasting: Spatio-temporal early warning capabilities of different geo-social media topics in the context of state-level political leaning
Objectives: Due to political polarization, adherence to public health measures varied across US states during the COVID-19 pandemic. Although social media posts have been shown effective in anticipating COVID-19 surges, the impact of political leaning on the effectiveness of different topics for early warning remains mostly unexplored. Our study examines the spatio-temporal early warning potential of different geo-social media topics across repub…
An Aspect-Based Emotion Analysis Approach on Wildfire-Related Geo-Social Media Data—A Case Study of the 2020 California Wildfires
Natural disasters like wildfires pose significant threats to communities, which necessitates timely and effective disaster response strategies. While Aspect-based Sentiment Analysis (ABSA) has been widely used to extract sentiment-related information at the sub-sentence level, the corresponding field of Aspect-based Emotion Analysis (ABEA) remains underexplored due to dataset limitations and the increased complexity of emotion classification. In …
Legal and ethical considerations for demand-driven data collection and AI-based analysis in flood response
During a disaster, the timely provision of customised and relevant data is of utmost importance. In the case of floods, data from remote sensing (satellite-based or airborne) is often used, but in recent years data from social media platforms has also been increasingly utilised. Focusing on these data sources, this study provides an in-depth assessment of requirements by emergency responders. Furthermore, the paper sheds light on the legal and et…
Computer Science (25 works) · Data science (16 works) · Geography (16 works) · World Wide Web (14 works) · Social media (12 works) · Human Mobility and Location-Based Analysis (11 works) · Artificial Intelligence (9 works) · Political science (9 works) · Cartography (8 works) · Geographic Information Systems Studies (8 works)