WorldCat Identities

Resch, Bernd

Overview
Works: 21 works in 28 publications in 1 language and 334 library holdings
Genres: Conference papers and proceedings 
Roles: Author, Editor, Other
Classifications: G70.212, 910.01
Publication Timeline
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Most widely held works by Bernd Resch
GEOProcessing 2010 : proceedings, the Second International Conference on Advanced Geographic Information Systems, Applications, and Services : 10-16 February, 2010, St. Maarten, Netherlands Antilles by Applications, and Services International Conference on Advanced Geographic Information Systems( )

2 editions published in 2010 in English and held by 273 WorldCat member libraries worldwide

Live geography : standardised geo-sensor webs for real-time monitoring in urban environments by Bernd Resch( Book )

3 editions published in 2012 in English and held by 28 WorldCat member libraries worldwide

The digital layer: how innovative firms relate on the web by Miriam Krüger( Book )

2 editions published in 2020 in English and held by 4 WorldCat member libraries worldwide

In this paper, we introduce the concept of a Digital Layer to empirically investigate inter-firm relations at any geographical scale of analysis. The Digital Layer is created from large-scale, structured web scraping of firm websites, their textual content and the hyperlinks among them. Using text-based machine learning models, we show that this Digital Layer can be used to derive meaningful characteristics for the over seven million firm-to-firm relations, which we analyze in this case study of 500,000 firms based in Germany. Among others, we explore three dimensions of relational proximity: (1) Cognitive proximity is measured by the similarity between firms’ website texts. (2) Organizational proximity is measured by classifying the nature of the firms’ relationships (business vs. non-business) using a text-based machine learning classification model. (3) Geographical proximity is calculated using the exact geographic location of the firms. Finally, we use these variables to explore the differences between innovative and non-innovative firms with regard to their location and relations within the Digital Layer. The firm-level innovation indicators in this study come from traditional sources (survey and patent data) and from a novel deep learning-based approach that harnesses firm website texts. We find that, after controlling for a range of firm-level characteristics, innovative firms compared to non-innovative firms maintain more numerous relationships and that their partners are more innovative than partners of non-innovative firms. Innovative firms are located in dense areas and still maintain relationships that are geographically farther away. Their partners share a common knowledge base and their relationships are business-focused. We conclude that the Digital Layer is a suitable and highly cost-efficient method to conduct large-scale analyses of firm networks that are not constrained to specific sectors, regions, or a particular geographical level of analysis. As such, our approach complements other relational datasets like patents or survey data nicely
Analysing and predicting micro-location patterns of software firms by Jan Kinne( )

3 editions published in 2017 in English and held by 3 WorldCat member libraries worldwide

While the effects of non-geographic aggregation on inference are well studied in economics, research on geographic aggregation is rather scarce. This knowledge gap together with the use of aggregated spatial units in previous firm location studies result in a lack of understanding of firm location determinants at the microgeographic level. Suitable data for microgeographic location analysis has become available only recently through the emergence of Volunteered Geographic Information (VGI), especially the OpenStreetMap (OSM) project, and the increasing availability of official (open) geodata. In this paper, we use a comprehensive dataset of three million street-level geocoded firm observations to explore the location pattern of software firms in an Exploratory Spatial Data Analysis (ESDA). Based on the ESDA results, we develop a software firm location prediction model using Poisson regression and OSM data. Our findings demonstrate that the model yields plausible predictions and OSM data is suitable for microgeographic location analysis. Our results also show that non-aggregated data can be used to detect information on location determinants, which are superimposed when aggregated spatial units are analysed, and that some findings of previous firm location studies are not robust at the microgeographic level. However, we also conclude that the lack of high-resolution geodata on socio-economic population characteristics causes systematic prediction errors, especially in cities with diverse and segregated populations
Standardised Geo-Sensor Webs for Integrated Urban Air Quality Monitoring by Bernd Resch( )

2 editions published in 2011 in English and held by 3 WorldCat member libraries worldwide

Standardised Geo-Sensor Webs for Integrated Urban Air Quality Monitoring
Analysing and predicting micro-location patterns of software firms by Jan Kinne( )

1 edition published in 2017 in English and held by 2 WorldCat member libraries worldwide

Beyond Spatial Proximity: Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data by Anna Kovacs-Györi( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Abstract: Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study c
Investigating the Emotional Responses of Individuals to Urban Green Space Using Twitter Data: A Critical Comparison of Three Different Methods of Sentiment Analysis by Lee Chapman( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Abstract: In urban research, Twitter data have the potential to provide additional information about urban citizens, their activities, mobility patterns and emotion. Extracting the sentiment present in tweets is increasingly recognised as a valuable approach to gathering information on the mood, opinion and emotional responses of individuals in a variety of contexts. This article evaluates the potential of deriving emotional responses of individuals while they experience and interact with urban green space. A corpus of over 10,000 tweets relating to 60 urban green spaces in Birmingham, United Kingdom was analysed for positivity, negativity and specific emotions, using manual, semi-automated and automated methods of sentiment analysis and the outputs of each method compared. Similar numbers of tweets were annotated as positive/neutral/negative by all three methods; however, inter-method consistency in tweet assignment between the methods was low. A comparison of all three methods on the same
#London2012 : towards citizen-contributed urban planning through sentiment analysis of Twitter data by Anna Kovacs-Gyori( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Abstract: The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment
Analysing and predicting micro-location patterns of software firms by Jan Kinne( Book )

1 edition published in 2017 in English and held by 2 WorldCat member libraries worldwide

The Digital Layer: how innovative firms relate on the web by Miriam Krüger( )

1 edition published in 2020 in English and held by 2 WorldCat member libraries worldwide

Examining Trade-Offs between Social, Psychological, and Energy Potential of Urban Form by Martin Bielik( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

Deriving Hospital Catchment Areas from Mobile Phone Data by Bernd Resch( )

1 edition published in 2016 in Undetermined and held by 1 WorldCat member library worldwide

Deriving Hospital Catchment Areas from Mobile Phone Data by Bernd Resch( )

1 edition published in 2016 in Undetermined and held by 1 WorldCat member library worldwide

Abundant topological outliers in social media data and their effect on spatial analysis by René Westerholt( )

1 edition published in 2016 in English and held by 1 WorldCat member library worldwide

A Geoprivacy by Design Guideline for Research Campaigns That Use Participatory Sensing Data( )

in English and held by 1 WorldCat member library worldwide

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 guideline with practical relevance, containing the steps that a research campaign should follow to preserve the participants' privacy. We first examine the technical aspects of geoprivacy in the context of participatory sensing data. Then, we propose privacy-preserving steps in four categories, namely, ensuring secure and safe settings, actions prior to the start of a research survey, processing and analysis of collected data, and safe disclosure of datasets and research deliverables
Greenwashing in the US metal industry? a novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining by Sebastian Schmidt( )

1 edition published in 2022 in English and held by 1 WorldCat member library worldwide

This Discussion Paper deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO2), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO2 concentrations is through satellite data from the Sentinel-5P programme, which represents a major advance due to its unprecedented spatial resolution. In this paper, Sentinel-5P remote sensing data was combined with a plant-level firm database to investigate the relationship between the US metal industry and SO2 concentrations using a spatial regression analysis. Additionally, this study considered web text data, classifying companies based on their websites in order to depict their self-portrayal on the topic of sustainability. In doing so, we investigated the topic of greenwashing, i.e. whether or not a positive self-portrayal regarding sustainability is related to lower local SO2 concentrations. Our results indicated a general, positive correlation between the number of employees in the metal industry and local SO2 concentrations. The web-based analysis showed that only 8% of companies in the metal industry could be classified as engaged in sustainability based on their websites. The regression analyses indicated that these self-reported ”sustainable” companies had a weaker effect on local SO2 concentrations compared to their ”non-sustainable” counterparts, which we interpreted as an indication of the absence of general greenwashing in the US metal industry. However, the large share of firms without a website and lack of specificity of the text classification model were limitations to our methodology
Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

ABSTRACT: 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 information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection. Our results demonstrate that earthquake footprints can be reliably and accurately identified in our use case. More, a number of relevant semantic topics can be automatically identified without a priori knowledge, revealing clearly differing temporal and spatial signatures. Furthermore, we are able to generate a damage map that indicates where significant losses have occurred. The validation of our results using statistical measures, complemented by the official earthquake footprint by US Geological Survey and the results of the HAZUS loss model, shows that our approach produces valid and reliable outputs. Thus, our approach may improve current disaster management procedures through generating a new and unseen information layer in near real time
Polish-German-Austrian cross-border cooperation : in the field of logistical support for rescue operations in natural disaster situations( Book )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

 
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Live geography : standardised geo-sensor webs for real-time monitoring in urban environments
Covers
Standardised Geo-Sensor Webs for Integrated Urban Air Quality Monitoring
Alternative Names
Bernd Resch wetenschapper

Languages
English (25)