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Refugee movements in recent years have caused enormous challenges for relief organizations and public authorities, but especially for refugees themselves. Organizations which have to allocate their resources to regions where large groups of arrivals are expected struggle to prepare the refugees’ admission, transfer, care and accommodation in time. Events like the refugee movement of 2015/16 in Austria and Germany in the wake of the Syrian civil war have shown that many of these issues are caused by a lack of up-to-date information about logistical requirements. We evaluate various methods to acquire this information that utilize semantic, spatial and temporal features to analyse geo-social network data. A multimodal analysis of these features leads to information about refugee movements across borders and regions. Approaches based on user trajectories and attempts to identify refugees by the language they used showed little promise, whereas using spatiotemporal aggregation and hotspot analysis of keyword-based filtered data allowed us to retrace refugees’ collective movement patterns. Using temporal bins, we were able to detect changes in these patterns caused by external factors such as border closures.

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 updates to LODES or the availability of an appropriate substitute desirable. In this paper, we derive mobility flows from a set of over 40 million georeferenced tweets of the study area and compare them with LODES data. These tweets are publicly available and offer fine spatial and temporal resolution. Based on an exploratory analysis of the Twitter data, we pose research questions addressing different aspects of the integration of LODES and Twitter data. Furthermore, we develop methods for their comparative analysis on different spatial scales: at the county, census tract, census block, and individual street segment level. We thereby show that Twitter data can be used to approximate LODES on the county level and on the street segment level, but it also contains information about non-commuting-related regular travel. Leveraging Twitter’s high temporal resolution, we also show how factors like rush hour times and weekends impact mobility. We discuss the merits and shortcomings of the different methods for use in urban planning and close with directions for future research avenues.

Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to extrapolate to overall traffic demand is indeed justified. This means that expensive and rarely comprehensive surveys are now only needed to capture trip purposes. Regardless of trip purpose (e.g., shopping, regular recreational activities, dropping kids at school), the LODES data is an excellent predictor of overall road segment loads.

Multiple digital data streams forecast COVID-19 activity weeks before traditional epidemiological surveillance., Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.

There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant&rsquo, s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a &ldquo, gold standard&rdquo, of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant&rsquo, s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.

This paper summarizes the research conducted to improve the automatic generalization of man-made water networks for topographic maps by context-dependent pruning (Altena, 2014). The aim of this study was to improve existing thinning methods for map generalization by accounting for landscape types. The results show that it is possible to improve the thinning of water networks by taking into account separate landscape types. On a more abstract level, the study delivers a methodology for the pruning of man-made networks with regard to landscape typology. In addition, it provides a method for evaluating the quality of generalization results for networks. First, previous research on both thinning and evaluation of thinning results is described. Secondly, a selection of existing algorithms are implemented and evaluated by several experiments: identification of landscape variation based on feature morphology and humidity; selection of representative test areas; and geometric network improvement. Results show that the connectivity of the network can be significantly increased. This is important to obtain better generalization results. The final experiments investigated the effectiveness on various landscape types of three different thinning algorithms. The results are evaluated in terms of the amount of thinning, the resemblance of the results to the input data, and the deviation in connectivity. The findings of this research can be used to improve the thinning of artificial networks by applying a customized thinning method to each unique landscape type. In addition, the proposed metrics to measure the effectivity of thinning algorithms – reduction, resemblance and connectivity – have been proved to be appropriate criteria for the comparison of results of alternative thinning approaches.

Even though much research has been conducted on the safety of cycling infrastructures, most previous approaches only make use of traditional and proven methods based upon datasets such as accident statistics, road infrastructure data, or questionnaires. Apart from typical surveys, which are known to face numerous limitations from a psychological and sociological viewpoints, the question of how perceived safety can best be assessed is still widely unexplored. Thus, this paper presents an approach for bio-physiological sensing to identify places in urban environments which are perceived as unsafe by cyclists. Specifically, a number of physiological parameters like ECG, skin conductance, skin temperature and heart rate variability are analysed to identify moments of stress. Together with data gathered through a People as Sensors app, these stress levels can be mapped to specific emotions. This method was tested in a pilot study in Cambridge, MA (USA), which is presented in this paper. Our findings show that our method can identify places with emotional peaks, particularly fear and anger. Although our results can be qualitatively interpreted and used in urban planning, more research is necessary to quantitatively and automatically generate recommendations from the measurements for urban planners.

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 corpus of tweets, using character emojis as an additional quality control, identifies a number of limitations associated with each approach. The results presented have implications for urban planners in terms of the choices available to identify and analyse the sentiment present in tweets, and the importance of choosing the most appropriate method. Future attempts to develop more reliable and accurate algorithms of sentiment analysis are needed and should focus on semi-automated methods.

Provision of urban green spaces is largely unbalanced. Therefore, urban municipalities are concerned to improve citizens' accessibility to vegetated areas. This study proposes a workflow that identifies spatial inequalities in provision of already existing green spaces and suggests locations for potential green space implementations. The workflow was probed in the city of Szeged, Hungary; however its flexibility allows transferability to other urban settings. First, potential vegetation classes were identified based on their heights and spectral behavior, all sourced from very high resolution stereo satellite imagery. Then locations of societal demand for green space provision were identified by using attribute and spatial information from census tracks. By using OpenStreetMap road network, the data analysis will measure distances between population with green space demand and existing green spaces, compares the results with actual green space visits, and finally proposes potential vegetation patches which can be transferred to community utilization. The expected outcomes will be threefold: differences between potential travel distances to potential green spaces and actual travelled distances to existing green spaces; map of hot spots of spatial inequalities in urban green space availability; and map of locations of unused green surfaces that have a potential for community use.

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 massively for research purposes, and the processing or publication of these data may further compromise individual privacy. On the other hand, consistent and increasing research on location protection measures promises solutions that mitigate disclosure risks. In this paper, we examine recent research efforts on the spectrum of privacy issues related to geosocial network data and identify the contributions and limitations of these research efforts. Furthermore, we provide protection recommendations to researchers that share, anonymise, and store social media data or publish scientific results.