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Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis. [ABSTRACT FROM AUTHOR]

Across Europe, and probably the world, messages of the extreme right surface with increasing and alarming regularity in both public and virtual space. Within the virtual space, geomedia are increasingly used in nationalist propaganda, a trend which is embedded in a wider development in public discourse, election results, and policies. In Austria and Germany, particular sensibilities due to the responsibility for the Holocaust have developed, leading to various specific education initiatives that address this part of history. This paper presents an activist learning environment using geomedia to deconstruct right-wing extremist discourses, and has been tested used in university settings in Salzburg and Vienna. Using a combination of Instagram and geospatial technologies not visible to learners, results of reflection and learning processes are analyzed qualitatively across two slightly different enactments of the learning environment. Experiences suggest an encouraging potential of geomedia use in critical learning processes, as long as the technology is hidden from learners, forming an incentive, support, and contextualization of the learning process. [ABSTRACT FROM AUTHOR]

The research focuses on detecting tourist flows in the Province of Styria in Austria based on crowdsourced data. Twitter data were collected in the time range from 2008 until August 2018. Extracted tweets were submitted to an extensive filtering process within non-relational database MongoDB. Hotspot Analysis and Kernel Density Estimation methods were applied, to investigate spatial distribution of tourism relevant tweets under temporal variations. Furthermore, employing the VADER method an integrated semantic analysis provides sentiments of extracted tweets. Spatial analyses showed that detected Hotspots correspond to typical Styrian touristic areas. Apart from mainly successful sentiment analysis, it pointed out also a problematic aspect of working with multilingual data. For evaluation purposes, the official tourism data from the Province of Styria and federal Statistical Office of Austria played a role of ground truth data. An evaluation with Pearson's correlation coefficient was employed, which proves a statistically significant correlation between Twitter data and reference data. In particular, the paper shows that crowdsourced data on a regional level can serve as accurate indicator for the behaviour and movement of users. [ABSTRACT FROM AUTHOR]

Health care accessibility studies are well established in the US but lacking in Austria, even though both experience high costs and have hospital care as the largest contributor to health care spending. This study aims to examine multiscale spatial accessibility to acute hospitals in Carinthia, Austria. Using the most recent data at census block and 250 meter grid levels, we refine proximity and generalized two-step floating catchment area (G2SFCA) methods while accounting for the modifiable areal unit problem (MAUP) and edge effects. For census blocks and 250 meter grids, the mean travel times to the nearest acute hospitals are 16 and 21 min, respectively, covering 58.8% and 76.2% of the population, which, however, increases to 25 and 31 min to the three nearest hospitals with similar populations. People bypassing the nearest hospital to seek hospitals at a longer distance, termed "bypass behavior", is more influential, as 20% more of the population living in mountainous or rural areas need to travel 30 min longer. The G2SFCA method with a more pronounced distance decay results in a more decentralized polycentric structure of accessibility and identifies poorer access areas. While urban advantage is most evident in Klagenfurt and Villach, not all areas near hospitals enjoy the highest accessibility. A combination of the proximity and G2SFCA methods identifies less accessible areas. The MAUP overestimates accessibility at a coarse level and in less populous areas. Edge effects occur at the border when using proximity only, but they are more sensitive when considering bypass behavior or a weak distance decay effect. This study contributes to our understanding of acute hospitals' accessibility in Carinthia and highlights the need to improve low-accessible areas in addition to universal health coverage. Cautions need to be exercised when using different geographic units or considering edge effects for health care planning and management. [ABSTRACT FROM AUTHOR]

Transport planning strategies regard cycling promotion as a suitable means for tackling problems connected with motorized traffic such as limited space, congestion, and pollution. However, the evidence base for optimizing cycling promotion is weak in most cases, and information on bicycle patterns at a sufficient resolution is largely lacking. In this paper, we propose agent-based modeling to simulate bicycle traffic flows at a regional scale level for an entire day. The feasibility of the model is demonstrated in a use case in the Salzburg region, Austria. The simulation results in distinct spatio-temporal bicycle traffic patterns at high spatial (road segments) and temporal (minute) resolution. Scenario analysis positively assesses the model's level of complexity, where the demographically parametrized behavior of cyclists outperforms stochastic null models. Validation with reference data from three sources shows a high correlation between simulated and observed bicycle traffic, where the predictive power is primarily related to the quality of the input and validation data. In conclusion, the implemented agent-based model successfully simulates bicycle patterns of 186,000 inhabitants within a reasonable time. This spatially explicit approach of modeling individual mobility behavior opens new opportunities for evidence-based planning and decision making in the wide field of cycling promotion [ABSTRACT FROM AUTHOR]

A continued shift of human mobility towards sustainable and active mobility modes is a major concern for society in order to reduce the human contribution to climate change as well as to improve liveability and health in urban environments. For this change to succeed, non-motorized modes of transport need to become more attractive. Cycling can play a substantial role for short to medium distances, but perceived safety and stress levels are still major concerns for cyclists. Therefore, a quantitative assessment of cyclists' stress sensations constitutes a valuable input for urban planning and for optimized routing providing low-stress routes. This paper aims to investigate stress sensations of cyclists through quantifying physiological measurements and their spatial correlation as an intersubjective indicator for perceived bikeability. We developed an automated workflow for stress detection and aggregation, and validated it in a case study in the city of Salzburg, Austria. Our results show that measured stress generally matches reported stress perception and can thus be considered a valuable addition to mobility planning processes. [ABSTRACT FROM AUTHOR]

The coordinate system of the Austrian cadastre is physically realised through control points provided by the national institution for surveying. Due to historical development over the centuries and changes in measurement technologies, inhomogeneities can occur within the local control point network. These inhomogeneities affect the derived boundary point coordinates. When staking out boundary points in an area with inhomogeneous control points, deviations from the boundary marks in the field can occur that exceed the accuracy requirements of the ordinance for surveying. Examples show that a suitable approach to tackle this issue has to be selected on a case-based strategy. Different situations might require different approaches. This needs to be considered in the legal framework to enable cadastral experts to select the optimal approach. [ABSTRACT FROM AUTHOR]

Austria aims to meet 100% of its electricity demand from domestic renewable sources by 2030 which means, that an additional 27 T W h / a of renewable electricity generation are required, thereof 11 T W h / a from photovoltaic. While some federal states and municipalities released a solar rooftop cadastre, there is lacking knowledge on the estimation of the potential of both, ground mounted installations and rooftop modules, on a national level with a high spatial resolution. As a first, in this work data on agricultural land-use is combined with highly resolved data on buildings on a national level. Our results show significant differences between urban and rural areas, as well as between the Alpine regions and the Prealpine- and Easter Plain areas. Rooftop potential concentrates in the big urban areas, but also in densely populated areas in Lower- and Upper Austria, Styria and the Rhine valley of Vorarlberg. The ground mounted photovoltaic potential is highest in Eastern Austria. This potential is geographically consistent with the demand and allows for a production close to the consumer. In theory, the goal of meeting 11 T W h / a in 2030 can be achieved solely with the rooftop PV potential. However, considering the necessary installation efforts, the associated costs of small and dispersed production units and finally the inherent uncertainty with respect to the willingness of tens of thousands of individual households to install PV systems, installing the necessary solar PV on buildings alone is constrained. [ABSTRACT FROM AUTHOR]

Landslides are one of the most detrimental geological disasters that intimidate human lives along with severe damages to infrastructures and they mostly occur in the mountainous regions across the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potential areas that are prone to landslides and could have an impact on decreasing the possible damages. The application of the fuzzy best-worst multi-criteria decision-making (FBWM) method was applied for LSM in Austria. Further, the role of employing a few numbers of pairwise comparisons on LSM was investigated by comparing the FBWM and Fuzzy Analytical Hierarchical Process (FAHP). For this study, a wide range of data was sourced from the Geological Survey of Austria, the Austrian Land Information System, Humanitarian OpenStreetMap Team, and remotely sensed data were collected. We used nine conditioning factors that were based on the previous studies and geomorphological characteristics of Austria, such as elevation, slope, slope aspect, lithology, rainfall, land cover, distance to drainage, distance to roads, and distance to faults. Based on the evaluation of experts, the slope conditioning factor was chosen as the best criterion (highest impact on LSM) and the distance to roads was considered as the worst criterion (lowest impact on LSM). LSM was generated for the region based on the best and worst criterion. The findings show the robustness of FBWM in landslide susceptibility mapping. Additionally, using fewer pairwise comparisons revealed that the FBWM can obtain higher accuracy as compared to FAHP. The finding of this research can help authorities and decision-makers to provide effective strategies and plans for landslide prevention and mitigation at the national level. [ABSTRACT FROM AUTHOR]

Law enforcement is very interested in knowing when a crime has happened. Unfortunately, the occurrence time of a crime is often not exactly known. In such circumstances, estimating the most likely time that a crime has happened is crucial for spatio-temporal analysis. The main purpose of this research is to introduce two novel temporal approximation methods, termed retrospective temporal analysis (RTA) and extended retrospective temporal analysis (RTAext). Both methods are compared to six existing temporal approximation methods and subsequently evaluated in order to identify the method that can most accurately estimate the occurrence time of crimes. This research is conducted with 100,000+ burglary crimes from the city of Vienna, Austria provided by the Criminal Intelligence Service Austria, from 2009–2015. The RTA method assumes that crimes in the immediate past occur at very similar times as in the present and in the future. Historical crimes with accurately known time stamps can therefore be applied to estimate when crimes occur in the present/future. The RTAext method enhances one existing temporal approximation method, aoristicext, with probability values derived from historical crime data with accurately known time stamps. The results show that the RTA method performs superiorly to all other temporal approximation methods, including the novel RTAext method, in two out of the three crime types analyzed. Additionally, the RTAext method shows very good results that are similar to the best performing existing approximation methods. [ABSTRACT FROM AUTHOR]

An address is a specification that refers to a unique location on Earth. While there has been a considerable amount of research on the syntactic structure of addressing systems in order to evaluate and improve their quality, aspects of semantics and pragmatics have been less explored. An address is primarily associated by humans to the elements of their spatial mental representations, but may also influence their spatial knowledge and activities through the level of detail it provides. Therefore, it is not only important how addressing components are structured, but it is also of interest to study their meaning as well as the pragmatics in relation to an interpreting agent. This article studies three forms of addresses (i.e., structured as in Austria, semi-formal as in Japan, and descriptive as in Iran) under the principles of semiotics (i.e., through levels of syntax, semantics, and pragmatics). Syntax is discussed through formal definitions of the addressing systems, while semantics and pragmatics are assessed through an agent-based model to explore how they influence spatial knowledge acquisition and growth. [ABSTRACT FROM AUTHOR]