The International Committee of the Red Cross (ICRC) has been using satellite imagery for more than 10 years now; initially for hydrogeological analysis in arid and semi-arid areas, as well as for vegetation follow-up in agricultural projects (irrigation, seeds distributions, etc.). Today, the humanitarian organization most frequently uses very high-resolution imagery for urban water projects, where the necessity to identify population densities, water resources, water supply needs, and pipeline routes is relevant. A growing field of interest is the use of imagery to analyze hostilities during conflict situations. Data and imagery is hosted on our servers enabling our field staff to access them from any of our field offices through web clients from ICRC’s intranet. This approach allows the sharing of simple PDF-maps, but also web-maps, web-applications, or entire dashboards within the ICRC, depending on the needs.

In contrast to previous approaches, Urban Emotions defines Smart Cities not only as technology-enriched cities, but also emphasises the human factor – Smart Citizens. This is due to the fact that the question of how people perceive a city and how they feel about it has always been important in urban planning and management. In this paper we present our approach for crowdsourcing physiological conditions and subjective emotions by combining data from technical sensors (measuring psycho-physiological parameters) and human sensors (“People as Sensors” contributing subjectively perceived emotions). Furthermore, we couple this crowdsourcing approach with a technical architecture to harmonise and integrate the sensor data via standardised service interfaces (Sensor Web Enablement – SWE) to allow for generic access for further analysis and visualisation. Finally, we discuss the use of emotion information in urban planning and point out related privacy issues together with a number of strategies to mitigate those.

Lung cancer is still at the top of the list of commonly preventable diseases, for both men and women, and smoking is the major trigger that causes lung cancer. The Austrian Health Ministry implements different actions to decrease lung cancer in the future. For instance, it offers smoking cessation services; but are these services always located in the right places to efficiently reach the target groups? This paper highlights the advantages of location analysis methods to spatially estimate the need at local level (i.e. where are clusters of smokers distributed at a small spatial level), and correspondingly position (more accurately) the offer of such services. With the help of our solution framework called simSALUD, we estimate the smoking prevalence by using Spatial Microsimulation Modelling in this project. Then, location-allocation modelling can be applied to find the places with the highest demand for smoking cessation services. The results of our project might be highly relevant for future health policy planning to decrease smoking prevalence, and therefore lung cancer.

The present study as part of the joint research project “Smart-Power-Flow”1 (at Reiner Lemoine Institute for Renewable Energies) focuses on modelling PV systems´ distribution in German rural communities. Although solar power energy systems in Germany have been increasing exponentially for the last 20 years (WRITH 2014), the majority of literature on PV potential has focused on rooftop PV systems in urban regions, and a small number of publications consider the typology of small rural communities. These areas, although less populated than cities, are where the highest PV potential in Germany is expected (DENA 2010), and, at the same time, where the availability of laser scanning data is highly incomplete, or not affordable for small administrations or projects. Several authors have used remotely-sensed imagery to quantify the PV potential on a regional scale, but only few authors (KJELLSSON 2000, BERGAMASCO & ASINARI 2011, JO & OTANICAR 2011) have attempted to use high-resolution images to quantify the suitable rooftop surface on a building basis, and none of them have addressed the particularities of rural communities. The aim of this study is to create a methodology, which predicts the size and location of future photovoltaic systems on rooftops, based on generally accessible data, and that is easily reproducible on a building scale for other rural villages. The methodology’s input data comprises high-resolution aerial imagery, GIS building footprints from the Landregister map, and the Bavarian database of photovoltaic systems. In addition, the method is tested using two types of images: a) official orthophotos from the Bavarian Land-survey Office, and b) Google Earth™ orthophotos, to assess the accuracy of freely available data to the project. The results are compared in the discussion section.

This research project deals with the implementation and evaluation of the Risk Terrain Modeling (RTM) technique, which allows localizing places, where the probability is high that a crime event will take place. RTM does not focus on previous events that happened, but on risk factors, which have an influence on the environment and can increase the probability of the risk that a crime will be committed. RTM is a recently developed approach, that has not yet been tested in Austria. Using the example of the city of Salzburg, predictions are made for the crime events assault, auto theft, burglary, and robbery for 2013 and 2014. In addition, the results of 2013 are evaluated and compared. Using the RTMDx Utility software, risk factors that correlate with the crime event as well as their influence can be identified. Based on these results, the risk factors can be operationalized to risk map layers, which is done using two models developed within ArcGIS. After that, the risk map layers are combined to a final risk terrain map, which is classified and finalized according to cartographic aspects. The evaluation for the predictions of 2013 is done using the Predictive Accuracy Index (PAI), based on a model developed in ArcGIS. The results of the evaluation and the percentage of correctly predicted crime events in respect to the size of the predicted areas are shown. In sum, 27 models were calculated and predictions made, because crime events have been partly separated into seasons or sub-types. The best predictive accuracy is reached for assaults, which includes results for the seasons spring and summer and for robberies, with PAI values of 31, 23, and 18, respectively. In contrast, the predictions for burglaries and auto thefts performed rather poorly, with PAI values between two and four. Overall, the RTM technique can be applied to Austrian cities, although the accuracy of the predictions varies. Additionally, the availability and quality of the risk factor data are crucial for the accuracy of the predictions.

Global climate is constantly changing, and such changes have irreversible impacts on the fauna and flora at all scales. Some progress has been made in order to properly understand the impact of climate on some vulnerable taxa; and species distribution models coupled with GIS and climate change scenarios have become crucial to understanding the relations between species distribution and environmental conditions, identifying threats and determining biodiversity vulnerability. With the use of predictive models, important changes in the species’ suitable areas were obtained. Laurel forest land snails, which are highly dependent on precipitation and relative humidity, may face major losses of their future suitable areas, leading to the possible extinction of several endangered species. Despite the complexity of the biological systems, the intrinsic uncertainty of species distribution models, and the lack of information about land snails’ functional traits, this analysis contributed to a pioneer study on the impacts of climate change on endemic species of Madeira Island. The future inclusion of predictions of the effect of climate change on species distribution as part of International Union for Conservation of Nature (IUCN) assessments could contribute to species prioritizing, promoting specific management actions and maximizing conservation investment.