Verfahren der digitalen Photogrammetrie bei der Auswertung historischer Luftbilder zur Erfassung von Altlastverdachtsflächen

In dieser Arbeit werden Verfahren und Methoden entwickelt und angewendet, mit deren Hilfe historische Luftbilder für die Erfassung von Altlastverdachtsflächen photogrammetrisch ausgewertet werden können. Diese werden an exemplarisch ausgewähltem Bildmaterial getestet und hinsichtlich der geometrischen Verwendbarkeit der Ergebnisse bewertet. Dabei wird vornehmlich den folgenden Fragen nachgegangen: Mit welchen Hilfsmitteln und Methoden kann ungeeignet erscheinendes Bildmaterial stereoskopisch ausgewertet werden, wie genau sind die Kartierungen im 3D-Stereomodell mit historischem Bildmaterial und in wie weit lassen sich Geländeveränderungen mit Hilfe von Höhenmodellen aus historischen Luftbildern analysieren? Ein Schwerpunkt der Betrachtungen wird auf Kriegsluftbilder gelegt, die aufgrund der Aufnahmetechnik, widriger Aufnahmesituationen und Überlieferungseinflüsse ein Problem für die Verarbeitung mit Verfahren der digitalen Photogrammetrie darstellen

Geospatial data analysis and economic evaluation of companies for sustainable business development

Sustainable business development is connected with environmental impact, natural resources and people. This makes the location a crucial factor for the operation of a business. Therefore, a combination of both geo-spatial data analysis and traditional economic evaluation of companies are advantageous. The consideration of geolocation is beneficial with calculations for process optimizations and cost efficiency as well as ecological and social compliance. Since integrating geospatial methods into economics is a rather new interdisciplinary approach, it seems necessary to establish innovative teaching concepts for the education of experts in this field. Creating and testing such new teaching concepts based on playful learning is the idea behind the ERASMUS+ project SPATIONOMY ("Spatial exploration of economic data — methods of interdisciplinary analytics"). An interdisciplinary team of teachers educates an interdisciplinary assembled group of international students. Hence, the fields of economics/business informatics and geography/geomatics are represented by participants and staff. Based on initial lessons about basic knowledge in the connected subjects, the central elements of the teaching concept are case studies and a simulation game, each with interdisciplinary challenges. The principal aim of the project — to educate students to become specialists in spatial economics — could be achieved. This paper aims to present, evaluate and discuss the methodological approach as well as the results from the application of the simulation game. The results show that the gamification of education is worthy. Simulation game-based learning appears to be more playful and experiential compared to traditional teaching approaches. Further research in this area should focus on the students' engagement evaluation and attitude towards sustainable behaviour in their own business

Combined small- and large-scale geo-spatial analysis of the Ruhr area for an environmental justice assessment

This paper investigates spatial relationships regarding the accessibility of urban green space, the overall yearly vitality of the surrounding vegetation, and additional indicators such as air and noise pollution, in urban areas. The analysis uses socio-economic data sets derived from a sophisticated disaggregation approach. It results from applying a new tool that processes data from coarse and small-scale data sets to smaller spatial units in order to derive more fine-grained insights into the characteristics of the smallest suburb. The consequent data sets are then augmented by comprehensive raster-based accessibility network analysis and the incorporation of measured data on air and noise pollution. Gaining an overview over the whole area on the one hand, and looking at smaller city districts in detail on the other, unveils whether there is an imbalance regarding all combined indicators. After correlating two socio-economic indicators, a spatial comparison of the preliminary results determines whether this approach reveals neighborhoods wherein residents of a lower socio-economic status are exposed to multiple threats at once. As a result, the paper presents a workflow to obtain a broader and, at the same time, more small-scale overview of polycentric agglomeration. Simultaneously, it provides a large-scale insight into single sites, right down to the city block level. Consequently, this study provides a sophisticated approach that helps to assess the quality, quantity and characteristics of the specific spatial distribution of environmental justice in small- to large-scale urban areas at a glance. The results help to identify regions of inequalities and disadvantages. They allow for querying additional values assigned to large-scale spatial units. These versatile variables provide a means to reveal other noticeable indicators. Furthermore, this entails the opportunity to evaluate the distinct living conditions of locally affected demographic groups, and improve them with tailored approaches. Finally, the results can enhance the perception of these living conditions, and be used to promote the capacity for organizing the lives of the respective residents more sustainably, helping the neighborhood to grow accordingly

Geo-spatial analysis of population density and annual income to identify large-scale socio-demographic disparities

This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary out- come the increase or decrease in patients’ Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs . 0.69, P < 0.01 [paired t -test with 95% confidence interval]). Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources

Implications of early respiratory support strategies on disease progression in critical COVID-19: a matched subanalysis of the prospective RISC-19-ICU cohort

Background: Uncertainty about the optimal respiratory support strategies in critically ill COVID-19 patients is wide‑ spread. While the risks and benefts of noninvasive techniques versus early invasive mechanical ventilation (IMV) are intensely debated, actual evidence is lacking. We sought to assess the risks and benefts of diferent respiratory sup‑ port strategies, employed in intensive care units during the frst months of the COVID-19 pandemic on intubation and intensive care unit (ICU) mortality rates. Methods: Subanalysis of a prospective, multinational registry of critically ill COVID-19 patients. Patients were subclas‑ sifed into standard oxygen therapy ≥10 L/min (SOT), high-fow oxygen therapy (HFNC), noninvasive positive-pressureBackground: Uncertainty about the optimal respiratory support strategies in critically ill COVID-19 patients is widespread. While the risks and benefits of noninvasive techniques versus early invasive mechanical ventilation (IMV) are intensely debated, actual evidence is lacking. We sought to assess the risks and benefits of different respiratory support strategies, employed in intensive care units during the first months of the COVID-19 pandemic on intubation and intensive care unit (ICU) mortality rates. Methods: Subanalysis of a prospective, multinational registry of critically ill COVID-19 patients. Patients were subclassified into standard oxygen therapy ≥10 L/min (SOT), high-flow oxygen therapy (HFNC), noninvasive positive-pressure ventilation (NIV), and early IMV, according to the respiratory support strategy employed at the day of admission to ICU. Propensity score matching was performed to ensure comparability between groups. Results: Initially, 1421 patients were assessed for possible study inclusion. Of these, 351 patients (85 SOT, 87 HFNC, 87 NIV, and 92 IMV) remained eligible for full analysis after propensity score matching. 55% of patients initially receiving noninvasive respiratory support required IMV. The intubation rate was lower in patients initially ventilated with HFNC and NIV compared to those who received SOT (SOT: 64%, HFNC: 52%, NIV: 49%, p = 0.025). Compared to the other respiratory support strategies, NIV was associated with a higher overall ICU mortality (SOT: 18%, HFNC: 20%, NIV: 37%, IMV: 25%, p = 0.016). Conclusion: In this cohort of critically ill patients with COVID-19, a trial of HFNC appeared to be the most balanced initial respiratory support strategy, given the reduced intubation rate and comparable ICU mortality rate. Nonetheless, considering the uncertainty and stress associated with the COVID-19 pandemic, SOT and early IMV represented safe initial respiratory support strategies. The presented findings, in agreement with classic ARDS literature, suggest that NIV should be avoided whenever possible due to the elevated ICU mortality risk