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In this paper we analyze changes in the intra-urban spatial distribution of employment across six U.S. and German city regions between 2002 and 2015. Our methodological approach allows for a systematic and spatially consistent comparison of urban spatial structures across the two different countries. The empirical results show major national, regional, and sectoral differences in the spatial distribution of employment. In the German case studies traditional core cities play a more important role for the regional labor market than in the U.S. Only relatively small shares of metropolitan employment are concentrated in subcenters. While employment concentrations are spatially less persistent in the U.S. case study regions, we did not find any evidence of common or country-specific trends toward increased polycentricity or employment dispersal. Changes in the spatial concentration of employment seem to be highly context-specific and influenced by the individual geographic and institutional frameworks of the analyzed metropolitan areas. [ABSTRACT FROM AUTHOR]

The global number of vulnerable citizens in areas of landslide risk is expected to increase due to the twin forces of climate change and growing urbanization. Self-constructed or informal settlements are frequently built in hazardous terrain such as on landslide-prone slopes. They are characterized by high dynamics of growth, simple construction methods and strong social dynamics and are exposed to unsteady political approaches. Landslide early warning systems (LEWSs) can contribute to decreasing their vulnerability, but precise, affordable and culturally integrated LEWSs need to be further developed. In this paper, we present a 4-year living-lab research project called Inform@Risk that aimed to develop a LEWS prototype in the neighborhood of Bello Oriente, located on the urban–rural border of Medellín, Colombia. Its research team is composed of landscape architects, geo-engineers, and remote sensing and geo-informatics experts. The research team collaborated with a multitude of stakeholders: civil society, private enterprises, non-governmental agencies and various branches of government. A preliminary LEWS with the last functionalities still to be developed has been designed, implemented and handed over to the government. It has entered a test and calibration phase (i.e., warning-threshold development, procedures for warning and alert dissemination through the sensor system), which is on hold due to legal constraints. Our first findings indicate that the integrative development of technical aspects of a LEWS in informal settlements can be challenging, albeit manageable, whereas the level of social and political support is beyond the control of the designer. Steady political will is needed to increase technical capacities and funding of the operation and maintenance of an increased amount of monitoring equipment. Social outreach has to be continuous in order to inform, train, maintain the trust and increase the self-help capacities of the often rapidly changing population of an informal settlement. Legal requirements for a transfer of academic research projects to municipal authorities have to be clear from the start. Satisfying replacement housing options for the case of evacuation have to be in place in order to not lose the overall acceptance of the LEWS. As political will and municipal budgets can vary, a resilient LEWS for informal settlements has to achieve sufficient social and technical redundancy to maintain basic functionality even in a reduced-governmental-support scenario. [ABSTRACT FROM AUTHOR]

Geolocated social media data hold a hitherto untapped potential for exploring the relationship between user mobility and their interests at a large scale. Using geolocated Twitter data from Nigeria, we provide a feasibility study that demonstrates how the linkage of (1) a trajectory analysis of Twitter users' geolocation and (2) natural language processing of Twitter users' text content can reveal information about the interests of migrants. After identifying migrants via a trajectory analysis, we train a language model to automatically detect the topics of the migrants' tweets. Biases of manual labelling are circumvented by learning community‐defined topics from a Nigerian web forum. Results suggest that differences in users' mobility correlate with varying interests in several topics, most notably religion. We find that Twitter data can be a flexible source for exploring the link between users' mobility and interests in large‐scale analyses of urban populations. The joint use of spatial techniques and text analysis enables migration researchers to (a) study migrant perspectives in greater detail than is possible with census data and (b) at a larger scale than is feasible with interviews. Thereby, it provides a valuable complement to interviews, surveys and censuses, and holds a large potential for further research. [ABSTRACT FROM AUTHOR]

Context: Remote sensing time series (hereafter called time series) and telemetry data are widely used to study animal-environment relationships. However, both data sources are subject to uncertainties that can cause erroneous conclusions. To date, only the uncertainty of telemetry data can be estimated, e.g. through movement modelling, while information on the uncertainty of time series is often lacking. Consequently, it remains challenging to assess if and how the results of animal-environment studies are affected by cumulative uncertainties of telemetry and time series data. Objectives: To address this gap, we proposed an approach to approximate time series uncertainties. Coupled with movement modelling, this allows to determine whether the results of animal-environment studies are robust to the cumulative uncertainties of time series and telemetry data. We demonstrated the procedure with a study that used time series to distinguish periods of favourable/poor prey accessibility for white storks. Our objective was to test whether the storks' preference for fields during periods of favourable prey accessibility could be validated despite the uncertainties. Methods: We estimated the telemetry data uncertainties based on continuous-time movement modelling, and approximated time series uncertainties based on data subsampling. We used Monte Carlo simulations to propagate the uncertainties and to generate several estimates of the stork habitat use and levels of prey accessibility. These data were applied in two habitat selection analyses to derive probability distributions of the analyses results, allowing us to characterise the output uncertainties. Results: We found that, after accounting for uncertainty, favourable and poor prey accessibility periods were well discriminated, with storks showing the expected degree of preference/avoidance for them. However, our uncertainty analysis also showed, that compared to croplands, grasslands required more temporal NDVI samples to reliably identify these periods. Furthermore, the NDVI itself did not appear to be a coherent predictor of stork habitat selection when uncertainties were accounted for. Conclusion: Our findings highlight the importance of validating results by assessing and quantifying the effect of input data uncertainties in animal-environment studies. To our knowledge, the approach presented is the first to assess the cumulative uncertainty of time series and telemetry data, hopefully raising awareness of the consequences of input data uncertainties for future studies. [ABSTRACT FROM AUTHOR]

Landslides are socio-natural hazards. In Colombia, for example, these are the most frequent hazards. The interplay of climate change and the mostly informal growth of cities in landslide-prone areas increases the associated risks. Landslide early warning systems (LEWSs) are essential for disaster risk reduction, but the monitoring component is often based on expensive sensor systems. This study presents a data-driven approach to localize landslide-prone areas suitable for low-cost and easy-to-use LEWS instrumentation, as well as to estimate the associated costs. The approach is exemplified in the landslide-prone city of Medellín, Colombia. A workflow that enables decision-makers to balance financial costs and the potential to protect exposed populations is introduced. To achieve this, city-level landslide susceptibility is mapped using data on hazard levels, landslide inventories, geological and topographic factors, and a random forest model. Then, the landslide susceptibility map is combined with a population density map to identify highly exposed areas. Subsequently, a cost function is defined to estimate the cost of LEWS monitoring sensors at the selected sites, using lessons learned from a pilot LEWS in Bello Oriente, a neighbourhood in Medellín. This study estimates that LEWS monitoring sensors could be installed in several landslide-prone areas with a budget ranging from EUR 5 to EUR 41 per person (roughly COP 23 000 to 209 000), improving the resilience of over 190 000 exposed individuals, 81 % of whom are located in precarious neighbourhoods; thus, the systems would particularly reduce the risks of a social group of very high vulnerability. The synopsis of all information allows us to provide recommendations for stakeholders on where to proceed with LEWS instrumentation. These are based on five different cost-effectiveness scenarios. This approach enables decision-makers to prioritize LEWS deployment to protect exposed populations while balancing the financial costs, particularly for those in precarious neighbourhoods. Finally, the limitations, challenges, and opportunities for the successful implementation of a LEWS are discussed. [ABSTRACT FROM AUTHOR]

Failures in urban areas' solid waste management lead to clandestine garbage dumping and pollution. This affects sanitation and public human hygiene, deteriorates quality of life, and contributes to deprivation. This study aimed to test a combination of machine learning, high-resolution earth observation and GIS data to detect diverse categories of residual waste on the streets, such as sacks and construction debris. We conceptualised five different classes of solid waste from image interpretation: "Sure", "Half-sure", "Not-sure", "Dispersed", and "Non-garbage". We tested a combination of k-means-based segmentation and supervised random forest to investigate the capabilities of automatic classification of these waste classes. The model can detect the presence of solid waste on the streets and achieved an accuracy of up from 73.95%–95.76% for the class "Sure". Moreover, a building extraction using an EfficientNet deep-learning-based semantic segmentation allowed masking the rooftops. This improved the accuracy of the classes "Sure" and "Non-garbage". The systematic evaluation of all parameters considered in this model provides a robust and reliable method of solid waste detection for decision-makers. These results highlight areas where insufficient waste management affects the citizens of a given city. The best segmentation using simple linear iterative clustering (SLIC) was achieved with the parameter values 8,000 segments and 0.3 compactness. The following supervised classification of the segmented images using Random Forest yielded an average overall accuracy of 80.18%. The model can detect the presence of solid waste on the streets and achieved an accuracy of up from 73.95%–95.76% for the class "Sure". The average reflectance values of the classes "Sure" and "non-Garbage" overlapped. Removing the building rooftops from the orthotiles reduced the overlap of the classes mentioned above. This allowed better identification of the class "Sure". Moreover, rooftop removal helped improve the accuracy of the classifier, from 59.51% to 90.18% to 71.53% to 95.76% in study areas with and without rooftops, respectively. [ABSTRACT FROM AUTHOR]

Subsequently, new technologies including a plethora of sensor data and numerical modeling and machine learning techniques are identified which have the potential to generate risk assessment strategies that match the aforementioned requirements. 10.1007/s11069-021-05125-6 2 Bhuyan K, Van Westen C, Wang J, Meena SR. Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence. To extract relevant thematic information from the data, novel methods related to the field of machine learning and Artificial Intelligence (AI) are increasingly deployed. In detail, the authors present a semi-automated workflow for the development of an elements-at-risk database of buildings by detecting building footprints with deep learning techniques and characterizing the footprints with building occupancy information using both building morphological metrics and open-source auxiliary data for flood exposure analysis. [Extracted from the article]

Exposure is an essential component of risk models and describes elements that are endangered by a hazard and susceptible to damage. The associated vulnerability characterizes the likelihood of experiencing damage (which can translate into losses) at a certain level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) of risk assessment procedures. Existing models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow the collection of spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of constituting morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we establish dasymetric methods for disaggregation. The results are presented for the city of Santiago de Chile, which is prone to natural hazards such as earthquakes. We present loss estimations due to seismic ground shaking and corresponding sensitivity as a function of the resolution properties of the exposure data used in the model. The experimental results underline the benefits of deploying modern earth observation technologies for refined exposure mapping and related earthquake loss estimation with enhanced accuracy properties. [ABSTRACT FROM AUTHOR]

Landslides are often deadly natural events. Steep slopes and certain loose soil types are predestined areas for them. Moreover, in the context of climate change, extreme weather events such as heavy rainfall, which often trigger landslides, are becoming even more likely. While all this is well known, it, therefore, stands to reason that this knowledge will lead to the avoidance of these risks. On the other hand, however, there are highly dynamic urbanization processes that often overtake formal urban planning processes by rising population figures and areal expansion. In the course of these processes, economically deprived population groups often have no other option than to informally build on high-risk areas. Against these backgrounds, we systematically examine in this study how these risks develop over a 24-year period from 1994 to 2018 taking into account three time steps, with respect to the citywide exposure and in particular with respect to different social groups. For this purpose, we use heterogeneous input data from remote sensing, landslide hazard maps, and census data. Our case study is the city of Medellín in Colombia. We develop and apply a set of methods integrating the heterogenous data sets to map, quantify and monitor exposure and social vulnerability at a fine spatial granularity. Our results document first of all the highly dynamic growth in total population and urban areas. However, our results reveal that the city's expansion is socially unevenly distributed. People of higher vulnerability proxied by informal settlements are found to settle in considerably higher shares of areas exposed to landslides. This study proposes a methodological setup that allows for monitoring exposure and social vulnerability over long time spans at a fine spatial resolution, allows to bring inequality into the spotlight, and provides decision-makers with better information to develop socially responsible policies. [ABSTRACT FROM AUTHOR]

We live in a rapidly changing and globalized society. The increasing connectivity of our economic, social and technical systems, growing urbanization and the consequences of climate change might lead to more complex risks and multi-dimensional vulnerability. The complex relationships between multiple and consecutive natural hazards, exposed population and built environment result in a variety of cascading effects which, if are often not considered appropriately by decision makers, can result to inadequate or even misleading risk management strategies. Thus, hindering efficient prevention and mitigation measures, and ultimately undermining the resilience of societies. International efforts to identify global risks such as within the Sustainable Development Goals, the Sendai Framework for Disaster Risk Reduction 2015-2030 or the Paris Agreement help to raise awareness, set priorities and face such global problems. However, even if the formulated goals for risk reduction are comprehensible in their abstraction, decision-makers still face major challenges when it comes to local implementation. In this paper, we present a conceptual approach for multi-risk assessment which was designed to serve potential users like disaster risk managers, urban planners or operators of critical infrastructures to increase their capabilities. Based on recent scientific and technical capabilities, we developed a tool through an iterative participative approach which has allowed users to explore various scenarios of multiple hazards, cascading effects and their impacts. As an illustrative example, the experiences during this stimulating process are documented for Lima Metropolitan area (Peru), a megacity exposed to various natural hazards, among them, earthquakes and tsunamis. We believe that such an approach for exploring, describing and quantifying different What-if scenarios can constitute a valuable approach for understanding complex multi-risk processes, preparing for such situations, and serving as a good practice that can be replicated for other areas of interest in the future. [ABSTRACT FROM AUTHOR]

Develoment of image recognition AI algorithms for flower-visiting arthropods has the potential to revolutionize the way we monitor pollinators. Ecologists need light-weight models that can be deployed in a field setting and can classify with high accuracy. We tested the performance of three deep learning light-weight models, YOLOv5nano, YOLOv5small, and YOLOv7tiny, at object recognition and classification in real time on eight groups of flower-visiting arthropods using open-source image data. These eight groups contained four orders of insects that are known to perform the majority of pollination services in Europe (Hymenoptera, Diptera, Coleoptera, Lepidoptera) as well as other arthropod groups that can be seen on flowers but are not typically considered pollinators (e.g., spiders-Araneae). All three models had high accuracy, ranging from 93 to 97%. Intersection over union (IoU) depended on the relative area of the bounding box, and the models performed best when a single arthropod comprised a large portion of the image and worst when multiple small arthropods were together in a single image. The model could accurately distinguish flies in the family Syrphidae from the Hymenoptera that they are known to mimic. These results reveal the capability of existing YOLO models to contribute to pollination monitoring. [ABSTRACT FROM AUTHOR]

The population experiencing high temperatures in cities is rising due to anthropogenic climate change, settlement expansion, and population growth. Yet, efficient tools to evaluate potential intervention strategies to reduce population exposure to Land Surface Temperature (LST) extremes are still lacking. Here, we implement a spatial regression model based on remote sensing data that is able to assess the population exposure to LST extremes in urban environments across 200 cities based on surface properties like vegetation cover and distance to water bodies. We define exposure as the number of days per year where LST exceeds a given threshold multiplied by the total urban population exposed, in person ⋅ day. Our findings reveal that urban vegetation plays a considerable role in decreasing the exposure of the urban population to LST extremes. We show that targeting high-exposure areas reduces vegetation needed for the same decrease in exposure compared to uniform treatment. This study uses earth observation data and proposes a method to evaluate and optimize the increment of urban greening to reduce the population exposure to extreme land surface temperatures in cities. [ABSTRACT FROM AUTHOR]

In the past decade, the number of refugees and internally displaced people (IDP) has doubled. This prompted the construction of more refugee camps and the proliferation of existing camps with diverse structural morphologies. Satellite imagery and machine learning (ML) are increasingly utilized to map these camps. However, there exists no standardized inventory that systemizes the built-up structures of these camps. In this study, we conceptualize the settlement morphology of refugee and IDP camps from satellite images and create a structure catalogue. Using visual image interpretation (VII) of very-high-resolution and multitemporal imagery, we compile a global database of settlement structures from 285 camps across 1,053 observations. This catalogue is subsequently used to synthesize patterns in camp structures and temporal dynamics. The results show stark variations in settlement structures across camps. Despite some similar regional patterns, stark differences in morphologies are a testament to the global heterogeneous landscape of refugee and IDP camp structures. These findings highlight the importance of considering morphological differences in image analyses across camps in future designs of ML-based automated detection and monitoring efforts. Therein, the Structure Catalogue serves as an important foundation for future earth observation for humanitarian applications. [ABSTRACT FROM AUTHOR]

The population experiencing high temperatures in cities is rising due to anthropogenic climate change, settlement expansion, and population growth. Yet, efficient tools to evaluate potential intervention strategies to reduce population exposure to Land Surface Temperature (LST) extremes are still lacking. Here, we implement a spatial regression model based on remote sensing data that is able to assess the population exposure to LST extremes in urban environments across 200 cities based on surface properties like vegetation cover and distance to water bodies. We define exposure as the number of days per year where LST exceeds a given threshold multiplied by the total urban population exposed, in person ⋅ day. Our findings reveal that urban vegetation plays a considerable role in decreasing the exposure of the urban population to LST extremes. We show that targeting high-exposure areas reduces vegetation needed for the same decrease in exposure compared to uniform treatment. This study uses earth observation data and proposes a method to evaluate and optimize the increment of urban greening to reduce the population exposure to extreme land surface temperatures in cities. [ABSTRACT FROM AUTHOR]

The global number of vulnerable citizens in areas of landslide risk is expected to increase due to the twin forces of climate change and growing urbanization. Self-constructed or informal settlements are, due to shortage of urban land, frequently built in hazardous terrain such as landslide-prone slopes. They are characterized by high dynamics of growth, simple construction methods, strong social dynamics, and are exposed to unsteady political approaches. Landslide Early Warning Systems (LEWS) can contribute to decrease their vulnerability, but precise, affordable and culturally integrated LEWS need to be further developed. In this paper, we present a four-year living lab research project that aimed to design, implement, and evaluate a LEWS in the neighborhood of Bello Oriente, located in the urban-rural border of Medellín, Colombia. Its research team is composed of landscape architects, geo-engineers, remote sensing and geo-informatic experts. The research team collaborated with a multitude of stakeholders: civil society, private enterprises, non-governmental agencies and various branches of government. A prototypical LEWS has been designed, implemented and handed over to the government. It has entered a test and calibration phase. First findings indicate that the integrative development of technical aspects of a LEWS in informal settlements can be challenging, but manageable; whereas, the social and political support can vary and be beyond the control of the designer. It can be concluded that a resilient LEWS for informal settlements has to achieve sufficient social and technical redundancy to maintain basic functionality even in a reduced support scenario. Keywords: landslides, early warning systems, risk, natural hazard, informal settlements, living lab. [ABSTRACT FROM AUTHOR]

The fastest growing regional crisis is happening in West Africa today, with over 8 million people considered persons of concern. A culmination of identity politics, climate-driven disasters, and extreme poverty has led to this humanitarian crisis in the region and is exacerbated by a lack of political will and misplaced media attention. The current state of the art does not present sufficient investigations of the thematic and spatial coverage of news media of this crisis in this region. This paper studies the spatial coverage of this crisis as reported in the media, and the themes associated with those locations, based on a curated dataset. For the time frame 12 March to 15 September 2021, 2017 news articles related to the refugee crisis in West Africa were examined and manually coded based on (1) the geographical locations mentioned in each article; (2) the themes found in the articles in reference to a location (e.g., Relocation of people in Abuja). The dataset introduces a thematic dimension, as never achieved before, to the conflict-ridden areas in West Africa. A comparative analysis with UNHCR (United Nations High Commissioner for Refugees) data showed that 96.8% of refugee-related locations in West Africa were not covered by news during the considered time frame. Contrastingly, 80.4% of locations mentioned in the news do not appear in the UNHCR repository. Most news articles published during this time frame reported on Development aid or Political statements. Linear multiple regression analysis showed GDP per capita and political stability to be among the most influential determinants of news coverage. [ABSTRACT FROM AUTHOR]

Earth observation data provides valuable information and support along the disaster management cycle. However, information from satellite remote sensing is often not available in the first hours a crisis occurs, due to several reasons, e.g. pre-defined acquisition times, cloud coverage, downlink capacities. To fill this time gap and add value to the incoming results from remote sensing data, ancillary datasets such as Twitter data become useful to enrich data and get insights into events by leveraging their spatio-temporal and thematic references. However, the main disadvantage of using Twitter data is the noise that is introduced into analyses by these data. Among other reasons, this is mainly caused by the use of insignificant search criteria that are used to harvest the data, that often result in irrelevant, noisy data (e.g. using insignificant keywords or incorrect geotags to filter data). This paper presents a method to identify crisis-event specific signal words, that are then used together with Part Of Speech (POS) tagging to filter the Twitter streams, and gather crisis-event specific data. These data are then used to estimate the location hotspots of the crisis events. The developed methods are applied as a proof-of-concept to determine flood events in May of 2022. [ABSTRACT FROM AUTHOR]

Green space (GS) is a crucial resource in urban areas, but not spatially uniformly distributed. We compare the availability of GS on a national scale using green land cover (GLC) and public green space (PGS). Using spatial census data we analyse GS availability and accessibility for the German population. The average GS availability differs by a factor of three between GLC and PGS. 19.2 % of Germans find less than the WHO defined target for PGS in their neighbourhood. PGS is less equally distributed among the population than GLC, and green space equity varies significantly between rural and urban areas. In areas with multi-family homes, a higher share of the population has access to sufficient PGS than in areas with predominantly single-family homes. We find a negative relation between GLC availability and share of immigrant population, which does not extend to PGS. [ABSTRACT FROM AUTHOR]

Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, statistical and machine learning methods have been developed to estimate population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises a digital elevation model, local climate zone, land use proportions, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated approaches in the field of population estimation. Measurement(s) human population distribution Technology Type(s) remote sensing Factor Type(s) remote sensing data Sample Characteristic - Location Europe [ABSTRACT FROM AUTHOR]

High-resolution population mapping is of high relevance for developing and implementing tailored actions in several fields: From decision making in crisis management to urban planning. Earth Observation has considerably contributed to the development of methods for disaggregating population figures with higher resolution data into fine-grained population maps. However, which method is most suitable on the basis of the available data, and how the spatial units and accuracy metrics affect the validation process is not fully known. We aim to provide recommendations to researches that attempt to produce high-resolution population maps using remote sensing and geospatial information in heterogeneous urban landscapes. For this purpose, we performed a comprehensive experimental research on population disaggregation methods with thirty-six different scenarios. We combined five different top-down methods (from basic to complex, i.e., binary and categorical dasymetric, statistical, and binary and categorical hybrid approaches) on different subsets of data with diverse resolutions and degrees of availability (poor, average and rich). Then, the resulting population maps were systematically validated with a two-fold approach using six accuracy metrics. We found that when only using remotely sensed data the combination of statistical and dasymetric methods provide better results, while highly-resolved data require simpler methods. Besides, the use of at least three relative accuracy metrics is highly encouraged since the validation depends on level and method. We also analysed the behaviour of relative errors and how they are affected by the heterogeneity of the urban landscape. We hope that our recommendations save additional efforts and time in future population mapping. [ABSTRACT FROM AUTHOR]

Agricultural activities and vegetation growth cause rapid small‐scale vegetation changes which dynamically alter habitat suitability. Time series enable to track down such variations of vegetation structure and are promising to examine their impact on animals' life. Nevertheless, their potential to characterize vegetation dynamics in ways pertinent to animals' fine‐scale habitat use has not been adequately explored and ecologically meaningful proxies are lacking. To address this gap, we exemplary investigated foraging activities of breeding white storks in an agricultural landscape. Reflecting on the understanding that storks require short vegetation to access prey, we examined if good foraging conditions – early growth and post‐harvest/mowing periods – are detectable using the points between local minima/maxima in NDVI profiles (half‐maximum). We processed 1 year of Landsat imagery to identify half‐maximum periods (HM: good prey access) and non‐half‐maximum periods (non‐HM: poor prey access) on field‐scale in croplands and grasslands. Additionally, we mapped used/unused fields and retrieved foraging duration/daily visitation rates from GPS tracks of the storks. We then explored habitat use, compared habitat use with habitat availability and tested temporal predictors distinguishing between HM/non‐HM in habitat selection models. Examining habitat use, storks revisited croplands and grasslands significantly more often during HM than during non‐HM, while foraging duration was only prolonged in croplands during HM. However, comparing habitat use with habitat availability, we observed that storks used croplands and grasslands in significantly higher proportions during HM than during non‐HM. Additionally, we found that temporal information affected storks' habitat selection and improved model performance. Our findings emphasize that the half‐maximum proxy enables to coarsely distinguish temporal resource variations in storks' foraging habitats, highlighting the potential of time series for characterizing behaviorally‐relevant vegetation dynamics. Such information helps to create more species‐centered landscape scenarios in habitat models, allowing to unravel effects of small‐scale environmental changes on wildlife to ultimately guide conservation and management. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation. Methods: We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021. Results: The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible. Conclusions: We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units. [ABSTRACT FROM AUTHOR]

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This study proposes a methodology based on machine learning (ML) algorithms for rapid and robust classification of building structural types (STs) in multispectral remote sensing imagery aiming to assess buildings' seismic vulnerability. The seismic behavior of buildings is strongly affected by the ST, including material, age, height, and other main structural features. Previous works deployed in situ data integrated with remote sensing information to statistically infer STs through supervised ML methods. We propose a transferable methodology with specific focus on situations with imbalanced in situ data (i.e., the number of available labeled samples for model learning differs largely between different STs). We learn a transferable model by selecting features from an exhaustive set. The transferability relies on deploying geometric features characterizing individual buildings; thus, the model is less sensitive to domain adaption problems frequently induced by e.g., changes in acquisition parameters of remotely sensed imagery. Thereby, we show that few geometry features enable generalization capabilities similar to models learned with a large number of features describing spectral, geometrical or contextual building properties. We rely on an extensive geodatabase containing almost 18,000 building footprints. We follow a Random Forest (RF)-based feature selection strategy to objectively identify most valuable features for prediction. Furthermore, the problem of unbalanced classes is addressed by adopting two approaches: downsampling the majority class and modifying the classifier internally (weighted RF). The implemented model is transferred on the challenging urban morphology of the Val d'Agri area (Italy). Results confirm the statistical robustness of the model and the importance of the geometry features, allowing for reliable identification of STs. [ABSTRACT FROM AUTHOR]

Measuring spatial patterns is a crucial task in spatial sciences. Multiple indicators have been developed to measure patterns in a quantitative manner. However, most comparative studies rely on relative comparisons, limiting their explanatory power to specific case studies. Motivated by advancements in earth observation providing unprecedented resolutions of settlement patterns, this paper suggests a measurement technique for spatial patterns to overcome the limits of relative comparisons. We design a model spanning a feature space based on two metrics – largest patch index and number of patches. The feature space is defined as 'dispersion index' and covers the entire spectrum of possible two-dimensional binary (settlement) patterns. The model configuration allows for an unambiguous ranking of each possible pattern with respect to spatial dispersion. As spatial resolutions of input data as well as selected areas of interest influence measurement results, we test dependencies within the model. Beyond, common other spatial metrics are selected for testing whether they allow unambiguous rankings. For scenarios, we apply the model to artificially generated patterns representing all possible configurations as well as to real-world settlement classifications differing in growth dynamics and patterns. [ABSTRACT FROM AUTHOR]

Urbanization in China has been experiencing a remarkable dynamism in the past 40 years. The most evident implication of urbanization is the physical growth of cities. We analyze urban land growth rates and changes in spatial urban forms from the end of the 1980s to 2010 based on the authoritative National Land Use/Cover Database of China. We present new spatial measures that describe 'urban land growth types' and 'fluctuations in urban land growth' within the monitoring time span with a temporal interval of five-year steps. We evaluate the correlations between urban land growth rates and socioeconomic data. Results show that (1) distinct characteristics exist on the spatiotemporal evolutions of urban land growth rates in terms of area and perimeter, e.g. coastal areas exhibit the most dramatic growth rates; (2) the spatial distribution characteristics of 'urban land growth types' and 'fluctuations in urban land growth' follow similar spatial patterns across China, e.g. significant differences exist between the eastern region and other regions; and (3) a moderate correlation exists between urban area growth rate and urban population growth rate at an R² of 0.37. By contrast, we determine no significant correlation between urban area growth rate and tertiary industry value growth rate. [ABSTRACT FROM AUTHOR]

More than 900 million people worldwide live in slums. These slums mainly can be found in cities of the global south and are characterized by poor living conditions and usually insufficient access to basic infrastructure such as water or energy. In order to improve the living conditions of slum inhabitants, information about the number, location and size of the slums is required to plan supply infrastructure. We therefore identify morphological slums in eight different cities in Africa, South America and Asia, using remote sensing data and analyse their size distributions. We show that 84.6% of all observed morphological slums have a size between 0.001 and 0.1 km2. These results rely on a consistent approach using a clear ontology and conceptual frame for classification. However, classification methods for these underserved areas differ. We show slum classifications based on different methods reveal a strong dependency between the particular method and the resulting size distribution. The study shows the relevance of remote sensing for the investigation of slums and the results can be used for infrastructure planning, as infrastructure improvement projects are often limited to the large known slums. Whereas, the large number of small slums distributed across the city is often neglected. [ABSTRACT FROM AUTHOR]

Correction to: I Nature Communications i https://doi.org/10.1038/s41467-023-38596-1, published online 22 May 2023 The original version of this Article contained an error in Fig. The original article can be found online at https://doi.org/10.1038/s41467-023-38596-1. [Extracted from the article]

The delimitation of urban space is conceptually elusive and fuzzy. Commonly, urban areas are delimited through administrative boundaries. These artificial, fixed boundaries, however, do not necessarily represent the actual built-up extent, the urban catchment, or the economic linkage within and across neighboring metropolitan regions. For an approach to spatially delimit an urban corridor--a generically defined concept of a massive urban area--we use the Boston to Washington (Boswash) region as an example. This area has been consistently conceptualized in literature as bounded urban space. We develop a method to spatially delimit the urban corridor using multi-source geodata (built-up extent, infrastructure and socioeconomic data) which are based on a grid rather than on administrative units. Threshold approaches for the input data serve to construct Boswash as varying connected territorial spaces, allowing us to investigate the variability of possible spatial forms of the area, i.e., to overcome the simple dichotomous classification in favor of a probability-based differentiation. Our transparent multi-layer approach, validated through income data, can easily be modified by using different input datasets while maintaining the underlying idea that the likelihood of an area being part of an urban corridor is flexible, i.e., in our case a factor of how many input layers return positive results. [ABSTRACT FROM AUTHOR]

The impact of natural hazards on mankind has increased dramatically over the past decades. Global urbanization processes and increasing spatial concentrations of exposed elements induce natural hazard risk at a uniquely high level. To mitigate affiliated perils requires detailed knowledge about elements at risk. Considering a high spatiotemporal variability of elements at risk, detailed information is costly in terms of both time and economic resources and therefore often incomplete, aggregated, or outdated. To alleviate these restrictions, the availability of very-high-resolution satellite images promotes accurate and detailed analysis of exposure over various spatial scales with large-area coverage. In the past, valuable approaches were proposed; however, the design of information extraction procedures with a high level of automatization remains challenging. In this paper, we uniquely combine remote sensing data and volunteered geographic information from the OpenStreetMap project (OSM) (i.e., freely accessible geospatial information compiled by volunteers) for a highly automated estimation of crucial exposure components (i.e., number of buildings and population) with a high level of spatial detail. To this purpose, we first obtain labeled training segments from the OSM data in conjunction with the satellite imagery. This allows for learning a supervised algorithmic model (i.e., rotation forest) in order to extract relevant thematic classes of land use/land cover (LULC) from the satellite imagery. Extracted information is jointly deployed with information from the OSM data to estimate the number of buildings with regression techniques (i.e., a multi-linear model from ordinary least-square optimization and a nonlinear support vector regression model are considered). Analogously, urban LULC information is used in conjunction with OSM data to spatially disaggregate population information. Experimental results were obtained for the city of Valparaíso in Chile. Thereby, we demonstrate the relevance of the approaches by estimating number of affected buildings and population referring to a historical tsunami event. [ABSTRACT FROM AUTHOR]

The article focuses on geospatial data for multiscale mapping and characterization of elements at risk. Topics discussed include remote sensing as a source of geospatial data to analyze high risk areas and their vulnerabilities, high spatial resolution display of new sensor systems, and high spatial detail despite large area coverage of remote sensing.

The article discusses how remote sensing can help in mapping exposure and high risk areas and in analyzing potential losses and mitigating strategies in natural disasters. Remote sensing enables data to be derived with high spatial and temporal resolution through the use of satellites such as the RapidEye, the Disaster Monitoring Constellation, and SkySat-1. Unmanned aerial vehicles or drones as geodata gatherers are also mentioned.

The spatial patterns of landscapes are complex. Highly dense urban centers are not just mirrowed in a dichotomic sense by rural environments; landscapes are a spatially variable continuum. In this logic, nation-states (or any political or administrative unit) spatially integrate different types and physical appearances of land cover. Understanding regions in the sense that similar physical characteristics may construct alternative (natural) spatial entities which may sub-divide or cross-over adminstrative boundaries allows us to overcome common map projections. However, which indicators and which regional logics define and delimit regions is conceptually vague. With this paper we aim to add an empirical study to identify regional phenomena in Europe. To do so, we take advantage of a new data set from remote sensing, the Global Urban Footprint. It features European-wide consistent spatial information on settlement patterns. We use density and distribution of settlements as indicators for delimiting regions by similar characteristics. Our methodological approach classifies urban nodes (by settlement density and size), spans an unbounded soft space by the classification of spatial connectivity between nodes (by continuous settlement) and maps territorial entities (by density around nodes); the approach is following a space of place logic. From a geographic perspective we identify uneven development across Europe. The corridor streching from England via the Benelux areas via Germany, Switzerland, France to Northern Italy is mapped as the European backbone; however, new focal areas such as, e.g., towards eastern Europe are also detected. Applying a plausibility check reveals that the proxy settlement pattern corresponds well with regional conceptions presented in other studies. [ABSTRACT FROM AUTHOR]

Grid-based urbanization velocity analysis of remote sensing imagery is used to measure urban growth rates. However, it remains unclear how critical the spatial resolution of the imagery is to such grid-based approaches. This research therefore investigated how urbanization velocity estimates respond to different spatial resolutions, as determined by the grid sizes used. Landsat satellite images of the Pearl River Delta (PRD) in China from the years 2000, 2005, 2010 and 2015 were hierarchically aggregated using different grid sizes. Statistical analyses of urbanization velocity derived using different spatial resolutions (or grid sizes) were used to investigate the relationships between socio-economic indicators and the velocity of urbanization for 27 large cities in PRD. The results revealed that those cities with above-average urbanization velocities remain unaffected by the spatial resolution (or grid-size), and the relationships between urbanization velocities and socio-economic indicators are independent of spatial resolution (or grid sizes) used. Moreover, empirical variogram models, the local variance model, and the geographical variance model all indicated that coarse resolution version (480 m) of Landsat images based on aggregated pixel yielded more appropriate results than the original fine resolution version (30 m), when identifying the characteristics of spatial autocorrelation and spatial structure variability of urbanization patterns and processes. The results conclude that the most appropriate spatial resolution for investigations into urbanization velocities is not always the highest resolution. The resulting patterns of urbanization velocities at different spatial resolutions can be used as a basis for studying the spatial heterogeneity of other datasets with variable spatial resolutions, especially for evaluating the capability of a multi-resolution dataset in reflecting spatial structure and spatial autocorrelation features in an urban environment. [ABSTRACT FROM AUTHOR]

The massive growth of some urban areas has led to new constellations of urban forms. New concepts describing large urban areas have been introduced but are not always defined and mapped sufficiently and consistently. This article describes urban corridors as an example of such a concept with an ambiguous spatial definition. Based on the existing usage of the concept in scientific literature and the results of a questionnaire, we attempt to spatially parameterize and identify the main characteristics of urban corridors on a global scale. The parameters we use are physically measurable and therefore serve as a basis for a harmonized and scientifically sound mapping of urban corridors using remote sensing data and methods. Our results are presented in a global urban corridor map. [ABSTRACT FROM AUTHOR]

Density is among the most important descriptive as well as normative measures in urban research. While its basic concept is generally understandable, approaches towards the density measure are manifold, diverse and of multidimensional complexity. This evolves from differing thematic, spatial and calculative specifications. Consequently, applied density measures are often used in a subjective, non-transparent, unspecific and thus non-comparable manner. In this paper, we aim at a systematic deconstruction of the measure density. Varying thematic, spatial and calculative dimensions show significant influence on the measure. With both quantitative and qualitative techniques of evaluation, we assess the particular influences on the measure density. To do so, we reduce our experiment setting to a mere physical perspective; that is, the quantitative measures building density, degree of soil sealing, floor space density and, more specifically, the density of generic structural classes such as open spaces and highest built-up density areas. Using up-to-date geodata derived from remote sensing and volunteered geographic information, we build upon high-quality spatial information products such as 3-D city models. Exemplified for the comparison of two European megacities, namely Paris and London, we reveal and systemize necessary variables to be clearly defined for meaningful conclusions using the density measure. [ABSTRACT FROM AUTHOR]