In contrast to the ongoing worldwide uncontrolled expansion of urban development resulting in sprawled cities, compact cities have been argued by planners and researchers to be the more sustainable urban form. However, in compact cities, it has been shown that a low proportion of green spaces jeopardizes the sufficient supply of urban ecosystem services. This suggests that there remains a deficiency in clear visions for operationalizing compact and green cities. To remediate this, this paper introduces a systemic conceptual framework for compact and green cities by combining the concepts of smart growth and green infrastructure. The indicator-based, smartcompact-green city framework includes two aspects: 1) smart compact cities (considering the need to limit urban sprawl through smart growth) and 2) smart green cities (reflecting the preservation and (re-)development of urban green infrastructure). The paper suggests that there is the need to balance these two aspects to develop a systemic approach towards smart-compact-green cities. A hierarchical target system grounded on four characters for smart compact and smart green cities is developed. Smart-compact-green cities can be characterized through a 1) smart environment of compact and green cities, 2) smart multifunctionality of compact and green cities (economic, social, environmental), 3) smart government for compact and green cities and 4) smart governance for compact and green cities. The characters comprise twelve factors defined by 39 indicators for smart compact cities and 44 indicators for smart green cities, respectively. The systemic framework can support researchers and practitioners to develop visions of how existing or future cities can approach smart-compact-green cities in mainstreaming the ecology of and for cities by better understanding the complexity of urban systems and providing a basis for a systematic spatial monitoring.
The paper summarises the multiple benefits of urban green spaces for city dwellers and provides an overview of proximity approaches and common key parameters for green-space quantification in cities. We propose indicators for the assessment of the ecosystem service 'recreation in the city' on a national scale. The calculation procedure, which takes into account the best available data sets in Germany, is explained. The determination of threshold values regarding green-space standards comprising type, size and distance is crucial to such studies. The results, the degree of provision with public green spaces in all German cities with more than 50,000 inhabitants (n = 182) and their accessibility, are presented. In total, green spaces are accessible for daily recreation for 74.3% of the inhabitants in German cities, which means that underprovision affects 8.1 million city dwellers. Some indicator details are shown for the examples of Wiesbaden and Stuttgart. Finally, we discuss the approach and values of the proposed and quantified indicators in a German and European context.
SynopsisThe crystallinity, elastic modulus, and tensile strength of samples of various draw ratios together with the true stress-strain curves of high-density polyethylene were determined to establish correlations with morphological changes occurring during deformation. Changes of crystallinity at draw ratios below 5, Le., constancy during drawing of quenched film and a decrease. during drawing of annealed film, are explained by the formation of microfibrils with crystallinity independent of the thermal history of the film. The microfibrils slide past each other a t higher draw ratios, generating an increasing number of interfibrillar tie molecules, which is reflected in the increase of crystallinity, elastic modulus, and tensile strength. From the true stress-strain curves, the differential work density for the deformation of the volume element was calculated as a function of the draw ratio. It contains two components which reflect two different mechanisms of deformation. The first component, decreasing with increasing draw ratio, can be associated with the destruction of the original microspherulitic structure; the second one, increasing with increasing draw ratio, can be associated with the deformation of the new fiber structure, i.e., with the sliding motion of the microfibrils formed during the first deformation step.
Data, maps and services of the national mapping and cadastral agencies contain geometric information on buildings, particularly building footprints. However, building type information is often not included. In this paper, we propose a data-driven approach for automatic classification of building footprints that make use of pattern recognition and machine learning techniques. Using a Random Forest Classifier the suitability of five different data sources (e.g. topographic raster maps, cadastral databases or digital landscape models) is investigated with respect to the achieved accuracies. The results of this study show that building footprints obtained from topographic databases such as digital landscape models, cadastral databases or 3D city models can be classified with an accuracy of 90-95%. When classifying building footprints on the basis of topographic maps the accuracy is considerably lower (as of 76-88%). The automatic classification of building footprints provides an important contribution to the acquisition of new small-scale indicators on settlement structure, such as building density, floor space ratio or dwelling/population densities. In addition to its importance for urban research and planning, the results are also relevant for cartographic disciplines, such as map generalization, automated mapping and geovisualization.
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