Search

Copyright of Landscape Architecture Frontiers is the property of Higher Education Press Limited Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using an event study approach and a novel data set that links administrative information on German establishments with exact distance measures from geo-referenced address data, we analyze the net impact of mass layoffs on local employment. We find that local spillovers significantly attenuate the direct impact of mass layoffs on municipal-level employment. About a quarter of the 1-year direct employment loss due to a mass layoff event is absorbed within the same municipality. Local spillovers are especially pronounced very close to the mass layoff site; the majority of the absorption is concentrated within a 1000-m radius. There is little evidence of spillovers beyond the affected municipality. [ABSTRACT FROM AUTHOR]

The subject of the study is green city infrastructure. The content of the concept ‘green infrastructure’ is expounded, and the reasons why the term ‘green infrastructure’ is now mostly used. Special attention is paid to the structural properties of the green infrastructure, in particular to the main element that ensures its sustainability - the green framework. As a case in point, we considered the condition of the green infrastructure framework in the city of Ekaterinburg. Issues around the green infrastructure framework were identified and described regarding its structural properties. Long-term risks arising from the mismatch between the structural properties of the green infrastructure framework and the needs of city development are assessed. The novelty of the study lies in the fact that we used not only the traditional methods such as mapping, graphical analytic technique, and photo surveying, but also new modern approaches such as geo-referenced data, multispectral space images from which different indices are calculated, in particular combinations of images in different channels. The article shows how different indices can be applied and used to analyze green infrastructure. It is concluded that in a number of cases it is the use of geo-referenced data that allows obtaining unique information about the condition of the green infrastructure and the causes of long-term changes occurring in it. All the identified regularities are used to evaluate the design decisions laid down in the general plan of the city to forecast risks and create recommendations that contribute to the sustainable development of green infrastructure.

We study the comparative political economy within the territories ruled by the medieval republics in Northern Italy. Building on the historical evidence, we conceptualize the emergence of more inclusive vs. extractive institutions in these sovereign polities as driven by the interests of local rulers and their need to build state capacity. We provide novel insights on the role of geography and historical contingencies in the development of public governance, individual attitudes, and social-inclusion, which mutually supported each other and persisted over time. We measure the origin, territorial evolution, and disappearance of all the sovereign polities that ruled over the Italian peninsula during the period of 1000-1800 AD. The empirical analysis connects contemporary socio-economic outcomes across spatially disaggregated northern Italian localities, at the municipality level, to local political history. We distinguish between so-called "communal" and "maritime republics" and show that the intensity and stability of exposure to the different types of republican rule in pre-industrial times continues to shape local public good provision and individual fiscal compliance, and has left a tangible imprint on today's population diversity. [ABSTRACT FROM AUTHOR]

Abstract Background We introduce and study a recently proposed method for privacy-preserving distance computations which has received little attention in the scientific literature so far. The method, which is based on intersecting sets of randomly labeled grid points, is henceforth denoted as ISGP allows calculating the approximate distances between masked spatial data. Coordinates are replaced by sets of hash values. The method allows the computation of distances between locations L when the locations at different points in time t are not known simultaneously. The distance between $$L_1$$ L 1 and $$L_2$$ L 2 could be computed even when $$L_2$$ L 2 does not exist at $$t_1$$ t 1 and $$L_1$$ L 1 has been deleted at $$t_2$$ t 2 . An example would be patients from a medical data set and locations of later hospitalizations. ISGP is a new tool for privacy-preserving data handling of geo-referenced data sets in general. Furthermore, this technique can be used to include geographical identifiers as additional information for privacy-preserving record-linkage. To show that the technique can be implemented in most high-level programming languages with a few lines of code, a complete implementation within the statistical programming language R is given. The properties of the method are explored using simulations based on large-scale real-world data of hospitals ( $$n=850$$ n = 850 ) and residential locations ( $$n=13,000$$ n = 13 , 000 ). The method has already been used in a real-world application. Results ISGP yields very accurate results. Our simulation study showed that—with appropriately chosen parameters – 99 % accuracy in the approximated distances is achieved. Conclusion We discussed a new method for privacy-preserving distance computations in microdata. The method is highly accurate, fast, has low computational burden, and does not require excessive storage.

Herbaceous biomass will contribute significantly to meeting renewable energy goals. Harvesting equipment for hay is generally suitable for mowing, raking, and baling grasses such as switchgrass; however, there is a need for field data to better understand machine performance in energy crops. The purpose of this study was to collect field data to estimate baler field capacity, throughput, and speed. Data gathered with a Differential Global Positioning System (DGPS) unit during baling provided time-motion studies of baler productivity. Six fields were used to compare field capacity, speed, and throughput results from four round balers and one large-square baler. The results show that in-field performance of round balers is significantly affected by yield, but that the relationship can be represented with machinery management concepts, knowledge of maximum throughput, and wrap-eject time. Baler performance will be overestimated if the yield, maximum throughput, and wrap-eject time are not correctly accounted for.

Model simulations for bioenergy harvest planning need to utilize equipment-capacity relationships for equipment operating under the high-yield conditions typical of a biomass crop. These performance assumptions have a direct bearing on the estimates of machine capacity, the number of machines required, and, therefore, the cost to fulfill the biorefinery plant demands for a given harvest window. Typically, two major issues in these models have been poorly understood: the available time required to complete the harvest operation (often called probability of workdays) and the capacity of the harvest equipment as impacted by yield. Simulations use annual yield estimates, which incorporate weather events, to demonstrate year-to-year effects. Some simulations also incorporate potential yield increases from genetically modified energy crops. There are limited field performance data for most current forage equipment used for harvesting high-yield biomass crops. Analysis shows that the impact of wrap/eject time for round balers resulted in a 50% reduction in achieved throughput capacity (Mg/h). After the maximum throughput is reached, the cost of the round bale operation (3.23 USD/Mg) is double that of the large-square baler (1.63 USD/Mg). The round baler achieved throughput capacity is 50% less (32.7 Mg/h compared to 71.0 Mg/h) than the large-square baler.

Background: We introduce and study a recently proposed method for privacy-preserving distance computations which has received little attention in the scientific literature so far. The method, which is based on intersecting sets of randomly labeled grid points, is henceforth denoted as ISGP allows calculating the approximate distances between masked spatial data. Coordinates are replaced by sets of hash values. The method allows the computation of distances between locations L when the locations at different points in time t are not known simultaneously. The distance between L 1 and L 2 could be computed even when L 2 does not exist at t 1 and L 1 has been deleted at t 2 . An example would be patients from a medical data set and locations of later hospitalizations. ISGP is a new tool for privacy-preserving data handling of geo-referenced data sets in general. Furthermore, this technique can be used to include geographical identifiers as additional information for privacy-preserving record-linkage. To show that the technique can be implemented in most high-level programming languages with a few lines of code, a complete implementation within the statistical programming language R is given. The properties of the method are explored using simulations based on large-scale real-world data of hospitals ( n = 850 ) and residential locations ( n = 13 , 000 ). The method has already been used in a real-world application. Results: ISGP yields very accurate results. Our simulation study showed that—with appropriately chosen parameters – 99 % accuracy in the approximated distances is achieved. Conclusion: We discussed a new method for privacy-preserving distance computations in microdata. The method is highly accurate, fast, has low computational burden, and does not require excessive storage. [ABSTRACT FROM AUTHOR]

Herbaceous biomass will contribute significantly to meeting renewable energy goals. Harvesting equipment for hay is generally suitable for mowing, raking, and baling grasses such as switchgrass; however, there is a need for field data to better understand machine performance in energy crops. The purpose of this study was to collect field data to estimate baler field capacity, throughput, and speed. Data gathered with a Differential Global Positioning System (DGPS) unit during baling provided time-motion studies of baler productivity. Six fields were used to compare field capacity, speed, and throughput results from four round balers and one large-square baler. The results show that in-field performance of round balers is significantly affected by yield, but that the relationship can be represented with machinery management concepts, knowledge of maximum throughput, and wrap-eject time. Baler performance will be overestimated if the yield, maximum throughput, and wrap-eject time are not correctly accounted for. [ABSTRACT FROM AUTHOR]

Model simulations for bioenergy harvest planning need to utilize equipment-capacity relationships for equipment operating under the high-yield conditions typical of a biomass crop. These performance assumptions have a direct bearing on the estimates of machine capacity, the number of machines required, and, therefore, the cost to fulfill the biorefinery plant demands for a given harvest window. Typically, two major issues in these models have been poorly understood: the available time required to complete the harvest operation (often called probability of workdays) and the capacity of the harvest equipment as impacted by yield. Simulations use annual yield estimates, which incorporate weather events, to demonstrate year-to-year effects. Some simulations also incorporate potential yield increases from genetically modified energy crops. There are limited field performance data for most current forage equipment used for harvesting high-yield biomass crops. Analysis shows that the impact of wrap/eject time for round balers resulted in a 50% reduction in achieved throughput capacity (Mg/h). After the maximum throughput is reached, the cost of the round bale operation (3.23 USD/Mg) is double that of the large-square baler (1.63 USD/Mg). The round baler achieved throughput capacity is 50% less (32.7 Mg/h compared to 71.0 Mg/h) than the large-square baler. [ABSTRACT FROM AUTHOR]

In this paper I investigate undergraduate students' discrete location choices in presence of a plethora of potential destinations and psychic costs. I demonstrate how enrolment into institutions of tertiary education is influenced by personality and social preferences. More importantly, these individual traits are found to affect the valuation of location-specific conditions in alternative study locations. Eventually, the relevance of location attributes, such as urban or labour market characteristics, varies substantially with respect to distance and individuals' personality. This has direct implications for student recruitment, since prospective students display distinct geographic sorting patterns along these traits: students featuring higher levels of patience integrate post-graduation opportunities into their decision-making. As a consequence, institutions in economically less prosperous regions might attract a specific subset of the overall student population, which might also have repercussions on student performance. [ABSTRACT FROM AUTHOR]

Water distribution networks are the backbone of any municipal water supply. Their task is to supply the population regardless of the respective demand. High resilience of these infrastructures is of great importance and has brought these infrastructures into the focus of science and politics. At the same time, the data collected is highly sensitive and often openly unavailable. Therefore, researchers have to rely on models that represent the topology of these infrastructures. In this work, a model is developed that allows the topology of an urban water infrastructure to be mapped using the example of Cologne, Germany by combining freely available data. On the one hand, spatial data on land use (local climate zones) are used to disaggregate the water demand within the city under consideration. On the other hand, the parallelism of water and urban transportation infrastructures is used to identify the topology of a network by applying optimization methods. These networks can be analyzed to identify vulnerable areas within urban structures.

We describe an applied methodology to build fuzzy models of geographical expressions, which are meant to be used for natural language generation purposes. Our approach encompasses a language grounding task within the development of an actual data-to-text system for the generation of textual descriptions of live weather data. For this, we gathered data from meteorologists through a survey and built consistent fuzzy models that aggregate the interpersonal variations found among the experts. A subset of the models was utilized in an illustrative use case, where we generated linguistic descriptions of weather maps for specific geographical expressions. These were used in a task-based evaluation to determine how well potential readers are able to identify the geographical expressions grounded on the models.

In recent years, advances in computer hardware, graphics rendering algorithms and computer vision have enabled the utilization of 3D building reconstructions in the fields of archeological structure restoration and urban planning. This paper deals with the reconstruction of realistic 3D models of buildings façades, in the urban environment for cultural heritage. The proposed approach is an extension of our previous work in this research topic, which introduced a methodology for accurate 3D realistic façade reconstruction by defining and exploiting a relation between stereoscopic image and tacheometry data. In this work, we re-purpose well known deep neural network architectures in the fields of image segmentation and single image depth prediction, for the tasks of façade structural element detection, depth point-cloud generation and protrusion estimation, with the goal of alleviating drawbacks in our previous design, resulting in a more light-weight, robust, flexible and cost-effective design.

Governments use a variety of policies to increase the impact of foreign investment on economic growth. An increasingly popular policy is to require that foreign companies provide public goods near the communities where their commercial investments are sited. This approach seeks to crowd in additional investments, create clusters of interconnected firms, and set in motion economic agglomeration processes. Post-2006 Liberia represents an ideal empirical setting to test the effectiveness of this approach. We construct a new dataset that measures the precise locations of 557 natural resource concessions granted to investors. We then merge these data with a remotely sensed measure of nighttime light growth at the 1 km × 1 km grid cell level and analyze it using a matched difference-in-differences strategy. We find heterogeneous treatment effects across sectors and investor types: mining (specifically iron-ore) investments projects have positive growth effects, while agriculture and forestry investment projects do not; furthermore, concessions granted to Chinese investors have positive growth effects while those given to U.S. investors do not. These patterns of heterogeneous treatment effects across sectors and investor types are consistent with the theory of change underpinning the government’s development corridor strategy. [ABSTRACT FROM AUTHOR]

For the development of advanced driving assistance and automation systems the simulation plays an important role. Urban areas get increasing emphasis, especially in the context of future Car2X-communication. This article describes an approach developed in the project Virtual World. Its goal is to model such virtual three-dimensional (3D) environments and logical road descriptions automatically based on a tool chain from heterogeneous geographic datasets (e.g., cadastral data, road surveying, aerial pictures, and crowd-sourced data). As proof of concept, the urban area of Braunschweig, Germany, was chosen. The article focuses on the generation of a 3D city model with corresponding road network description suitable for driving and traffic simulations as used in research and industry. The article gives technical descriptions of the major work steps and discusses issues regarding the availability of data. It concludes with the current project outcome and further development in the project. [ABSTRACT FROM AUTHOR]

Geographical Indications (GIs), as Protected Designation of Origin (PDO)and Protected Geographical Indication (PGI), offer a unique protection scheme to preserve high-quality agri-food productions and support sustainable rural development at the territorial level. However, not all the areas with traditional agri-food products are acknowledged with a GI. Examining the Italian wine sector by a geo-referenced database and a machine learning framework, we show that municipalities which obtain a GI within the subsequent 10 year period (2002–2011) can be predicted using a large set of (lagged) municipality-level data (1981–2001). We find that the Random Forest algorithm is the best model to make out-of-sample predictions of municipalities which obtain GIs. Results show that there is a sort of optimal territorial condition characterized by the successful matching of wine-growing profession (vineyards), local actors involved (number of farmers), and physical dimension of farms (middle farms). Being in a vital economic system and the distance from major urban centers also emerges among the main relevant features in predicting the success of GIs. The methodology adopted and the evidence provided lead to policy reflections, in the light of the future Common Agricultural Policy (CAP) programming period and the scheduled reform of the GI's quality scheme. • A method to predict Geographical Indications areas is developed. • The analysis uses a geo-referenced database, lagged data, and machine learning models. • Random Forest is the best model to make out-of-sample predictions. • Socio-economic and spatial features emerge as determinants. • Reflections for policy recommendations tools are provided. [ABSTRACT FROM AUTHOR]

The purpose of this project is to use a game engine to integrate geo-referenced research data, whether experimental or simulated, to present it interactively to the user. Geo-referenced means that every image, video, or sound file, every pressure map, and every simulated temperature chart is attached to a specific point on a map or body. These data may also be time-referenced, so that different data sets may be available at the same location for different times of the day or seasons of the year. Target users for the interactive applications are high-school and college students who can then conduct their own "experiments" or "explorations" as a way to get exposed to the problems and methodologies of science and research. We use two examples of projects to illustrate the approach. [ABSTRACT FROM AUTHOR]

Abstract: NameSet is a system that translates a set of geographic coordinates into a textual name based on the geographic regions where the coordinates occur. One possible application of NameSet is to concisely present the geographical scope of a set of geo-referenced observations to a human user. Another application is to generate text to depict a set of coordinates that appear on a web site—text that could later be used for information retrieval applications. NameSet’s computation is based on a simple algorithm, using off-the-shelf and web-based data sources. The system was proven effective in an application that automatically organizes and names sets of geo-referenced digital photographs. [Copyright &y& Elsevier]

Abstract: In this paper we explore the advantages of using Self-Organized Maps (SOMs) when dealing with geo-referenced data. The standard SOM algorithm is presented, together with variants which are relevant in the context of the analysis of geo-referenced data. We present a new SOM architecture, the Geo-SOM, which was especially designed to take into account spatial dependency. The strengths and weaknesses of the different variants proposed are shown through a set of tests based on artificial data. A real world application of these techniques is given through the analysis of geodemographic data from Lisbon''s metropolitan area. [Copyright &y& Elsevier]

Factor analysis of multivariate spatial data is considered. A systematic approach for modeling the underlying structure of potentially irregularly spaced, geo-referenced vector observations is proposed. Statistical inference procedures for selecting the number of factors and for model building are discussed. We derive a condition under which a simple and practical inference procedure is valid without specifying the form of distributions and factor covariance functions. The multivariate prediction problem is also discussed, and a procedure combining the latent variable modeling and a measurement-error-free kriging technique is introduced. Simulation results and an example using agricultural data are presented. [Copyright &y& Elsevier]

Mulrivariate spatial or geo-referenced data arise naturally in such disciplines as ecology, agriculture, geology, and atmospheric sciences. In practice, interest often lies in modeling underlying structure and representing interrelationships in terms of a smaller number of variables. For such situations, statistical analysis using a latent variable model is proposed. We present a general model that incorporates spatial correlation and potential lagged or shifted dependencies and that can represent subject matter theory or serve as a practical exploratory model. Procedures for model lifting, parameter estimation, inferences, and latent variables prediction are developed without restrictive assumptions on distribution and covariance function forms. The properties and usefulness of the proposed approaches are assessed by asymptotic theory and an extensive simulation study. An example from precision agriculture is also presented. [ABSTRACT FROM AUTHOR]

The rapid development of analysis tools, such as Geographic Information Systems and web-based tools for viewing, accessing, and analyzing of geo-referenced information, and the growing abundance of openly available Earth observations will increase the already large dependency of science and society on georeferenced information. However, most geo-referenced information comes without sufficient information on quality and applicability. The Group on Earth Observations (GEO) has embarked on establishing a so-called GEO Label that would provide easy-to-understand, globally available information on aspects of quality, user rating, relevance, and fit-for-usage of the products and services accessible through the Global Earth Observing System of Systems (GEOSS). There is a wide range of potential goals for the GEO Label including an attractive incentive for involvement; promotion of data sharing; creating trustworthiness; and providing information on usage. Here we propose a tri-faceted label that combines quality, user feedback, and relevance.

Water distribution networks are the backbone of any municipal water supply. Their task is to supply the population regardless of the respective demand. High resilience of these infrastructures is of great importance and has brought these infrastructures into the focus of science and politics. At the same time, the data collected is highly sensitive and often openly unavailable. Therefore, researchers have to rely on models that represent the topology of these infrastructures. In this work, a model is developed that allows the topology of an urban water infrastructure to be mapped using the example of Cologne, Germany by combining freely available data. On the one hand, spatial data on land use (local climate zones) are used to disaggregate the water demand within the city under consideration. On the other hand, the parallelism of water and urban transportation infrastructures is used to identify the topology of a network by applying optimization methods. These networks can be analyzed to identify vulnerable areas within urban structures. [ABSTRACT FROM AUTHOR]