Estimating Size Distributions of Building Areas for Natural Hazards Assessments

1990 ◽  
Vol 6 (3) ◽  
pp. 497-505 ◽  
Author(s):  
Barclay G. Jones ◽  
Blane D. Lewis
Keyword(s):  
Urban Areas ◽  
Building Stock ◽  
Data Set ◽  
Floor Area ◽  
Response Recovery ◽  
Error Distributions ◽  
Regression Techniques ◽  
Negative Exponential ◽  
Different Characteristics ◽  
Best Fit

It is very helpful to be able to estimate the number of buildings in floor area size categories for various purposes in risk assessment, vulnerability reduction, emergency response, recovery and reconstruction planning. We compile a frequency distribution by floor area from a data set for 900,000 buildings using complete enumerations of buildings from several urban areas. Three models are fit to the data: the lognormal distribution and two regression models based on the negative exponential. A regression model estimating the percentage of buildings larger than a given size provides the best fit and is a useful technique for making predictions. Since estimates produced by regression techniques have known error distributions, the results will only be approximate. For places for which the building stock has considerably different characteristics from the data set, the parameters of the model will have to be recalculated. An actual example of an application of the model is presented.

Construction of regional building typologies with a material catalog

2016 ◽  
Vol 27 (6) ◽  
pp. 663-680 ◽  
Author(s):  
Marcelo Esteban Muñoz Hidalgo
Keyword(s):  
Urban Areas ◽  
Financial Incentives ◽  
Regional Differences ◽  
Embodied Energy ◽  
Building Stock ◽  
Data Set ◽  
Content Type ◽  
Heat Demand ◽  
Building Typology ◽  
Building Components

Purpose The purpose of this paper is to develop a new building typology for: the estimation of heat demand of urban agglomerations; and the assessment of the environmental impact linked to urban re-development policies. Design/methodology/approach In order to: capture regional differences of urban areas; and describe individual building components of neighbourhoods, the author proposes the construction of a new building typology based upon a regional material catalog (Klauß et al. 2009a). Findings The main findings of this analysis are primarily on method. The author presents a method to estimate the building shell from available information on the digital cadastre and the first attempt to link material databases with a ranking algorithm. The analysis application presented in this paper shows that the embodied energy on insulation materials and the corresponding energetic payback time depends on the “real” building shell, making it important to accurately compute this value. Practical implications Results from this analysis present an heat demand urban model able to capture: regional differences, thanks to the use of the regional material catalog, local characteristics of the building stock, thanks to the detailed information of the digital cadastre, and ability to link building stock models with rich Live Cycle Inventory (LCI) databases for the explicit consideration of the embodied energy of retrofit measures. Further applications of the developed method could be used to assess new urban development plans of the city as well as financial incentives packages for building retrofits. Originality/value This analysis shows the first step towards the development of a new building typology constructed upon a regional material catalog. This innovation allows taking regional differences into account. Because the author uses a detailed catalog of building components, an accounting of embodied energy by linking data of a LCI database is possible. In this paper the author presents an application of the enriched data set, the presented example shows the needed embodied energy by adding an extra layer to the predefined building components of selected buildings of the digital cadastre.

Do firm characteristics of concentrated ownership firms affect dividend payout beyond traditional motivations?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Avinash Jawade
Keyword(s):  
Agency Cost ◽  
Growth Potential ◽  
Firm Characteristics ◽  
Data Set ◽  
Holistic View ◽  
Dividend Payout ◽  
Content Type ◽  
Concentrated Ownership ◽  
Indian Context ◽  
Best Fit

Purpose This study aims to analyze the influence of firm characteristics in dividend payout in a concentrated ownership setting. Design/methodology/approach This study is probably the first to use the lasso technique for model selection and error prediction in the study of dividend payout in India. The lasso method comprises subsampling the available data set and performing reiterative regressions on those samples to generate the model with the best fit. This study incorporates four different ways of performing lasso treatment to get the best fit among them. Findings This study analyzes the influence of firm characteristics on dividend payout in the Indian context and asserts that firms with growth potential and earnings volatility do not hesitate to cut dividends. This study does not find evidence for signaling, agency cost and life cycle theories in a concentrated ownership setting. Earnings is the single most important factor to have a positive influence on dividend, while excessively leveraged firms are restrictive of dividend payout. Taxation has a prominent role in altering the way firms pay dividend. Research limitations/implications The recent changes in buyback taxation offer another opportunity to test the reactive behavior of firms. Also, given the disregard for traditional motivations, further research needs to be done to determine if dividend adjustments (on the lower side) help enhance firm value or not. Practical implications This study may help investors view dividends in a proper perspective. Firms give importance to investments over dividends and thus investors need not dwell on dividend changes if firms fulfill their growth potential. Social implications It lends perspective to investors about dividend changes and its importance. Originality/value The methodology used for analysis is absolutely original in the literature pertaining to dividend policy in the Indian context. The literature is abundant with theories advocating or opposing the eminence of dividend payout; however, this study takes a holistic view of all influential dividend determinants in literature to understand dividend payout.

scholarly journals A Differentiated Spatial Assessment of Urban Ecosystem Services Based on Land Use Data in Halle, Germany

Land ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 101 ◽  
Author(s):  
Janis Arnold ◽  
Janina Kleemann ◽  
Christine Fürst
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Ecosystem Services ◽  
Urban Areas ◽  
Urban Ecosystem ◽  
Data Set ◽  
Urban Ecosystem Services ◽  
Climate Regulation ◽  
Land Use Types ◽  
Cooling Effects

Urban ecosystem services (ES) contribute to the compensation of negative effects caused by cities by means of, for example, reducing air pollution and providing cooling effects during the summer time. In this study, an approach is described that combines the regional biotope and land use data set, hemeroby and the accessibility of open space in order to assess the provision of urban ES. Hemeroby expresses the degree of naturalness of land use types and, therefore, provides a differentiated assessment of urban ES. Assessment of the local capacity to provide urban ES was conducted with a spatially explicit modeling approach in the city of Halle (Saale) in Germany. The following urban ES were assessed: (a) global climate regulation, (b) local climate regulation, (c) air pollution control, (d) water cycle regulation, (e) food production, (f) nature experience and (g) leisure activities. We identified areas with high and low capacity of ES in the urban context. For instance, the central parts of Halle had very low or no capacity to provide ES due to highly compact building styles and soil sealing. In contrast, peri-urban areas had particularly high capacities. The potential provision of regulating services was spatially limited due to the location of land use types that provide these services.

scholarly journals Building Asset Value Mapping in Support of Flood Risk Assessment: A Case Study of Shanghai, China

2017 ◽  
Author(s):  
Jidong Wu ◽  
Xu Wang ◽  
Elco Koks
Keyword(s):  
Risk Assessment ◽  
Natural Disaster ◽  
Flood Risk ◽  
Statistical Data ◽  
Disaster Risk ◽  
Flood Risk Assessment ◽  
Building Stock ◽  
Floor Area ◽  
Asset Value ◽  
Natural Disaster Risk

Abstract. Exposure is an integral part of any natural disaster risk assessment. As one of the consequences of natural disasters, damage to buildings is one of the most important concerns. As such, estimates of the building stock and the values at risk can assist natural disaster risk management, including determining the damage extent and severity. Unfortunately, only little information about building asset value is readily available in most countries (especially its spatial distributions) including in China, given that the statistical data on building floor area (BFA) is collected by administrative unities in China. In order to bridge the gap between aggregated census statistical buildings floor-area data to geo-coded building asset value data, this article introduces a methodology for a city-scale building asset value mapping using Shanghai as an example. It consists of a census BFA disaggregation (downscaling) by means of a building footprint map extracted from high-resolution remote sensing data and LandScan population density data, and a financial appraisal of building asset values. A validation with statistical data confirms the feasibility of the modelled building storey. The example of the use of the developed building asset value map in exposure assessment of a flood scenario of Shanghai demonstrated that the dataset offers immense analytical flexibility for flood risk assessment. The method used in this paper is transferable to be applied in other cities of China for building asset value mapping.

scholarly journals Building Smarter Cities through Social Entrepreneurship

2017 ◽  
pp. 327-362 ◽  
Author(s):  
Susana Bernardino ◽  
J. Freitas Santos
Keyword(s):  
Urban Areas ◽  
Smart Cities ◽  
Qualitative Approach ◽  
Non Profit ◽  
Social Interventions ◽  
Social Ventures ◽  
Different Characteristics ◽  
The City ◽  
Disadvantaged People

The objective of the present study is to examine the extent to which social ventures are able to increase the “smartness” of cities. To achieve this goal, we adopt a qualitative approach using a case study method to obtain valuable insights about different characteristics and strategies of Cais (a non-profit association dedicated to helping disadvantaged people in urban areas). Through our analysis of Cais's activities, we assess whether its social interventions match the dimensions proposed by Giffinger et al. (2007) to rank smart cities' performance; specifically, it has smart: economy, people, governance, mobility, environment, and living. The research shows that the action pursued comprises elements from all the above-mentioned dimensions. Further, the analysis reveals that Cais reinforces the smartness of the city in which it acts (in terms of attributes such as living, economy, people, and environment).

scholarly journals Artificial Intelligence Techniques for river flow forecasting in the Seyhan River Catchment, Turkey

2007 ◽  
Vol 4 (3) ◽  
pp. 1369-1406 ◽  
Author(s):  
M. Firat
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
River Flow ◽  
Fuzzy Inference ◽  
Flow Data ◽  
Data Set ◽  
Inference System ◽  
Forecasting Models ◽  
River Flow Forecasting ◽  
Best Fit

Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), for forecasting of daily river flow is investigated and the Seyhan catchment, located in the south of Turkey, is chosen as a case study. Totally, 5114 daily river flow data are obtained from river flow gauges station of Üçtepe (1818) on Seyhan River between the years 1986 and 2000. The data set are divided into three subgroups, training, testing and verification. The training and testing data set include totally 5114 daily river flow data and the number of verification data points is 731. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN methods. The results of ANFIS, GRNN and FFNN models for both training and testing are evaluated and the best fit forecasting model structure and method is determined according to criteria of performance evaluation. The best fit model is also trained and tested by traditional statistical methods and the performances of all models are compared in order to get more effective evaluation. Moreover ANFIS, GRNN and FFNN models are also verified by verification data set including 731 daily river flow data at the time period 1998–2000 and the results of models are compared. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily River flow forecasting.

scholarly journals From pole to pole: 33 years of physical oceanography onboard R/V <i>Polarstern</i>

2017 ◽  
Vol 9 (1) ◽  
pp. 211-220 ◽  
Author(s):  
Amelie Driemel ◽  
Eberhard Fahrbach ◽  
Gerd Rohardt ◽  
Agnieszka Beszczynska-Möller ◽  
Antje Boetius ◽  
...  
Keyword(s):  
Data Collection ◽  
Water Cycle ◽  
Heat Budget ◽  
Ocean Dynamics ◽  
The Arctic ◽  
Sensor Calibration ◽  
Data Sets ◽  
Calibration Data ◽  
Data Set ◽  
Different Characteristics

Abstract. Measuring temperature and salinity profiles in the world's oceans is crucial to understanding ocean dynamics and its influence on the heat budget, the water cycle, the marine environment and on our climate. Since 1983 the German research vessel and icebreaker Polarstern has been the platform of numerous CTD (conductivity, temperature, depth instrument) deployments in the Arctic and the Antarctic. We report on a unique data collection spanning 33 years of polar CTD data. In total 131 data sets (1 data set per cruise leg) containing data from 10 063 CTD casts are now freely available at doi:10.1594/PANGAEA.860066. During this long period five CTD types with different characteristics and accuracies have been used. Therefore the instruments and processing procedures (sensor calibration, data validation, etc.) are described in detail. This compilation is special not only with regard to the quantity but also the quality of the data – the latter indicated for each data set using defined quality codes. The complete data collection includes a number of repeated sections for which the quality code can be used to investigate and evaluate long-term changes. Beginning with 2010, the salinity measurements presented here are of the highest quality possible in this field owing to the introduction of the OPTIMARE Precision Salinometer.

scholarly journals CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 317-323
Author(s):  
J. Schachtschneider ◽  
C. Brenner
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Point Clouds ◽  
Urban Environments ◽  
Seasonal Effects ◽  
Data Set ◽  
Multi Temporal ◽  
One Year

Abstract. The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics.In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results.

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