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Jeff Wright
Jeff Wright

Modeling Electric Distribution With GIS.epubl

Abstract:There is an increasing impetus for the use of digital city models and sensor network data to understand the current demand for utility resources and inform future infrastructure service planning across a range of spatial scales. Achieving this requires the ability to represent a city as a complex system of connected and interdependent components in which the topology of the electricity, water, gas, and heat demand-supply networks are modelled in an integrated manner. However, integrated modelling of these networks is hampered by the disparity between the predominant data formats and modelling processes used in the Geospatial Information Science (GIS) and Building Information Modelling (BIM) domains. This paper presents a software systems approach to scale-free, multi-format, integrated modelling of evolving cross-domain utility infrastructure network topologies, and the analysis of the spatiotemporal dynamics of their resource flows. The system uses a graph database to integrate the topology of utility network components represented in the CityGML UtilityNetwork Application Domain Extension (ADE), Industry Foundation Classes (IFC) and JavaScript Object Notation (JSON) real-time streaming messages. A message broker is used to disseminate the changing state of the integrated topology and the dynamic resource flows derived from the streaming data. The capability of the developed system is demonstrated via a case study in which internal building and local electricity distribution feeder networks are integrated, and a real-time building management sensor data stream is used to simulate and visualise the spatiotemporal dynamics of electricity flows using a dynamic web-based visualisation.Keywords: GIS-BIM integration; infrastructure networks; utility resource flows; data streaming; sensor networks; smart cities; real-time simulation; data visualisation

Modeling Electric Distribution With GIS.epubl

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We describe a new methodology for performing regionalized life cycle assessment and systematically choosing the spatial scale of regionalized impact assessment methods. We extend standard matrix-based calculations to include matrices that describe the mapping from inventory to impact assessment spatial supports. Uncertainty in inventory spatial data is modeled using a discrete spatial distribution function, which in a case study is derived from empirical data. The minimization of global spatial autocorrelation is used to choose the optimal spatial scale of impact assessment methods. We demonstrate these techniques on electricity production in the United States, using regionalized impact assessment methods for air emissions and freshwater consumption. Case study results show important differences between site-generic and regionalized calculations, and provide specific guidance for future improvements of inventory data sets and impact assessment methods.

There are two common performance measures used for police district design: the average response time and the variation of workload [4]. Quick response to citizen calls for service can 1) improve the chances of catching the offender at the scene or nearby 2) increase the changes of identify and locate witnesses, 3) provide immediate gathering of physical evidence, 4) provide immediate lifesaving first aid, 5) enhances the reputation of police department, 6) creates citizen satisfaction with the police [5, 6]. The spatial distribution and allocation of police cars to districts affect both performance measures. In a 1971 study of the New York Police Department more than half of all dispatches were inter-sector dispatches (between districts). Usually, the nearest police car responds to the CFS incident so it may cross the patrol boundaries to respond. The average response time for these inter-sector responses was approximately 40% greater than that for intra-sector responses and the average travel distance was about 53% greater [7]. This large proportion of inter-sector cases indicates that the district design may not be efficient with regard to the first measure, response time.

The workload metric has similar complexities. Since the actual workload depends on travel times, the complexities described above hold for workload. Additionally the types of incidents and their spatial distribution within and across the districts affect workload. The type of incident is also stochastic and cannot be calculated by single variable functions typical of districting problems.

Copyright 2022 Nofal, van de Lind and Zakzouk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Sub-national figures providing information about the wealth of the population are useful in defining the spatial distribution of both economic activity and poverty within any given country. Furthermore, since several health indicators such as life expectancy are highly correlated with household welfare, sub-national figures allow for the estimation of the distribution of these health indicators within countries when direct measurement is difficult.

In Figure 4, we can observe a continuous distribution of the points with breaks indicated by the vertical and horizontal lines. These breaks (5, 10 and 25 %) are therefore used for grouping the countries (170 countries used for this analysis as 1 variable is missing for 1 country part of the initial sample) and the same approach than the one described earlier is applied in order to find the regression giving the best prediction for each group as follow:

Trying to generate a consistent global or regional poverty map desegregated to some sub-national level, the grouping of the sample at disposal by climate or percentage of GPD due to agricultural level is definitively improving the prediction but does not provide us with a model generating consistent estimates for all the countries. Between these two grouping the preference again goes to the second one which improves the estimation for the middle and high income countries. The result obtained when applying this model to South Africa as well as the distribution of the prediction error expressed in percentages are reported in Figure 12 as an example. Despite being the best regional model analysed in the context of this research the grouping by agricultural level is still producing significant error when applying it on a country by country basis (Figure 12b) compare to the country specific model itself (Figure 11b).

The more recent global night-time light mosaic that NOAA has compiled is another element which might improve the results presented in the context of this work. This new global mosaic has the advantage of covering a longer period of observation (full years instead of the six month composite used in this study) which would improve the homogeneity of the distribution of the number of observation per pixel and also reduce the effect of the snow observed on images collected during winter. These new products were processed with major improvements in the exclusion of all but the highest quality segments of the individual orbits. They also include one information that was missing in the data set used for the context of this work: the digital number brightness of the lights. It has been found that adding in brightness of the lighting greatly improves the relationship to variables such as electric power consumption and GDP. Even if some difficulties to make products with brightness values remains, due to saturation in urban centers and the lack of on-board calibration, this new generation of grids could allow a significant improvement of the models described in the present paper. Another advantage of this data set is that the NGDC is producing a full global composite for each year from 1992 through 2004 allowing therefore for trend analysis.

The night-time light grid used has been provided by NOAA's National Geophysical Data Center (NGDC). This grid data set is the result of a 6-month 1 km resolution composite based on images collected between October 1994 and March 1995 by the U.S. Air Force Defence Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) [10]. Only the grid with the distribution of lights associated with human settlements has been used in the context of the present research. Due to improvements in the algorithms used during the processing, this grid is different than the one used by Doll et al. for their publication [14]. By comparing these two grids it has been possible to identify some of these differences and to take advantage of their respective specificity for generating the grid used here. From this grid it possible to extract 4 parameters connected to light observation at night which are capturing a different information making it possible to extract the figures at the country or sub-national level for the analysis:

This SWMM add-in tool will help you create a times series of rainfall intensities for a design storm of a given depth, duration, and return period. You can use from a variety of standard deign storm distributions and use Intensity-Duration-Frequency curve information if available. The result can be copied or saved for later use within the EPA Storm Water Management Model (SWMM).

Since 2000, a combination of factors, including an increase in computing power, development and refinement of Geographic Information Systems, improvement in resolution, accuracy, availability of remote sensing data and the advent of machine learning have revolutionized ENM. The ability to access and download accurate and detailed georeferenced remote sensing data representing climate and environmental variables for a region opened the door to new biogeographical analyses. Originally used for determining potential ecological niches of plants and animals, ENMs are now being used in a variety of applications, creating new specializations across many fields. One such specialization is disease biogeography, which examines and predicts the spatial and temporal distribution of disease by employing the skills and tools of epidemiologists and ecologists. ENMs are now being utilized within this specialty to determine risk of disease for a given population or habitat for disease within a given geographic range.


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