Data Preparation. The Contractor approaches data preparation in a way that is ongoing, automated wherever feasible, scalable, and auditable. The Contractor’s preparation approach must be flexible and extensible to future data sources as well, including State datasets and systems. For the CCRS, data preparation will consist of the following at a minimum:
Data Preparation. (s,t)∈→−x x=1 i=1 P ((i, j)|f (t), e(s); ←θ−),, (31) Although it is appealing to apply our approach to dealing with real-world non-parallel corpora, it is time-consuming and labor-intensive to manually construct a ground truth parallel corpus. There-
Data Preparation s,t⟩∈→−x x=1 i=1 P (⟨i, j⟩|f (t), e(s); ←θ−),, (31) Although it is appealing to apply our approach to dealing with real-world non-parallel corpora, it is time-consuming and labor-intensive to manually construct a ground truth parallel corpus. There- ⟨ ⟩| where P ( i, x x(s), f (t); →−θ ) is source-to-target ⟨ ⟩ link posterior probability of the link i, j be- ing present (or absent) in the word align- ment according to the source-to-target model, ⟨ ⟩| P ( i, x x (t), e(s); ←θ−) is target-to-source link pos- terior probability. We follow Xxxxx et al. (2006) to use the product of link posteriors to encourage the agreement at the level of word alignment. xxxx, we follow Xxxx et al. (2015) to build syn- thetic E, F , and G to facilitate the evaluation. We first extract a set of parallel phrases from a sentence-level parallel corpus using the state- of-the-art phrase-based translation system Xxxxx (Xxxxx et al., 2007) and discard low-probability parallel phrases. Then, E and F can be con- structed by corrupting the parallel phrase set by 0.40 0.35 agreement ratio 0.30 0.25 noise inner outer no agreement iteration C → E E → C Outer Inner C E 0 10K 41.0 54.4 83.6 83.8 0 20K 28.3 48.3 80.1 81.2 10K 0 54.7 43.1 84.9 84.3 20K 0 50.4 31.4 83.8 83.6 10K 10K 34.9 34.4 80.0 79.7 20K 20K 22.4 23.1 73.6 74.3 Table 2: Effect of noise in terms of F1 on the de- velopment set. Figure 4: Comparison of agreement ratios on the development set. seed C → E E → C Outer Inner 50 4.1 4.8 60.8 66.2 100 5.1 5.5 65.6 69.8 500 7.5 8.4 70.4 72.5 1,000 22.4 23.1 73.6 74.3 Table 1: Effect of seed lexicon size in terms of F1 on the development set. adding irrelevant source and target phrases ran- domly. Note that the parallel phrase set can serve as the ground truth parallel corpus G. We refer to the non-parallel phrases in E and F as noise. From LDC Chinese-English parallel corpora, we constructed a development set and a test set. The development set contains 20K paral- lel phrases, 20K noisy Chinese phrases, and 20K noisy English phrases. The test test contains 20K parallel phrases, 180K noisy Chinese phrases, and 180K noisy English phrases. The seed parallel lex- icon contains 1K entries.
Data Preparation. The years that were focused on in this analysis were graduating years of 2017, 2018, and 2019. This is because the senior and one- year out surveys were both available and contained similar formatting. The variables that were focused on were: • Major and major department • Major satisfaction rating • Employment status • Employment position • Employment relation to major • Would you pick Etown again if you started your college search over today? The data preparation and organization were a large part of this project. Although each senior survey contained similar questions, there were some key fields that differed. Each year of the one-year out and the senior survey information was contained within separate excel files. There were years of the surveys that did not contain student id numbers. This is the key identifier of students and acted as the method of joining the senior survey and one-year out survey information to collect those who answered both surveys. Therefore, to correct this information gap, the id numbers needed to be brought in. In addition, there were years of the survey that only contained the major department while other years only contained the individual major itself. Therefore, for years that contained the specific major, the major department was brought in. In addition to the important field gaps, there were smaller adjustments that needed to be made. Although the answer choices for all of the rating field contained Very Dissatisfied, Dissatisfied, Neither Satisfied nor Dissatisfied, Satisfied, and Very Satisfied the capitalization differed and therefore was adjusted for the aggregation of the data to be successful.
Data Preparation. In preparing the data for subsequent analyses, several iterations were required to detect potential outliers, errors and other data anomalies. Reviews included multiple scatter plot comparisons, source plot card reviews, as well as between-measurement data checks. Corrections were made where noted, and plot measurement deletions only occurred in a few instances. SAS programs were written so that compilations could be easily adjusted or modified (e.g., changes in utilization standards). All SAS programs and input data files will be made available to ASRD.
Data Preparation. Esri will support the City with preparing the source data requested as part of Task 2. The prepared data will then be published as feature services to the City’s ArcGIS Online Organization (AGOL), enabling these services to be used and manipulated by ArcGIS Urban once it has been deployed. It is anticipated the following data preparation steps will be performed: Reproject data to appropriate coordinate system. Clean up parcel geometries using geoprocessing tools (repair geometry, generalize, multipart to single part, etc.). Assign standard road classification to centerlines. Assign parcel edge information. Interpret zoning code parameters (e.g., floor area ratio [FAR], setbacks, heights, coverage) for up to 5 zones, 1 overlay, 5 current land uses, and 5 future land uses. Fresno Fiscal Impact Analysis of the General Plan Buildout Proposed Work Program April 9, 2021 Prepare approximately 10 residential and nonresidential space uses and building types based on the development typologies identified in Task 2. Load parcel, zoning, project, plan, and indicator geometries and attributes into the ArcGIS Urban data model. Publish loaded layers as feature services to the City’s AGOL. ArcGIS Urban Application Deployment Once all necessary feature services are published, Esri will support the City by conducting the following ArcGIS Urban deployment tasks: Populate ArcGIS Urban configuration tables to read to the previously published services, including previously created services for existing 3D buildings. Configure ArcGIS Online permissions, enabling specified groups and accounts to access the ArcGIS Urban Web application. Configure the plan area, focused project, and up to four custom indicators identified during the project kickoff meeting and deployed to ArcGIS Urban. Esri anticipates configuration include tasks such as adding descriptions, URL links, charts, etc., to the deployed features using the Web-based interface.
Data Preparation. The seismic data set must exhibit certain characteristics to be applicable for SHPM. In particular, this includes that induced seismicity occurred on a single fault plane and that the events share a common rake direction. According to Xxxxx et al. (2017), induced seismicity of the St. Gallen geothermal project is interpreted to occur on a pre-existing sub-vertical fault, thus confirming a potential single fault plane scenario. Although Fault Plane Solutions (FPS) are generally not well constrained, compound FPS seem to be reasonably consistent with a common mechanism similar to the one of the largest events determined by Xxxxx et al., 2014 (124°/72°/-174°; Figure 15). Figure 15: Stereographic projection (lower hemisphere) of the polarity data of the St. Gallen seismic stations together with the fault plane solution determined by Xxxxx et al. (2014) for the ML = 3.5 event which occurred on 20 July 2013, 03:30:54 UTC. Plus signs denote a positive first movement of the P-phase, circles denote a negative movement, respectively. All events listed in the provided earthquake catalogue were used. Based on these findings, shear-deformation can be superimposed to analyse cumulative slip (Xxxxxx et al., 2009). The slip map (Figure 16) indicates cumulated slip exceeding a few centimeters with a maximum value of approx. 5 cm. For an earthquake in the magnitude range under consideration, the Xxxxx model yields a slip of approx. 2-14 mm. Hence, observed cumulative slip provides proof of the occurrence of repeated slip. Since the repeated slip is not as pronounced as in the Xxxxxx Basin example, the averaging effect of uncertainties of the source geometry of individual events will be less strong here and therefore, data scattering is expected to be larger. Figure 16: Cumulative shear slip related to induced seismicity of the St. Gallen geothermal project looking from an angle of view perpendicular to the subvertical shearing plane. The horizontal coordinate system is aligned with an assumed SH direction of 160° N (Moeck et al., 2015). Shear slip is displayed in millimeters according to the colour map. A constant stress drop of 1 MPa is assumed. As the St. Gallen data set met the requirements, the SHPM method could be applied using the same procedure as for the Xxxxxx Basin data (see Chapter 3.1.2). Seismic events were projected onto the best-fitting plane and rotated into the horizontal orientation. In addition, the coordinate system was aligned with the direction of the ...
Data Preparation. The data available from various sources was collected. The ground maps, contour information, etc. were scanned, digitized and registered as per the requirement. Data was prepared depending on the level of accuracy required and any corrections required were made. All the layers were geo-referenced and brought to a common scale (real coordinates), so that overlay could be performed. A computer programme was used to estimate the soil loss. The formats of outputs from each layer were firmed up to match the formats of inputs in the program. The grid size to be used was also decided to match the level of accuracy required, the data availability and the software and time limitations. The format of output was finalized. Ground truthing and data collection was also included in the procedure.
Data Preparation. HDM-4’s required input is organized into data sets that describe road networks, vehicle fleets, pavement preservation standards, traffic and speed flow patterns, and climate conditions. Most of the required pavement performance information was obtained from 2002 data within the Washington State Pavement Management System (WSPMS) (Xxxxxxxxxxxx et al., 2002). Other data were obtained through available literature and interviews with WSDOT personnel. The Road Networks data set contains a detailed account of each road section’s physical attributes. HDM-4 uses this information to model pavement deterioration and to provide input to other models. The Vehicle Fleet data set contains vehicle characteristics that are used for calculating speeds, operating costs, and travel times to determine traffic impacts on roads and the resulting costs for the economic analysis. The WSPMS vehicle classification was used for HDM-4 input and included passenger cars, single-unit trucks, double-unit trucks, and truck trains (Xxxxxxxxxxxx et al., 2003). Preservation standards define pavement preservation practices, including their costs and effects on pavement conditions when they are applied. Although WSDOT uses a number of different preservation practices, the most common one for flexible pavement is a 45-mm HMA overlay (Xxx et al., 1993). The typical target distress for application of a 45-mm HMA overlay is when the total area of pavement cracking is ≥ 10 percent (total roadway area), rut depth is ≥ 10 mm, or the IRI is ≥ 3.5 m/km (although the “trigger” IRI used by WSDOT may be reduced to about 2.8 m/km). Table 1 lists the major inputs. Specific inputs shown in Table 1 are not described in this report. Table 1: Maintenance standard of 45-mm HMA overlay in HDM-4 version 1.3 General Name: 45-mm HMA Overlay Short Code: 45 OVER Intervention Type: Responsive Design Surface Material: Asphalt Concrete Thickness: 45 mm Dry Season a: 0.44 CDS: 1 Intervention Responsive Criteria: Total cracked area ≥ 10% or Rutting ≥ 10 mm or IRI ≥ 3.5 m/km Min. Interval: 1 Max. Interval: 9999 Last Year: 2099 Max Roughness: 16 m/km Min ADT: 0 Max ADT: 500,000 Costs Overlay Economic: 19 dollars/m2 * Financial: 19 dollars/m2 * Patching Economic: 47 dollars/m2 * Financial: 47 dollars/m2 * Edge Repair Economic: 47 dollars/m2 Financial: 47 dollars/m2 Effects Roughness: Use generalized bilinear model a0 = 0.5244 a1 = 0.5353 a2 = 0.5244 a3 = 0.5353 Rutting: Use rutting reset coefficient = 0 Texture Depth: Use def...
Data Preparation. The following subsections provide insight into the data issues involved when using the new version of HDM-4.
