Analysis Methodology Clause Samples
The Analysis Methodology clause defines the specific procedures, standards, or techniques that must be used when conducting analyses under the agreement. It typically outlines the required steps, data sources, or analytical tools to ensure consistency and reliability in results. By establishing a clear and agreed-upon approach, this clause helps prevent disputes over analytical outcomes and ensures that all parties interpret findings based on the same criteria.
Analysis Methodology. The analysis methodology employed in this report follows the AG Guidance and is informed by an extensive review of wildfire research papers and guidelines. A list of references is provided at the end of this memo. To analyze the evacuation events, CR Associates (CRA) conducted simulations using Vissim, a microscopic, multimodal traffic flow modeling software used to simulate different traffic conditions. In Vissim simulations, roadway capacity is accounted for and each vehicle in the traffic system is individually tracked through the model, and comprehensive measures of effectiveness, such as average vehicle speed and queueing, are collected on every vehicle during each 0.1-second of the simulation. This software enables drivers’ behaviors during an evacuation to be replicated. A total of 20 simulations were conducted to yield a reasonable sample size to determine the performance of the study area roadways and impacts during evacuation scenarios. As previously noted, to be conservative, CRA applied worst-case assumptions, including a nighttime evacuation and a scenario in which all vehicles belonging to the households in the study area would be used in the evacuation, instead of the necessary number of vehicles needed to evacuate the impacted population. Detailed evacuation analysis information is provided in Attachment B. Evacuation Routes The evacuation areas under each scenario are anticipated to utilize the following facilities as evacuation routes: Otay Lakes Road – Otay Lakes Road is a two-lane roadway within the County of San Diego. It is classified as a four-lane Major Road with Intermittent Turn Lane (4.1B) between the County/City boundary and the proposed Project Driveway #2. However, the Project proposes to reclassify this segment from a 4.1B to a 4.2A Boulevard with Raised Median. Therefore, this facility is being analyzed as a 4.2A from this point forward. The widening of Otay Lakes Road is included only under the “with Alternative I” scenarios, i.e. Scenarios 3 and 4.
Analysis Methodology. Both sharing patterns and NoC-data compression opportunities must be analyzed under the same premises and thus they must share the applied methodology and tools. However, they also differ in several aspects. An analysis framework has been built in any case to make these analyses possible. In this section we show the main characteristics of such framework. The steps followed to analyze both issues are similar as can be seen in Fig. 11. First of all, in both cases, we trace memory accesses requested by the cores when running real applications. For the NoC-data compression analysis we perform a second step aimed at simulating the transfer over the NoC of data blocks included in the previously traced memory accesses over the NoC. Finally in both cases, we obtain statistics for the previously obtained traces.
Analysis Methodology. The first step was to unpack the KPI calculation formulas into data sets. Most of the KPIs are not available ready-calculated, and an indicator often consists of two or more single data sets. The same data set can be shared by multiple indicators, being referred here as “common data sets”. Therefore when common data sets were identified, and with a list of distinct data sets, it was possible to start collecting and analysing the respective data sources. The following presents two examples of how an indicator is translated into needed data sets. First example is the indicator “Ratio of green and water spaces”, which is defined in the framework as follows “Share of green and water surface area of total land area”. In mathematical notation it would be: (𝑔𝑟𝑒𝑒𝑛 ∧ 𝑤𝑎𝑡𝑒𝑟 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 𝑎𝑟𝑒𝑎) ∗ 100 From there we can see, that in order to calculate the indicator we need the data sets Green surface area, Water surface area and Total land area. When only a division of two numbers is done, there are not any more requirements for those data sets. They can either be plain values (e.g. Total land area = 690 000 m2), or a spatial feature representing city borders in GIS-format, where the Total land area could then be calculated on the fly. Both data sets have their pros and cons, but in this case will lead into same result. Another example of indicator is “Access to public transport”. Its definition is “% of inhabitants with a public transport stop/transportation connection (train, tram, subway) within reasonable (500m) distance”. The associated calculation formula is: (𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑤𝑖𝑡ℎ 𝑎 𝑝𝑢𝑏𝑙𝑖𝑐 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 𝑠𝑡𝑜𝑝⁄𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛(𝑡𝑟𝑎𝑖��,𝑡𝑟𝑎𝑚,𝑠𝑢𝑏𝑤𝑎𝑦)<500𝑚) ∗ 100. The needed data sets are Total population, population data with coordinates and locations of public transport stops/connections. Total population can be a plain value, but locations of public transport stops/connections and population data with coordinates both have to contain exact feature coordinates. Population data needed for this indicator could be for example centre coordinates of buildings, with an attribute telling how many persons live in that building. Then the indicator would be calculated with the following steps:
1. Locate buildings, where the distance to the nearest bus stop is less than 500m
2. Calculate how many persons live in those buildings
3. Divide it by total amount of persons living in all buildings
4. Multiply it with 100% to get percentages.
Analysis Methodology. An algorithm, that the designer want to translate in hardware, has to be analyzed for several characteristics from the designer point of view. Inputs and outputs of the algorithm, performance issues and the basic data structures and operations are the most important. In addition, the communication overhead for the synchronization of the portions of the algorithm which run in specialized hardware with the aspects that run in software has to be considered, as excessive fragmentation may lead to poor performance (e.g. too many forks/joints for little work done in hardware).Performance issues are very important in the scope of QualiMaster, as performance quality tradeoff is of main importance as it has been analyzed in D 2.1.
Analysis Methodology. Implementing the Proposed Project would not require any short-term construction activities. Long-term operational activities would involve changes in water supply provided to the Plaintiff Water Contractors. These changes would not require additional worker trips or heavy-duty equipment beyond existing conditions. The proposed changes to SWP water supplied to the Plaintiff Water Contractors would require changes in electricity consumption to provide SWP water to those areas. Table 3.3-1 National and California Ambient Air Quality Standards California Standards1 National Standards2 Pollutant Averaging Time Concentration3 Primary3,4 Secondary3,5 Ozone 1 hour 0.09 ppm (180 μg/m3) — Same as primary standard 8 hour 0.070 ppm (137 μg/m3) 0.075 ppm (147 μg/m3) Respirable particulate matter (PM10) 24 hour 50 μg/m3 150 μg/m3 Same as primary standard Annual arithmetic mean 20 μg/m3 — Fine particulate matter (PM2.5) 24 hour — 35 μg/m3 Same as primary standard Annual arithmetic mean 12 μg/m3 15 μg/m3 Carbon monoxide 8 hour 9.0 ppm (10 mg/m3) 9 ppm (10 mg/m3) None 1 hour 20 ppm (23 mg/m3) 35 ppm (40 mg/m3) 8 hour (Lake Tahoe) 6 ppm (7 mg/m3) — — Nitrogen dioxide6 Annual arithmetic mean 0.030 ppm (57 μg/m3) 0.053 ppm (100 μg/m3) Same as primary standard 1 hour 0.18 ppm (339 μg/m3) 0.100 ppb (188 μg/m3) None Sulfur dioxide7 Annual arithmetic mean — 0.030 ppm (for certain areas)7 — 24 hour 0.04 ppm (105 μg/m3) 0.14 ppm (for certain areas)7 — 1 hour 0.25 ppm (655 μg/m3) 0.075 ppm (196 μg/m3) — Lead8,9 30-day average 1.5 μg/m3 — — Calendar quarter — 1.5 μg/m3 (for certain areas)9 Same as primary standard Rolling 3-month average — 0.15 μg/m3 Visibility-reducing particles10 8 hour See footnote 10 No national standards Sulfates 24 hour 25 μg/m3 Hydrogen sulfide 1 hour 0.03 ppm (42 μg/m3) Vinyl chloride10 24 hour 0.01 ppm (26 μg/m3) Notes: mg/m3 = milligrams per cubic meter; PM2.5 = fine particulate matter with an aerodynamic resistance diameter of 2.5 micrometers or less; PM10 = respirable particulate matter with an aerodynamic resistance diameter of 10 micrometers or less; ppm = parts per million; ppb = parts per billion; µg/m3 = micrograms per cubic meter. 1 California standards for ozone, carbon monoxide (except 8-hour Lake Tahoe), sulfur dioxide (1 and 24 hour), nitrogen dioxide, and particulate matter (PM10, PM2.5, and visibility-reducing particles), are values that are not to be exceeded. All others are not to be equaled or exceeded. California ambient air quality standa...
Analysis Methodology. The first step was to unpack the KPI calculation formulas into data sets. Most of the KPIs are not available ready-calculated, and an indicator often consists of two or more single data sets. The same data set can be shared by multiple indicators, being referred here as “common data sets”. Therefore when common data sets were identified, and with a list of distinct data sets, it was possible to start collecting and analysing the respective data sources. The following presents two examples of how an indicator is translated into needed data sets. First example is the indicator “Ratio of green and water spaces”, which is defined in the framework as follows “Share of green and water surface area of total land area”. In mathematical notation it would be: