Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the …Show full descriptionAbstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on the known details at the time of acquisition. This is a collection of AGL activity related features digitised from the AGL Director General report (Figure sourced from AECOM, 2009, prepared for AGL Gloucester). Features digitised from this report map and used in BA GLO report map images include: AGL pipeline Corridor Petroleum Exploration Licence 285 boundary (PEL 285) AGL Concept Area Stage 1 Gas field Development Area The original bitmap and georeferenced version form part of the data set. Filenames: original: AGL DG report location map.bmp georeferenced: AGL DG report location map1.tif This dataset has been provided to the BA Programme for use within the programme only. Third parties may request a copy of the data from AGL. http://www.agl.com.au Purpose Features used as map context for BA report maps. Dataset History A bitmap of the AGL Dircetor General report location map (Figure sourced from AECOM, 2009, prepared for AGL Gloucester) was supplied by the Bioregional Assessment Programme. This bit map was georeferenced and rectified to MGA zone 56 geotiff using know locations on the report map as control points. Selected features shown on the map were manually digitised off the computer screen using this georectified map as a backdrop. Features were saved as ESRI shapefiles in the MGA z56 projection. The features digitised and included in this dataset are: AGL pipleline Corridor Petroleum Exploration Licence 285 boundary (PEL 285) AGL Concept Area Stage 1 Gas filed Development Area Prepared for AGL Gloucester L E Pty Ltd AECOM (2009) Gloucester Gas Project Environmental Assessment - Appendix F Air Quality Assessment Fig. 1, pp 37. https://majorprojects.affinitylive.com/public/80e245a5b45b9feeaa22b81782910dc8/Volume 2_Part 02_Appendix F.pdf Main report: https://majorprojects.affinitylive.com/public/6292c8af2d506df412e52b5dccae282b/Volume1_Environmental_Assessment_11Nov09.pdf The source bmp and geotiff are also included in this dataset. Filenames: original: AGL DG report location map.bmp georeferenced: AGL DG report location map1.tif Dataset Citation AGL (2014) AGL Gloucester Gas Project AECOM report location map features. Bioregional Assessment Source Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/7fca4f35-6e4e-4d37-bbae-1c1029a93a40.

There are three primary objectives of this work; first: to establish a gas concentration map; second: to estimate the point of emission of the gas; and third: to generate a path from any location to the point of emission for UAVs or UGVs. A mountable array of MOX sensors was developed so that the angles and distances among the sensors, alongside sensors data, were utilized to identify the influx of gas plumes. Gas dispersion experiments under indoor conditions were conducted to train machine learning algorithms to collect data at numerous locations and angles. Taguchi’s orthogonal arrays for experiment design were used to identify the gas dispersion locations. For the second objective, the data collected after pre-processing was used to train an off-policy, model-free reinforcement learning agent with a Q-learning policy. After finishing the training from the training data set, Q-learning produces a table called the Q-table. The Q-table contains state-action pairs that generate an autonomous path from any point to the source from the testing dataset. The entire process is carried out in an obstacle-free environment, and the whole scheme is designed to be conducted in three modes: search, track, and localize. The hyperparameter combinations of the RL agent were evaluated through trial-and-error technique and it was found that ε = 0.9, γ = 0.9 and α = 0.9 was the fastest path generating combination that took 1258.88 seconds for training and 6.2 milliseconds for path generation. Out of 31 unseen scenarios, the trained RL agent generated successful paths for all the 31 scenarios, however, the UAV was able to reach successfully on the gas source in 23 scenarios, producing a success rate of 74.19%. The results paved the way for using reinforcement learning techniques to be used as autonomous path generation of unmanned systems alongside the need to explore and improve the accuracy of the reported results as future works.

This feature layer contains the locations of Natural Gas Import/Export Pipeline Facilities along the borders between the Continental United States, Canada, and Mexico for the Homeland Infrastructure Foundation-Level (HIFLD) Database (https://hifld-dhs-gii.gov/HIFLD) as well as the Energy modeling and simulation community. A Natural Gas Import/Export Pipeline Facility delivers natural gas in and out of the Continental United States between foreign countries.

Exercise - Exercise - ExerciseData layers, Analysis, Modeling represent the collective efforts of the geospatial community to provide data driven and science based content &analysis to inform planning efforts for exercise scenarios and emergencymanagement. The purpose of Shaken Fury 2019 (SF19) is to evaluate and improve the wholecommunity’s ability to establish and implement a coordinated strategy of rapidresponse and recovery operations for the prioritization and application of accessibleresources and capabilities in response to a “no-notice” earthquake incident.Utilizing a common scenario of a 7.7 magnitude earthquake off the CottonwoodGrove Fault (Southwest Segment of the New Madrid Seismic Zone [NMSZ])outside of Memphis, Tennessee, whole community partners will come togetherfrom May 29 – June 7, 2019 to exercise response and recovery missionsthroughout and beyond the NMSZ Region.Through a series of integrated tabletop, functional, and full-scale exercises, the states of Alabama, Arkansas, Kentucky, Mississippi, Missouri, and Tennessee will coordinate response and recovery activities with FEMA Regions IV, V, VI, and VII state, FEMA headquarters, U.S. Department of Energy, and Department of Defense (DOD).Credits and much thanks GIS partner agencies:USGS, DOE, CUSEC, FEMA Hazus HQ Team, FEMA Recovery GIS, DoD, FEMA HQ Response Geosaptial Office, DHS NCC/ESF2

The California Department of Transportation (Caltrans) and the California Energy Commission (CEC) are partnering to implement the federal National Electric Vehicle Infrastructure (NEVI) Program, which allocates $5 billion to the states to create a nationwide, interconnected network of DC fast chargers along the National Highway Systems. California's share will be $384 million over 5 years. This map was developed to help prospective applicants and interested parties identify eligible areas for infrastructure deployment.InstructionsViewers can display corridor groups, corridor segments, electric vehicle (EV) charging stations, Justice40 disadvantaged communities, Tribal lands, California-designated low-income or disadvantaged communities, metropolitan planning organizations, regional transportation planning agencies, California state legislative districts, counties, Caltrans districts, utility districts, and congressional districts in this interactive map. The map initially displays corridor groups and their corridor segments included in the Round 2 NEVI solicitation. Viewers can toggle individual layers on and off using the map layers menu located to the right of the map. Some layers are organized into groups; viewers can toggle all layers within a group or select specific ones. The legend to the left of the map will show the layers that have been turned on. There is a search tool to the right of the map that enables viewers to type in an address and locate the address on the map. A basemap selector allows viewers to view road detail. Additional information on the map can be found under the information icon. Viewers can download the map files by clicking on the Data and Supplemental Links icon. Map layers include:A Corridor groups layer that shows designated corridor groups for California's NEVI funding program. Users can click on a corridor segment to view the start and end of each segment within a corridor group. When selected, a pop-up window will appear that identifies the corridor group number, corridor segment, corridor name, minimum number of charging stations required, minimum number of ports required, and needed locations, if applicable, for the corridor segment. Corridor group labels for enhanced accessibility. Note that labels are only visible at certain ranges (zoom in and out to view labels). A NEVI 2 corridors layer shows corridor groups eligible for Round 2 of California's NEVI funding program. NEVI 2 corridor group labels for enhanced accessibility. Note that labels are only visible at certain ranges (zoom in and out to view labels). NEVI 2 corridor segment labels for enhanced accessibility. Note that labels are only visible at certain ranges (zoom in and out to view labels). A Round 1 solicitation corridor groups layer that shows corridor groups eligible for Round 1 of California's NEVI funding program. A layer showing California and Justice40 disadvantaged or low-income communities. A layer showing California-designated disadvantaged or low-income communities. A layer showing Justice40-designated disadvantaged communities. A layer showing California Federally Recognized Tribal Lands. A layer showing Metropolitan Planning Organizations. A layer showing Regional Transportation Planning Agencies. A layer showing California State Senate Districts. A layer showing California State Assembly Districts. A layer showing California Counties. EV charging stations layers (existing DC fast charging stations that are located within one mile of a NEVI-eligible corridor offramp). One layer shows locations of EV charging stations with DC fast charging capabilities that meet the NEVI power level and four-port minimum requirement and could likely become part of the NEVI network if these stations became compliant with other NEVI program requirements such as data reporting. The other layer shows DC fast charging stations that do not meet NEVI power-level or port count requirements but could be upgraded to be NEVI-compliant. Users can click on EV charging stations and a pop-up window will appear with more information on the station (i.e., station address, total port count, minimum NEVI standard, etc.). These data were last updated in March 2024. Please refer to the Department of Energy's Alternative Fuels Data Center and PlugShare for up-to-date existing and planned DC fast charger site information. A layer showing Caltrans Districts. A layer showing Electric Utilities (IOUs and POUs). A layer showing California Congressional Districts. BackgroundThe $5 billion NEVI Program is part of the $1.2 trillion Infrastructure Investment and Jobs Act (IIJA) signed into law by President Biden in November 2021. IIJA commits significant federal funding to clean transportation and energy programs throughout the U.S. to reduce climate changing greenhouse gas emissions. Caltrans is the designated lead agency for NEVI. The CEC is their designated state energy partner. Caltrans and the CEC have partnered to create California's Deployment Plan for the National Electric Vehicle Infrastructure Program that describes how the state plans to allocate its $384 million share of federal NEVI funds to build out a network of modern, high-powered DC fast chargers along federally designated Alternative Fuel Corridors throughout California. California's latest NEVI Deployment Plan was submitted to the Joint Office of Energy and Transportation on August 1, 2023 and approved on September 29, 2023. The Plans must be updated each year over 5 years.NEVI funds must be used initially on federally-designated Alternative Fuel Corridors (shown on the map).Each NEVI-funded DC fast charge station will have a minimum of four 150 kW Combined Charging System (CCS) connectors. Stations will be located no more than 50 miles apart along freeways and highways and no more than 1 mile from a freeway exit or highway roadway. States are required to emphasize equity, with at least 40 percent of NEVI benefits going to disadvantaged, low income, rural and Tribal communities.Data SourcesData are from the Federal Highway Administration's Alternative Fuel Corridors website, the U.S. Department of Energy's Alternative Fuels Data Center Station Data for Alternative Fuel Corridors (as of September 2022), Argonne National Laboratory's Electric Vehicle Charging Justice40 Map, and the California Air Resources Board's Map of California Climate Investments Priority Populations 2022 CES 4.0. ContactPlease submit questions and comments to mediaoffice@energy.ca.gov

Abstract

This polygon outlines the cumulative prospective area of interest for three gas play types (shale, tight and deep coal gas) in the Cooper GBA region. As detailed in Lech et al. (2019), the area of interest for each play type was derived from the relative prospectivity maps using a cut-off value of 0.2. This value was chosen as it best represented the envelop around key wells targeting each play type. Significant overlap is present between the three play types.

Attribution

Geological and Bioregional Assessment Program

History

This polygon outlines the cumulative prospective area of interest for three gas play types (shale, tight and deep coal gas) in the Cooper GBA region. \r As detailed in Lech et al. (2019), the area of interest for each play type was derived from the relative prospectivity maps using a cut-off value of 0.2. This value was chosen as it best represented the envelop around key wells targeting each play type. Significant overlap is present between the three play types.\r Reference . \r \r Lech ME, Wang L, Hall LS, Bailey A, Palu T, Owens R, Skeers N, Woods M, Dehelean A, Orr M, Cathro D and Evenden C (2019) Shale, tight and deep coal gas prospectivity of the Cooper Basin. Technical appendix for the Geological and Bioregional Assessment Program: Stage 2. Department of the Environment and Energy, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia.

The City’s 100,000 street lights are in the early stages of conversion from high pressure sodium vapour lamps to LED luminaires. About 17,000 are converted now; the goal is to have the rest done within five years.

Sodium vapour lamps ranged from 70 to 1,000 watts. The LED lamps, which use 16 to 200 watts, save about 35% in both energy costs and greenhouse gas emission spin-off. It is believed that the total saving could reach 50% by 2020 with technological improvements.

Analysis of ‘Offshore Oil Leases’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4986e7ed-aae8-40fe-87d8-87f8462d3145 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Analysis of ‘Offshore Oil Leases’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7dbfeba0-c6d3-4ccd-9885-9e6751da10c4 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Best available knowledge of location of abandoned Hobart Town gas mains throughout Glenorchy municipality. Please consult Workplace Standards for safety information. http://workplacestandards.tas.go…Show full descriptionBest available knowledge of location of abandoned Hobart Town gas mains throughout Glenorchy municipality. Please consult Workplace Standards for safety information. http://workplacestandards.tas.gov.au/safety/alerts/redundant_town_gas_pipes The custodian for this infrastructure no longer exists. Whilst GCC holds copyright over the dataset, it does not have, and has never had, any liability or responsibility for the actual infrastructure. We release the data in the interests of public safety as we believe we hold the only information regarding this infrastructure. Digitised off Glenorchy Water and Sewer Engineering Detail plans c1938-1972. The purpose of the plans was to map municipal assets, not non-council gas lines. The depiction of the gas lines was to show nearby infrastructure where it was known as the water and sewer surveys were completed. Hence the information in this dataset cannot be relied upon as a complete record of gas locations.

Overview

This dataset contains the modeled gridded fractional source contribution results for the 2021 Global Burden of Disease - Major Air Pollution Sources (GBD-MAPS) - Global study. Fractional results aggregated to the regional, national, and sub-national scales are available as Supplementary Datasets in McDuffie et al., Nature Communications, 2021.

This GBD-MAPS-Global methodology and results are described in the following article:

McDuffie, E. E., Martin, R. V., Spadaro, J. V., Burnett, R., Smith, S. J., O'Rourke, P., Hammer, M., van Donkelaar, A., Bindle, L., Shah, V., Jaegle, L., Luo, G., Yu, F., Adeniran, J., Lin, J., Brauer, M. Source Sector and Fuel Contributions to Ambient PM2.5 Attributable Mortality Across Multiple Spatial Scales, Nature Communications

Notes:

All results provide the modeled fractional contribution of each designated source category to total surface PM2.5 mass and attributable disease burden. Fractional source contribution results are derived from emission sensitivity simulations using the GEOS-Chem 3D global chemical transport model (DOI: 10.5281/zenodo.4718622) and emissions from the Community Emissions Data System (GBD-MAPS update) (DOI: 10.5281/zenodo.3754964), unless otherwise noted. Results are provided at the 0.01°x0.01° (~1 km x 1 km) spatial resolution and are based on original simulations conducted at 2°x2.5° (global) and 0.5°x0.625° (Europe, North America, and Asia regions) degree resolutions. All units are in fractional percentages (e.g., 0.0245 is 2.45%).

To calculate gridded absolute contributions of each source to PM2.5 mass (used in the GBD-MAPS analysis):

1) Multiply the 0.01°x0.01° fractional source contributions by downscaled 0.01°x0.01° resolution PM2.5 mass estimates, available in the DownscaledPM.zip folder as part of the GBD-MAPS-Global: Analysis Input Dataset (DOI: 10.5281/zenodo.4642700)

Data File Descriptions:

LatLon - The latitude and longitude values that correspond to each data point in the source-specific files (0.01°x0.01° resolution)

Sector Files (sum to 100%):

AFCID - Anthropogenic Fugitive, Combustion, and Industrial Dust

AGR - Agriculture - includes manure management, soil fertilizer emissions, rice cultivation, enteric fermentation, and other agriculture

ENEcoal - Energy Production (coal combustion only) - Includes electricity and heat production, fuel production and transformation, oil and gas fugitive/flaring, and fossil fuel fires

ENEother - Energy Production (all non-coal combustion) - Includes electricity and heat production, fuel production and transformation, oil and gas fugitive/flaring, and fossil fuel fires

GFEDagburn - Agricultural Waste Burning - Includes solid waste disposal, waste incineration, waste-water handling, and other waste handling (from the GFED fires inventory)

GFEDoburn - Other Open Fires - Includes deforestation, boreal forest, peat, savannah, and temperate forest fires (from the GFED fires inventory)

INDcoal - Industry (coal combustion only) - Includes Industrial combustion (iron and steel, non-ferrous metals, chemicals, pulp and paper, food and tobacco, non-metallic minerals, construction, transportation equipment, machinery, mining and quarrying, wood products, textile and leather, and other industry combustion) and non-combustion industrial processes and product use (cement production, lime production, other minerals, chemical industry, metal production, food, beverage, wood, pulp, and paper, and other non-combustion industrial emissions)

INDother - Industry (all non-coal combustion) - Includes Industrial combustion (iron and steel, non-ferrous metals, chemicals, pulp and paper, food and tobacco, non-metallic minerals, construction, transportation equipment, machinery, mining and quarrying, wood products, textile and leather, and other industry combustion) and non-combustion industrial processes and product use (cement production, lime production, other minerals, chemical industry, metal production, food, beverage, wood, pulp, and paper, and other non-combustion industrial emissions)

NRTR - non-road/ off-road transportation - Includes Rail, Domestic navigation, Other transportation

OTHER - all remaining sources, including: volcanic SO2, lightning NOx, biogenic soil NO, ocean emissions, biogenic emissions, very short lived iodine and bromine species, decaying plants (misc. inventories)

RCOC - Commercial Combustion - Includes commercial and institutional combustion

RCOO - Other Combustion - Includes combustion from agriculture, forestry, and fishing

RCORbiofuel - Residential combustion (solid biofuel combustion only) - includes residential heating and cooking

RCORcoal - Residential combustion (coal combustion only) - includes residential heating and cooking

RCORother - Residential Combustion (all non-coal and non-solid biofuel) - includes residential heating and cooking

ROAD - Road Transportation - includes cars, motorcycles, heavy and light duty trucks and buses

SHP - International Shipping - Includes international shipping and tanker loading

SLV - Solvents - Includes solvents production and application (degreasing and cleaning, paint application, chemical products manufacturing and processing, and other product use)

WDUST - Windblown Dust - (from the DEAD dust model)

WST - Waste - Includes solid waste disposal, waste incineration, waste-water handling, and other waste handling

Fuel Categories (do not sum to 100% for each grid cell as these only include combustion sources of PM2.5):

BIOFUEL - Solid Biofuel (or biomass) Combustion- Includes solid biofuel

COAL - Total Coal Combustion- Includes hard coal, brown coal, coal coke

OILGAS- Liquid Oil and Natural Gas Combustion- Includes light and heavy oil, diesel oil, and natural gas

ABSTRACT: This article describes the run time characteristics of a gas turbine performance simulation using different solvers and components off-design performance database formats. Two different nonlinear systems of equation solvers, Newton-Raphson's and Broyden's, and two different formats of compressor and turbine off-design performance database (maps), tabulated values and fitted surface equations, were compared. Based on the results it is then possible to trade off and select the most appropriate combination of solver and component map type for the gas turbine performance simulation for real-time application.

This dataset includes maps and locations of potential gas seepage in lakes within the immediate and surrounding area around Fairbanks, Alaska, a region underlain by discontinuous permafrost and characterized by thermokarst lake formation. Gas bubbling from lakes in the Fairbanks area is usually rich in methane, a potent greenhouse gas that is difficult to quantify. A new remote sensing method was used for detecting potential gas seepage, defined as areas of suspected perennial ebullition, by using a previously ground-truthed seep that appeared as a high-backscatter perennial feature in winter L-band Synthetic Aperture Radar (SAR) data from 2006-2011. Based on threshold values determined by the sigma-naught backscatter of this training seep, 1,690 areas of potential seepage were detected in 459 lakes out of 658 lakes analyzed. Results were validated through a different ground-truthed seep. The data are provided in shapefile format.

The power outages in this layer are pulled directly from the utility public power outage maps and is automatically updated every 15 minutes. This dataset represents only the most recent power outages and does not contain any historical data. The following utility companies are included:Pacific Gas and Electric (PG&E)Southern California Edison (SCE)San Diego Gas and Electric (SDG&E)Sacramento Municipal Utility District (SMUD)Los Angeles Water & Power (LAWP)Layers included in this dataset:Power Outage Incidents - Point layer that shows data from all of the utilities and is best for showing a general location of the outage and driving any numbers in dashboards.Power Outage Areas - Polygon layer that shows rough power outage areas from PG&E only (They are the only company that feeds this out publicly). With in the PG&E territory this layer is useful to show the general area out of power. The accuracy is limited by how the areas are drawn, but is it good for a visual of the impacted area.Power Outages by County - This layer summaries the total impacted customers by county. This layer is good for showing where outages are on a statewide scale.If you have any questions about this dataset please email GIS@caloes.ca.gov

This dataset is produced and published annually by Natural Resources Canada. It contains a variety of statistics on Canada’s mineral production, and provides the geographic locations of significant metallic, nonmetallic and coal mines, oil sands mines, selected metallurgical works and gas fields for the provinces and territories of Canada. Related product: - Top 100 Exploration Projects

The polylines represent underground utilities such as cable TV, gas, oil, and telephone lines across various areas of Los Angeles County. All data were collected from LA County Substructure Grid Maps drawing. The utility lines are from cities that were contracted with LA County. To download the hardcopy maps, please visit: Los Angeles County Substructure Maps | County of Los Angeles Open Datahttps://egis-lacounty.hub.arcgis.com/maps/los-angeles-county-substructure-maps-1/explore?location=34.094631%2C-118.256950%2C7.82Every reasonable effort has been made to assure the accuracy of this data and the maps referenced. Some cities may provide substructure information for the areas not covered by our map grids. Additional and more accurate substructure data and information may also be obtained through the utility companies. The County of Los Angeles makes no warranty, representation, or guarantee as to the content, sequence, accuracy, timeliness, or completeness of any of the data provided herein or of any maps referenced. Los Angeles County Public Works recommends that all utility research be conducted under the supervision of a licensed civil engineer.

The discovery of oil has had a huge impact on economics and politics within the Middle East, as well as the region’s relationship with the west and the way regional standards of living. Before the discovery of oil, fishing and pearling were the primary economic sectors of many Gulf States. After the discovery of oil and due to the immense value of oil, many Middle East countries made oil their economic focus, changing livelihood of their people in just a few decades. One example is Kuwait, whose economy focused mainly on fishing and pearling prior to the discovery of oil in 1934. Now, oil extraction and processing accounts for 50% of the country’s GDP, 90% of export earnings, and 75% of government revenues1. Typically, the more oil a country exports the less economically diverse it is. Booz & Company did a study to look at the economic diversity of the Gulf States, which are very oil-rich, in comparison to the rest of the world, and found that the economic diversity of the GCC (the countries of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) was much lower than that of European or other “western” states3. Since oil is a nonrenewable resource it will become important for these countries to diversify their economies and become independent of oil as reserve levels decline. Recently, attempts of economic diversification have been made in several oil diverse nations such as the aluminum smelting industry in Bahrain, Qatar, and the UAE, taken up as an attempt to diversify their economy6; however, the reason that the industry of aluminum smelting has grown in these counties is because aluminum smelting requires immense amounts of oil. Therefore, the economics of these counties is in reality not that diversified. The Export Diversity Index is defined as the number of prominent commodities a country exports. Goods made from the same derivative, such as crude oil and petroleum products, were categorized as belonging in the same industry for simplicity purposes. The data represented in the map was obtained from lists of each country's ten most lucrative exports, and the index ranges on a scale of 1 to 10 different exports4. We noticed that the countries with the greatest volume oil resources had the lowest score on the index because more goods they produced were related to the oil industry. The map of oil reserves gives a good visual representation of which Middle Eastern countries are the most oil-rich, and shows a high concentration of marks in the Gulf states, particularly the in the Persian Gulf where off-shore reserves are located. The countries with the lowest score on the index were Saudi Arabia (with a score of 2), Kuwait (4), Bahrain (2), and Qatar (2). It is interesting to note that although other countries may have high concentrations of certain resources within their borders it is only the oil-rich countries that have the lowest levels of export diversity. The only exceptions to this trend are countries with a government that has made particularly strong efforts to become less oil-reliant, such as the United Arab Emirates7. Although, we recognize that a country's economic diversity also accounts for its domestic economy, which generally relies heavily on the country's exports. Therefore this analysis concludes that the Export Diversity Index is an indicator of a country's economic index. The data we have compiled has implications for the future of many of the Gulf States, especially Saudi Arabia, as the international community attempts to wean itself off of fossil fuels.Amanda Doyle, March 2012WORKS CITED1.“Kuwait Economy”. Encycopedia of the Nations, Advameg, Inc. 2011. http://www.nationsencyclopedia.com/Asia-and-Oceania/Kuwait-ECONOMY.html.2.Burke, Edmund, and Yaghoubian, David N. Struggle and Survival in the Modern Middle East. 2nd ed. University of California Press: Berkley, CA, 2006.3.“Economic Diversification”. The Ideation Center. 2011. http://www.ideationcenter.com/home/ideation_article/economic_diversification.4."UN Data: Country Profile”. UN Division of Statistics, United Nations. 2011. http://data.un.org/CountryProfile.aspx5."USGS identifies potential giant oil and gas fields in Israel/Palestine”. EnerGeoPolitics. 2010. http://energeopolitics.com/2010/04/09/usgs-identifies-potential-giant-oil-and-gas-fields-in-israelpalestine/6. "A Summary of Existing and New-Buuild Smelters in the Middle East". Aluminium International Today. January /February 2009. http://www.improvingperformance.com/papers/Primary%20Article%20AIT.pdf.7. "UAE to Diversify Economy - To Reduce Dependence on Oil and Natural Gas Revenues". Oil Gas Articles. 2011. http://www.oilgasarticles.com/articles/416/2/UAE-to-Diversify-Economy---To-Reduce-Dependence-on-oil-and-Natural-Gas-Revenues/Page2.html?PHPSESSID=e10561d4a9d2cf87f64fbdeb2e00f65d.

The data was collected from various sources including easement documents and company rights of way maps for transmission lines. This layer is a work in progress, updated monthly: where available, the county deed book and page number for the recorded easement have been provided. If you have questions or comments, please contact us at LandServicesGIS@duke-energy.com.DISCLAIMER: All information provided by Duke Energy is for general informational purposes only. Duke Energy makes no representation or warranty whatsoever regarding the completeness or accuracy of this information and assumes no responsibility or liability for any damages arising out of recipient’s use of or reliance upon this information. This information shall not be used or relied upon by recipient to relieve it of complying with or undertaking any necessary or prudent acts, diligence or obligations in connection with the subject matter addressed herein or recipient’s intended purposes, including, without limitation, use of 811 before performing any digging and obtaining an encroachment permit from Piedmont Natural Gas.”