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Solar powered internet methods

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===Geographical potential using GIS===
In this section we will use Geographical Information Systems (GIS) to identify optimal areas for the PV mesh network deployment. Information on obtaining a copy of ArcGIS can be found at [http://www.esri.com/products/index.html]. In order to assess various aspects of the variables influencing PV system implementation, a geographical representation of the variables deployment potential is necessaryuseful in identifying optimal locations. Local level spatial disparities are necessary to assess to find locate places lacking infrastructure and technology for broadband access (Grubesic and Murray 2001).
Due to the potential of emerging technology and because of its relatively high solar irradiance availability, California was used as an initial case study.Among the geo-processing capabilities of GIS perhaps the most useful is the overlay function. GIS has shown its usefulness in rural and urban planning, sustainable development, and identifying site specific locations (Refs). The different layers, including electric networks, broadband networks, and To do this we will mainly look at population density, can be synced to identify rural locations for technology dissemination. The geographical information system (GIS), ArcGIS, was used to organize spatial variables to find cells, of resolution 1 km2, that is appropriate for standalone PV system installation. We computed the locations of remote sites for SAPV installation from creating 6 layers incorporating buffers from the electric grid and current broadband access at 500m, 1,000m, and >1,000m (Muselli et al. 1999). Additionally, a layer of population density is used to interpret potential market and impact.
Please ==Getting Started==First, base layers are needed for creating a quality map. Many free shape files can be found online. For our map we used California Basemap [http://atlas.ca.gov/download.html#/casil/imageryBaseMapsLandCover/baseMaps] and a general basemap for the United States and Pacific Ocean [https://projects.atlas.ca.gov/frs/?group_id=119&release_id=295]. After unzipping the files, open ArcMap and ‘add data’. Select the shapefile you would like to use. If one of the files has labels, for example state names, you can double click on the file and select the ‘Display’ tab and select the field that contains names next to the ‘Field’ drop down window. The window should look like this. <center>[[Basemap 1.jpg]]</center> Next we need to download and add links to the map county boundaries, which were found here: [http://www.census.gov/cgi-bin/geo/shapefiles2010/file-download] These files are in the same geographical format as census data setswhich will be helpful in the next step. After uploading the map will look like this. <center>[[ ]]</center>  Now we need to find census data in order to map population density. These files can be found at the U.S. Census Bureau’s FactFinder page. [http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml] Download the data that is the most appropriate for your application. Download the data in txt format in order to transfer to excel. This will allow us to link the data with our shapefiles in ArcMap. Open the file in excel. If you downloaded population data, calculate the population density with this equation.  Population Density=Population/(Land Area*2,589,988)  Now the file can be linked with a shapefile in our map. To do this. Click on the file you downloaded from census.gov and select ‘Join and Relate’ and then ‘Join’. This will pop up this window to select the file and table to connect. Note: The files can only be linked if they share at least one attribute. <center>[[ ]]</center> After identifying the symbology to be displayed and number of categories, you should get a map that looks something like this. <center>[[ ]]</center> Next, lets try to find some utilities data for California. Specifically, we are interested in broadband providers. Luckily the California Public Utilities Commission has already compiled much of this data and keeps ongoing and up to date files of various service providers. Maps with broadband service are found here: [http://www.cpuc.ca.gov/PUC/Telco/Information+for+providing+service/Broadband+Availability+Maps.htm] After adding this layer, the combined maps will look like this. <center>[[ ]]</center> An "underserved" area is an area where broadband is available, but no wireline or wireless facilities- based provider offers service at advertised speeds of at least 6 mbps download and 1.5 mbps upload. “Served” areas exceed these advertised speeds. Refining the data to look at certain demographics or population densities is easy with “Select by Attributes”. This allows you to define specific parameters in a table or shapefile and export it as a new database or map. In this case we wanted to isolate the higher population density areas. <center>[[ ]]</center> In the next map we will combine the high population density file (in purple) with the “underserved” areas to see if there are areas with relatively high population density but no broadband access. This can be done with the “Select by Location” function in GIS.  <center>[[ ]]</center> Overlaying this file results in two optimal locations, roughly 2x3 miles, with high population density and no access to broadband that would make ideal locations for mesh network pilot projects. The two areas are difficult to see in the below map. <center>[[ ]]</center> But by zooming in we can see the two areas.  <center>[[ ]]</center> <center>[[ ]]</center> Make sure that the area you use GRASS instead choose is not water by downloading a river and lake shapefile. Proper ground validation is important with GIS to ensure that the area does indeed provide the attributes you were looking for. Potential next steps include using high resolution satellite imagery for object recognition analysis. By identifying individual houses a more accurate map can be made of ArcGIShigh density housing clusters. Additionally this will allow for optimal routing algorithms to be applied for technology dissemination.
===Testing methodology===
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