${data.title[0]}${labels[j] && ` (${labels[j]})`} ${data.title[1]}${labels[i] && ` (${labels[i]})`}

2d:null 2e:["$","title",null,{"children":"Renewable Energy and Nonrenewable Energy Use Across the Contiguous United States. - Amanda & Nathan | Geographic Visualization (GEOG 3540) - University of Iowa | Observable"}] 33:T54f,md` In our project we look at comparing the consumption of nonrenewable energy sources such as coal, petroleum, and natural gas to the renewable energy sources wind and solar. From the comparisons we plan on finding what states in the contiguous United States renewable energy can be utilized to reduce greenhouse gas emissions and produce energy with wind and solar instead of coal, petroleum, and natural gas. Another goal we have is discovering where renewable energy is being used compared to other sources of energy and what states have a high potential to produce clean renewable energy and if they are doing that or not also compared to how much nonrenewable resources those states are using. The questions we start off by asking to satisfy our goals are… • What parts of the United States have high resource potential for wind and solar energy? • What parts of the United States are consuming high amounts of renewable energy? • What parts of the United States are dependent on fossil fuels, but have resource potential for wind and solar, if any? • Are the states with high resource potential producing high amounts of renewable energy? Key words: Consumption, Potential, renewable, nonrenewable, greenhouse gas emissions, clean energy, green energy, solar irradiance, wind, coal, petroleum, natural gas, contiguous United States.` 34:T56a,choropleth_2= { const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]); svg.append("g") .attr("transform", "translate(360,20)") .append(() => legend({ color: color_2, title: data_2.title, width: 500, tickFormat: ".2f", }) ); svg.append("g") .selectAll("path") .data(states.features) .join("path") .attr("stroke", "black") .attr("stroke-linejoin", "round") .attr("stroke-width", 1) .attr("fill", function(d){ console.log(color_2(data_2.get(d.properties.STATE_NAME))) return color_2(data_2.get(d.properties.STATE_NAME)); }) .attr("fill", d => color_2(data_2.get(d.properties.STATE_NAME))) .attr("d", path_1) .append("title") .text(d => " global horizontal irradiance " + data_2.get(d.properties.STATE_NAME)); // svg.selectAll(".subunit-label") // .data(states.features) // .enter().append("text") // .attr("class", function(d) { return "subunit-label " + d.id; }) // .attr("transform", function(d) { return "translate(" + path_1.centroid(d) + ")"; }) // .attr("fill", function(d){ /// var value = data_2.get(d.properties.STATE_NAME); // if(value > 0 { // return "black";}) return svg.node(); } 35:T5d1,choropleth_1= { const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]); svg.append("g") .attr("transform", "translate(360,20)") .append(() => legend({ color: color_1, title: data_1.title, width: 500, tickFormat: ".2%" }) ); svg.append("g") .attr("transform", "translate(100,20)") .append(() => legend({ color: nullcolorforlegend, title: "No Data", width: 50, tickFormat: ".1f" }) ); svg.append("g") .selectAll("path") .data(states.features) .join("path") .attr("stroke", "#969696") .attr("stroke-linejoin", "round") .attr("stroke-width", 1) .attr("fill", function(d){ if(data_1.get(d.properties.STATE_NAME) > 0){ return color_1(data_1.get(d.properties.STATE_NAME)); } else { return "black"; } }) //if (data_1.get(d.properties.STATE_NAME) ) //console.log(color_1(data_1.get(d.properties.STATE_NAME))) //return color_1(data_1.get(d.properties.STATE_NAME)); .attr("fill", d => color_1(data_1.get(d.properties.STATE_NAME))) .attr("d", path_1) .append("title") .text(d => " pct_suit " + data_1.get(d.properties.STATE_NAME)); return svg.node(); }36:T4c2,chart7 = { const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]); svg.append(legend_7) .attr("transform", "translate(870,150)") svg.append("g") .attr("transform", "translate(650,20)") .append(() => legend({ color: nullcolorforlegend, title: "No Data", width: 50, tickFormat: ".1f" }) ); svg.append("g") .selectAll("path") .data(topojson.feature(USA, USA.objects.states48).features) .join("path") .attr("fill", function(d){ if(data_f.get(d.properties.STATE_NAME)){ if(data_f.get(d.properties.STATE_NAME)[0] && data_f.get(d.properties.STATE_NAME)[1]){ return color_f(data_f.get(d.properties.STATE_NAME)); }else return nancolor; } else{ return nancolor; }}) .attr("d", path_1) .attr("stroke", "white") .attr("stroke-linejoin", "round") .attr("stroke-width", .5) .append("title") .text(d => `${d.properties.STATE_NAME}, ${format_f(data_f.get(d.properties.STATE_NAME))}`); return svg.node(); }37:T427,md ` This visualization of solar energy potential compared to coal consumption in the United States shows that there is more coal consumption in the north than in the south where solar potential is higher. Based on the average solar irradiance compared to the consumption in oil in each state spatial patterns are present. Solar potential starts strong in the southern states and begins to become weak further north you go. Oil consumption is high in a few southern states and higher in northern states that don’t have the yearly availability of solar potential. If we use this map's information correctly, we should be able to form an answer to the questions. Are the states with high renewable resource potential producing high amounts of nonrenewable energy? Also, what arts of the United States are dependent on fossil fuels? To answer the questions, we can see that southern states that have high solar potential still use a lot of nonrenewable energy and northern states rely on fossil fuels because they aren’t able to produce more energy with solar. `38:T40c,md ` Renewable Energy in this case refers to the production of both solar AND wind production. The Great Plains is an area that has high wind energy potential and seems to produce a large amount of renewable energy. States like California, Oregon, Washington, Kentucky, South Carolina, etc. have medium or high renewable energy production, but little wind energy potential. These states might be utilizing other renewable energy sources like solar, offshore wind, wave power, or biofuels. The Southeast once again seems to not rely, produce, or consume much renewable energy. For the case of wind energy, this makes some since so those states have some of the slowest average wind speeds at 60m above ground surface. It is interesting that states in previous maps (Like Minnesota and Illinois) that have high wind resource wind potential and high wind consumption, DO NOT produce a high amount of renewable energy production. Perhaps the high consumption comes from surrounding states that produce a large amount of renewable energy? `39:T4a4,md ` This map compares wind resource potential and coal consumption (in million btu). There are a few main groups that the states fall under. States like Nevada, Idaho, and the Northeastern states all have medium wind potential and have low coal consumption. Perhaps they are using other forms of nonrenewable energy like natural gas or oil or a different renewable energy source like wave, solar, or biofuels. Most of the Southeastern states, Arizona, and Utah all have medium to high amounts of coal consumption, which might be partially related to the low average wind speeds of those states (not likely to be able to make as much wind energy or it is not profitable). It is interesting that some of the notable high wind potential states like Iowa, Wyoming, Montana and many others have both high potential and high coal consumption. Many of those states are also producing a relatively high amount of renewable energy, but there might be room for more infrastructure in larger, low population density states like Montana and Wyoming. This would be an interesting addition to help really answer the question of which states can/should be increase their renewable energy production. `3a:T501,md ` This map is visually similar to the one up above. The Northeast once again consumes a low amount of natural gas. The West Coast perhaps uses wave power or solar power in the case of California (has high resource potential). Some Southeastern states like Tennessee, North Carolina, South Carolina, Georgia, and Florida all use low amounts of natural gas, but have higher levels of coal consumption. Places like the Midwest, even with the high wind resource potential, consume high amounts of natural gas. When renewable energy production and natural gas production are both measured in British thermal unit (btu), the amount of million btu that states like Iowa, Montana, and other Midwestern/Great Plain states produce of renewable energy production does not even compare to the millions of btu spent on natural gas consumption. This is a frequent problem with natural gas versus renewable energy production. Renewable energy production is not on the same scale as the consumption of oil, natural gas, and coal. For example, Iowa produces 77.5 million btu of renewable energy compared to the 84.8 million btu of coal, 137.2 million btu of natural gas, and 135 million btu of oil. This is a similar pattern if you observe states like Montana, Wyoming, California, and Maine. `3b:T11bc,md ` # Overall Conclusions! • What parts of the United States have high resource potential for wind and solar energy? In summary, Montana, Wyoming, New Mexico, Texas, Oklahoma, Kansas, Nebraska, South Dakota, North Dakota, Minnesota, Iowa, and Michigan all were in the highest wind speed classification with a wind speed of at least 6.10 meters per second. Solar resource, estimated with global horizontal irradiance, is highest in the southernmost states and decreases with increasing latitude. The states with the most potential are California, Nevada, Utah, Arizona, New Mexico, Colorado, Texas, Oklahoma, Louisiana, Mississippi, Georgia, and Florida. California, New Mexico, Arizona, and Texas are especially important states to consider because the small buildings within that states are very suitable for solar panels which is another way to produce solar energy. • What parts of the United States are consuming high amounts of renewable energy? Florida, Idaho, California, Nevada, Utah, New Mexico, Arizona, Colorado, South Carolina, New Jersey, Vermont, and Massachusetts consume relatively large amounts of solar energy measured in million Btu. The states that consume the most wind energy measured in million Btu are very similar to the states that have the greatest wind resource potential except for Colorado (this state is in the second highest classification). This category includes the states of Montana, Wyoming, New Mexico, Texas, Oklahoma, Kansas, Nebraska, South Dakota, North Dakota, Minnesota, and Iowa. States like California, Washington, Oregon, Michigan, and Maine may benefit from off-shore wind production. Idaho, Missouri, Illinois, Indiana, West Virginia, and Michigan also consume a relatively high amount of wind energy. These states are in the second wind speed category and have speeds of at least 5.76 meters per second. • Are the states with high resource potential producing high amounts of renewable energy? Solar Energy versus Renewable Energy In general, Kansas, Oklahoma, New Mexico, Texas, and Nevada all produce high amounts of renewable energy (wind + solar) and also have high solar resource potential. States like California, Utah, Colorado, and New Mexico are not far behind in terms of production but may be able to increase their renewable energy production since they have such high average global horizontal irradiance. Other states like Louisiana, Mississippi, Georgia, South Carolina, and Florida have high solar potential, but low renewable energy production. Wind Energy versus Renewable Energy Many states seem to utilize wind energy when they have high resource potential. Much of the Great Plains and midwest falls into this category. However, Wisconsin (low renewable production), Michigan(low renewable production), Indiana(low renewable production), and even Ohio (low renewable production) have medium to high resource potential but are not producing as much renewable energy as states like Nebraska, Kansas, and Iowa (all have high production). • What parts of the United States are dependent on fossil fuels, but have resource potential for wind and solar if any? A big part of this project has been realizing that the production of renewable energy and consumption of nonrenewable energy is on two completely different scales. Even if a state produces "a lot" of renewable energy, they were also really likely to consume large amounts of natural gas, oil, and coal (see the bivariate maps comparing these variables, pay close attention to states like Texas, Iowa, Montana, Wyoming, etc.). So largely, most of the states are dependent on at least one form of nonrenewable energy. It is concerning that more is not being done to take advantage of the solar resource that many of the Southern states have because one paper, Furkan et al. 2011 found that the United States has "high insolation (think solar resource), the greatest electricity demand in the world, and ample available land for PV development." On top of that, PV rigged small buildings could produce as much as 1,432 Twh as a national estimate. This is equivalent to 4.88619e+15 Btu. A step in the right direction for harnessing wind power more in states like Ohio, Illinois, Indiana, and even states like Iowa, Wyoming, and the Dakotas would be to increase the number of transmission lines. One study in Wyoming found that wind power plants were clustered by transmission lines even if those areas were not the windiest and would not produce energy the most efficiently (Hitaji, 2013). `3c:T412,md ` # Sources Intro https://www.climate.gov/news-features/climate-qa/if-carbon-dioxide-hits-new-high-every-year-why-isn%E2%80%99t-every-year-hotter-last Solar Energy Map Pattern Analysis Gagnon, Pieter; Margolis, Robert; Phillips, Caleb (2019): Rooftop Photovoltaic Technical Potential in the United States. National Renewable Energy Laboratory. 10.7799/1575064 Furkan Dinçer, The analysis on photovoltaic electricity generation status, potential and policies of the leading countries in solar energy, Renewable and Sustainable Energy Reviews, Volume 15, Issue 1, 2011, Pages 713-720, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2010.09.026. (https://www.sciencedirect.com/science/article/pii/S1364032110003138) Wind Energy Map Pattern Analysis Claudia Hitaj, Wind power development in the United States, Journal of Environmental Economics and Management, Volume 65, Issue 3, 2013, Pages 394-410, ISSN 0095-0696, https://doi.org/10.1016/j.jeem.2012.10.003 (https://www.sciencedirect.com/science/article/pii/S0095069612001003) `3d:T45f,legend_1 = () => { const k = 30; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data.title[0]}${labels[j] && ` (${labels[j]})`} ${data.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data.title[0]} ${data.title[1]} `; }//make a legend3e:T467,legend_2 = () => { const k = 30; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data_a.title[0]}${labels[j] && ` (${labels[j]})`} ${data_a.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data_a.title[0]} ${data_a.title[1]} `; }//make a legend3f:T467,legend_3 = () => { const k = 30; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data_b.title[0]}${labels[j] && ` (${labels[j]})`} ${data_b.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data_b.title[0]} ${data_b.title[1]} `; }//make a legend40:T464,legend_4 = () => { const k = 30; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data_c.title[0]}${labels[j] && ` (${labels[j]})`} ${data_c.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data_c.title[0]} ${data_c.title[1]} `; }//make a leg41:T464,legend_5 = () => { const k = 30; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data_d.title[0]}${labels[j] && ` (${labels[j]})`} ${data_d.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data_d.title[0]} ${data_d.title[1]} `; }//make a leg42:T464,legend_6 = () => { const k = 30; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data_e.title[0]}${labels[j] && ` (${labels[j]})`} ${data_e.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data_e.title[0]} ${data_e.title[1]} `; }//make a leg43:T464,legend_7 = () => { const k = 30; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data_f.title[0]}${labels[j] && ` (${labels[j]})`} ${data_f.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data_f.title[0]} ${data_f.title[1]} `; }//make a leg44:T464,legend_8 = () => { const k = 30; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data_g.title[0]}${labels[j] && ` (${labels[j]})`} ${data_g.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data_g.title[0]} ${data_g.title[1]} `; }//make a leg45:T464,legend_h = () => { const k = 30; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data_h.title[0]}${labels[j] && ` (${labels[j]})`} ${data_h.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data_h.title[0]} ${data_h.title[1]} `; }//make a leg46:T464,legend_i = () => { const k = 30; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data_i.title[0]}${labels[j] && ` (${labels[j]})`} ${data_i.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data_i.title[0]} ${data_i.title[1]} `; }//make a leg47:T645,md ` ## Goal The overarching goal of this project is to explore patterns in nonrenewable and renewable energy use across the lower 48 states. Our motivation for investigating the energy use patterns in the contiguous US is that renewable energies are more environmentally friendly. For example, they release less carbon into the atmosphere, and a surplus of carbon in the atmosphere is one of the causes of climate change. The problem our series of maps hopes to answer is where are areas of the lower 48 states that can increase the amount of solar power and/or wind power that they produce. When discussing renewable energy, it is also important to understand the distribution or use of nonrenewable energy. For example, one of the specific questions our map story will answer will be "Do areas that produce lots of renewable energy still use predominantly nonrenewable energy or fossil fuels?. Another broad purpose of our map story will be to explore areas that can reduce their dependence on fossil fuels by using a renewable energy source that their land has potential for. Specific Questions Specifically, we are looking into what parts of the US are utilizing two renewable energy options: solar and wind. What parts of the lower 48 states have solar or wind energy potential but are not using it? How can we display or visualize the solar or wind potential? What parts of the United States are dependent on fossil fuels but have good resource potential for solar or wind power? Do areas that produce lots of renewable energy still use predominantly nonrenewable energy or fossil fuels? `48:T1d5c,md ` ## Related Work ### Solar Related Work The geovisualization methods I have seen used in the papers down below have been isarithmic maps and choropleth maps. The isarithmic maps are good for the irradiance data because that data is continuous and smooth. Where this data is not collected at true points, it was most likely filled using some sort of spatial interpolation method. The choropleth maps are useful for displaying the suitability of the buildings. This is because the data is continuous for a smaller area like a zip code, but can abruptly change from zip code to zip code. This paper, https://www.sciencedirect.com/science/article/pii/S1364032117313485#f0010, explores the global status, limitations, benefits, and future directions of solar energy. This paper showcases two isarithmic maps. This paper reports that '"the National Renewable Energy Laboratory (NREL) in the United States has estimated that the solar energy potential within the USA is capable enough to provide 400 zettawatt-hours annually (ZWh) [15], hugely exceeding the current electrical generation capacity (22,813 terawatt-hours (TWh)"'. This paper also reports that high installation cost is still a limitation of PV solar technology. Additionally, solar energy is not the most reliable source of energy in regions with unsustainable weather or climate conditions (is not as efficient if it is cloudy or rainy). For the benefits, solar power is environmentally green and clean because it has very little emissions and produces little noise. This paper reports "The range of CO2 emission per kilowatt-h generated from coal, natural gas, and solar are estimated as 0.64 ̴ 1.63, 0.27 ̴ 0.91, and 0.03 ̴ 0.09 kg (emission ratio of 18:9.5:1), respectively." this shows that solar emits much less CO2 than conventional energy sources. This paper (https://www.sciencedirect.com/science/article/pii/S1364032110003138) does not include any maps I am interested in making, but it does do have a review kind of format of each of the leading solar PV countries, the US being one of them. They find that the US has "high insolation, the greatest electricity demand in the world, and ample available land for PV development." The final paper discusses the PV suitability of small, medium, and large buildings. The sheer number of buildings in the small class gives small buildings the greatest technical potential. Small building rooftops could generate 926 TWh/year of PV energy, which is over half of all rooftop PV's total technical potential. They conclude by estimating that the total national technical potential of rooftop PV is 1,118 GW of installed capacity and 1,432 TWh of annual energy generation. This equates to 39% of total national electric-sector sales. https://www.nrel.gov/gis/solar-resource-maps.html https://www.sciencedirect.com/science/article/pii/S1364032117313485#f0010 https://www.sciencedirect.com/science/article/abs/pii/S1364032110003138 https://www.nrel.gov/docs/fy16osti/65298.pdf ### Wind Related Work https://ieeexplore.ieee.org/abstract/document/4449210?casa_token=9c0KyToQ67IAAAAA:8WeNnqkDMHm8xMqFpoWb_1izt4jCLtlhulPlc6p7lGORkYWE2MSzBaHslMdPBg7EaOVy8ZKToq0 Offshore Wind Resource Potential of the United States Many coastal areas have large electricity demand but have limited access to high-quality, land-based wind resources, so offshore wind resources have the potential to help these areas meet their energy demands. This paper takes into account the wind power class (wind speed at 50m), water depth, distance from shore, and some waterway exclusions like protected marine environments. In the future, another study such as this could include high traffic seaways and underwater infrastructure as places where offshore wind turbines cannot be placed. Depth of water was concerned to be an important factor because it heavily influences what technology would be used to develop a wind resource project (something to add to our analysis). Distance from the shoreline is another variable to look at because it will influence how much the project will cost. The farther a wind project is away from the shore, the more underwater cable will be needed to connect the turbines to a plant on land. Lastly, they looked at protected environmental areas that could significantly impact how much energy is able to be produced by these projects, however, for most states (except California) the impact of the environmental exclusions is minimal https://www.sciencedirect.com/science/article/pii/S0095069612001003?casa_token=w-2Fcz5FV-kAAAAA:3WI59uZCvDF4oklvEVFHKV3ppNE-l8rVKnYBI8JE1i98MwFhunJvV-gUi2UDymguqRnowpRfYXEV#f0025 Wind power development in the United States The key things I took away from this paper are that the important determinants of wind power profitability are wind speed, distance to the electricity transmission grid, and policy. The windiness is so important. IF the wind speed is doubled, this causes the power to increase by a factor of 8. In general, locations with a wind power rating of 4 or higher are preferred for large-scale wind plants (at least 7-7.5 m/s). Distance to the transmission grid is important because this paper finds instances in the state of Wyoming where the wind plants are clustered by the transmission lines and are not necessarily in the areas that are the highest wind power class. https://www.sciencedirect.com/science/article/pii/S0360544221002930?casa_token=uIP4X6Mar0oAAAAA:hKmNpP2mAA0m8Rf5cPqYuCFkT6GPBa31XNC8-7iJYTQr9CYKGDCP67Iw1AK3M0yzTKdKUCsNk0Wv#fig2 Land use and turbine technology influences on wind potential in the United States This paper included very fine-scaled objects such as roads, railways, and radar towers! The authors mentioned that land use classifications like the NLCD might be too broad or generalized for where to place the individual turbines. Since my analysis is broader, NLCD land classifications might be something to look into (add to the unavailable land variable). Radar towers and their exclusion zones are very important in the placement of turbines because the turbines can interfere with the radar systems. This could be something to look into. Other papers/government websites did not mention a specific height, but since 2 papers have now mentioned 50m and the power classes that are associated with those speeds, for the final project I will change the 100m to 50m wind speed going forward. ### Nonrenewable Resource Related Work https://www.eia.gov/tools/faqs/faq.php?id=427&t=3 https://www.sciencedirect.com/science/article/pii/036031999090005J https://www.sciencedirect.com/science/article/pii/S0306261917304154 https://www.eia.gov/electricity/data/browser/#/topic/3?agg=1,0,2&fuel=8&geo=vvvvvvvvvvvvo&sec=o3g&linechart=ELEC.CONS_EG.COW-TX-99.A~ELEC.CONS_EG.COW-TX-1.A~ELEC.CONS_EG.COW-TX-94.A&columnchart=ELEC.CONS_EG.COW-TX-99.A~ELEC.CONS_EG.COW-TX-1.A~ELEC.CONS_EG.COW-TX-94.A&map=ELEC.CONS_EG.COW-TX-99.A&freq=A&start=2001&end=2021&ctype=map<ype=pin&rtype=s&pin=&rse=0&maptype=0 There has been many studies done on how harmful nonrenewable resources and fossil fuels are to the environmetn and the health of people who live near power plants and fuel refinereies. In 2021, 61% of the energy that was generated at a utility scale was from fossil fuels. Previous work shows the amount of fossil fuels that are used by states and the comparison between states in classed maps. They also split up the types of nonrenewable resources and also show the difference between regions or states in their usage. `49:T1ece,md ` ## Data ### Solar #### Rooftop Solar Photovoltaic Technical Potential in the United States. A Detailed Assessment https://www.osti.gov/biblio/1575064-rooftop-photovoltaic-technical-potential-united-states https://www.osti.gov/biblio/1236153-rooftop-solar-photovoltaic-technical-potential-united-states-detailed-assessment A small building is less than 5,000 square feet, a medium building is between 5,000 and 25,000 square feet, and a large building is greater than 25,000 square feet. The suitability of roof area for hosting PV was determined based on shading (energy production not less than 70% of an identical unshaded system in the same location or is the building mostly covered in shade during the day), tilt (no greater than 60-degree tilt), azimuth (not facing north), and contiguous roof area (at least 10 square meters available) criteria. Unfortunately, this dataset does not have the whole United States, and it only includes small buildings (large and medium buildings were not available at the zip code level, which is the only data they provided). Also, if a zip code was not completely covered by lidar, then a model was used to fill in for the rest of the area. However, if there was insufficient input data, then that zip code was not represented (explains random and large gaps). The lack of medium and large buildings is a source of underestimation for solar potential, as is the lack of some of the zip codes/states. However, it is still useful, because it is a good indication of the solar power capacity if small buildings (like homes and businesses) were to install solar panels! To make this observable friendly, change of the coordinate system to WGS1984 was needed and the split of the data into two topojsons was needed because one file was too large to import into observable. Then the use of map shaper to change it into a topojson was used and it was uploaded like that as a file attachment. This file has data attached to it, so a csv asntt needed. Attributes Needed: Percent Suitability #### GHI https://www.nrel.gov/gis/solar-resource-maps.html Irradiance = The primary source of energy to the Earth is radiant energy from the Sun. This radiant energy is measured and reported as solar irradiance. (according to https://www.nasa.gov/mission_pages/sdo/science/solar-irradiance.html). GHI or Global Horizontal Irradiance is considered the total solar irradiance. It is made up of the Direct Normal Irradiance (how much irradiance is received if you are on the sun's direct path) + Diffuse Irradiance (energy received after it has been scattered) (https://www.nrel.gov/grid/solar-resource/solar-glossary.html). For this data set, I need to convert it to WGS1984. I also need to decide if I want to use it as a vector or a raster format. After that, I can import it into observable as a topojson (if using a polygon). Then I will need to examine the histogram and determine the classification method, the number of classes, and the final color scheme. This dataset should be pretty accurate because it is "averaged over surface cells of 0.038 degrees in both latitude and longitude" according to the metadata. It does not have any missing data. Based on the metadata available, I am unsure of how to determine the data uncertainty. This file has data attached to it, so I should not need a csv. Attribute needed: GHI measurement ### Wind #### Unavailable Land Cities-infrastructure and currently developed places, plus people often do not like the noise pollution associated with wind turbines. https://www.arcgis.com/home/item.html?id=432bb9246fdd467c88136e6ffeac2762&sublayer=1 https://www.arcgis.com/home/item.html?id=432bb9246fdd467c88136e6ffeac2762 This layer is managed by ESRI and is from the 2010 Census where they then derived Urbanized Areas (UA) and Urban Clusters (UC). This data is also derived from the urban areas layer of the Digital Chart of the World (DCW). This dataset provides information about the locations, names, and populations of urbanized areas. Based on what I can see from the website, this does not lack any data. It covers the whole US and the District of Columbia. This layer does not contain smaller urban areas but that is okay because I am choosing to focus on the larger Urban Areas. I wanted a dataset mapping urban areas to represent unavailable land. This data is in WGS 1984, so I do not have to change that. I can then upload this to mapshaper and turn it into a topojson as it is made up of polygons. This topojson will have the data in it so a separate csv full of attributes is not needed. In GIS, I made a buffer around each city of 10 km. I was unable to find a standard distance that wind turbines are built outside of cities, so I am thinking this may vary greatly depending on local policy. Attributes needed: City name, City Area National Park Boundaries-Land that cannot be developed https://uiowa.maps.arcgis.com/home/item.html?id=c8d60ffcbf5c4030a17762fe10e81c6a&sublayer=2 I found this layer from an account related to the National Park Service. I am confident then that the large majority of national parks have made it onto this shapefile. I am also certain the area of the national park polygons should be correct. This data is useful because national parks would be an example of land that is unavailable for development and cannot be used for wind turbines. One limitation of this dataset is that when it is opened in ArcGIS, it says that there are too many features to load. This might mean that I am not able to visualize all the parks. To process this data, I need to make sure its coordinate systems and spatial extents match up with WGS1984. After that, I will use mapshaper.org to change it into topojson. This file has data attached to it, so I should not need a csv. Attributes needed: Park Name, Park Area I am thinking that I can develop a variable called "unavailable land" for every state. I can calculate that variable by adding up the total area of each state covered by cities (and the buffer) and national parks. I would take that number and divide it by the TA of the state to make a percentage of unavailable land. That can be visualized using a choropleth map. #### Wind Speed https://www.nrel.gov/gis/wind-resource-maps.html This data is the Annual Average Wind Speed (2007-2013) in meters per second. This dataset is wind speeds at 100 meters above surface level. There are other height options, and I have seen maps and articles use varying heights to measure resource potential. This data provides modeled annual average offshore and onshore wind speed for the contiguous United States. I am once again uncertain of any uncertainty with this data except that since it's filled in by a model, it is an estimate of sorts. Models are necessary as it's impossible to measure the exact windspeed at every single point over the contiguous USA. A similar concept applies to the GHI solar resource. This data is useful because wind speed is the primary determiner of wind power capacity or production. If a location does not have high wind speeds, it is unlikely to be able to produce an economic or useable amount of energy from a wind turbine. There is no missing data, so that increases its usefulness and my certainty that this data is accurate. To process this raster, I need to clip it down to the contiguous US for the on-shore wind assessment, as this includes off-shore data as well. I also need to make sure before making maps in observable that the shapefile is in WGS 1984 and has been converted into a vector and then a topojson. However, I need to play around with how to display it as a vector because it does not make a lot of sense (similar to what happened with the solar GHI). The topojson can be used in observable to make isarithmic maps. That would be the ideal way to show this data. Attribute Needed: wind speed value in meters per second`4a:Ta90,md `Wind Speed Map description Usefulness Wind speed is an important predictor of where wind turbines can be built to incorporate renewable energy. The great plains and much of the midwest are ideal places for wind turbines. Offshore areas around the East/West Coasts and the State of Michigan also seem like good places to install offshore wind turbines. This may be helpful since the coastal states, in general, have lower wind speeds at 100m above ground than the central parts of the country. Those states are also more populated, so they might have more developed land, the ocean could be a good place to set up wind turbines as they will then not take up land needed for agriculture or cities. Process This dataset is not in WGS1984 so I need to convert it. I then clipped the raster down (top map) to showcase and isolate just the patterns of the land wind patterns (ignoring offshore). To help visualize the data for the proposal, I used a quantile classification system with 4 classes. ### Nonrenewable Energy and Resource Data Although we might not like the sound of it 61% of energy generation in the united states is from nonrenewable resources such as coal, oil, and natural gas. The data sets from the sources below show the amount of fossil fuel usage in the United States for each state. https://www.arcgis.com/home/item.html?id=b2e6b4945c3b4796beaa46916a65ac88 https://www.eia.gov/state/seds/data.php?incfile=/state/seds/sep_sum/html/sum_btu_1.html&sid=US The U.S Energy Information Administration has alot of data to use for nonrenewable resources and energy consumption. The website also has spatial information on the locations of power plants that burn these fuels where we could look at information on how the emissions can also impact the surrounding areas. The goal of these data sets are to show how much nonrenewable resources are used compared between states and to then use that information to find where nonrenewable resources can be used less often. The information from the data sets could allow for third parties to locate where nonrenewable energy dependent areas can be convereted into renewable energy dependent areas. https://www.eia.gov/electricity/data/browser/#/topic/3?agg=1,0,2&fuel=8&geo=vvvvvvvvvvvvo&sec=o3g&linechart=ELEC.CONS_EG.COW-TX-99.A~ELEC.CONS_EG.COW-TX-1.A~ELEC.CONS_EG.COW-TX-94.A&columnchart=ELEC.CONS_EG.COW-TX-99.A~ELEC.CONS_EG.COW-TX-1.A~ELEC.CONS_EG.COW-TX-94.A&map=ELEC.CONS_EG.COW-TX-99.A&freq=A&start=2001&end=2021&ctype=map<ype=pin&rtype=s&pin=&rse=0&maptype=0 Limitations to the data can consist of difficulties from data gathering and differences in the local preferences based on what people in certain areas feel about energy usage.`4b:Tc39,md ` ## Planned representation techniques ### Solar Section and Wind Section I plan to use a choropleth map to show the percentage of small buildings suitable in each zip code. Solar Energy is not like wind energy where you necessarily need large amounts of open space for it: homes, businesses, any building that can support a PV solar panel which could be used to produce energy and reduce the need for fossil fuels. That is the information or the story that the small building suitability assessment is supposed to tell. I plan to use an isarithmic (isometric, as there are true points where the solar irradiance is measured) map to show how the solar energy resource (energy from the sun) changes across the contiguous US. GHI is the most useful one for deciding solar resource potential because it takes into account both types of solar irradiance (direct and scattered). For interaction techniques (for wind and solar), I would like to be able to have map readers hover over a polygon and see the value of the percent suitability. I would also like to allow them to pick a state in case they want to look more closely at the state that they live in. For the solar resource potential (wind and solar), I would like them to be able to hover over the map and see the specific numbers for those locations. I found a few notebooks that contain different ways to make a state map interactive: https://observablehq.com/@jwcarroll/interactive-state-map https://observablehq.com/@27ed378f60dd4c20/us-map-with-state-tooltips https://observablehq.com/@jeantimex/us-state-county-map https://observablehq.com/@jwcarroll/interactive-state-map. https://observablehq.com/@uwdata/interaction-This notebook makes me think I might be able to combine the solar and wind resource map, but I got a little lost in the code and what it all does...It seems like I could set up a map and make it wear the map reader can select which energy (solar, wind, coal, etc) they want to look at. This notebook describes how to call text when hovering over a map: https://observablehq.com/@amanda-car/choropleth-mapping-geog-3540-assignment-3-amanda-c "Raster Notebooks" (in case I do not like how my data looks when transferred into a polygon or vector) https://observablehq.com/@joewdavies/visualizing-eurostat-grid-data-using-three-js-d3 ### Nonrenewable Energy Section For the nonrenewable energy section, the plan is to use data that is able to show the total usage of nonrenewable energy resources by state. We can then take this information and create a map showing information that will allow the viewer to decipher where non-renewable energy usage is the highest or lowest. Using that data we can show where it would be best to limit nonrenewable energy usage and implement wind and solar power. Representation of this data will be with a classed map (natural breaks or whichever class turns out to be better with the data we are working with). In order to make the data show information that can be used by viewers, we will also try to implement the interactive state map notebooks found for solar and wind into the nonrenewable energy section.`4c:T4fa,// choropleth_4= { // const svg = d3.create("svg") // .attr("viewBox", [0, 0, width, height]); // svg.append("g") // .attr("transform", "translate(270,20)") .append(() => /// legend({ color: nullcolorforlegend, title: "Not Ranked", width: 50, tickFormat: ".1f" }) ); svg.append("g") .attr("transform", "translate(360,20)") .append(() => legend({ color: color_4, title: data_4.title, width: 500, tickFormat: ".1f" }) ); svg.append("g") .selectAll("path") .data(states.features) .join("path") .attr("stroke", "black") .attr("stroke-linejoin", "round") .attr("stroke-width", 1) .attr("fill", function(d){ console.log(color_4(data_4.get(d.properties.STATE_NAME))) return color_4(data_4.get(d.properties.STATE_NAME)); }) .attr("fill", d => color_4(data_4.get(d.properties.STATE_NAME))) .attr("d", path_1) .append("title") .text(d => " Coal Production (short tons) " + data_4.get(d.properties.STATE_NAME)); return svg.node(); 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31:{"id":"88c6e96294034eda","slug":"amanda-nathan","title":"Renewable Energy and Nonrenewable Energy Use Across the Contiguous United States. - Amanda & Nathan","thumbnail":"78f8f55811b63be19ecf4f35904b8e7c0b8edc6420406971bdb267940283810a","access_level":"public","create_time":"$D2022-03-13T00:09:41.802Z","update_time":"$D2022-05-13T01:43:52.525Z","view_time":null,"first_public_time":"$D2022-03-20T18:36:16.124Z","has_unpublished_edits":null,"fork":false,"stars":5,"starred":false,"owner":{"type":"team","id":"22130e106df70095","login":"geoviz","name":"Geographic Visualization (GEOG 3540) - University of Iowa","avatar_url":"https://avatars-next.observableusercontent.com/d69028c51a1cbcb1540188a2e16578dcc6abeea0c267ae7e3a59a69cf8245596","tier":"starter_2024"},"version":1951,"publish_version":1951,"role":null,"authored":false,"authors":[{"type":"individual","id":"60e9af384dfce94c","login":"caglarkoylu","name":"Caglar 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Nonrenewable Energy Use Across the Contiguous United States. - Amanda & Nathan\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1410,"value":"md ` # What is a Nonrenewable Energy Source versus a Renewable Energy Source?\n\nNonrenewable Energy are energy sources like coal, petroleum, and natural gas that are limited in supply and cannot be used sustainably. Renewable Energy refers to energy sources such as wind and solar that naturally replenish themselves and produce less greenhouse gas emissions. Although greenhouse gas emissions from nonrenewable energy sources cause immense amounts of harm to life on earth, it is necessary to provide electricity produced by coal, petroleum, and natural gas to allow the United States to function at productive rates. Renewable energy sources are shown to produce clean energy to sustainably supply energy to communities producing far less greenhouse gas emissions than nonrenewable sources.\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1568,"value":"md` # Why are we concerenced about nonrenewable energy use?\n\nClimate change is a side effect of Green House Gas emissions produced by the burning of coal, petroleum, and natural gas along with other chemicals and fuels. One piece of evidence that connects nonrenewable energy sources adn climate change is the growing amount of CO2 in the atmosphere. The figure below show that over time, more and more CO2 is going into the atmosphere and the concentration is increasing. This also seems to be correlated with the rising temperatures represented by the red lines. The tall red lines represent years that where the surface temperature was significantly higher compared to the average globla temperature. As CO2 concentration increases in the atmosphere, so does the difference between the average global surface temperature and the recorder surface temperature. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1562,"value":"FileAttachment(\"climate change.jpg\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1023,"value":"md `## Our Goals`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1482,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1294,"value":"md ` # Exploring the relationship between Solar Energy Resource Potential and Nonrenewable Energy Consumption.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1310,"value":"md ` ### In this section, we hope to explore these key questions:\n\n#### What areas of the Lower 48 States have high resource potential for solar energy?\n#### What states consume a large amount of nonrenewable energy sources but have high resource potential?\n#### Do areas that have high solar resource potential produce high amounts of renewable energy?\n#### What areas of the Lower 48 States consume more solar energy than expected?\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1798,"value":"md `### Average Global Horizontal Irradiance`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1801,"value":"md ` The choropleth map below shows the average Global Horizontal Irradiance. Global Horizontal Irradiance (GHI) is the measure of the amount of shortwave radiation from the sun that makes it to the ground. There are a number of factors that go into calculating the GHI of an area including Direct Normal Irradiance and Diffuse Horizontal Irradiance. This information allows us to decide what states are able to produce solar energy best. \n\nGHI decreases with increasing latitude. So the Southernmost states have the highest GHI values and the best solar resource access.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":423,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1472,"value":"md ` ### Percentage of Small buildings that can support solar panels. \n#### Rooftop Solar Photovoltaic Technical Potential in the United States. A Detailed Assessment\n\nA small building is defined to be less than 5,000 square feet. The suitability of roof area for hosting PV technology was determined based on shading (energy production not less than 70% of an identical unshaded system in the same location or is the building mostly covered in shade during the day), tilt (no greater than 60-degree tilt), azimuth (not facing north), and contiguous roof area (at least 10 square meters available) criteria.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":394,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1919,"value":"md ` Unfortunately, the dataset referenced up above did not include all of the states. For the states where this study did not occur, they are defined as NaN or No Data. However, this map has some interesting patterns because this solar energy \"resource\" is not completely determined by the latidtidue of the state. As stated above, it considers a lot of fine, specific details about every building. States such as Iowa, California, Arizona, New Mexico, Texas, and Alabama should consdier increasing public outreach and education about placing Solar PV panels on small apartments, homes, schools, and businesses. If buildings of any size could be built with being suitable for solar panels, then that would be a great way to increase every states renewable energy production.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1811,"value":"md `### Solar Potential Compared to Solar Panel Suitability`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1818,"value":"md` \n\nThis Bivariate Choropleth map shows where solar potential and solar panel suitability is highest to help decide where the best states would be to produce solar energy. This map is based off solar energy potential from Global Horizontal Irradiance and solar panel suitability on small buildings. States like Iowa have great solar panel suitability but are not in the best location for Global Horizontal Irradiance. Then there are states like California and Texas that have great solar potential and solar panel suitability. This visualization helps us gather information that will tell us where the best places are to produce solar energy. Unfortunately, with data being insufficient in most states it is hard to tell where there would be more locations for the best solar energy production. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1284,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1807,"value":"md` ### Solar Energy consumption choropleth\nThis map gives information on where the most solar energy is consumed. We can look at information that tells us where solar energy potential is higher and compare it to where it's consumed the most to show what states should prioritize producing solar energy and reducing the usage of fossil fuels. Alot of the states that consume the most solar energy are in the southern United States where Global Horizontal Irradiance is on average higher.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":875,"value":"choropleth_9= {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(\"g\")\n .attr(\"transform\", \"translate(360,20)\")\n .append(() => \n legend({\n color: color_9,\n title: data_9.title,\n width: 600,\n tickFormat: \".6f\",\n })\n );\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(states.features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .attr(\"fill\", function(d){\n console.log(color_9(data_9.get(d.properties.STATE_NAME)))\n return color_9(data_9.get(d.properties.STATE_NAME));\n })\n .attr(\"fill\", d => color_9(data_9.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .append(\"title\")\n .text(d => data_9.get(d.properties.STATE_NAME) + \" in Million Btu\");\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1759,"value":"md ` ### Bivariate Choropleth map showing solar potential compared to Renewable energy Production`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1769,"value":"md ` In this choropleth map the correlation between the amount of potential solar energy and the production of renewable energy is strong when it comes to seeing patterns within the map. A viewer can assume that places like Texas where there is a high solar energy potential are taking advantage of it. It is easy to tell because Texas also has high renewable energy production. We can answer the question on which states are using higher amounts of renewable energy but if we compare this map to one of wind energy potential, we can further conclude that solar is not the pure cause of Texas, New Mexico, Oklahoma, and Kansas being at the top for taking advantage of solar potential. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1195,"value":"chart5 = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend_5)\n .attr(\"transform\", \"translate(800,810)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(USA, USA.objects.states48).features)\n .join(\"path\")\n .attr(\"fill\", d => color_d(data_d.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .append(\"title\")\n .text(d => `${d.properties.STATE_NAME}, ${format_d(data_d.get(d.properties.STATE_NAME))}`);\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1699,"value":"md ` #### Summary Map of the relationship between Solar energy potential and all nonrenewable energy consumption. \n\nto explore this map in an interactive way please visit https://geog3540.github.io/adcaraballo/solar_energy.html `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1694,"value":"FileAttachment(\"Final_solar.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1947,"value":"md ` This html page is made up of four linked choropleth maps that can be used to explore the data mentioned in this section all at once. Instead of comparing Solar Energy Potential to just one other energy source, now it can be compared to all three nonrenewable energy sources all at once.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1775,"value":"md `### Solar Potential Versus Oil Consumption\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1779,"value":"md ` This Bivariate Choropleth Map shows that southern states consume a lot of oil when they could be taking advantage of Solar energy potential. The map also shows that states where solar potential is lower, Oil consumption is higher. States that are dark blue consume a lot of oil and have high solar energy potential. This means that those states are not taking advantage of renewable energy as much as they should be. This visualization can answer the question, what parts of the United States are dependent on fossil fuels, but have resource potential for wind and solar, if any? we can say that the states of Texas, New Mexico, Oklahoma, Louisiana, and Mississippi are using more Oil than what they could produce with solar energy.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1449,"value":"chart8 = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend_8)\n .attr(\"transform\", \"translate(800,810)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(USA, USA.objects.states48).features)\n .join(\"path\")\n .attr(\"fill\", d => color_g(data_g.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .append(\"title\")\n .text(d => `${d.properties.STATE_NAME}, ${format_g(data_g.get(d.properties.STATE_NAME))}`);\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1783,"value":"md ` ### Solar Potential Compared to Coal Consumption `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1787,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1225,"value":"chart6 = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend_6)\n .attr(\"transform\", \"translate(800,810)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(USA, USA.objects.states48).features)\n .join(\"path\")\n .attr(\"fill\", d => color_e(data_e.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .append(\"title\")\n .text(d => `${d.properties.STATE_NAME}, ${format_e(data_e.get(d.properties.STATE_NAME))}`);\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1791,"value":"md `### Solar Potential Compared to Natural Gas Consumption`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1795,"value":"md ` Solar potential in the north states is low due to seasonal changes preventing higher amounts of sun in those states. Higher amounts of natural gas is consumed in these states based on what we know about the amount of yearly solar irradiance. Further answering the question on what states are dependent on fossil fuels for an energy source and what states are still dependent when they should be capable of using solar energy.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1003,"value":"chart3 = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend_3)\n .attr(\"transform\", \"translate(800,810)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(USA, USA.objects.states48).features)\n .join(\"path\")\n .attr(\"fill\", d => color_b(data_b.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .append(\"title\")\n .text(d => `${d.properties.STATE_NAME}, ${format_b(data_b.get(d.properties.STATE_NAME))}`);\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1333,"value":"md ` # Exploring the relationship between Wind Energy Resource Potential and Nonrenewable Energy Consumption.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1337,"value":"md ` ### In this section, we hope to explore these key questions:\n\n#### What areas of the Lower 48 States have high resource potential for wind energy?\n#### What states consume a large amount of nonrenewable energy sources but have high resource potential?\n#### Do areas that have high wind resource potential produce high amounts of renewable energy?\n#### What areas of the Lower 48 States consume more wind energy than expected?\n\n`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1298,"value":"md ` ### Wind energy Resource is estimated by the average wind speed at 60 m above ground surface. \n\nThis map will help us answer \"what areas of the lower 48 states have high reosuce potential for wind energy?\" The areas with high average wind speeds at about 60m will reveal which states would be able to produce the most wind generated electricity. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":476,"value":"choropleth_3= {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(\"g\")\n .attr(\"transform\", \"translate(360,20)\")\n .append(() => \n legend({\n color: color_3,\n title: data_3.title,\n width: 500,\n tickFormat: \".2f\",\n font: \" 60px\",\n })\n );\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(states.features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .attr(\"fill\", function(d){\n console.log(color_3(data_3.get(d.properties.STATE_NAME)))\n return color_3(data_3.get(d.properties.STATE_NAME));\n })\n .attr(\"fill\", d => color_3(data_3.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .append(\"title\")\n .text(d => data_3.get(d.properties.STATE_NAME) + \" meters per second\");\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1711,"value":"md ` According to the wind energy resource map above, most of the Midwest and Great Plain states are in the highest wind speed category. This includes states like Michigan, Minnesota, Iowa, North Dakota, South Dakota, Nebraska, Kansas, Oklahoma, Texas, Montana, Wyoming, and New Mexico. The legend may appear to be close together because of the numbers, but increasing the wind speed by a factor of 2 can increase the power output of those turbines by a factor of 8 (Hitaj, 2013). So, smaller changes in wind speed can have large power output consequences. The highest class measured in this map (dark blue) is at a good wind speed, those states could produce Some factors that might allow the wind speed to be faster here is the areas of low topography and the relative flatness of these states. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1712,"value":"md ` ### What states consume the most energy produced from wind turbines? \n\nThe map below depcits Wind Energy Consumption in Million Btu for each of the lower 48. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":832,"value":"choropleth_8= {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(\"g\")\n .attr(\"transform\", \"translate(360,20)\")\n .append(() => \n legend({\n color: color_8,\n title: data_8.title,\n width: 600,\n tickFormat: \".2f\",\n })\n );\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(states.features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .attr(\"fill\", function(d){\n console.log(color_8(data_8.get(d.properties.STATE_NAME)))\n return color_8(data_8.get(d.properties.STATE_NAME));\n })\n .attr(\"fill\", d => color_8(data_8.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .append(\"title\")\n .text(d => data_8.get(d.properties.STATE_NAME) + \" Million Btu\");\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1720,"value":"md ` The spatial pattern of this map is very similar to the wind resource energy map. That is a good sign! That implies that the states that have high resource potential are also comsuming that energy (that they may produce). The states that use over 16,680,000 btu align well with the states that have the highest average widn speeds. Coastal states that consume more wind energy than expected like California, Oregon, and Wahsington, may be consuming off-shore wind energy that other states do not have access too. A question that this map brings up that should be investigated is if off shore wind infrastructure can be belt in the Gulf-Coast region or on the Eastern side of the country. A more in depth look at the relationshp between wind resource potential and wind energy consumption is shown in the bivariate map below.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1731,"value":"md ` ### A Bivariate Choropleth Map Exploring the Relationshp between wind energy resource potential and wind energy consumption.\n\n\nThis bivaraite map can help answer \"What areas of the Lower 48 States consume more wind energy than expected?\"\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":915,"value":"chart = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend_1)\n .attr(\"transform\", \"translate(800,810)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(USA, USA.objects.states48).features)\n .join(\"path\")\n .attr(\"fill\", d => color(data.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .append(\"title\")\n .text(d => `${d.properties.STATE_NAME}, ${format(data.get(d.properties.STATE_NAME))}`);\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1823,"value":"md ` This map is a combination of the first two maps. It more clearly shows the patterns discussed up above. The Great Plains and Midwest are regions that have high wind speeds and high wind energy consumption. The South Eastern United States is kind of the opposite of the Great Plains/ Midwest. There is little wind potential (low wind speeds) and the consumption is also low. The West Coast (California, Oregon, and Washington consumes more energy than expected based on their wind resource potential.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1734,"value":"md ` ### A Bivariate Choropleth Map Exploring the Relationshp between wind energy resource potential and renewable energy production.\n\nThis map helps us determine \"Do areas that have high wind resource potential produce high amounts of renewable energy?\"`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":990,"value":"chart4 = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend_4)\n .attr(\"transform\", \"translate(800,810)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(USA, USA.objects.states48).features)\n .join(\"path\")\n .attr(\"fill\", d => color_c(data_c.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .append(\"title\")\n .text(d => `${d.properties.STATE_NAME}, ${format_c(data_c.get(d.properties.STATE_NAME))}`);\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1865,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1706,"value":"md ` ### Summary Map of the relationship between Wind energy potential and all nonrenewable energy consumption.\n\n#### to explore this map in an interactive way please visit\n\nhttps://geog3540.github.io/adcaraballo/wind_energy.html`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1702,"value":"FileAttachment(\"final_wind.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1868,"value":"md ` This is a static image representing the interactive map that can be found at the link above. The benefit of this map over some of the other maps is that they are linked to one another. So, if you hover over Iowa on one of the maps, it will be highlighted in the other three. This makes it easy to see patterns and how they change depending on the variable. There are multiple ways to customize the map. The suggestion for this data is four classes and a quantile classification. Four classes are a sweet spot of a number, it allows you to see some variation and detail, but it is not too many classes. Quantile classification is required. If the classification is not quantile, the maps are no longer comparable because they have different data distributions and ranges! `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1741,"value":"md ` ### A Bivariate Choropleth Map Diving Deeper into the Relationship between wind energy resource potential and coal consumption measured in million btu (this variable basically explains how much of the energy consumes comes from coal). `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":975,"value":"chart2 = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend_2)\n .attr(\"transform\", \"translate(800,810)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(USA, USA.objects.states48).features)\n .join(\"path\")\n .attr(\"fill\", d => color_a(data_a.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .append(\"title\")\n .text(d => `${d.properties.STATE_NAME}, ${format_a(data_a.get(d.properties.STATE_NAME))}`);\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1876,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1744,"value":"md ` ### A Bivariate Choropleth Map Looking into the Relationship between wind energy resource potential and natural gas consumption measured in million btu (this variable basically explains how much of the energy consumed comes from natural gas). `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1676,"value":"charth = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend_h)\n .attr(\"transform\", \"translate(800,810)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(USA, USA.objects.states48).features)\n .join(\"path\")\n .attr(\"fill\", d => color_h(data_h.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .append(\"title\")\n .text(d => `${d.properties.STATE_NAME}, ${format_h(data_h.get(d.properties.STATE_NAME))}`);\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1888,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1748,"value":"md ` ### A Bivariate Choropleth Map Exploring into the Relationship between wind energy resource potential and oil/petroleum consumption measured in million btu (this variable basically explains how much of the energy consumed comes from oil/petroleum sources). `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1679,"value":"charti = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend_i)\n .attr(\"transform\", \"translate(800,810)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(USA, USA.objects.states48).features)\n .join(\"path\")\n .attr(\"fill\", d => color_i(data_i.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .append(\"title\")\n .text(d => `${d.properties.STATE_NAME}, ${format_i(data_i.get(d.properties.STATE_NAME))}`);\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1896,"value":"md ` This is the final nonrenewable energy being examined. Maine, Vermont, and New Hampshire so far have used relatively low amounts of coal and natural gas even though all those states have low potential for wind and solar energy. These states rely mostly on oil compared to the other energy options examined here. The Southeastern states once again are kind of flip flop between the nonrenewable energy sources they use. Though Florida is notable as an exception in all three of those cases. The Great Lakes area does not utilize as much oil compared to natural gas, but it is a large amount compared to the amount of renewable energy they produce. Once again, states like Texas, Iowa, Montana, the Dakotas, and Nebraska use extremely copious amounts of million btu in nonrenewable energy when many of them have high solar potential (Oklahoma, New Mexico), high wind potential (Iowa, Nebraska) or both (Texas). `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1840,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1913,"value":"md `Overall, we were able to effectively answer the questions that were raised for our project using Geographic Visualization methods such as choropleth maps, bivariate choropleth maps, and choropleth maps in small multiples. We found that there were many states dependent on fossil fuels such as coal, petroleum, and natural gas. Then we found that there were multiple states that had high renewable resource potential for wind and solar energy along with states that were already consuming high amounts of renewable energy. From that information we found states that had high renewable resource potential that were consuming more fossil fuels when they could be utilizing wind and solar energy. Although there were limitations brought up during the project with data retrieval, we believe that our geographic visualization can successfully answer the research questions. The limitations were discussed down below in the Data Sections of the proposal. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1616,"value":"md ` # Supplementary Charts!\n\n#### What areas of the Lower 48 States consume large amounts of nonrenewable energy sources like coal, petroleum, and natural gas? ` ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1833,"value":"md ` ### This map is meant as a supplementary map that can be used to visualize coal consumption on its own without another variable.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":660,"value":"choropleth_6= {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(\"g\")\n .attr(\"transform\", \"translate(360,20)\")\n .append(() => \n legend({\n color: color_6,\n title: data_6.title,\n width: 600,\n tickFormat: \".2f\",\n })\n );\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(states.features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .attr(\"fill\", function(d){\n console.log(color_6(data_6.get(d.properties.STATE_NAME)))\n return color_6(data_6.get(d.properties.STATE_NAME));\n })\n .attr(\"fill\", d => color_6(data_6.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .append(\"title\")\n .text(d => data_6.get(d.properties.STATE_NAME) + \" Million Btu\");\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1754,"value":"md ` Coal consumption has a more complicated spatial pattern. The Great Plains and the Midwest seem more coal dependent compared to places like the North East and the Western Coast. This map is meant as a supplementary map that can be used to visualize coal consumption on its own without another variable. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1830,"value":"md ` ### This map is meant as a supplementary map that can be used to visualize natural gas consumption on its own without another variable.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":522,"value":"choropleth_5= {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(\"g\")\n .attr(\"transform\", \"translate(360,20)\")\n .append(() => \n legend({\n color: color_5,\n title: data_5.title,\n width: 600,\n tickFormat: \".3f\",\n })\n );\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(states.features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .attr(\"fill\", function(d){\n console.log(color_5(data_5.get(d.properties.STATE_NAME)))\n return color_5(data_5.get(d.properties.STATE_NAME));\n })\n .attr(\"fill\", d => color_5(data_5.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .append(\"title\")\n .text(d => data_5.get(d.properties.STATE_NAME) + \" in Million Btu \");\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1824,"value":"md ` Natural Gas consumption has an almost random spatial pattern. Some states along the gulf coast (Texas, Louisana, Missisipi, and Alabama) consume some of the highest amounts of natural gas (in million btu). The North East, California, and Washington all use little natural gas compared to other states. This is slightly similar to the pattern presented in the coal consumption choropleth map. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1837,"value":"md ` ### This map is meant as a supplementary map that can be used to visualize petrolium consumption on its own without another variable.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":729,"value":"choropleth_7= {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(\"g\")\n .attr(\"transform\", \"translate(360,20)\")\n .append(() => \n legend({\n color: color_7,\n title: data_7.title,\n width: 600,\n tickFormat: \".2f\",\n })\n );\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(states.features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1)\n .attr(\"fill\", function(d){\n console.log(color_7(data_7.get(d.properties.STATE_NAME)))\n return color_7(data_7.get(d.properties.STATE_NAME));\n })\n .attr(\"fill\", d => color_7(data_7.get(d.properties.STATE_NAME)))\n .attr(\"d\", path_1)\n .append(\"title\")\n .text(d => data_7.get(d.properties.STATE_NAME) + \" in Million Btu\");\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1844,"value":"md ` This last nonrenewable energy consumption map looks similar to the previous two. In the Southern US... New Mexico, Texas, Oklahoma, Louisana, and Mississipi are very oil dependent. Other states that have high petroleum consumption include those in the Northern Great Plain states (Montana, Wyoming, North Dakota, and South Dakota). This spatial pattern might be due to the location of currently producing oil rigs. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1029,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":898,"value":"viewof colors = {\n const form = html`

color scheme`;\n form.i.selectedIndex = 1;\n form.oninput = () => form.value = schemes[form.i.selectedIndex].colors;\n form.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":237,"value":"md ` # MAP IN PROGRESS`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"topojson = require(\"topojson-client@3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":189,"value":"import {legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":143,"value":"simple = require(\"simple-statistics@7.0.7/dist/simple-statistics.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1058,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":929,"value":"format = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data.title[0]}${labels[x(a)] && ` (${labels[x(a)]})`}\n${b} ${data.title[1]}${labels[y(b)] && ` (${labels[y(b)]})`}`;\n}//formats the values and labels for the legend and data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1038,"value":"format_a = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data_a.title[0]}${labels[x_a(a)] && ` (${labels[x_a(a)]})`}\n${b} ${data_a.title[1]}${labels[y_a(b)] && ` (${labels[y_a(b)]})`}`;\n}//formats the values and labels for the legend and data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1044,"value":"format_b = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data_b.title[0]}${labels[x_b(a)] && ` (${labels[x_b(a)]})`}\n${b} ${data_b.title[1]}${labels[y_b(b)] && ` (${labels[y_b(b)]})`}`;\n}//formats the values and labels for the legend and data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1049,"value":"format_c = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data_c.title[0]}${labels[x_c(a)] && ` (${labels[x_c(a)]})`}\n${b} ${data_c.title[1]}${labels[y_c(b)] && ` (${labels[y_c(b)]})`}`;\n}//formats the values and labels for the legend and data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1199,"value":"format_d = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data_d.title[0]}${labels[x_d(a)] && ` (${labels[x_d(a)]})`}\n${b} ${data_d.title[1]}${labels[y_d(b)] && ` (${labels[y_d(b)]})`}`;\n}//formats the values and labels for the legend and data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1227,"value":"format_e = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data_e.title[0]}${labels[x_e(a)] && ` (${labels[x_e(a)]})`}\n${b} ${data_e.title[1]}${labels[y_e(b)] && ` (${labels[y_e(b)]})`}`;\n}//formats the values and labels for the legend and data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1281,"value":"format_f = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data_f.title[0]}${labels[x_f(a)] && ` (${labels[x_f(a)]})`}\n${b} ${data_f.title[1]}${labels[y_f(b)] && ` (${labels[y_f(b)]})`}`;\n}//formats the values and labels for the legend and data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1446,"value":"format_g = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data_g.title[0]}${labels[x_g(a)] && ` (${labels[x_g(a)]})`}\n${b} ${data_g.title[1]}${labels[y_g(b)] && ` (${labels[y_g(b)]})`}`;\n}//formats the values and labels for the legend and data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1669,"value":"format_h = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data_h.title[0]}${labels[x_h(a)] && ` (${labels[x_h(a)]})`}\n${b} ${data_h.title[1]}${labels[y_h(b)] && ` (${labels[y_h(b)]})`}`;\n}//formats the values and labels for the legend and data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1671,"value":"format_i = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data_i.title[0]}${labels[x_i(a)] && ` (${labels[x_i(a)]})`}\n${b} ${data_i.title[1]}${labels[x_i(b)] && ` (${labels[y_i(b)]})`}`;\n}//formats the values and labels for the legend and data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"USA = FileAttachment(\"states48_1.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"states = topojson.feature(USA, USA.objects.states48)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":885,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":985,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1010,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":992,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1201,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1229,"value":"$42","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1278,"value":"$43","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1443,"value":"$44","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1662,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1664,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":888,"value":"// http://www.joshuastevens.net/cartography/make-a-bivariate-choropleth-map/\nschemes = [\n {\n name: \"RdBu\", \n colors: [\n \"#e8e8e8\", \"#e4acac\", \"#c85a5a\",\n \"#b0d5df\", \"#ad9ea5\", \"#985356\",\n \"#64acbe\", \"#627f8c\", \"#574249\"\n ]\n },\n {\n name: \"BuPu\", \n colors: [\n \"#e8e8e8\", \"#ace4e4\", \"#5ac8c8\",\n \"#dfb0d6\", \"#a5add3\", \"#5698b9\", \n \"#be64ac\", \"#8c62aa\", \"#3b4994\"\n ]\n },\n {\n name: \"GnBu\", \n colors: [\n \"#e8e8e8\", \"#b5c0da\", \"#6c83b5\",\n \"#b8d6be\", \"#90b2b3\", \"#567994\",\n \"#73ae80\", \"#5a9178\", \"#2a5a5b\"\n ]\n },\n {\n name: \"PuOr\", \n colors: [\n \"#e8e8e8\", \"#e4d9ac\", \"#c8b35a\",\n \"#cbb8d7\", \"#c8ada0\", \"#af8e53\",\n \"#9972af\", \"#976b82\", \"#804d36\"\n ]\n }\n]//color schemes?","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":891,"value":"labels = [\"low\", \"Med\", \"high\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":894,"value":"n = Math.floor(Math.sqrt(colors.length))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":896,"value":"color = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y(b) + x(a) * n];\n };\n}//color function to return function based on color scheme","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1084,"value":"color_a = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y_a(b) + x_a(a) * n];\n };\n}//color function to return function based on color scheme","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1109,"value":"color_b = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y_b(b) + x_b(a) * n];\n };\n}//color function to return function based on color scheme","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1104,"value":"color_c = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y_c(b) + x_c(a) * n];\n };\n}//color function to return function based on color scheme","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1234,"value":"color_d = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y_d(b) + x_d(a) * n];\n };\n}//color function to return function based on color scheme","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1239,"value":"color_e = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y_e(b) + x_e(a) * n];\n };\n}//color function to return function based on color scheme","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1275,"value":"color_f = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y_f(b) + x_f(a) * n];\n };\n}//color function to return function based on color scheme","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1440,"value":"color_g = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y_g(b) + x_g(a) * n];\n };\n}//color function to return function based on color scheme","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1656,"value":"color_h = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y_h(b) + x_h(a) * n];\n };\n}//color function to return function based on color scheme","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1658,"value":"color_i = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y_i(b) + x_i(a) * n];\n };\n}//color function to return function based on color scheme","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":902,"value":"x = d3.scaleQuantile(Array.from(data.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":900,"value":"y = d3.scaleQuantile(Array.from(data.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1086,"value":"x_a = d3.scaleQuantile(Array.from(data_a.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1088,"value":"y_a = d3.scaleQuantile(Array.from(data_a.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1090,"value":"x_b = d3.scaleQuantile(Array.from(data_b.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1093,"value":"y_b = d3.scaleQuantile(Array.from(data_b.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1096,"value":"x_c = d3.scaleQuantile(Array.from(data_c.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1101,"value":"y_c = d3.scaleQuantile(Array.from(data_c.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1213,"value":"x_d = d3.scaleQuantile(Array.from(data_d.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1209,"value":"y_d = d3.scaleQuantile(Array.from(data_d.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1241,"value":"x_e = d3.scaleQuantile(Array.from(data_e.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1245,"value":"y_e = d3.scaleQuantile(Array.from(data_e.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1267,"value":"x_f = d3.scaleQuantile(Array.from(data_f.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1270,"value":"y_f = d3.scaleQuantile(Array.from(data_f.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1431,"value":"x_g = d3.scaleQuantile(Array.from(data_g.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1434,"value":"y_g = d3.scaleQuantile(Array.from(data_g.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1645,"value":"x_h = d3.scaleQuantile(Array.from(data_h.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1640,"value":"y_h = d3.scaleQuantile(Array.from(data_h.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1648,"value":"x_i = d3.scaleQuantile(Array.from(data_i.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1651,"value":"y_i = d3.scaleQuantile(Array.from(data_i.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":905,"value":"data= Object.assign(new Map(d3.csvParse(await FileAttachment(\"Bivariate_map_1.csv\").text(), ({STATE_NAME, POP, Wind_PO, W_con_btu}) => [STATE_NAME, [+Wind_PO, 1000000*+W_con_btu/POP]])), {title:[\"Wind Potential\",\"Wind Consumption\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":977,"value":"data_a= Object.assign(new Map(d3.csvParse(await FileAttachment(\"Bivariate_map_1.csv\").text(), ({STATE_NAME, POP, Wind_PO, Coal_con}) => [STATE_NAME, [+Wind_PO, +(Coal_con/POP)/1000000]])), {title:[\"Wind Potential\",\"Coal Consumption\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":978,"value":"data_b= Object.assign(new Map(d3.csvParse(await FileAttachment(\"Bivariate_map_1.csv\").text(), ({STATE_NAME, POP, Solar_PO, NG_con}) => [STATE_NAME, [+Solar_PO, +(NG_con/POP)/1000000]])), {title:[\"Solar Potential\",\"NG Consumption\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1000,"value":"data_c= Object.assign(new Map(d3.csvParse(await FileAttachment(\"Bivariate_map_1.csv\").text(), ({STATE_NAME, POP, Wind_PO, ren_pro}) => [STATE_NAME, [+Wind_PO, +(ren_pro/POP)/1000000]])), {title:[\"Wind Potential\",\"Renewable E Pro\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1205,"value":"data_d= Object.assign(new Map(d3.csvParse(await FileAttachment(\"Bivariate_map_1.csv\").text(), ({STATE_NAME, POP, Solar_PO, ren_pro}) => [STATE_NAME, [+Solar_PO, +(ren_pro/POP)/1000000]])), {title:[\"Solar Potential\",\"Renewable E Pro\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1247,"value":"data_e= Object.assign(new Map(d3.csvParse(await FileAttachment(\"Bivariate_map_1.csv\").text(), ({STATE_NAME, POP, Solar_PO, Coal_con}) => [STATE_NAME, [+Solar_PO, +(Coal_con/POP)/1000000]])), {title:[\"Solar Potential\",\"Coal Consumption\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1263,"value":"data_f= Object.assign(new Map(d3.csvParse(await FileAttachment(\"Bivariate_map_1@1.csv\").text(), ({STATE_NAME, Solar_PO, pct_suit}) => [STATE_NAME, [+Solar_PO, +pct_suit*100]])), {title:[\"Solar Potential\",\"Solar Panel Suitablity\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1418,"value":"data_g= Object.assign(new Map(d3.csvParse(await FileAttachment(\"Bivariate_map_1.csv\").text(), ({STATE_NAME, POP, Solar_PO, Pet_con}) => [STATE_NAME, [+Solar_PO, +(Pet_con/POP)/1000000]])), {title:[\"Solar Potential\",\"Oil Consumption\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1629,"value":"data_h= Object.assign(new Map(d3.csvParse(await FileAttachment(\"Bivariate_map_1.csv\").text(), ({STATE_NAME, POP, Wind_PO, NG_con}) => [STATE_NAME, [+Wind_PO, +(NG_con/POP)/1000000]])), {title:[\"Wind Potential\",\"NG Consumption\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1634,"value":"data_i= Object.assign(new Map(d3.csvParse(await FileAttachment(\"Bivariate_map_1.csv\").text(), ({STATE_NAME, POP, Wind_PO, Pet_con}) => [STATE_NAME, [+Wind_PO, +(Pet_con/POP)/1000000]])), {title:[\"Wind Potential\",\"Oil Consumption\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":548,"value":"md ` ## Map 1`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"csv_data_1 = d3.csvParse(await FileAttachment(\"Bivariate_map_1@1.csv\").text(),({STATE_NAME, pct_suit}) => [STATE_NAME, +pct_suit])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":378,"value":"data_1=Object.assign(new Map(csv_data_1), {title: \" Average Percent Suitability of Small Buildings\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":380,"value":"pct_ss = Array.from(csv_data_1.values(), d => d[1]).filter(d => d != NaN)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":532,"value":"nancolor = d3.color(\"black\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":539,"value":"nullcolorforlegend = d3.scaleOrdinal()\n .domain([NaN])\n .range([nancolor])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":386,"value":"color_1 = d3.scaleQuantile()\n .domain(pct_ss) // pass the whole dataset to a scaleQuantile’s domain\n .range(YlGnBu)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":384,"value":"YlGnBu = [d3.color(\"#fee0d2\"), d3.color(\"#fc9272\"), d3.color(\"#de2d26\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":556,"value":"md ` ## Map 2`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":410,"value":"ghi_data = d3.csvParse(await FileAttachment(\"ghi_state_1.csv\").text(),({STATE_NAME, MEAN}) => [STATE_NAME, +MEAN])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":414,"value":"data_2 = Object.assign(new Map(ghi_data), {title: \"Average Global Horizontal Irradiance\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":418,"value":"ghi = Array.from(ghi_data.values (), d=> d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":420,"value":"color_2 = d3.scaleQuantile()\n .domain(ghi) // pass the whole dataset to a scaleQuantile’s domain\n .range(orange)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":432,"value":"orange = [d3.color(\"#feedde\"), d3.color(\"#fdbe85\"), d3.color(\"#fd8d3c\"), d3. color (\"#d94701\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":569,"value":"md ` ## Map 3`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":463,"value":"wind_data = d3.csvParse(await FileAttachment(\"wind_po_by_state.csv\").text(),({STATE_NAME, MEAN}) => [STATE_NAME, +MEAN])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":467,"value":"data_3 = Object.assign(new Map(wind_data), {title: \"Average Wind Speed at 60m above Ground Surface\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":470,"value":"wind = Array.from (wind_data.values (), d=> d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":440,"value":"color_3 = d3.scaleQuantile()\n .domain(wind) // pass the whole dataset to a scaleQuantile’s domain\n .range(blue)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":451,"value":"blue = [d3.color(\"#eff3ff\"), d3.color(\"#bdd7e7\"), d3.color(\"#6baed6\"), d3. color (\"#2171b5\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":577,"value":"md ` ## Map 4-No longer in use`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":492,"value":"coal_production_data = d3.csvParse(await FileAttachment(\"Coal Production_Normalized@1.csv\").text(),({STATE_NAME, Btu_2019, STATE_POP}) => [STATE_NAME, +Btu_2019/STATE_POP])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":495,"value":"data_4 = Object.assign(new Map(coal_production_data), {title: \"Coal Production in each state in Btu\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":498,"value":"coal_production = Array.from (coal_production_data.values (), d=> d[1]).filter (d => d != 0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":502,"value":"color_4 = d3.scaleQuantile()\n .domain(coal_production) // pass the whole dataset to a scaleQuantile’s domain\n .range(Grey)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":503,"value":"Grey = [d3.color(\"#f7f7f7\"), d3.color(\"#cccccc\"), d3. color (\"#969696\"), d3.color (\"#525252\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":581,"value":"md ` ## Map 5`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":529,"value":"Natural_Gas_Consumption = d3.csvParse(await FileAttachment(\"NG_btu_info.csv\").text(),({STATE_NAME, NG_tbut, POP}) => [STATE_NAME, [+((NG_tbut/POP)/1000000)]])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":592,"value":"data_5 = Object.assign(new Map(Natural_Gas_Consumption), {title: \"Natural Gas Consumption in Million Btu\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":603,"value":"ng_consumption = Array.from (Natural_Gas_Consumption.values (), d=> d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":607,"value":"gcolor = [d3.color(\"#ffffcc\"), d3.color(\"#c2e699\"), d3. color (\"#78c679\"), d3.color (\"#238443\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":600,"value":"color_5 = d3.scaleQuantile()\n .domain(ng_consumption)\n .range(gcolor)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":587,"value":"md ` ## Map 6`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":526,"value":"Coal_Consumption = d3.csvParse(await FileAttachment(\"Coal_Trillion_Btu_Consumed.csv\").text(),({STATE_NAME, Btu_Coal, STATE_POP}) => [STATE_NAME, +((Btu_Coal/STATE_POP)/1000000)])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":636,"value":"data_6 = Object.assign(new Map(Coal_Consumption), {title: \"Coal Consumption in Million Btu\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":651,"value":"c_consumption = Array.from (Coal_Consumption.values (), d=> d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":644,"value":"color_6 = d3.scaleQuantile()\n .domain(c_consumption)\n .range(Grey)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":590,"value":"md ` ## Map 7`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":692,"value":"Petroleum_Consumption = d3.csvParse(await FileAttachment(\"Petro_consumption@1.csv\").text(),({STATE_NAME, Pet_tbut, POP}) => [STATE_NAME, [+((Pet_tbut/POP)/1000000)]])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":696,"value":"data_7 = Object.assign(new Map(Petroleum_Consumption), {title: \"Petroleum Consumption in Million Btu\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":700,"value":"p_consumption = Array.from (Petroleum_Consumption.values (), d=> d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":709,"value":"color_7 = d3.scaleQuantile()\n .domain(p_consumption)\n .range(pcolor)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":717,"value":"pcolor = [d3.color(\"#f2f0f7\"), d3.color(\"#cbc9e2\"), d3. color (\"#9e9ac8\"), d3.color (\"#6a51a3\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":823,"value":"md ` ## Map 8`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":839,"value":"wc = d3.csvParse(await FileAttachment(\"ren_consump_2019.csv\").text(),({STATE_NAME, Wind_btu, POP}) => [STATE_NAME, +((Wind_btu/POP)/1000000)])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":844,"value":"data_8 = Object.assign(new Map(wc), {title: \"Wind Energy Consumption in Million Btu\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":852,"value":"w_consumption = Array.from (wc.values (), d=> d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":855,"value":"color_8 = d3.scaleQuantile()\n .domain(w_consumption) // pass the whole dataset to a scaleQuantile’s domain\n .range(blue)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":836,"value":"md ` ## Map 9`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":866,"value":"sc = d3.csvParse(await FileAttachment(\"ren_consump_2019.csv\").text(),({STATE_NAME, Solar_btu, POP}) => [STATE_NAME, +((Solar_btu/POP)/1000000)])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":870,"value":"data_9 = Object.assign(new Map(sc), {title: \"Solar Energy Consumption in Million Btu\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":873,"value":"s_consumption = Array.from (sc.values (), d=> d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":865,"value":"color_9 = d3.scaleQuantile()\n .domain(s_consumption) // pass the whole dataset to a scaleQuantile’s domain\n .range(orange)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":677,"value":"md ` ## Other Random Stuff `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":160,"value":"width = 1200","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":162,"value":"height = 900","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":166,"value":"margin = 100","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":390,"value":"projection_1 = d3.geoAlbers().fitExtent([[margin,margin], [width-margin, height-margin]], states);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":392,"value":"path_1= d3.geoPath().projection(projection_1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":684,"value":"md ` # OLD PROPOSAL `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"md `### Wind Data \nhttps://www.nrel.gov/gis/wind-resource-maps.html\nhttps://www.arcgis.com/home/item.html?id=432bb9246fdd467c88136e6ffeac2762\nhttps://www.arcgis.com/home/item.html?id=432bb9246fdd467c88136e6ffeac2762 \nhttps://uiowa.maps.arcgis.com/home/item.html?id=c8d60ffcbf5c4030a17762fe10e81c6a&sublayer=2 \nhttps://nps.maps.arcgis.com/apps/webappviewer/index.html?id=43bc9db4736140e88e661c67460936e6 \n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"md ` ## Division of Project/ Team Work\nThe students working on this project are Nathan and Amanda. We are communicating mostly through text and email, but if necessary we could meet over zoom! We divided the work between renewable energy and nonrenewable energy. Amanda is responsible for doing each section for Wind and Solar Energy (so data for wind and solar, etc), as well as adding information to the goal section. Nate is responsible for the section information for nonrenewable energy and filling out the goal section as well. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":41,"value":"md ` ### Solar Data\n\nsources\nhttps://www.osti.gov/biblio/1575064-rooftop-photovoltaic-technical-potential-united-states \nhttps://www.nrel.gov/gis/solar-resource-maps.html\n`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":280,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":735,"value":"md ` ## Coal Production `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":512,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4d","stdlib":"1","titling":"auto"}} 32:"$31:body"