25:["$","title",null,{"children":"Data Centers and Their Impact on Local Communities | DATA 200 | Observable"}] 49:T5de,** Figure 3.** Interactive line graph demonstrating housing price trends from 2015 to 2025 among 5 NOVA counties[5].

When first looking at this data the increase in data centers could be seen as a positive thing due to the decreased cost of housing in the nearby areas. However, this viewpoint doesn't consider all factors involved. When housing prices decrease there is nearly always a reason, and it typically has to do with the fact that people don't want to live in the area for a particular reason. The increase in data centers (Figure 2) correlates with the decrease in housing prices, and we attribute this to the negative impacts like electricity price hikes, unsightliness, and the noise that data centers come with[8].

In our analysis of these community impacts data centers come with, the main complaint seems to be that they lower quality of life for nearby residents. Housing price is a consistent metric that demonstrates how badly people want to live in a certain place, and in a housing market where prices rarely dip a decrease typically means something about the area changed for the worse.

The correlation between housing prices and data centers has even more backing when looking at which counties have seen the least increase in data centers and the housing price trends associated with those counties. These data show that prices have not dropped to the same degree as counties that have seen a much greater spike in data center number (Figure 2, 3).4a:T7ae,**Figure 4**. A line graph illustrating the average National electrical price in cent per kW/Hr between 2015 and 2025 in red and the average electrical price in NOVA in cent per kW/Hr between 2015 and 2025 in blue. It is worth noting that average electrical price data for NOVA has not been published for all counties within NOVA for 2025, and was not able to be included in this analysis. This plot was created by compiling data from multiple sources to gain reliable averages[1][2][3][7].

From the comparison between the national and NOVA average price per kW/hr it is clear that there are two trends in the NOVA data when compared to the national averages. From 2015 to 2020, the price of energy was dropping significantly to a point where it was significantly below the national average. Then, from 2020 onward, we see a sharp increase back to 2019 levels with the price continuing to be on the rise, narrowing the margin between the average NOVA energy price and the national average. This is likely due to the increased number of data centers in the region, as they put heavy strains on the grid, require expensive projects to expand the grid to support them, and will require more costly maintenance to support the new infrastructure. It is likely that this margin will become much narrower as time goes on.
These raised rates burden average consumers by requiring them to spend more money on basic things, which removes money that could be used in other sectors of the economy and would lower the quality of life for people living in this area, as they can afford to buy less as electricity gets more expensive. Dominion is also looking to increase its rates in the area, which would add an average of $131 yearly to the household electric bill[6]. They are reportedly also looking to create a new rate class, which would allow for data centers to pay a higher rate for electricity to allow for the increase in the residential rate to slow[6]. 4b:T411,# Conclusion Overall, we have shown that the number of data centers within the US, and especially in NOVA, have blown up a great deal, with some counties in NOVA currently having more than double the amount of data centers in operation than they did in five years ago. As for the real estate prices, it is clear that the counties in NOVA with a large amount of data centers show a downward trend in the average new house price, which is not seen in the counties where data centers are less prevalent. For the electricity prices, it is clear that, while they remain under the national average, the increase in data centers on operation in the area is correlated with a residential electricity rate that is rising at an increasing rate. Without intervention, this rate will likely increase over the national rate, which would greatly increase the cost of living and of services in the NOVA area. These are the main points by which we argue that the growth of data centers has a negative effect on the lives of the people that live around them.4c:T5b4,# References
1. NOVA Electricity Prices Yearly—U.S. Energy Information Administration (EIA). Retrieved December 10, 2025, from https://www.eia.gov/electricity/data/eia861/

2. Electric Power Monthly—U.S. Energy Information Administration (EIA). (n.d.-a). Retrieved December 10, 2025, from https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_03

3. Electric Power Monthly—U.S. Energy Information Administration (EIA). (n.d.-b). Retrieved December 10, 2025, from https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_6_a

4. OpenStreetMap contributors. (2015) Planet dump from 12/02/2025. Retrieved from https://planet.openstreetmap.org.

5. Counties | fred | st. Louis fed. (n.d.). Retrieved December 11, 2025, from https://fred.stlouisfed.org/categories/30199

6. Staff, W. C. 6 W. (2025, November 26). Virginia partially approves Dominion’s rate increase requests. How much will bills go up next year? CBS 6 News Richmond WTVR. https://www.wtvr.com/news/local-news/dominion-rate-increases-nov-25-2025
7. Federal Reserve Bank of St. Louis. Average Price: Electricity per Kilowatt-Hour in Arlington-Alexandria, VA https://fred.stlouisfed.org/series/APUS35A72610 8. Murphy, R. (2025, July 17). Why more residents are saying “No” to AI data centers in their backyard. NPR. https://www.npr.org/2025/07/17/nx-s1-5469933/virginia-data-centers-residents-saying-no 4d:[{"id":"dd2687eea597f701e3e38fb20d255e4d85d0c2e5e34d81ac494a28436a93875bdcae70d740c36c7195bc37a4381c4a8413afa4726a7c4a7748bcd1924cc55e66","href":"https://static.observableusercontent.com/files/dd2687eea597f701e3e38fb20d255e4d85d0c2e5e34d81ac494a28436a93875bdcae70d740c36c7195bc37a4381c4a8413afa4726a7c4a7748bcd1924cc55e66","name":"data_centers.csv","type":"text/csv","lastModified":1763673179449.214,"size":15219},{"id":"ff73290f3fccc5db55f09031bb845e639a07e444ca9fbcf83f2dbb4600a38bf4baccb2a85df3c24dba57a4ed64c230dcf1a87604453969495db835c5f3cbebf2","href":"https://static.observableusercontent.com/files/ff73290f3fccc5db55f09031bb845e639a07e444ca9fbcf83f2dbb4600a38bf4baccb2a85df3c24dba57a4ed64c230dcf1a87604453969495db835c5f3cbebf2","name":"us-counties-10m.json","type":"application/json","lastModified":1763673336109.271,"size":842177},{"id":"bda2b83a142d37c3d3e3fc4e803a29044d5dc11a11e1247550af03f0a77bebd6b2c9d9392340cb714acbd4c4ec809b8d79b88d88167f1f5436de0188d8e950fc","href":"https://static.observableusercontent.com/files/bda2b83a142d37c3d3e3fc4e803a29044d5dc11a11e1247550af03f0a77bebd6b2c9d9392340cb714acbd4c4ec809b8d79b88d88167f1f5436de0188d8e950fc","name":"data-xQ5ws.csv","type":"text/csv","lastModified":1764711935995.128,"size":304434},{"id":"682f6f25c8f7d65abc614f7cc6e83c977c5df6a79e1584030d7b30a91ce678e0c24e71df3c41c3ef85a0386df04173f00abcdd6340d7361311d9532d0e2b6d35","href":"https://static.observableusercontent.com/files/682f6f25c8f7d65abc614f7cc6e83c977c5df6a79e1584030d7b30a91ce678e0c24e71df3c41c3ef85a0386df04173f00abcdd6340d7361311d9532d0e2b6d35","name":"2020_data centers.csv","type":"text/csv","lastModified":1764710857433.014,"size":5132},{"id":"f9253f561e7d8ab3224662ff93ea86dfff3fbe3c1b11d8ccaa4fa4f5ccb03b9a612e3465a1472fba1f150506a4b283c226c575878ed403221f7fc60fe2620a9f","href":"https://static.observableusercontent.com/files/f9253f561e7d8ab3224662ff93ea86dfff3fbe3c1b11d8ccaa4fa4f5ccb03b9a612e3465a1472fba1f150506a4b283c226c575878ed403221f7fc60fe2620a9f","name":"datacenters_per_county@1.csv","type":"text/csv","lastModified":1765331997685.902,"size":277},{"id":"ac0e05de2f4c5a65388eeb5b6be34647fd2a7f0053005f2f9463a1552d4824251f47e764cde0c7a26436791d43407bfc907f3aea0085606cafd338ad9fddc9f3","href":"https://static.observableusercontent.com/files/ac0e05de2f4c5a65388eeb5b6be34647fd2a7f0053005f2f9463a1552d4824251f47e764cde0c7a26436791d43407bfc907f3aea0085606cafd338ad9fddc9f3","name":"national_electric_price@2.csv","type":"text/csv","lastModified":1765411119986.289,"size":157},{"id":"829c2d76e194f159b482b1f0a845c63a07e790c2ef699bfd05ff9ef93d5073eb0834ab63eaf92abbe4fc7777a2167775221bc0257bee0740f8775e29518db80d","href":"https://static.observableusercontent.com/files/829c2d76e194f159b482b1f0a845c63a07e790c2ef699bfd05ff9ef93d5073eb0834ab63eaf92abbe4fc7777a2167775221bc0257bee0740f8775e29518db80d","name":"Energy Prices NOVA 2015-2024.csv","type":"text/csv","lastModified":1765410254883.297,"size":198},{"id":"f49419a1f9024167602a288477d239d2355b9625b9f6627e8f8435d13df09ae454031090faf95a3aafe8094088e5b36e41cc24a296d73fbabcecadeffc55fbbf","href":"https://static.observableusercontent.com/files/f49419a1f9024167602a288477d239d2355b9625b9f6627e8f8435d13df09ae454031090faf95a3aafe8094088e5b36e41cc24a296d73fbabcecadeffc55fbbf","name":"average_listing_price@3.csv","type":"text/csv","lastModified":1765396545270.537,"size":1624},{"id":"ad133890af247ed1d2f8a6d3649fcf4708a37b39ef2fc7dc4310bc2a6fc7ad52d7d459dd63546c72cfae102c802b3b49d5d8b3bfa2f7551d13700329ad7f51aa","href":"https://static.observableusercontent.com/files/ad133890af247ed1d2f8a6d3649fcf4708a37b39ef2fc7dc4310bc2a6fc7ad52d7d459dd63546c72cfae102c802b3b49d5d8b3bfa2f7551d13700329ad7f51aa","name":"US Average Electric Price@1.csv","type":"text/csv","lastModified":1765405642788.837,"size":173},{"id":"d67316a2ed48282afe4e72bfd62f4cb180d51365c5c20669a76f1efeec0c3616fb93d8c8fbf717b2352503d46b9dbb3f2c8efb42b8d7ed2aadc770fbc4dc5e14","href":"https://static.observableusercontent.com/files/d67316a2ed48282afe4e72bfd62f4cb180d51365c5c20669a76f1efeec0c3616fb93d8c8fbf717b2352503d46b9dbb3f2c8efb42b8d7ed2aadc770fbc4dc5e14","name":"nova_power_pricing.csv","type":"text/csv","lastModified":1765468612305.061,"size":159}] 29:{"id":"6e8be0748bf29bd3","slug":null,"title":"Data Centers and Their Impact on Local Communities","thumbnail":"007131068c5c190daf5297dd1163f652acf365bf4aefb5a63551da8a5b04fbc0","access_level":"public","create_time":"$D2025-11-18T21:59:55.078Z","update_time":"$D2025-12-11T17:56:13.088Z","view_time":null,"first_public_time":"$D2026-03-18T00:56:37.792Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"350509c2c2b2bb2f","login":"jmu-data200","name":"DATA 200","avatar_url":"https://avatars-next.observableusercontent.com/30f8c3aad5eb54975c2dc7e1585024a2406c81487cb46dcf2a0d111f3a5d76a5","tier":"education_2026","bio":"","followed_by_you":false,"follows_you":false},"version":797,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"1e39b4740b652f79","login":"acdonaghy","name":"Acdonaghy","avatar_url":"https://avatars-next.observableusercontent.com/83ee2915d64e81bdc5a3c83415e5fed7768b825f8698378b36f22e534e56997a","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"1e39b4740b652f79","login":"acdonaghy","name":"Acdonaghy","avatar_url":"https://avatars-next.observableusercontent.com/83ee2915d64e81bdc5a3c83415e5fed7768b825f8698378b36f22e534e56997a","tier":null,"role":"editor"},{"type":"individual","id":"d5c8d7ee1e480add","login":"maxtyree","name":"Max","avatar_url":"https://avatars-next.observableusercontent.com/9177ad61125926aa77c6b7acfe40dc3bd082543271aa4056ecbd5ed32e6a1cda","tier":null,"role":"editor"},{"type":"individual","id":"ad49e0ea86e16e66","login":"rhody-brown","name":"Rhody Brown","avatar_url":"https://avatars-next.observableusercontent.com/4d65606a2468b52abed822d763733275c40bedd31f094bea717aae3be2e995c4","tier":null,"role":"editor"},{"type":"team","id":"350509c2c2b2bb2f","login":"jmu-data200","name":"DATA 200","avatar_url":"https://avatars-next.observableusercontent.com/30f8c3aad5eb54975c2dc7e1585024a2406c81487cb46dcf2a0d111f3a5d76a5","tier":"education_2026","role":"viewer"},{"type":"individual","id":"9f316fa2aba52cf9","login":"scresawn","name":"Steven Cresawn","avatar_url":"https://avatars-next.observableusercontent.com/e2727956835daa3b5e9995b66c1111489c2e403c176b4fb761ec6ad64b97786b","tier":null,"role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Data Centers and Their Impact on Local Communities","cells":[{"id":0,"value":"# Data Centers and Their Impact on Local Communities\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"With the growth of cloud services and AI usage, there is a growing need for data processing centers to handle the large amounts of data that need to be stored and processed. This increase in need has led to a larger number of data centers popping up all over the US in recent years. These developments haven't been received well by everyone though, residents of local areas of where these data centers are being built say that it has had a negative impact on their communities. We will use data on data center development, electricity prices, and real estate prices to demonstrate that the rise in data centers negatively impacts the people who live in the places they are being built.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":173,"value":"# Choropleths Illustrating Data Center Frequency From 2020 to 2025
\n2020 Data Center Density","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":166,"value":"Plot.plot({\n width: 990,\n height: 600,\n projection: \"albers\",\n r: {\n range: [0, 0.25]\n },\n marks: [\n Plot.geo(nation, { fill: \"#eaeaea\", stroke: \"Black\" }),\n Plot.geo(statemesh, { strokeOpacity: 0.4 }),\n //Plot.dot(data_centers, {x: \"longitude\", y: \"latitude\", symbol: \"circle\"})\n Plot.density(old_data_centers, {\n x: \"longitude\",\n y: \"latitude\",\n bandwidth: 12,\n fill: \"density\"\n }),\n Plot.dot(old_data_centers, {\n x: \"longitude\",\n y: \"latitude\",\n r: 1.6,\n fill: \"Black\"\n })\n ]\n //fill: { legend: true, domain: [0, 8] }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":171,"value":"2025 Data Center Density","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":68,"value":"Plot.plot({\n width: 990,\n height: 600,\n projection: \"albers\",\n r: {\n range: [0, 0.25]\n },\n marks: [\n Plot.geo(nation, { fill: \"#eaeaea\", stroke: \"Black\" }),\n Plot.geo(statemesh, { strokeOpacity: 0.4 }),\n //Plot.dot(data_centers, {x: \"longitude\", y: \"latitude\", symbol: \"circle\"})\n Plot.density(data_centers, {\n x: \"longitude\",\n y: \"latitude\",\n bandwidth: 25,\n fill: \"density\"\n }),\n Plot.dot(data_centers, {\n x: \"longitude\",\n y: \"latitude\",\n r: 1.5,\n fill: \"Black\"\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":155,"value":"**Figure 1.** These choropleths represents individual data centers using black dots, and the density of data centers within a given area is represented using a color fill that moves towards red to represent higher data center density. These maps were created with data taken from OpenStreetMap, an open source map that tags data centers with labels that we can filter for[4]. Overpass API was used to isolate the names and locations of objects with data center tags, and we then transformed the downloaded data into a CSV that was imported into this workbook.

\nBy comparing the maps from 2020 and 2025, you can see how even in just the past 5 years there has been an explosion in the amount of data centers across the country. It is important to establish that data centers are increasing in abundance before addressing claims of negative community impacts. If there weren't actually more data centers being built then claims about their increasingly negative impacts would have no basis.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":661,"value":"# Comparison of Data Center Count in Northern Virginia Counties from 2020 to 2025","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":293,"value":"Plot.plot({\n width: 900,\n height: 500,\n marks: [\n Plot.barY(data_per_county, {\n x: \"Years\",\n y: \"Total_Datacenters\",\n fill: \"Years\"\n })\n ],\n facet: {\n data: data_per_county,\n x: \"County\"\n },\n color: {\n domain: [2020, 2025],\n range: [\"#007bff\", \"#dc3545\"]\n },\n grid: true\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":663,"value":"**Figure 2.** Bar graph illustrating data center frequency in Northern Virginia (NOVA) counties between 2020 and 2025. Red bars represent data from 2025 and blue bars represent data from 2020

\nMost data center development has been concentrated in Northern Virginia, especially when comparing concentrations from 2020 to 2025 (Figure 1). This concentration in data center location goes even further though. The burden of increasing density of NOVA data centers has fallen primarily on Loudon county. Despite already having the most data centers of the surrounding counties in 2020, Loudon has seen the greatest increase in data centers with nearly 130 more being built in the last 5 years (Figure 2). One of the main reasons data centers have been getting so much attention is that they're being built near residential areas which disturbs the people living there, evidenced by the trends seen in housing prices (Figure 3).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":672,"value":"# Housing Prices in NOVA Counties Between 2015 and 2025","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":330,"value":"viewof county = Inputs.select(\n Array.from(new Set(average_listing_price.map((d) => d.County))),\n {\n label: \"Counties:\",\n value: \"Fairfax County\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":358,"value":"Plot.plot({\n width: 900,\n marginLeft: 60,\n x: {\n label: \"Year\",\n domain: [2015, 2025],\n tickFormat: \"d\"\n },\n y: {\n label: \"Average House Price\",\n grid: true\n },\n marks: [\n Plot.ruleY([0]),\n Plot.lineY(\n average_listing_price.filter((d) => d.County === county),\n { x: \"Year\", y: \"Price\" }\n ),\n Plot.dot(\n average_listing_price.filter((d) => d.County === county),\n { x: \"Year\", y: \"Price\", fill: \"steelblue\", r: 4 }\n ),\n Plot.ruleX([2020], {\n stroke: \"red\",\n strokeWidth: 2\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":714,"value":"$49","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":627,"value":"# Electricity Prices: National vs NOVA
\n - National
\n - NOVA","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":485,"value":"Plot.plot({\n color: { legend: true },\n x: { label: \"Year\", tickFormat: \"d\" },\n y: { label: \"Average Retail Electric Price (Cent/kWh)\" },\n marks: [\n Plot.ruleY([0]),\n Plot.lineY(National_NOVA_Prices, {\n x: \"Year\",\n y: \"NOVA_Average_(¢/kWh)\",\n stroke: \"blue\"\n }),\n Plot.lineY(National_NOVA_Prices, {\n x: \"Year\",\n y: \"NOVA_Average_(¢/kWh)\",\n y: \"Average Electric Price (Cent/kWhr)\",\n stroke: \"red\"\n }),\n Plot.ruleX([2020], {\n stroke: \"black\",\n strokeWidth: 2\n })\n ],\n grid: true\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":680,"value":"$4a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":690,"value":"$4b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":254,"value":"$4c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":185,"value":"## Code Used for Overpass Queries
\n### 2020 Data
\n[out:json][timeout:180][date:\"2020-01-01T00:00:00Z\"];\n\n{{geocodeArea:United States}}->.searchArea;\n\n(\n node[\"amenity\"=\"data_center\"](area.searchArea);\n way[\"amenity\"=\"data_center\"](area.searchArea);\n relation[\"amenity\"=\"data_center\"](area.searchArea);\n\n node[\"building\"=\"data_center\"](area.searchArea);\n way[\"building\"=\"data_center\"](area.searchArea);\n relation[\"building\"=\"data_center\"](area.searchArea);\n\n node[\"building\"=\"server_farm\"](area.searchArea);\n way[\"building\"=\"server_farm\"](area.searchArea);\n relation[\"building\"=\"server_farm\"](area.searchArea);\n);\n\nout center;\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":783,"value":"# Data Table Joining Two Data Sources Using SQL","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":564,"value":"select *\nfrom table1\njoin table2 on table1.Year = table2.Year\n","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"National_NOVA_Prices","database":"var:db"},{"id":78,"value":"us = FileAttachment(\"us-counties-10m.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"data_centers = await FileAttachment(\"data_centers.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"data_centers","database":"$undefined"},{"id":182,"value":"Proptery_Price_Taxes = await FileAttachment(\"data-xQ5ws.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"Proptery_Price_Taxes","database":"$undefined"},{"id":193,"value":"House_Prices = new Map(\n Proptery_Price_Taxes.map(({ geoid, med_housing_value_23 }) => [\n geoid,\n med_housing_value_23\n ])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"nation = topojson.feature(us, us.objects.nation)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"statemesh = topojson.mesh(us, us.objects.states, (a, b) => a !== b)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":206,"value":"counties = topojson.feature(us, us.objects.counties)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":163,"value":"old_data_centers = await FileAttachment(\"2020_data centers.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"old_data_centers","database":"$undefined"},{"id":427,"value":"average_listing_price = await FileAttachment(\"average_listing_price@3.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"average_listing_price","database":"$undefined"},{"id":467,"value":"double line graph, with national rates and nova rates plotted.\n2025 reference: https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_6_a\nAverage zip codes for: https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_03 \ncent per kilowatt hour\nConclusion:\nWrap up what the data shows\nrestate thesis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":482,"value":"usAverageElectricPrice1 = await FileAttachment(\"US Average Electric Price@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"usAverageElectricPrice1","database":"$undefined"},{"id":551,"value":"NOVA_energy_price_20152024 = await FileAttachment(\"Energy Prices NOVA 2015-2024.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"NOVA_energy_price_20152024","database":"$undefined"},{"id":558,"value":"db = DuckDBClient.of({\n table1: national_electric_price,\n table2: nova_power_pricing\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":598,"value":"national_electric_price = await FileAttachment(\"national_electric_price@2.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"national_electric_price","database":"$undefined"},{"id":296,"value":"data_per_county = FileAttachment(\"datacenters_per_county@1.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":715,"value":"nova_power_pricing = await FileAttachment(\"nova_power_pricing.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"nova_power_pricing","database":"$undefined"}],"files":"$W4d","stdlib":"1"}} 24:null