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25.7356433,1.34698743 C25.1669153,1.34698743 24.7179195,1.43678659 24.4086113,1.6263626 C24.0993031,1.81593861 23.9396601,2.10529147 23.9396601,2.51437654 C23.9396601,2.79375171 24.039437,3.03321614 24.2389906,3.22279215 C24.4385443,3.41236816 24.8077187,3.60194417 25.336536,3.77156481 L26.7533672,4.22056062 C27.4717605,4.45004737 27.9906001,4.76933328 28.2999083,5.17841835 C28.6092165,5.58750342 28.7588818,6.0564546 28.7588818,6.5752942 C28.7588818,7.00433464 28.6690826,7.39346435 28.499462,7.73270563 C28.3198637,8.0719469 28.0803992,8.37127745 27.771091,8.61074188 C27.4617828,8.86018399 27.0926085,9.03978232 26.663568,9.16949222 C26.2145722,9.30917981 25.745621,9.3790236 25.2367591,9.3790236 Z" id="Shape" fill="#FFFFFF"></path><g id="Group" transform="translate(0.000000, 11.184211)" fill="#FFFFFF"><path d="M27.1225415,3.04396746 C23.8398833,5.46854484 19.07055,6.75566616 14.9697216,6.75566616 C9.2225752,6.75566616 4.04415686,4.63041933 0.132904461,1.09831895 C-0.176403764,0.818943781 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src="https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/63fe2e386888335276a1763b_Microsoft_Azure.svg.png" loading="lazy" sizes="100vw" srcset="https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/63fe2e386888335276a1763b_Microsoft_Azure.svg-p-500.png 500w, https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/63fe2e386888335276a1763b_Microsoft_Azure.svg-p-800.png 800w, https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/63fe2e386888335276a1763b_Microsoft_Azure.svg-p-1080.png 1080w, https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/63fe2e386888335276a1763b_Microsoft_Azure.svg-p-1600.png 1600w, https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/63fe2e386888335276a1763b_Microsoft_Azure.svg.png 1920w" alt="" class="icon-embed-2"/><div class="link-wrap"><div class="link">Azure</div><div class="link__underline"></div></div></a></div><div class="col-1--company"><a href="https://carto.com/snowflake/spatial-extension/" 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author__and--main">and</div><a href="#" class="post__author post__author--main w-dyn-bind-empty"></a><div class="post__author post__author--main w-condition-invisible">CARTO Contributor</div><div class="dot-separator dot-separator--main">·</div><div class="post__date post__date--main">Mar 15, 2023</div></div></div><div class="author__header post__author--condition w-condition-invisible"><div class="authors__avatars"><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/637df4198f6e3b862d36b670_helen-mckenzie.jpg" loading="lazy" alt="Helen McKenzie" class="post__avatar"/><img src="" loading="lazy" alt="" class="post__avatar w-dyn-bind-empty"/><img src="" loading="lazy" alt="" class="post__avatar w-dyn-bind-empty"/></div><div class="authors__links"><a href="/authors/helen-mckenzie" class="post__author post__author--main">Helen McKenzie</a><div class="author__comma author__comma--main">,</div><a href="#" class="post__author post__author--main w-dyn-bind-empty"></a><div class="author__and author__and--main">and</div><a href="#" class="post__author post__author--main w-dyn-bind-empty"></a><div class="dot-separator">·</div><div class="post__date post__date--main">Mar 15, 2023</div></div></div></div><div class="post__link--heading post__link--heading-main"><h1 class="heading--1">How To Optimize Location Planning For Wind Turbines</h1></div><div class="blog-post__info"><div class="blog-post__info--item"><a href="/categories/use-cases" class="post__link--category blog-post__link--category w-inline-block"><div class="text-block-3">Use cases</div></a></div><div class="blog-post__info--item"><div class="blog-post__time"><img src="https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/6364faaf4443c45a2771c43d_clock.svg" loading="lazy" alt="" class="time-icon"/><div class="w-dyn-list"><div role="list" class="w-dyn-items"><div role="listitem" class="time-read__item w-dyn-item"><div id="reading-time" class="time-read">4</div><div class="time-read--text">mins read</div></div></div></div></div></div><div class="blog-post__info--item last-item"><div class="blog-post__share"><a href="#" class="blog-post__share--link w-inline-block"><img src="https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/6364fbc48ae88d49a31107ec_share.svg" loading="lazy" alt="" class="share-icon"/><div>Share</div></a><div class="blog__share--wrapper"><div class="blog__share--block"><a fs-socialshare-element="facebook" href="https://www.facebook.com/CartoDB" target="_blank" class="social-link facebook w-inline-block"><img src="https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/6369504e280a6605e5325e9e_facebook.svg" loading="lazy" alt="Facebook icon" class="blog__share--social-icon"/><div title="Facebook" class="blog__share--social-link"><div>Facebook</div></div></a><a fs-socialshare-element="twitter" href="https://twitter.com/CARTO" target="_blank" class="social-link twitter w-inline-block"><img src="https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/6369504edb07d45c6aa54f56_twitter.svg" loading="lazy" alt="Twitter icon" class="blog__share--social-icon"/><div title="Twitter" class="blog__share--social-link"><div>Twitter</div></div></a><a fs-socialshare-element="linkedIn" href="https://www.linkedin.com/company/carto" target="_blank" class="social-link linkedin w-inline-block"><img src="https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/6369504eb4df2a36e60a1973_linkedin.svg" loading="lazy" alt="Linkedin icon" class="blog__share--social-icon"/><div title="Linkedin" class="blog__share--social-link"><div>LinkedIn</div></div></a><div class="blog__share--arrow"></div></div></div></div></div></div></header><div class="summary__content hide"><p class="post__summary">Summary</p><p class="post__intro">Optimize wind farm locations with our guide on location planning for wind turbines. Explore criteria, spatial analysis, and use CARTO for a free trial.</p></div><div class="warning w-embed w-script"><div class="baner-alert hide"> <svg width="20" height="20" viewBox="0 0 20 20" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M9 13H11V15H9V13ZM9 5H11V11H9V5ZM10 0C4.47 0 0 4.5 0 10C0 12.6522 1.05357 15.1957 2.92893 17.0711C3.85752 17.9997 4.95991 18.7362 6.17317 19.2388C7.38642 19.7413 8.68678 20 10 20C12.6522 20 15.1957 18.9464 17.0711 17.0711C18.9464 15.1957 20 12.6522 20 10C20 8.68678 19.7413 7.38642 19.2388 6.17317C18.7362 4.95991 17.9997 3.85752 17.0711 2.92893C16.1425 2.00035 15.0401 1.26375 13.8268 0.761205C12.6136 0.258658 11.3132 0 10 0ZM10 18C7.87827 18 5.84344 17.1571 4.34315 15.6569C2.84285 14.1566 2 12.1217 2 10C2 7.87827 2.84285 5.84344 4.34315 4.34315C5.84344 2.84285 7.87827 2 10 2C12.1217 2 14.1566 2.84285 15.6569 4.34315C17.1571 5.84344 18 7.87827 18 10C18 12.1217 17.1571 14.1566 15.6569 15.6569C14.1566 17.1571 12.1217 18 10 18Z" fill="#eb1510" /> </svg> <div class="alert__text"> This post may describe functionality for an <a href="https://carto.com/login" target="_blank">old version </a> of CARTO. Find out about the latest and cloud-native version here. </div> </div> <script> const postDate = new Date('Mar 15, 2023') const topPostDate = new Date('2021-10-12'); const banner = document.querySelector('.baner-alert') if(postDate < topPostDate && false == false){ banner.classList.remove("hide") } </script></div><div class="main-image w-condition-invisible"><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/6411e150f266233697b868ca_Blogpost_Asset1_LinkedIn_1200x628(5).png" loading="lazy" alt="How To Optimize Location Planning For Wind Turbines" sizes="100vw" srcset="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/6411e150f266233697b868ca_Blogpost_Asset1_LinkedIn_1200x628(5)-p-500.png 500w, https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/6411e150f266233697b868ca_Blogpost_Asset1_LinkedIn_1200x628(5)-p-800.png 800w, https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/6411e150f266233697b868ca_Blogpost_Asset1_LinkedIn_1200x628(5)-p-1080.png 1080w, https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/6411e150f266233697b868ca_Blogpost_Asset1_LinkedIn_1200x628(5).png 1200w" class="main-image__img"/></div><div class="rich-text-blog w-richtext"><p>With a global goal of reaching <a href="https://netzeroclimate.org/what-is-net-zero/">net zero</a> by 2050, renewable energy sources such as solar energy, wind power, and other forms of kinetic energy, are set to play a huge role in powering us through the 21st century and beyond. It is estimated that an additional <a href="https://www.carbontrust.com/news-and-insights/news/global-offshore-wind-industry-joins-forces-with-the-carbon-trust-to">70-80 GW</a> of wind power that creates electricity will need to be installed annually to reach net zero. </p><p>While the future for wind power looks bright, one of the biggest questions facing organizations looking to drive this growth from renewable energy projects is… where?</p><figure class="w-richtext-align-center w-richtext-figure-type-image"><div><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/670fc245a6190c7c42c4c743_64106098a2a1dc116220afe3_wind-turbines.jpeg" loading="lazy" alt="A photograph of 7 wind turbines in a field"/></div><figcaption>Photo by <a href="https://unsplash.com/@tollbooth?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">m h</a> on <a href="https://unsplash.com/photos/MQWgosx6bNk?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a></figcaption></figure><p>Site Selection for wind farms represents a challenging contradiction for spatial analysts. The ideal location should be somewhere remote but not <strong>too</strong> remote. Not too close to residents, but with a sufficient power and transport infrastructure able to support construction and operations. A site should experience high wind speeds (often found in upland areas) but should not be ecologically sensitive to avoid the energy development causing negative environmental impacts. </p><p>In this guide, we’ll be seeking sites in the north west of the USA which meet these contradictory criteria. Make sure you grab a free two-week CARTO trial <a href="https://app.carto.com/signup/">here</a> so you can try out this analysis yourself!</p><figure class="w-richtext-align-center w-richtext-figure-type-image"><div><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/670fc245a6190c7c42c4c75c_6410613dd20c10dca26dee7e_north-west-states.png" loading="lazy" alt="A map showing the boundaries of our study area: Washington, Oregon, Idaho, Montana and Wyoming."/></div><figcaption>Our study area in the north-east of the United States: Washington, Oregon, Idaho, Montana and Wyoming.</figcaption></figure><div class="w-embed"><h2 style="font-family:montserrat">Multi-criteria analysis for wind turbine Site Selection</h2></div><p>For our Site Selection process, we’ll have two criteria types; boolean and ranked. Boolean criteria will return a yes or no based on specified conditions; locations will be discarded if they don’t meet these. Ranked criteria will be used to rank the remaining locations in terms of suitability. </p><p>Please note that this guide is intended as an illustrative guide to the types of data and analysis which can be used to optimize the potential locations of wind farms; it is not intended to be exhaustive of all considerations. </p><div class="w-embed"><h4 style="font-family:montserrat">Boolean criteria</h4></div><ul role="list"><li>Must be within 25 miles of a >=400KV powerline, with data sourced from the <a href="https://hub.arcgis.com/datasets/fedmaps::u-s-electric-power-transmission-lines/explore?location=43.006436%2C-87.946834%2C14.83">Homeland Infrastructure Foundation.</a></li><li>Must be within 15 miles of a motorway or trunk level highway.</li><li>Must not intersect a large protected area (please note Native American lands are not included as many Native American communities are <a href="https://www.windpowermonthly.com/article/966022/us-focus-tribal-wind-ambition-victim-policy-void">reported to be</a> pro wind farm developments).</li></ul><p>The highway and protected area (see definition <a href="https://wiki.openstreetmap.org/wiki/Tag:boundary%3Dprotected_area">here</a>) data were sourced from <a href="https://www.openstreetmap.org">OpenStreetMap</a> - a fantastic global free database often dubbed “Wikipedia for maps.” While the crowdsourced nature of this dataset means quality and consistency can vary, major highways and protected areas are typically accurate due to their significance.</p><p>You can access this data for free from the Google BigQuery OpenStreetMap <a href="https://wiki.openstreetmap.org/wiki/BigQuery_dataset">public dataset</a> by modifying the below code. This code extracts protected areas which intersect our selected states (accessible via our Spatial Data Catalog <a href="https://carto.com/spatial-data-catalog/browser/geography/cdb_state_c435f81f/">here</a>). Note you’ll need to change “xxxxxxxx” to your Data Observatory dataset - you can find this in the sample SQL code for any dataset you have a subscription to. This code also extracts only areas which have an area greater than or equal to 0.73km². Why? This is the <a href="https://h3geo.org/docs/core-library/restable/">average area</a> of a H3 cell at resolution 8, which is the <a href="https://carto.com/solutions/spatial-indexes/">geographic support system</a> we’ll be using for this analysis (keep reading for more information). </p><p>You can read our full guide to working with the BigQuery OpenStreetMap dataset <a href="https://carto.com/blog/osm-bigquery">here</a>.</p><div class="w-embed"><pre class="line-numbers"> <code class="language-sql"> WITH aoi AS ( SELECT ST_UNION_AGG(geom) AS geom FROM `carto-data.ac_xxxxxxxx.sub_carto_geography_usa_state_2019` WHERE do_label IN ('Washington', 'Oregon', 'Idaho', 'Montana', 'Wyoming')), protected AS ( SELECT (SELECT osm_id) osmid, (SELECT value FROM UNNEST(all_tags) WHERE KEY = "boundary") AS boundary, (SELECT value FROM UNNEST(all_tags) WHERE KEY = "name") AS name, (SELECT geometry) AS geom FROM bigquery-public-data.geo_openstreetmap.planet_features) SELECT protected.* FROM protected, aoi WHERE ST_CONTAINS(aoi.geom, protected.geom) AND protected.boundary = 'protected_area') AND ST_AREA(protected.geom) >= 737327.598 </code> </pre></div><div class="w-embed"><h4 style="font-family:montserrat">Ranked critera</h4></div><p>Following this assessment, locations will be ranked based on the following criteria:</p><ul role="list"><li>Average wind speeds, with higher speed being preferable</li><li>Lowest population within 18.6 miles or 30 kilometers; the distance beyond which visual impacts from wind farms are <a href="https://www.greenrhinoenergy.com/renewable/wind/wind_visual.php">deemed negligible</a>. </li></ul><p>This data will be extracted from our Spatial Features dataset, available via our Spatial Data Catalog <a href="https://carto.com/spatial-data-catalog/browser/dataset/cdb_spatial_fea_94e6b1f/">here</a>. These tables include data on demographics and climatology. In this instance, we’re interested in the total population and average monthly wind speed.</p><p>We’ll be using the H3 version of this dataset. H3 is a Spatial Index; a global, multi-resolution grid system which is geolocated by a short reference ID rather than a long geometry string. This makes Spatial Indexes small to store and super quick to analyze - ideal for large-scale yet detailed analyses like this!</p><p>Want to know more Spatial Indexes? We’ve written a whole ebook about them which you can download for FREE <a href="https://go.carto.com/report-spatial-indexes-101">here</a>!</p><p>Explore these input variables for the whole of the US in <a href="https://clausa.app.carto.com/map/0cccac81-a036-41a4-9bec-ea886c2a6662">the map</a> below!</p><div class="w-embed w-iframe"><div class="Wrap blog-hidden-mobile"><iframe loading="lazy" width="100%" height="600" frameborder="0" src="https://clausa.app.carto.com/map/0cccac81-a036-41a4-9bec-ea886c2a6662" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe></div></div><div class="w-embed"><div class="Wrap blog-mobile"> <a href="https://clausa.app.carto.com/map/0cccac81-a036-41a4-9bec-ea886c2a6662"target="_blank"><img loading="lazy" width="100%" frameborder="0" src="https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/670fc006805f117b224c6e57_Map%20with%20data%20extracted%20from%20our%20Spatial%20Features%20dataset.png"></a></div></div><div class="w-embed"><h2 style="font-family:montserrat">A low-code analytical framework</h2></div><p>For this analysis, we’ll be using <a href="https://carto.com/blog/no-code-workflows-beta">CARTO Workflows</a> - a visual, low-code analytics tool currently in beta.</p><div class="w-embed"><h3 style="font-family:montserrat">Boolean criteria</h3></div><figure class="w-richtext-align-center w-richtext-figure-type-image"><div><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/670fc245a6190c7c42c4c740_64106302a171630ecd6dd61a_wind-turbines-workflows-full.png" loading="lazy" alt="A screenshot showing the workflow in full, with a series of input datasets and analytical components connected by lines."/></div></figure><p>This workflow filters our Spatial Features H3 dataset from the 1,780,870 cells which cover our study area, down to the 226,854 features which meet our criteria. Let’s break this down to see how it works!</p><div class="w-embed"><h4 style="font-family:montserrat">Step 1: Select cells within 25 miles of a >=400KV powerline</h4></div><figure class="w-richtext-align-center w-richtext-figure-type-image"><div><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/670fc246a6190c7c42c4c77e_6410635f90f63d6e805d73b6_wind-turbines-workflows-step1.png" loading="lazy" alt="A screenshot of the workflow outlined in the steps below, with a panel for the SQL and showing the map results."/></div></figure><p>In this first section of the analysis we:</p><ol start="" role="list"><li>Convert the H3 table “NW_spatialfeatures” to their center points using <a href="https://docs.carto.com/data-and-analysis/analytics-toolbox-for-bigquery/sql-reference/h3#h3_center">H3_CENTER</a>, as a geometry field is required for the subsequent step. </li><li>Simplifies the geometry of “MW_HF_powerlines” using <a href="https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions#st_simplify">ST_SIMPLIFY</a> to improve the processing speed of the subsequent steps. <a href="https://docs.carto.com/data-and-analysis/analytics-toolbox-for-bigquery/sql-reference/transformations#st_buffer">A_BUFFER</a> is then used to create a buffer of 25 miles around the powerlines. </li><li> A SPATIAL_FILTER component is then used to select only H3 centroids which intersect the buffers. If you want to learn more about Spatial Filters, check out <a href="https://carto.com/blog/guide-to-spatial-joins-and-predicates-with-sql">this guide</a>. </li><li>As the buffer layer contains multiple geometries, it is likely that some H3 cells intersect with more than one buffer which would generate duplicate cells during the filter. Therefore, the final step of this section is to use <a href="https://docs.carto.com/carto-user-manual/workflows/components/data-preparation#select-distinct">SELECT_DISTINCT</a> to eliminate duplicates. </li></ol><p>The 499,215 resulting cells can be seen in the map preview at the bottom of the Workflows screen.</p><div class="w-embed"><h4 style="font-family:montserrat">Step 2: Select cells within 15 miles of a major highway</h4></div><figure class="w-richtext-align-center w-richtext-figure-type-image"><div><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/670fc245a6190c7c42c4c759_641063d6a9ae10d4fceae327_wind-turbines-workflows-step2.png" loading="lazy" alt="A screenshot of the workflow outlined in the steps below, with a panel for the SQL and showing the map results."/></div></figure><div class="w-embed"><ol type="1"> <li>As with the previous section, first we need to use H3_CENTER to create centerpoint geometries from our H3 table, this time using the results of step 1.</li> <li>We then repeat the steps from section 1, this time using the table “NW_osm_highways” and a buffer distance of 15 miles. However, as our highway table contains 3,728 unique geometries, there is the potential for the creation of a lot of H3 duplicates during our Spatial Filter, which could slow things down significantly. To combat this, we’ve added a CUSTOM_SQL_SELECT and used the ST_UNION_AGG function on the results of A_BUFFER to merge all of the buffers into one feature. Note how we can use $a to call the first connected inputs (or $b and $c if multiple inputs are connected). </li> </ol> <pre class="line-numbers"> <code class="language-sql"> SELECT ST_UNION_AGG(geom_buffer) AS geom FROM $a </code> </pre> <ol type="1" start="3"> <li>And finally run another SPATIAL_FILTER to select only the H3 cells which intersect the buffer, taking us down to 242,830 cells - we're getting there!</li> </ol></div><div class="w-embed"><h4 style="font-family:montserrat">Step 3: Deselect cells which intersect a large protected area</h4></div><figure class="w-richtext-align-center w-richtext-figure-type-image"><div><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/670fc245a6190c7c42c4c756_64106494d4f4ba743a456566_wind-turbines-workflows-step3.png" loading="lazy" alt="A screenshot of the workflow outlined in the steps below, with a panel for the SQL and showing the table of results."/></div></figure><p>Nearly there! This final section is a little different.</p><div class="w-embed"><ol type="1"> <li>We run another SPATIAL_FILTER; this time we use this to establish which H3 cell centers intersect the protected areas.</li> <li>Again, H3 cells could potentially intersect multiple protected areas which would create duplicates, so we’ll use SELECT_DISTINCT to remove these.</li> <li> And finally, we want to select the H3 cells from our original table (which contains the population and wind variables needed for the second half of our analysis) which intersect with the powerline and highway buffers, but do not intersect with the protected areas. <br></br>To do this, we connect the original table, the resulting SPATIAL_FILTER from step 2, and the resulting SELECT_DISTINCT from step 3 to another CUSTOM_SQL_SELECT component. We then use the following SQL code to select the required H3 cells: </li> </ol> <pre class="line-numbers"> <code class="language-sql"> SELECT $a.* FROM $a WHERE h3 IN (SELECT h3 FROM $b) AND h3 NOT IN (SELECT h3 FROM $c) </code> </pre></div><p>Finally, add a <a href="https://docs.carto.com/carto-user-manual/workflows/components/import-export#save-as-table">SAVE_AS_TABLE</a> component to the end of this - and let’s <a href="https://clausa.app.carto.com/map/e0e67be8-8b5c-4442-9dd9-a4f7ba001ffe">take a look</a> at what we’ve got!</p><div class="w-embed w-iframe"><div class="Wrap blog-hidden-mobile"><iframe loading="lazy" width="100%" height="600" frameborder="0" src="https://clausa.app.carto.com/map/e0e67be8-8b5c-4442-9dd9-a4f7ba001ffe" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe></div></div><div class="w-embed"><div class="Wrap blog-mobile"> <a href="https://clausa.app.carto.com/map/e0e67be8-8b5c-4442-9dd9-a4f7ba001ffe"target="_blank"><img loading="lazy" width="100%" frameborder="0" src="https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/670fc00521f8b3655ba393d1_Map%20using%20a%20low-code%20analytical%20framework.png"></a></div></div><div class="w-embed"><h3 style="font-family:montserrat">Ranked criteria</h3></div><p>Now we have our potential locations for a wind farm, we can rank their suitability. We already have the average wind speeds, but we still need to calculate the population size within 18.6 miles / 30 kilometers of each cell.</p><div class="w-embed"><h4 style="font-family:montserrat">Step 4: Calculating population within a distance</h4></div><p>Using traditional geometries, this would be a really expensive and slow operation. Thankfully, Spatial Indexes make this much faster, thanks to a function called <a href="https://docs.carto.com/data-and-analysis/analytics-toolbox-for-bigquery/sql-reference/h3#h3_kring">H3_KRING</a> which calls an array of cells which are a specified number of “steps” from the origin cell. Our H3 cells are a resolution 8, which means we need a ring of 32 cells to approximate 30 kilometers (based on <a href="https://h3geo.org/docs/core-library/restable/">this table</a> of average H3 cell sizes). </p><p>Check out the following SQL code to see how this works!</p><div class="w-embed"><pre class="line-numbers"> <code class="language-sql"> WITH --Create an area of interest AOI as (SELECT st_union_agg(geom) as geom FROM `carto-data.ac_xxxxxxxx.sub_carto_geography_usa_state_2019` where do_label IN ('Washington','Oregon','Idaho', 'Montana','Wyoming')), --Select Spatial Features H3 cells within 30km of area of interest SF AS ( SELECT geoid AS h3,population FROM `carto-data.ac_xxxxxxxx.sub_carto_derived_spatialfeatures_usa_h3res8_v1_yearly_v2` SF, AOI WHERE ST_DISTANCE(aoi.geom,`carto-un`.carto.H3_CENTER(SF.geoid)) < 30000), --Create a K-ring of 32 for each cell ring AS (SELECT * FROM yourproject.wind_site_selection.NW_H3_potential_locations wind, unnest(`carto-un`.carto.H3_KRING(wind.h3, 32)) as ring_h3) --Join the Spatial Features to the cells generated in step 3, summing the population in each K-ring SELECT ring.h3, ring.population, ring.avg_wind, SUM(SF.population) AS population_30km FROM ring LEFT JOIN SF on ring.ring_h3 = SF.h3 GROUP BY ring.h3, ring.population, ring.avg_wind </code> </pre></div><p>With the results of this query exported as a table, all that’s left to do is normalize our two input variables and create a final composite index which indicates suitability for wind farms.</p><div class="w-embed"><h2 style="font-family:montserrat">Step 5: Normalize and index! </h2></div><p>Workflows makes normalization really simple.</p><figure class="w-richtext-align-center w-richtext-figure-type-image"><div><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/670fc245a6190c7c42c4c75f_64106b1fdd5c4bfffbdd6961_wind-turbines-workflows-normalization.png" loading="lazy" alt="A screenshot of the normalization process in CARTO Workflows."/></div></figure><p>We just need to add two <a href="https://docs.carto.com/carto-user-manual/workflows/components/data-preparation#normalize">NORMALIZE</a> components in order to - you guessed it - normalize each of our variables. Then finally - and this really is the final FINAL step - we add a <a href="https://docs.carto.com/carto-user-manual/workflows/components/custom#custom-sql-select">CUSTOM_SQL_SELECT</a> component. This SQL will inverse the population normalization (so the lowest population would score 1, as we would prefer our wind farm to not be located near a large population). It also adds the two normalization scores together to create a wind farm suitability index - with a maximum score of 2 indicating an area with the highest wind speeds and the smallest proximal population.</p><div class="w-embed"><pre class="line-numbers"> <code class="language-sql"> WITH norm AS ( SELECT $a.*, 1-population_30km_norm AS population_30km_norm_inv FROM $a) SELECT norm.*, population_30km_norm_inv+avg_wind_norm AS INDEX FROM norm </code> </pre></div><p>Use SAVE_AS_TABLE again to export these results… and let’s check them out!</p><div class="w-embed"><h2 style="font-family:montserrat">The results!</h2></div><div class="w-embed w-iframe"><div class="Wrap blog-hidden-mobile"><iframe loading="lazy" width="100%" height="600" frameborder="0" src="https://clausa.app.carto.com/map/699eb078-c245-4020-a4a2-2234db32c6cd" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe></div></div><div class="w-embed"><div class="Wrap blog-mobile"> <a href="https://clausa.app.carto.com/map/699eb078-c245-4020-a4a2-2234db32c6cd"target="_blank"><img loading="lazy" width="100%" frameborder="0" src="https://cdn.prod.website-files.com/6345207a1b18e581fcf67604/670fc005a6190c7c42c2b67e_Maps%20using%20CARTO%20Workflows%20for%20normalization.png"></a></div></div><p>‍</p><p>Access the interactive map <a href="https://clausa.app.carto.com/map/699eb078-c245-4020-a4a2-2234db32c6cd">here</a> to see that areas of low suitability are more common to the west coast, around highly populated areas like Seattle and Portland. More suitable areas can be found particularly in the sparsely populated Central Montana, a state ranked 5th for potential wind energy power by the <a href="https://www.montana.edu/wind/windinmontana.html">National Renewable Energy Laboratory</a>. Wyoming is the only state where no suitable areas can be found, as there are no areas where the requisite power and transport infrastructure can be found in close proximity.</p><div class="w-embed"><h2 style="font-family:montserrat">Location Intelligence for Renewable Energy Site Selection</h2></div><p>This has been a simplified look at how different spatial datasets and analytical techniques can be used to pinpoint optimal locations for wind farms. Of course in reality, many more factors would contribute to this process - you can read more about using Location Intelligence for climate and environmental insights <a href="https://carto.com/solutions/environmental-management/">here</a>. </p><p>Want to learn more watch this process in action in our mini-webinar below👇</p><figure style="padding-bottom:33.723653395784545%" class="w-richtext-align-center w-richtext-figure-type-video"><div><iframe allowfullscreen="true" frameborder="0" scrolling="no" src="https://www.youtube.com/embed/xgjeHmAmeqA" title="Optimizing location planning for wind turbines"></iframe></div></figure><p>Ready to see how you can using Location Intelligence to gain a competitive advantage? 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From location intelligence to GIS, spatial data science, industry trends, and much more, we’ve crafted relevant content to accompany you at every stage of your journey, whether you have a technical or business background. With our Blog, you are one step closer to taking spatial analysis to the next level.<a href="https://carto.com/authors/carto-contributors"><br/></a></p><a href="https://carto.com/authors/carto-contributors" class="more-posts w-inline-block"><div class="author-link">More Posts from </div><div class="author-link">CARTO Contributors</div></a></div></div></div></div><div class="author__about--condition w-condition-invisible"><div class="banner__author"><div class="author_wrapper"><div class="author-content"><p class="paragraph--1 p-banner__author">About the author</p><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/637df4198f6e3b862d36b670_helen-mckenzie.jpg" loading="lazy" alt="Helen McKenzie" class="author-avatar"/></div><div class="author-bio"><p class="author-bio-text">Helen is CARTO's Geospatial Advocate where she works to get the data science community excited about thinking about “where?” Whether it's electric vehicle Site Selection, analyzing caffeination deprivation or choosing the next Eurovision Song Contest venue, Helen's posts are all about opening your eyes to the possibilities of geospatial.</p><a href="/authors/helen-mckenzie" class="more-posts w-inline-block"><div class="author-link">More Posts from </div><div class="author-link">Helen McKenzie</div></a></div></div><div class="author_wrapper"><div class="author-content"><p class="paragraph--1 p-banner__author">About the author</p><img src="" loading="lazy" alt="" class="author-avatar w-dyn-bind-empty"/></div><div class="author-bio"><p class="author-bio-text w-dyn-bind-empty"></p><a href="#" class="more-posts w-inline-block"><div class="author-link">More Posts from </div><div class="author-link w-dyn-bind-empty"></div></a></div></div></div></div><div class="author__about--condition w-condition-invisible"><div class="banner__author"><div class="author_wrapper"><div class="author-content"><p class="paragraph--1 p-banner__author">About the author</p><img src="https://cdn.prod.website-files.com/63483ad423421bd16e7a7ae7/637df4198f6e3b862d36b670_helen-mckenzie.jpg" loading="lazy" alt="Helen McKenzie" class="author-avatar"/></div><div class="author-bio"><p class="author-bio-text">Helen is CARTO's Geospatial Advocate where she works to get the data science community excited about thinking about “where?” Whether it's electric vehicle Site Selection, analyzing caffeination deprivation or choosing the next Eurovision Song Contest venue, Helen's posts are all about opening your eyes to the possibilities of geospatial.</p><a href="/authors/helen-mckenzie" class="more-posts w-inline-block"><div class="author-link">More Posts from </div><div class="author-link">Helen McKenzie</div></a></div></div><div class="author_wrapper"><div class="author-content"><p class="paragraph--1 p-banner__author">About the author</p><img src="" loading="lazy" alt="" class="author-avatar w-dyn-bind-empty"/></div><div class="author-bio"><p class="author-bio-text w-dyn-bind-empty"></p><a href="#" class="more-posts w-inline-block"><div class="author-link">More Posts from </div><div class="author-link w-dyn-bind-empty"></div></a></div></div><div class="author_wrapper"><div class="author-content"><p class="paragraph--1 p-banner__author">About the author</p><img src="" loading="lazy" alt="" class="author-avatar w-dyn-bind-empty"/></div><div class="author-bio"><p class="author-bio-text w-dyn-bind-empty"></p><a href="#" class="more-posts w-inline-block"><div class="author-link">More Posts from </div><div class="author-link w-dyn-bind-empty"></div></a></div></div></div></div></div><div class="post-nav"><nav aria-label="Posts" class="post-nav__nav"><div class="post-nav__column"><div fs-cmsprevnext-element="previous" class="post-nav__previous"></div><div class="post-nav__previous-row"><div class="post-nav__text">Previous Post</div></div></div><div class="post-nav__column"><div fs-cmsprevnext-element="next" class="post-nav__next"></div><div class="post-nav__next-row"><div class="post-nav__text">Next Post</div></div></div></nav><div class="post-nav__list w-dyn-list"><div fs-cmsprevnext-element="list" role="list" class="w-dyn-items"><div role="listitem" class="w-dyn-item"><a href="/blog/presidential-elections-us" class="post-nav__link">What divides the U.S.? 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