25:["$","title",null,{"children":"Re(port)ing for Duty | Vekta Group | Observable"}] 49:T4ce,The dataset is currently targeted towards mariners whose main use case is to access vital information as a planning tool before docking. Although not the largest port dataset in the world, it appears that the WPI is attempting to provide the largest variety of port associated variables. This is where we feel the WPI could benefit the offshore wind industry as some of these variables are vital for wind turbine integration, such as: - Entrance Widths - Channel Depths - Maximum Vessel Lengths - Entrance Restrictions - Pier Depths - Floating/Fixed Cranes

The following list shows competitors of the WPI and how they differ: - **S&P**: the largest global port dataset, offering similar data such as port terminal, berth locations and vessel restrictions. Lacks data about crane capacities, entrance restrictions and harbour dimensions. - **U.S. Army Corps of Engineers Port Directory**: tailored to port services. Lacks physical data such as quay lengths and channel depths. - **Lloyds List Intelligence**: data is offered as polygons rather than points. Data includes berthing, anchorage, terminal and sub-port areas, which helps display port orientation. Lacks physical characteristics of water surrounding the port.4a:T46e,***Where can Offshore Wind Benefit?***
A fully populated WPI dataset would reduce the need for large scale port feasibility studies, which could bring research time from months down to days. The WPI already provides data that is vital to port selection during planning and development, such as water depths, environmental factors and existing infrastructure. Increased data would improve risk management and contingency planning by informing port capabilities and infrastructure, allowing for smarter decision making that saves money and time.

***Where can Offshore Wind Help?***
As highlighted in this Insight, the scale required to gather missing data is immense. **This is where you come in**. There is a three step process on the NGA website where **you can become an editor for the WPI** and add information. This allows anyone with useful data to upload it without the need for WPI to gather it. Data gathered from previous port feasibility studies could be useful to others. Sharing data encourages innovation, improves decision making and promotes transparency, which benefits multiple offshore industries.4b:[{"id":"66647d8ebe10f4960130bce002915f664aaf0b952c7ff9a846505ad22920cb4785311d5f2d060ffacd7e58a8b111454f7ebf87863e7f960e527ad493ec4d6b49","href":"https://static.observableusercontent.com/files/66647d8ebe10f4960130bce002915f664aaf0b952c7ff9a846505ad22920cb4785311d5f2d060ffacd7e58a8b111454f7ebf87863e7f960e527ad493ec4d6b49","name":"Size vs Missing data@1.csv","type":"text/csv","lastModified":1742903435501.346,"size":1152},{"id":"c49a537bb6e9fe7972c8f7359f46bdc45ca2627e2c584863fe39a8e80522866071843d159d54ec927d595bb569e579e94037be787ffac977b3d065b2b0473b1f","href":"https://static.observableusercontent.com/files/c49a537bb6e9fe7972c8f7359f46bdc45ca2627e2c584863fe39a8e80522866071843d159d54ec927d595bb569e579e94037be787ffac977b3d065b2b0473b1f","name":"Observable - Week 4.geojson","type":"application/geo+json","lastModified":1742910969865.404,"size":885408},{"id":"eb3cf2004901ac67c600e74bb895394d6e415f6d6dcd96e1576eb8b6f0f7ca2d2277b9165904b6abc883f7e2c85210e9ada547aeeb72390815c7024c804b4577","href":"https://static.observableusercontent.com/files/eb3cf2004901ac67c600e74bb895394d6e415f6d6dcd96e1576eb8b6f0f7ca2d2277b9165904b6abc883f7e2c85210e9ada547aeeb72390815c7024c804b4577","name":"land-110m.json","type":"application/json","lastModified":1742911116861.453,"size":55207},{"id":"7a023b95258f4f4f413694b6d5b74a8f8e5c929eb7e4a7f8c7099101626647abc7c40f9051c5349cda881d8f9f3226fc961999206f9aa19dbc6cb75e56074d2e","href":"https://static.observableusercontent.com/files/7a023b95258f4f4f413694b6d5b74a8f8e5c929eb7e4a7f8c7099101626647abc7c40f9051c5349cda881d8f9f3226fc961999206f9aa19dbc6cb75e56074d2e","name":"Data Summary.xlsx","type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","lastModified":1742919782466.794,"size":8992},{"id":"b10af0992c9fcf1e88f07fb7bde3d6d07369b4bc5f9720775113ba49cf4b9e8f9d55d69e935f1c0c403a40eba2f74ff3adfaff06ce113b882cea78eabdc955f9","href":"https://static.observableusercontent.com/files/b10af0992c9fcf1e88f07fb7bde3d6d07369b4bc5f9720775113ba49cf4b9e8f9d55d69e935f1c0c403a40eba2f74ff3adfaff06ce113b882cea78eabdc955f9","name":"Lat, Long and Rating (1)@1.csv","type":"text/csv","lastModified":1742985045839.92,"size":195919}] 29:{"id":"30041470ec17dc82","slug":null,"title":"Re(port)ing for Duty","thumbnail":"e7a1e347cf95592953c1dbea757c6d7edbfa44abe13c6e127ad9d49216c24efd","access_level":"public","create_time":"$D2025-03-24T18:40:54.096Z","update_time":"$D2025-03-27T15:25:47.072Z","view_time":null,"first_public_time":"$D2025-03-27T09:29:56.059Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"3594fe6877d14711","login":"vekta-group","name":"Vekta Group","avatar_url":"https://avatars-next.observableusercontent.com/13f6934c06a2c847905f92615bfe639e05a96b39badbe0036f3139b0c24ef101","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":589,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"353739170940576d","login":"vekta-group","name":"Vekta Group","avatar_url":"https://avatars-next.observableusercontent.com/13f6934c06a2c847905f92615bfe639e05a96b39badbe0036f3139b0c24ef101","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"353739170940576d","login":"vekta-group","name":"Vekta Group","avatar_url":"https://avatars-next.observableusercontent.com/13f6934c06a2c847905f92615bfe639e05a96b39badbe0036f3139b0c24ef101","tier":null,"role":"editor"},{"type":"team","id":"3594fe6877d14711","login":"vekta-group","name":"Vekta Group","avatar_url":"https://avatars-next.observableusercontent.com/13f6934c06a2c847905f92615bfe639e05a96b39badbe0036f3139b0c24ef101","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":{"id":"a5603c9dcf7492ca","slug":"vekta-digital-insights","title":"Vekta Digital Insights ","owner":{"id":"3594fe6877d14711","login":"vekta-group","name":"Vekta Group"}},"type":"notebook","body":{"titling":"auto","title":"Re(port)ing for Duty","cells":[{"id":0,"value":"# Re(port)ing for Duty","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"This week's Insight will be looking again at the World Port Index (WPI), a dataset provided by the National Geospatial-Intelligence Agency (NGA). The WPI has the potential to be the go-to information source of ports for the offshore wind industry. However, it is missing significant portions of inputs and has some limitations with the data on offer.\n

\nThe WPI is a global dataset of 3802 ports with 91 environmental and physical conditions or service attributes. The table below shows size distribution of recorded ports. The WPI defines size as \"_based on several applicable factors, including area, facilities, and wharf space_\".","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":301,"value":"| Port Size | Number of Ports |\n| ------ | -------- |\n| Very Small | 2116 |\n| Small | 1019 |\n| Medium | 367 |\n| Large | 171 |\n| Unknown | 129 |","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":376,"value":"$49","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"## Where is the Data Missing?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":22,"value":"Missing data is collated by counting the NULL, 0 or \"Unknown\" values per port. The WPI is currently missing 220,630 data cells, 63.1% of all cells. A port rating is produced by totalling missing values per port, with a higher rating suggesting greater missing values. \n

\nThe interactive globe below shows the geospatial spread of the port rating. View the animation by pressing Play, and click on any of the sliders to stop. Use the Latitude and Longitude sliders for a custom view. \n

\nOver a third of global ports (1347) are missing 70% of their data (ratings over 63). Hotspots are noted in Mediterranean Europe, with Italy and Greece lacking significant amounts of data, and Northern Europe, specifically Norway and Scotland. North America is missing large amounts of data, specifically along the west coast of the US and east coast of Canada. This demonstrates a necessity for increased WPI data on a global scale.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":240,"value":"viewof rotate = Inputs.form([\n Inputs.range([-180, 180], {step: 0.1, label: \"Latitude\"}),\n Inputs.range([-90, 90], {step: 0.1, label: \"Longitude\"}),\n \n])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":248,"value":"play = {\n let frame;\n const button = Inputs.button(\"Play\", {reduce: animate});\n visibility().then(animate);\n function animate() {\n if (frame) cancelAnimationFrame(frame);\n let i = 0;\n const n = 400;\n const rotate = d3.piecewise([[-90, 0, 0], [90, -15, 0], [0, -30, 0]]);\n viewof rotate.addEventListener(\"mousedown\", () => cancelAnimationFrame(frame));\n (function tick() {\n if (!(++i <= n && button.isConnected)) return;\n viewof rotate.value = rotate(i / n);\n viewof rotate.dispatchEvent(new Event(\"input\"));\n frame = requestAnimationFrame(tick);\n })();\n }\n return button;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":238,"value":"globe.plot({\n width: 700,\n height: 700,\n projection: { type: \"orthographic\", rotate: rotate }, // Keep orthographic projection\n graticule: { stroke: \"lightgray\" }, // Grid lines\n water: { fill: \"none\" }, // Transparent ocean\n \n land: { fill: \"none\", stroke: \"none\" }, // Hollow land with black outline\n\n // Draw the points on top with color mapping based on rating\n marks: [\n Plot.geo(observableWeek4, {\n r: 3,\n fill: \"Rating\", // Color the points by Rating\n scale: ratingScale // Use the existing ratingScale\n })\n ],\n\n});\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":498,"value":"Plot.legend({\n color: {\n type: \"sequential\",\n domain: [0, 91],\n interpolator: d3.interpolateViridis, // Matches your ratingScale\n },\n label: \"Rating\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"## Data Breakdown","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":24,"value":"Missing port data is split by port size and categorised into 20 percentage point bins in the grouped bar chart below.\n

\nThere is an increase in known data with port size, with 28.07% of Large ports having less than 40% of their data missing compared to Very Small having just 0.94% missing. Equally, there is a decrease in the percentage of unknown data, with just 2.34% of Large ports in the 80-90% category compared to 17.82% in Very Small ports. This shows that larger ports currently have more data available, and so future studies to improve the WPI could focus on the characteristics of smaller ports. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":70,"value":"Plot.plot({\n title: \"Percentage of Ports by Size\", // Add your desired title here\n x: {axis: null},\n y: {tickFormat: \"s\", grid: true, labelAnchor:\"center\"},\n color: { \n type: \"ordinal\", // Ensures categorical mapping\n categories: [\"0-20\", \"20-40\", \"40-60\", \"60-80\", \"80-100\"], \n range: [\"#00FF00\", \"#C4E100\", \"#FFD700\", \"#F28C28\", \"#B22222\"],\n legend: true\n },\n fx: {\n domain: [\"Very Small\", \"Small\", \"Medium \", \"Large\", \"Unknown\"]\n },\n marks: [\n Plot.barY(sizeVsMissingData, {\n x: \"Group\",\n y: \"Percentage\",\n fill: \"Group\", // Use Percentage for color mapping\n fx: \"Harbor Size\",\n tip: true \n }),\n Plot.ruleY([0])\n ]\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":326,"value":"## Where Offshore Wind Could Benefit and Help","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":333,"value":"$4a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":9,"value":"
\n## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":282,"value":"data1 = workbook.sheet(0, {\n headers: false,\n // range: \"A1:J10\"\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":280,"value":"workbook = FileAttachment(\"Data Summary.xlsx\").xlsx()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"### Plot 1: Missing Data Interactive Globe ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":213,"value":"latLongAndRating1 = await FileAttachment(\"Lat, Long and Rating (1)@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"latLongAndRating1","database":"$undefined"},{"id":216,"value":"observableWeek4 = FileAttachment(\"Observable - Week 4.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":223,"value":"data = latLongAndRating1","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":228,"value":"land = topojson.feature(world, world.objects.land)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":230,"value":"world = FileAttachment(\"land-110m.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":232,"value":"ratingScale = d3.scaleSequential(d3.interpolateViridis)\n .domain([0, 91]); ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":234,"value":"globe = Plot.marks([\n Plot.graticule(),\n Plot.geo(land, { fill: \"none\", stroke: \"black\", strokeWidth: 1 }), // White fill with black outline\n Plot.sphere()\n])\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"### Plot 2: Percentage of Ports by Size","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":129,"value":"sizeVsMissingData = await FileAttachment(\"Size vs Missing data@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"sizeVsMissingData","database":"$undefined"},{"id":281,"value":"workbook.sheetNames","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":585,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4b","stdlib":"1"}} 24:null