Update blog (#331)

* Update blog

* Update dates

* Update social

blog/2025-01-21-amico/index.mdx → blog/2025-01-20-amico/index.mdx

@@ -2,11 +2,10 @@
 slug: fire-ratings
 title: "Calculating Fire Ratings with Overture Building and Places"
 authors: [amico, plinio]
-tags: [introduction]
-unlisted: true
-image: https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/social_jennings.png
+tags: [h3, places, buffer]
+image: https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/social_amico.png
 hide_table_of_contents: false
-keywords: [introduction]
+keywords: [h3, places, buffer]
 ---
 
 As communities continue to rebuild and recover from the devastation caused by natural disasters such as wildfires, the question remains: How can we quantify what was lost, especially in the built environment? With the ability to analyze detailed building footprints and overlay fire boundaries, we can begin to answer this by providing a rough estimate of the damage and identifying which structures were impacted by the flames.
@@ -58,7 +57,7 @@ First, we create a buffer around the fire perimeter to define the area of intere
 - Outside Perimeter: The building/point is within 10km of a historic perimeter
 
 
-import Image1 from '/blog/2025-01-21-amico/fire_buffers.png';
+import Image1 from '/blog/2025-01-20-amico/fire_buffers.png';
 
 <div style={{textAlign: 'center'}}>
 <img src={Image1} alt="File" style={{}} />
@@ -75,7 +74,7 @@ Next, we load the Overture Buildings dataset and spatially join it with the fire
 4. Spatially join buildings within the buffer zones to categorize them by proximity to the fire
 
 
-import Image2 from '/blog/2025-01-21-amico/fire_buildings.png';
+import Image2 from '/blog/2025-01-20-amico/fire_buildings.png';
 
 <div style={{textAlign: 'center'}}>
 <img src={Image2} alt="File" style={{}} />
@@ -94,7 +93,7 @@ Finally, we perform a spatial aggregation by calculating the H3 index for the ce
 5. Roll-up categories by H3, create categories primary set
 
 
-import Image3 from '/blog/2025-01-21-amico/fire_h3_broad.png';
+import Image3 from '/blog/2025-01-20-amico/fire_h3_broad.png';
 
 <div style={{textAlign: 'center'}}>
 <img src={Image3} alt="File" style={{}} />

blog/2025-01-14-kyle/index.mdx → blog/2025-01-23-kyle/index.mdx

@@ -2,14 +2,14 @@
 slug: how-pilot-fiber-creates-internal-tools-to-support-telecom-operations
 title: "How Pilot Fiber creates internal tools to support telecom operations"
 authors: [kyle,nelina]
-tags: [introduction]
+tags: [postgres,frontend,csv,apps]
 unlisted: true
 image: https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/pittman_social.png
 hide_table_of_contents: false
-keywords: [introduction]
+keywords: [postgres,frontend,csv,apps]
 ---
 
-[Pilot Fiber](https://www.pilotfiber.com/) is a commercial [Internet Service Provider](https://en.wikipedia.org/wiki/Internet_service_provider) primarily serving customers in New York City. Our primary value proposition in competing with national-scale ISPs is our commitment to customer service– we prioritize when incidents interrupt your service and will immediately jump into action to address the cause.
+[Pilot Fiber](https://www.pilotfiber.com/) is a commercial [Internet Service Provider](https://en.wikipedia.org/wiki/Internet_service_provider) primarily serving customers in New York City. Our primary value proposition in competing with national-scale ISPs is our commitment to customer service– we prioritize when incidents interrupt service and immediately jump into action to address the cause.
 
 
 import ReactPlayer from 'react-player'
@@ -28,12 +28,14 @@ import ReactPlayer from 'react-player'
 Almost all fiber optic cables in Manhattan run through a shared manhole-and-duct system. As such, road construction or work by other providers in a manhole has the potential to damage the equipment of multiple providers. Because of this, Pilot uses an active fiber monitoring system across our network: devices located in our data centers are constantly shooting light down the fibers in our network and looking for any anomalies by comparing with a reference a “snapshot” created when that fiber was initially installed to a building.
 
 
+
 When an anomaly is registered, it immediately fires an alert giving a fiber route and distance to the potential problem (i.e. “There is a light loss on the fiber running to 1234 5th Ave at a distance of 2.351 kilometers from the data center.”). When this happens, our engineering and support teams analyze the data within minutes to determine the exact location of the issue and, if necessary, get crews headed to the site to begin repairs.
 
 
+
 ## The Process
 
-Historically, this process would require the attention of an Outside Plant engineer with access to specialized software and network knowledge, regardless of the time of day or day of the week. This process had known friction and a single point of failure. Today we use  Fused to make this information accessible to more support team members and automate the creation of the reports field crews need to address the issue when they arrive on site.
+Historically, this process would require the attention of an Outside Plant engineer with access to specialized software and network knowledge, regardless of the time of day or day of the week. This process had known friction and a single point of failure. Today we use Fused to make this information accessible to more support team members and automate the creation of the reports field crews need to address the issue when they arrive on site.
 
 
 Using Fused as a back-end glue layer, we built a web app that allows a less technical user to select a route and distance and map where the system says the fault occurred, view nearby network infrastructure, and automatically generate the necessary field reports based on that nearby infrastructure.
@@ -44,7 +46,7 @@ The workflow requires a series of calls to UDFs that act as intermediaries to a
 
 The basic process is seen below:
 
-import Image1 from '/blog/2025-01-14-kyle/kyle1.png';
+import Image1 from '/blog/2025-01-23-kyle/kyle1.png';
 
 <div style={{textAlign: 'center'}}>
 <img src={Image1} alt="File" style={{}} />
@@ -55,30 +57,24 @@ _Workflow diagram._
 
 When the user selects a route and a distance to process, two separate processes are initiated. One retrieves the selected route to load onto the Mapbox-based map within the app, while the second kicks off a processing chain. This chain is utilizing UDFs that both assist in isolating the location of the fault and relevant nearby infrastructure as well as adding elements to the map display to assist the user in visualizing what may be occurring.
 
-UDF - returning the route itself that will show up on the map.
-UDF  get fault point php
-UDF - putting the red dot on the map.
-UDF - generate the splice report.
 
-When the user selects a route and a distance to process, two separate processes are initiated. One retrieves the selected route to load onto the Mapbox-based map within the app, while the second kicks off a processing chain. This chain calls UDFs that assist in isolating the location of the fault and relevant nearby infrastructure as well as adding elements to the map display to assist the user in visualizing what may be occurring.
 
-
-import Image2 from '/blog/2025-01-14-kyle/kyle2.png';
+import Image2 from '/blog/2025-01-23-kyle/kyle2.png';
 
 <div style={{textAlign: 'center'}}>
 <img src={Image2} alt="File" style={{}} />
 </div>
 _UDF to find fault location._
 
 
-After calculating the likely fault location, the tool loads splice cases and fiber slack loops along that route that point to where problems are most likely to have occurred and capacity enabling faster repair of more significant damage, we next determine which splice cases are closest to the likely damage point and if any of those are within 150m of the automated distance calculation.
+After calculating the likely fault location, the tool loads splice cases along that route that point to where problems are most likely to have occurred, and shows slack loops to locate capacity. This enables faster repair of more significant damage. We next determine which splice cases are closest to the likely damage point and if any of those are within 150m of the automated distance calculation.
 
 
-Once we have determined the relevant splice cases, we use Fused to build a CSV that reproduces what a splice report export looks like from our primary fiber mapping software and are able to submit that CSV to further processing to generate the Excel file that ultimately goes to the field teams detailing for them what to expect when working with the case– what cables enter the case, what fibers on which cables are spliced to which fibers on other cables, and what building and/or customer circuits are being carried along which fibers. This gives them the confidence to quickly address issues while minimizing the risk of damaging other circuits, as well as giving them the information needed to monitor services in the case while they work.
+Once we have determined the relevant splice cases, we use Fused to build a CSV that reproduces what a splice report export looks like from our primary fiber mapping software and are able to submit that CSV to further processing to generate the Excel file that ultimately goes to the field teams, detailing for them what to expect when working with the case– what cables enter the case, what fibers on which cables are spliced to which fibers on other cables, and what building and/or customer circuits are being carried along which fibers. This gives them the confidence to quickly address issues while minimizing the risk of damaging other circuits, as well as giving them the information needed to monitor services in the case while they work.
 
 
 
-## The Impact Fused
+## The Impact of Fused
 
 The impact of Fused across this process is many-fold:
 - The ability to easily work with data across several systems.
@@ -90,6 +86,4 @@ The impact of Fused across this process is many-fold:
 
 ## Conclusion
 
-Providing non-technical users a way to have access to the data in Postgres enables anybody at the company to gather the information to get the splice team headed in the right direction as fast as possible with the latest information on what they're looking for and what they're looking at when they get there. All this person would need to input is the distance along the fiber and the affected building. By using these different UDFs and Postgress the tool will figure out the point and if relevant, it will generate the documentation that they need to give to the field crew. This improves the turnaround time of dispatching crew to the site.
-
-Traditionally, I could do a lot of this just with Python but there were no visual maps to work with in these scenarios. Fused works so easily with the map viewer and the calculations also give a lot of context to the person running it. So even our more technical personnel can visually explore the data to immediately gain situational awareness.
+Providing non-technical users a way to have access to the data in Postgres enables anybody at the company to gather the information to get the splice team headed in the right direction quickly with the accurate information to quickly facilitate a repair when they arrive. All this user would need to input is the distance along the fiber and the affected building. By using these UDFs and Postgres the tool calculates the fault point and generate documentation for the field crew. This improves the turnaround time of dispatching crews to the site and enables the fastest restoration of service possible.