reference repo and clean few typo

ms-eda.qmd

@@ -11,31 +11,35 @@ Why are we doing it?
 
 > Premises and Premise Counts
 
-Currently we have FCC total count locations at census block level.
+Currently we have FCC total count of locations (BSL) at the census block level.
 
 # MS Building Foot print
 
 ## Overview
 
-MS used satellite data (from multiple campains/vintage) to get the footprint of buildings. 
+Microsoft (MS) used satellite data (from multiple campains/vintage) to get the footprint of buildings. 
 
 They are classifying pixels that are supposed to be part of a buildings (segmentation using a neural network) and then convert pixels to a shape. 
 
 It exists [worldwide](https://github.com/microsoft/GlobalMLBuildingFootprints) and for [US states](https://github.com/Microsoft/USBuildingFootprints?tab=readme-ov-file). 
 
 - We have processed the 51 states 
 
-- Additional works will be required for PR and US territories (parts of workflow from the US states can be reused).
+- Additional works will be required for Puero Rico and the US territories (parts of workflow from the US states can be reused).
 
-The precision of the model vary depending on the region: the Carribean is at 92,2% and the US at 98.5%. The rate of false positive is 1% for the US and 1.8% for the Carribean. (Oceania was not provided)
+The precision of their model vary depending on the region: the Carribean is at 92,2% and the US at 98.5%. The rate of false positive is 1% for the US and 1.8% for the Carribean. (Oceania was not provided)
 
 The license is [ODbL](https://github.com/Microsoft/USBuildingFootprints?tab=License-1-ov-file).
 
 ## Buildings to BSL?
 
 Buildings are shapes, BSL are points. We converted the buildings to single point (arbitrary: the first vertex of the shape) to lower the amout of data. Hence, now we have "buildings" summarized to points (lat/long).
 
-We do not have access to lat/long of BSL (fabrics). Our assumptions is if a count at block match they are describing the same reality (we can't do the "on the ground verification").
+:::{.column-margin}
+Our pipeline can be find [here](https://github.com/ruralinnovation/data-MS-buildings) (private repo)
+:::
+
+We do not have access to lat/long of BSL (this is only part of the fabric data). Our assumptions is if a count at block match they are describing the same reality (we can't do the "on the ground verification").
 
 The number of buildings reported for 51 states is: 130 099 920
 
@@ -53,11 +57,11 @@ While is the number of BSL is: 114 074 438
 
 **What could be the use cases?**
 
-Integrating those informations will have cost:  information "overload", documentation about is needed, and depending 
+Integrating those informations will have cost:  information "overload", documentation about it is needed
 
 We can: 
 
-- Provide the count per census block of MS building footprint and the user  
+- Provide the count per census block of MS building footprint and let the user decide on the quality of the data around their specific geography 
 
 - Add the dot to the map
 
@@ -69,7 +73,7 @@ How this data helps BEAD applicant?
 
 ## MS building footprint in VT
 
-We can count those points per block and compare to the number of location than FCC is descriving.
+We can count those points per block and compare to the number of location than FCC is describing.
 
 After that we build a small model that will provide either an estimate of BSL given MS footprint and how confidant the model is. 
 
@@ -116,10 +120,8 @@ lines(pot_val, conf_interval[, "upr"], col =" blue", lty = 2)
 
 :::
 
-- my model (just a linear one) is probably bad (log should correct that)
-
-- still strong relation
+- Strong relation
 
-- the model is "overconfidant", and the reality is more "spread"
+- the model is "overconfidant", and the reality is more "spread" (what could possibly be the cause of it?)
 
 - VT MS has also **more** locations (285333, versus 352618)