FCC NBM CORI metadata and storage

DB fields Descriptions

We are storing a transformed FCC’s NBM version for differents geographyical units:

• Census blocks: sch_broadband.bb_map_bl_2024june_data
• Census tracts: sch_broadband.bb_map_tr_2024june_data
• Census counties: sch_broadband.bb_map_co_2024june_data
• Census places (Incomming)

Here we will describe fields used and value stored for each of them:

source("R/table_with_options.R")
metadata <- read.csv("data/metadata_fcc.csv")
table_with_options(metadata)

Tests on data:

• Eyeballed them

TODO:

• wrap into an R script/function (should return 0 row): check logic ideas

select 
sign(cnt_total_locations - cnt_bcat_locations) as is_positive_dif_tot_bcat, 
sign(cnt_bcat_locations - cnt_fiber_locations) as is_positive_dif_bcat_fiber,
sign(cnt_100_20 - cnt_100_100) as is_positive_dif_20_100,
sign(cnt_25_3 - cnt_100_20) as is_positive_dif_25_100,
sign(cnt_fiber_cable - cnt_fiber_locations) as is_positive_dif_fiber_cable
from sch_broadband.bb_map_tr_2023dec_data
where 
    sign(cnt_total_locations - cnt_bcat_locations) = -1 or 
    sign(cnt_bcat_locations - cnt_fiber_locations) = -1 or 
    sign(cnt_100_20 - cnt_100_100) = -1 or
    sign(cnt_25_3 - cnt_100_20) = -1;
    
    
select 
sign(cnt_total_locations - cnt_bcat_locations) as is_positive_dif_tot_bcat, 
sign(cnt_bcat_locations - cnt_fiber_locations) as is_positive_dif_bcat_fiber,
sign(cnt_100_20 - cnt_100_100) as is_positive_dif_20_100,
sign(cnt_25_3 - cnt_100_20) as is_positive_dif_25_100,
sign(cnt_fiber_cable - cnt_fiber_locations) as is_positive_dif_fiber_cable
from sch_broadband.bb_map_co_2023dec_data
where 
    sign(cnt_total_locations - cnt_bcat_locations) = -1 or 
    sign(cnt_bcat_locations - cnt_fiber_locations) = -1 or 
    sign(cnt_100_20 - cnt_100_100) = -1 or
    sign(cnt_25_3 - cnt_100_20) = -1;

A quick check on 3 counties (also should be wrapped in R)

select * from sch_broadband.bb_map_co_2023dec_data
where geoid_co = any(array['06023', '15009', '41045']) ;

select 
    min(state_abbr) as state_abbr,
    min(geoid_st) as geoid_st,
    min(geoid_co) as geoid_co,
    --
    sum(cnt_total_locations) as cnt_bcat_locations,
    sum(cnt_bcat_locations) as cnt_total_locations,
    sum(cnt_fiber_locations) as cnt_fiber_locations,
    sum(cnt_100_100) as cnt_100_100,
    sum(cnt_100_20) as cnt_100_20,
    sum(cnt_25_3) as cnt_25_3,
    sum(cnt_gig_other) as  cnt_gig_other,
    sum(cnt_gig_fiber) as  cnt_gig_fiber,
    sum(cnt_fiber_cable) as cnt_fiber_cable,
    sum(cnt_dsl)

from 
    sch_broadband.bb_map_tr_2023dec_data
where geoid_co = any(array['06023', '15009', '41045'])
group by geoid_co
 ;

 select 
    min(state_abbr) as state_abbr,
    min(geoid_st) as geoid_st,
    min(geoid_co) as geoid_co,
    --
    sum(cnt_total_locations) as cnt_bcat_locations,
    sum(cnt_bcat_locations) as cnt_total_locations,
    sum(cnt_fiber_locations) as cnt_fiber_locations,
    sum(cnt_100_100) as cnt_100_100,
    sum(cnt_100_20) as cnt_100_20,
    sum(cnt_25_3) as cnt_25_3,
    sum(cnt_gig_other) as  cnt_gig_other,
    sum(cnt_gig_fiber) as  cnt_gig_fiber,
    sum(cnt_fiber_cable) as cnt_fiber_cable,
    sum(cnt_dsl)

from 
    sch_broadband.bb_map_bl_2023dec_data
where geoid_co = any(array['06023', '15009', '41045'])
group by geoid_co
 ;

S3 Archive:

We stored the raw data of FCC NBM we downloaded in a s3 bucket.

This is how the structure of the bucket look likes:

.
├── D22
│   ├── 01july2023
│   ├── 09august2023
│   ├── 10october2023
│   └── old-api
├── D23
│   └── 14may2024
├── J22
│   ├── 03november2023
│   ├── 10may2024
│   └── old-api
└── J23
    └── 14november2023

In each directory we get a zip file per csv (technology/state).