🚧 Work in progress!
Before committing code, please run yarn precommit
to format your code and run the tests. Only commit your code when it's formatted and the tests pass. You can add it as a git precommit hook if you like.
https://api.nycmesh.net/v1/nodes
https://api.nycmesh.net/v1/links
https://api.nycmesh.net/v1/buildings
https://api.nycmesh.net/v1/members
https://api.nycmesh.net/v1/requests
https://api.nycmesh.net/v1/search
https://api.nycmesh.net/v1/los
https://api.nycmesh.net/v1/kml
- Netlify Functions for hosting
- Express for handling requests
- PostgreSQL for main db
- PostGIS for line of sight db
- DigitalOcean Spaces (S3) for storing panorama images
- Auth0 for access control
Clone the repo: git clone [email protected]:olivernyc/nycmesh-api.git
Install dependencies: yarn install
Run the local server: yarn start
You'll need a .env
file with the following values:
DATABASE_URL=postgres://$user:$pass@$host:$port/$db
LOS_DATABASE_URL=postgres://$user:$pass@$host:$port/$db
S3_BUCKET=
S3_ENDPOINT=
S3_ID=
S3_KEY=
JWKS_URI=
JWT_AUDIENCE=
JWT_ISSUER=
SLACK_TOKEN=
SLACK_INSTALL_CHANNEL=
SLACK_PANO_CHANNEL=
SLACK_REQUEST_CHANNEL=
OSTICKET_API_KEY=
ACUITY_USER_ID=
ACUITY_API_KEY=
Currently, we use node numbers to represent join requests, members, and nodes. This schema is an attempt to detangle our data and create a common definition of the various components of the mesh.
A physical location.
id | address | lat | lng | alt | bin | notes |
---|
A person in the mesh community. For example, a node-owner, donor or installer.
id | name | phone |
---|
A specific location on the network. Typically one per building.
id | lat | lng | alt | status | name | location |
---|
- id
- lat
- lng
- alt
- status (active, dead)
- name (optional) // e.g. "Saratoga", "SN1"
- location (optional) // Human readable location, e.g. "Roof", "Basement"
- notes (optional)
- create_date
- abandon_date (optional)
- building_id
- member_id
- id
- date
- roof_access
- member_id
- building_id
- id
- url
- date
- request_id
- id
- name
- manufacturer
- range
- width
A unit of hardware. Routers, radios, servers, etc.
- id
- status (in stock, active, dead)
- name (optional)
- ssid (optional)
- notes (optional)
- lat
- lng
- alt
- azimuth (direction in degrees, default 0)
- create_date
- abandon_date (optional)
- device_type_id
- node_id
A connection between two devices. For example, an ethernet cable or wireless connection.
- id
- status (active, dead)
- create_date
- device_a_id
- device_b_id
SELECT
COUNT(members.id) AS count,
members.name AS member_name
FROM
requests
RIGHT JOIN members ON requests.member_id = members.id
GROUP BY
members.id
ORDER BY
count DESC;
SELECT
SUBSTRING(buildings.address, 1, 64) AS building_address,
COUNT(DISTINCT requests.member_id) AS request_count,
COUNT(DISTINCT nodes.member_id) AS node_count,
JSON_AGG(DISTINCT nodes.id) AS node_ids,
JSON_AGG(DISTINCT members.email) AS request_emails
FROM
buildings
JOIN requests ON buildings.id = requests.building_id
JOIN members ON members.id = requests.member_id
JOIN nodes ON buildings.id = nodes.building_id
WHERE
nodes.status = 'active'
GROUP BY
buildings.id
HAVING
COUNT(DISTINCT requests.member_id) > COUNT(DISTINCT nodes.member_id)
ORDER BY
request_count DESC
SELECT
buildings.alt,
COUNT(DISTINCT requests.id) as request_count,
SUBSTRING(buildings.address, 1, 64) as building_address,
ARRAY_AGG(DISTINCT nodes.id) as node_ids,
ARRAY_AGG(DISTINCT panoramas.url) as pano_ids
FROM buildings
JOIN requests
ON buildings.id = requests.building_id
FULL JOIN nodes
ON buildings.id = nodes.building_id
JOIN panoramas
ON requests.id = panoramas.request_id
WHERE requests.roof_access IS TRUE
GROUP BY buildings.id
ORDER BY buildings.alt DESC;
SELECT
SUBSTRING(buildings.address, 1, 64) as building_address,
COUNT(buildings.id) as count
FROM requests
RIGHT JOIN buildings
ON requests.building_id = buildings.id
GROUP BY buildings.id
ORDER BY count DESC;
SELECT
buildings.alt as building_height,
-- COUNT(requests.id) as request_count,
COUNT(buildings.id) as node_count,
SUBSTRING (buildings.address, 1, 64) as building_address
FROM nodes
RIGHT JOIN buildings
ON nodes.building_id = buildings.id
RIGHT JOIN requests
ON nodes.building_id = requests.building_id
GROUP BY buildings.id
ORDER BY node_count DESC;
SELECT array_agg(id) FROM nodes WHERE nodes.building_id = \$1;
SELECT
buildings.alt as building_height,
COUNT(buildings.id) as node_count,
SUBSTRING (buildings.address, 1, 64) as building_address
FROM nodes
RIGHT JOIN buildings
ON nodes.building_id = buildings.id
GROUP BY buildings.id
ORDER BY node_count DESC;
SELECT
buildings.id,
COUNT(DISTINCT requests.id) as request_count,
COUNT(DISTINCT nodes.id) as node_count,
ARRAY_AGG(DISTINCT nodes.id) as node_ids,
SUBSTRING(buildings.address, 1, 64) as building_address
FROM buildings
JOIN requests
ON buildings.id = requests.building_id
JOIN nodes
ON buildings.id = nodes.building_id
GROUP BY buildings.id
ORDER BY request_count DESC;
SELECT
buildings.alt as building_height,
COUNT(nodes.id) as node_count,
SUBSTRING(buildings.address, 1, 64) as building_address
FROM nodes
RIGHT JOIN buildings
ON nodes.building_id = buildings.id
GROUP BY buildings.id
ORDER BY building_height DESC;
SELECT
buildings.id as building_id,
buildings.alt as building_height,
COUNT(nodes.id) as node_count,
array_agg(nodes.id) as node_ids,
SUBSTRING(buildings.address, 1, 64) as building_address
FROM buildings
LEFT JOIN nodes
ON buildings.id = nodes.building_id
GROUP BY buildings.id
-- HAVING COUNT(nodes.id) > 0 -- Toggle this line to hide/show nodeless buildings
ORDER BY building_height DESC;
SELECT
buildings.id as building_id,
buildings.alt as building_height,
COUNT(requests.id) as request_count,
array_agg(requests.id) as request_ids,
SUBSTRING(buildings.address, 1, 64) as building_address
FROM buildings
LEFT JOIN requests
ON buildings.id = requests.building_id
GROUP BY buildings.id
-- HAVING COUNT(nodes.id) > 0 -- Toggle this line to hide/show nodeless buildings
ORDER BY building_height DESC;
Install lxml:
pip3 install lxml
Set up the db:
node scripts/reset-los-db.js
Download the building data:
curl -o building_data.zip http://maps.nyc.gov/download/3dmodel/DA_WISE_GML.zip
unzip building_data.zip -d building_data
rm building_data.zip
Insert the data
{
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA1_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA2_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA3_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA4_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA5_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA6_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA7_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA8_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA9_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA10_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA11_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA12_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA13_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA14_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA15_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA16_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA17_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA18_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA19_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA20_3D_Buildings_Merged.gml buildings
python3 ./scripts/gml_to_pgsql.py ./building_data/DA_WISE_GMLs/DA21_3D_Buildings_Merged.gml buildings
} | psql $LOS_DATABASE_URL
Now we are ready to make queries!
Let's check for line of sight between Supernode 1 and Node 3.
Use NYC GeoSearch or NYC Building Information Search.
Supernode 1 BIN: 1001389
Node 3 BIN: 1006184
SELECT ST_AsText(ST_Centroid((SELECT geom FROM ny WHERE bldg_bin = '1001389'))) as a,
ST_AsText(ST_Centroid((SELECT geom FROM ny WHERE bldg_bin = '1006184'))) as b;
# a | b
# ------------------------------------------+------------------------------------------
# POINT(987642.232749068 203357.276907034) | POINT(983915.956115596 198271.837494287)
# (1 row)
SELECT ST_ZMax((SELECT geom FROM ny WHERE bldg_bin = '1001389')) as a,
ST_ZMax((SELECT geom FROM ny WHERE bldg_bin = '1006184')) as b;
# a | b
# ------------------+------------------
# 582.247499999998 | 120.199699999997
# (1 row)
SELECT a.bldg_bin
FROM ny AS a
WHERE ST_3DIntersects(a.geom, ST_SetSRID('LINESTRINGZ (983915 198271 582, 987642 203357 120)'::geometry, 2263));
# bldg_bin
# ----------
# (0 rows)
There are no intersections. We have line of sight!