Identify Seattle bus stops that are within 1 hour of the Food Bank on public transportation.
- From where in the city is the Food Bank accessible within 1 hour?
- Where in the city is accessible from the Food Bank within 1 hour?
Identify Seattle bus stops that are within 1 hour of the Food Bank on public transportation.
Find a tutorial that works for you: AWS Jupyter Notebook
Key Pair
chmod 0400
Launch in a way that works for you: Command Line or Console
Configure Your Cluster
Grab data from data portal: http://www5.kingcounty.gov/gisdataportal/
Explore in QGIS
Export busstop.shp as geojson: busstop-layer.geojson
Generate routes.txt:
jq -r '.features | .[] | .properties | .ROUTES | split(",") | .[]' busstop-layer.geojson | sort -u >routes.txt
64 65 35235 28th Ave NE and NE 125th St | |
41 795 35233 NE 125th St and 25th Ave NE | |
41 64 35230 NE 125th St and Lake City Way NE | |
41 65 35230 NE 125th St and Lake City Way NE | |
41 795 35230 NE 125th St and Lake City Way NE | |
64 65 35230 NE 125th St and Lake City Way NE | |
64 795 35230 NE 125th St and Lake City Way NE | |
65 795 35230 NE 125th St and Lake City Way NE | |
41 795 35231 NE 125th St and 28th Ave NE | |
41 994 35231 NE 125th St and 28th Ave NE |
In the developing world, physical access to health care can be the number one factor in the utilization of services, and consequently, the health of a population. At Broad Street Maps, we believe that where you live shouldn't determine if you live.
Anna and Isabel will give a brief overview of Broad Street Maps’ current work, introduce some exciting opportunities emerging in the open source world for global health, and reflect on what it is like for non-developers engaging with open-source technology and communities.
<!doctype html> | |
<html lang="en"> | |
<head> | |
</head> | |
<body> | |
<svg width="720" height="120"> | |
<circle cx="40" cy="60" r="10"></circle> |
import csv | |
# out_file is a file path and out is an array of arrays of the data you want to write | |
def write_data_to_csv(out_file, out): | |
with open(out_file, "w") as f: | |
wr = csv.writer(f, delimiter='\t') | |
for row in out: | |
wr.writerow(row) |