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this gist helps parse an osm file to identify good running nodes in an area of interest
# python3
# this gist helps parse an osm file to identify good
# running nodes in an area of interest
import xml.etree.cElementTree as ET # to parse OSM file
import folium # to create map
from folium.plugins import MarkerCluster # to create map
import os # to view map
import webbrowser # to view map
Two pre-processing steps...
1. Go to and identify
Node ID for starting location (use Query features)
2. Find 4x4 mile box surrounding start location and
export OSM using Overpass API (see below for this)
# create static variables
start_location = '213774020'
start_lat = '40.8739800'
start_lon = '-73.3164560'
# define number of lat/lon degress roughly equivalent to 1 mile
one_degree = 69 #miles
one_mile = 1/one_degree #degrees
# find boundary of lat/lons from start location
upper_lat = float(start_lat) + (one_mile * 4)
lower_lat = float(start_lat) - (one_mile * 4)
upper_lon = float(start_lon) + (one_mile * 4)
lower_lon = float(start_lon) - (one_mile * 4)
# create map file variable
filename = 'osm-maps/northport-defined-small.osm'
# parse osm file
tree = ET.parse(filename)
root = tree.getroot()
# identify all geocoords that have meaningful tags
keywords = ['natural', 'waterway', 'bicycle',
'foot', 'checkpoint', 'mountain_pass',
'historic', 'path', 'hiking',
'unpaved', 'footway']
# create list for ways and nodes of interest
meaningful_ways = []
meaningful_nodes = []
# parse osm file to find nodes of interest
for way in root.findall('way'):
taglist = way.findall('tag')
for tag in taglist:
if tag.get('k') in keywords or tag.get('v') in keywords:
for node in way.findall('nd'):
# create list for geocoords of nodes of interest
node_coords = []
# create function to get geocoords from osm file
def get_node_geocoords():
''' parses osm file and returns lat lon coords
example input = '66566291'
example output = '[42.4436791,-71.0727793]' '''
for m_node in meaningful_nodes:
for node in root.findall(".//node[@id='"+m_node+"']"):
temp_node_coords = [node.get('lat'), node.get('lon')]
return node_coords
# get geocoords from osm file
node_coords = get_node_geocoords()
# create map and view
filepath = '/Users/thisisprobablyyou/hotspot_map.html'
m = folium.Map(location=[start_lat, start_lon], zoom_start=13) # create base map
marker_cluster = MarkerCluster().add_to(m) # add clustering feature
for point in range(0, len(node_coords)):
folium.Marker(node_coords[point]).add_to(marker_cluster) # add nodes to plot
folium.Marker([start_lat, start_lon], icon=folium.Icon(color='red')).add_to(marker_cluster) # to add marker of start location'file://' + filepath)
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