Category: Geek Hiking

Ground Truth: Information known to be true by direct observation, rather than by inference. In GIS (Geographical Information Systems), ground truth refers to verifying or correcting data on a map database by going out and measuring.

Truthiness: Assertion that a statement is true based on opinion, without regard to evidence. (Coined by Stephen Colbert on the Colbert Report)

The 1.0 version of the Gila X Loop is not workable, and no one should attempt to hike this route in its present form. One key connector trail between Glenwood and Gila Hot Springs, Holt Apache Trail #181, is in questionable condition after a fire, likely very brushy with thorny locust. On my route #181 connects to Turkeyfeather Mountain Trail #102, which is completely removed from the USFS Gila Interactive Map, although it still appears on plenty of printed maps, CalTopo, GaiaGPS, and OpenStreetMap. Likely there are other trails on my route that are actually non-trails, permanently abandoned by the USFS due to serious damage after fire and subsequent erosion.

I will need to re-run my algorithm, after removing trails that are not on the Gila Interactive Map and no longer recognized by the USFS. Look for v2.0 of the Gila X Loop sometime in 2025-2026, along with an attempted thru-hike for even more Ground Truth.

On another matter, I encountered difficulty hiking on the Deloche/Winn Canyon Trail #179. My algorithm attempted to exclude “deloche and winn canyon” (case insensitive), but openstreetmap.org and most sources used the name “deloche/wynn canyon”. Computer programs typically look for an exact match when comparing strings. Oops.

Lessons Learned:

• Programmers get caught up in the excitement of creating an algorithm, and do not allocate sufficient energy in vetting the input data. GIGO: garbage in, garbage out.
• Geographical names may have slight variations. Do not use a string “equals” matching function for cases where a string “similar” function should be used.

Related Posts:

• Trails and Graph Theory: The Series

In a previous article we sketched out how to create a databook for our long hike, a text summary of the route, which can include waypoints, elevation, trail names, and intersections. Our first result was somewhat crude, so we are circling back to make a better effort.

In a recent post we described how to break apart trail sections that had been concatenated together as part of our algorithm to find a maximum length route. We also found a different way to add elevation to our network, using built-in OSMNx functions. We have so many nodes in our desimplified network graph that it is better to take steps to only add elevation to important nodes.

def add_elevation(Q):
    H = nx.MultiGraph()
    H.graph['crs'] = ox.settings.default_crs

    for node, dat in Q.nodes(data=True):
        if 'street_count' in dat:
            street_count = dat['street_count']
            if street_count > 2:
                H.add_node(node,**dat)
    ox.settings.elevation_url_template = \
        'https://api.open-elevation.com/api/v1/lookup?locations={locations}'
    ox.elevation.add_node_elevations_google(H, api_key=None,
        batch_size=350,
        pause=1.0,
            )
    print('Added elevation data for ',H.number_of_nodes(), ' nodes')
    for node,dat in H.nodes(data=True):
        Q.add_node(node,**dat)
    return Q

Another improvement we should make is to document any road crossing or trail crossing on the route. Along with our trail graph G, we use the graph of all trails and roads GTR. Whenever a G node has a ‘street_count’ attribute greater than 2, we expect a crossing, and go back to GTR to attempt to find the name of the road or trail that our route crosses.

def report_path(Q,Q_orig, txt='',freedom_units=False,lat_long=False):
    meters_to_mile = 0.0006213712
    meters_to_feet = 3.28084
    meters_to_km = 0.001
    
    if nx.is_eulerian(Q):
        total_length = 0
        data = []
        
        for u,v,k in nx.eulerian_circuit(Q, keys=True): #fix: use predefined circuit, not arbitrary
             data_line = []
             if freedom_units:
                 data_line.append(str(round(total_length*meters_to_mile,1)))
             else:
                 data_line.append(str(round(total_length*meters_to_km,1)))
                 
             d = Q.get_edge_data(u,v)[k]
            # print(d)
             length=0                 
             if 'length' in d:
                 length = d['length'] #meters
                 total_length += length
             node_attr = Q.nodes(data=True)[v]
            # print( node_attr )
            
             street_count = node_attr['street_count']
             if street_count == 2:
                 continue
             
             long = node_attr['x']
             lat = node_attr['y']
             if lat_long:
                 data_line.append(lat)
                 data_line_append(long)
             #elevation = get_elevation(lat,long) #in meters
             if 'elevation' in node_attr:
                 elevation = node_attr['elevation']
                 if freedom_units:
                     data_line.append(str(round(elevation*meters_to_feet)))
                 else:
                     data_line.append(str(round(elevation)))
             else:
                 data_line.append(' ')

             trail_name=''
             if 'name' in d:
                 trail_name = d['name']
                 if not isinstance(trail_name, str):
                     trail_name = ','.join(trail_name) #in case is a list
             
             data_line.append(trail_name)                     
             
             crossing = set()
             crossing_txt = ''
             for x,neighbor,k,d in Q_orig.edges(v,data=True, keys=True):
                 if 'name' in d:
                     name = d['name']
                     if not isinstance(name, str): #FIX with a foreach
                         name = ','.join(name) #in case is a list
                     if name != trail_name:
                         crossing.add(name)
                 elif 'highway' in d:
                     name = d['highway']
                     name = '<' + name + '>'
                     crossing.add(name)
             if len(crossing)>0:
                 crossing_txt = ','.join(crossing)
             data_line.append(crossing_txt)
                              
             data.append(data_line)                 
        header = []
        if freedom_units:
            header.append('MILE')
        else:
            header.append('KM')
        if lat_long:
            header.append('LAT')
            header.append('LONG')
        header.append('ELEVATION')
        header.append('TRAIL')
        header.append('INTERSECTION')
        table = columnar(data, header, no_borders=True,terminal_width=132)
        print(table)     
    else:
        print('The graph is not eulerian')

Ideally, it might be useful to automatically add nearby water sources. We did document a way to find water sources, but inserting them into our databook looks challenging, so perhaps we will add that feature at a later date. Algorithmically adding other features, such as post offices, stores, or established campsites, are left as an exercise for the reader. 😁

A short example excerpt from the databook shows the improvements:

  MILE   ELEVATION  TRAIL                                    INTERSECTION                                                      
    
  234.6  8373       West Fork Trail #151                     Iron Creek Trail #172                                             
  234.8  8376       West Fork Trail #151                     Iron Creek Mesa Trail #171                                        
  237.4  7831       West Fork Trail #151                     Cooper Trail #141                                                 
  240.3  7841       Cooper Trail #141                        Clayton Mesa Trail #175                                           
  242.6  7106       Clayton Mesa Trail #175                  Middle Fork Trail #157                                            
  242.7  7090       Middle Fork Trail #157                   Iron Creek Mesa Trail #171                                        
  246.0  7326       Middle Fork Trail #157                   Snow Canyon Trail #142                                            
  246.6  7467       Snow Canyon Trail #142                   <path>                                                            
  246.8  7356       Snow Canyon Trail #142                   <path>                                                            
  246.9  7359       Snow Canyon Trail #142                   <service>  

Download source code and improved databook here.

Related Posts:

• Trails and Graph Theory: The Series