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ags_grabber
Commit a167f3f3 authored by Larkin Heintzman's avatar Larkin Heintzman
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      /matlab_data/
      /matlab_data_10km/
      /matlab_data_hiker_10km/
      /matlab_data_locale/
      /matlab_data_va_20km/
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      <?xml version="1.0" encoding="UTF-8"?>
      <module type="PYTHON_MODULE" version="4">
      <component name="NewModuleRootManager">
      <content url="file://$MODULE_DIR$" />
      <orderEntry type="inheritedJdk" />
      <orderEntry type="sourceFolder" forTests="false" />
      </component>
      <component name="TestRunnerService">
      <option name="PROJECT_TEST_RUNNER" value="Unittests" />
      </component>
      </module>
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      <component name="InspectionProjectProfileManager">
      <settings>
      <option name="USE_PROJECT_PROFILE" value="false" />
      <version value="1.0" />
      </settings>
      </component>
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      <?xml version="1.0" encoding="UTF-8"?>
      <project version="4">
      <component name="JavaScriptSettings">
      <option name="languageLevel" value="ES6" />
      </component>
      <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (tiny_ags_env)" project-jdk-type="Python SDK" />
      </project>
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      <?xml version="1.0" encoding="UTF-8"?>
      <project version="4">
      <component name="ProjectModuleManager">
      <modules>
      <module fileurl="file://$PROJECT_DIR$/.idea/ags_grabber.iml" filepath="$PROJECT_DIR$/.idea/ags_grabber.iml" />
      </modules>
      </component>
      </project>
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      <?xml version="1.0" encoding="UTF-8"?>
      <project version="4">
      <component name="VcsDirectoryMappings">
      <mapping directory="$PROJECT_DIR$" vcs="Git" />
      </component>
      </project>
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      arcgis_terrain.py 0 → 100644
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      from arcgis.gis import GIS
      from arcgis.geocoding import geocode
      from arcgis.geometry import Geometry
      import matplotlib
      import matplotlib.pyplot as plt
      import matplotlib.animation as animation
      import numpy as np
      from math import gcd
      import math
      from pyproj import Transformer, Proj, transform
      from scipy.interpolate import griddata
      # This function computes the factor of the argument passed
      def factorization(n):
      factors = []
      def get_factor(n):
      x_fixed = 2
      cycle_size = 2
      x = 2
      factor = 1
      while factor == 1:
      for count in range(cycle_size):
      if factor > 1: break
      x = (x * x + 1) % n
      factor = gcd(x - x_fixed, n)
      cycle_size *= 2
      x_fixed = x
      return factor
      while n > 1:
      next = get_factor(n)
      factors.append(next)
      n //= next
      factors = np.sort(factors, axis=None)
      # return [np.prod(factors[:-1]), factors[-1]]
      return factors
      def point_rotation(origin, pt, ang):
      # returns the pt rotated about the origin by ang (ang in degrees)
      c = math.cos(math.radians(ang))
      s = math.sin(math.radians(ang))
      # translate to origin
      pt_temp = [pt[0] - origin[0], pt[1] - origin[1]]
      pt_spun = [ pt_temp[0]*c - pt_temp[1]*s, pt_temp[0]*s + pt_temp[1]*c ]
      # translate back to frame
      pt_spun = [pt_spun[0] + origin[0], pt_spun[1] + origin[1]]
      return pt_spun
      def meters2lat_lon(xs, ys):
      transformer = Transformer.from_crs(3857, 4326) # meters -> lat lon
      ll_pts = transformer.transform(xs, ys)
      return ll_pts
      def lat_lon2meters(lats, lons):
      transformer = Transformer.from_crs(4326, 3857) # lat lon -> meters
      xy_pts = transformer.transform(lats, lons)
      return xy_pts
      # def lat_lon2meters(lat_lon_pts):
      # # '''Converts given lat/lon in WGS84 Datum to XY in Spherical Mercator EPSG:900913'''
      # originShift = 2 * math.pi * 6378137 / 2.0
      # x_y_pts = []
      # for ll in lat_lon_pts:
      # lat = ll[0]
      # lon = ll[1]
      # mx = lon * originShift / 180.0
      # my = math.log(math.tan((90 + lat) * math.pi / 360.0)) / (math.pi / 180.0)
      # my = my * originShift / 180.0
      # x_y_pts.append([mx,my])
      # return x_y_pts
      def center_geolocation(geolocations):
      """
      Provide a relatively accurate center lat, lon returned as a list pair, given
      a list of list pairs.
      ex: in: geolocations = ((lat1,lon1), (lat2,lon2),)
      out: (center_lat, center_lon)
      """
      x = 0
      y = 0
      z = 0
      for lat, lon in geolocations:
      lat = float(lat)
      lon = float(lon)
      x += math.cos(math.radians(lat)) * math.cos(math.radians(lon))
      y += math.cos(math.radians(lat)) * math.sin(math.radians(lon))
      z += math.sin(math.radians(lat))
      x = float(x / len(geolocations))
      y = float(y / len(geolocations))
      z = float(z / len(geolocations))
      return math.degrees(math.atan2(z, math.sqrt(x * x + y * y))), math.degrees(math.atan2(y, x))
      def centroid_calc(points_meters):
      return np.mean(points_meters, axis = 1)
      def get_terrain_map(lat_lon = [0,0], sample_dist = 10, extent = 100, heading = 0, show_plot = False, verbosity = False):
      # gis = GIS("pro")
      # gis = GIS(url="http://virginiatech.maps.arcgis.com", client_id="rluxzSWjZS6TfeXs", username="hlarkin3_virginiatech", password="arcgisheintzman97#26640", verify_cert=False)
      gis = GIS(username="larkinheintzman",password="Meepp97#26640")
      if verbosity:
      print("Successfully logged in as: " + gis.properties.user.username)
      elv_map = gis.content.get('58a541efc59545e6b7137f961d7de883')
      elv_layer = elv_map.layers[0]
      if len(lat_lon) > 2:
      print("Error, too many lat long points provided")
      return -1
      xy = lat_lon2meters(lat_lon[0], lat_lon[1])
      cen_pt = list(xy)
      lhc_pt = [cen_pt[0] - extent/2, cen_pt[1] - extent/2] # lefthand corner point
      max_samples = 30 # api limitation!
      if extent > max_samples*sample_dist:
      # multiple calls are required
      fac = factorization(int(extent/sample_dist))
      if len(fac) == 1: # random prime number incomming
      sc = fac[0]
      cc = 1
      else:
      sc = np.max(fac[fac <= max_samples])
      fac = np.delete(fac,np.argmax(fac[fac <= max_samples]))
      cc = np.prod(fac)
      else:
      # single call suffices
      cc = 1
      sc = int(np.round(extent/sample_dist))
      sample_extents = sc * sample_dist
      if verbosity:
      print("total calls: {}".format(np.square(cc)))
      # set max values
      elv_layer.extent['xmin'] = lhc_pt[0]
      elv_layer.extent['xmax'] = lhc_pt[0] + extent
      elv_layer.extent['ymin'] = lhc_pt[1]
      elv_layer.extent['ymax'] = lhc_pt[1] + extent
      # print(elv_layer.extent)
      # house keeping, initialize with empty lists
      x = np.empty([0,sc*cc])
      y = np.empty([0,sc*cc])
      e = np.empty([0,sc*cc])
      data = []
      for j in range(cc): # outter loop for y values
      # lists of values for a single row, reset at every y iteration
      x_row = np.empty([sc,0])
      y_row = np.empty([sc,0])
      e_row = np.empty([sc,0])
      for i in range(cc): # inner loop for x values O_O
      x_values = [np.linspace(lhc_pt[0] + i * sample_extents, lhc_pt[0] + (i + 1) * sample_extents, sc)]
      y_values = [np.linspace(lhc_pt[1] + j * sample_extents, lhc_pt[1] + (j + 1) * sample_extents, sc)]
      [X,Y] = np.meshgrid(x_values, y_values)
      # put in rotation here
      geo_points = [point_rotation(origin = [cen_pt[0],cen_pt[1]],pt = [xp,yp],ang = heading) for xv,yv in zip(X,Y) for xp,yp in zip(xv,yv)]
      # print(points)
      g = Geometry({"points": geo_points, "spatialReference": 3857})
      failed = True
      while failed: # keep trying until it works
      try:
      elv_samples = elv_layer.get_samples(g, sample_count=len(g.coordinates()), out_fields='location,values,value,resolution')
      failed = False
      except TimeoutError:
      print("failed call, trying again...")
      failed = True
      # extract location info JANK INCOMING
      xs = np.array([e['location']['x'] for e in elv_samples], dtype=float).reshape([sc,sc])
      ys = np.array([e['location']['y'] for e in elv_samples], dtype=float).reshape([sc,sc])
      es = np.array([e['values'][0] for e in elv_samples], dtype=float).reshape([sc,sc])
      if not np.all(xs == X) or not np.all(ys == Y):
      # xs = np.array([e['location']['x'] for e in elv_samples], dtype=float)
      # ys = np.array([e['location']['y'] for e in elv_samples], dtype=float)
      qs = np.stack([xs.reshape(len(elv_samples)), ys.reshape(len(elv_samples))]).T
      es = es.reshape(len(elv_samples))
      # data came back in weird ordering, need to re-order
      # print("re-ordering data ...")
      es_square = np.zeros([sc,sc])
      for scx in range(sc):
      for scy in range(sc): # maybe the least efficient way to do this
      idx = np.where(np.all(qs == np.asarray([X[scx,scy],Y[scx,scy]]),axis = 1))
      es_square[scx,scy] = es[idx]
      es = es_square
      # print("done re-ordering")
      # then just the tuple of all
      data_temp = []
      for s in elv_samples:
      xy_spun = point_rotation(origin = [cen_pt[0],cen_pt[1]],pt = [s['location']['x'],s['location']['y']],ang = -heading) # spin back to original frame
      data_temp.append((xy_spun[0], xy_spun[1],s['values'][0]))
      # data_temp = [(xy_spun[0], xy_spun[1],e['values'][0]) for e in elv_samples]
      # data_temp = [(e['location']['x'],e['location']['y'],e['values'][0]) for e in elv_samples]
      data = data + data_temp
      # append to larger arrays
      x_row = np.append(x_row, xs, axis=1)
      y_row = np.append(y_row, ys, axis=1)
      e_row = np.append(e_row, es, axis=1)
      x = np.append(x, x_row, axis=0)
      y = np.append(y, y_row, axis=0)
      e = np.append(e, e_row, axis=0)
      if show_plot:
      # Attaching 3D axis to the figure
      grid_x, grid_y = np.mgrid[min(x):max(x):100j, min(y):max(y):100j]
      points = np.array([x, y])
      grid_z = griddata(points.transpose(), z, (grid_x, grid_y), method='cubic', fill_value=np.mean(z))
      print(grid_z)
      fig = plt.figure()
      ax = fig.gca(projection='3d')
      ax.plot_surface(grid_x, grid_y, grid_z, cmap='viridis')
      if verbosity:
      print("x min: {}".format(np.min(x)))
      print("x max: {}".format(np.max(x)))
      print("x max - min: {}".format(np.max(x)-np.min(x)))
      print("x spacing: {}".format(abs(x[0][0] - x[0][1])))
      print("y min: {}".format(np.min(y)))
      print("y max: {}".format(np.max(y)))
      print("y max - min: {}".format(np.max(y)-np.min(y)))
      return [e,x,y,data,lat_lon]
      # savedimg = elv_layer.export_image(bbox=elv_layer.extent, size=[3840,2160], f='image', save_folder='.', save_file='testerino.jpg')
      if __name__ == "__main__":
      # lat_lon = [[[37.206026, -80.636010], [37.266475, -80.639015], [37.283674, -80.589005],
      # [37.224313, -80.585250], [37.205225, -80.604815], [37.206026, -80.636010]]]
      # lat_lon = [[[37.235195, -80.410403], [37.226427, -80.422368], [37.227209, -80.404514], [37.227209, -80.404504]]]
      lat_lon = [[[37.196791, -80.578343], [37.196874, -80.578430], [37.196974, -80.578518], [37.197229, -80.578093], [37.197482, -80.577689], [37.197401, -80.577610], [37.197319, -80.577528], [37.197066, -80.577928]]]
      # lat_lon should be a list of length 1, containing a set of 2-length lists
      # then the terrain map will be placed at the centroid of the lat-lon-points
      total_extent = 12000
      mesh_size = 30
      zoom_level = 4
      [z_array,x_array,y_array, data, cen_point_ll] = get_terrain_map(lat_lon, sample_dist = zoom_level*int(total_extent/mesh_size), extent = zoom_level*total_extent, heading=40, show_plot=False, verbosity=True)
      fig = plt.figure()
      ax = fig.gca(projection='3d')
      # plot results
      x, y, z = zip(*data)
      print(data)
      grid_x, grid_y = np.mgrid[min(x):max(x):100j, min(y):max(y):100j]
      points = np.array([x, y])
      grid_z = griddata(points.transpose(), z, (grid_x, grid_y), method='cubic', fill_value=np.mean(z))
      # print(grid_z)
      ax.plot_surface(grid_x, grid_y, grid_z, cmap='viridis')
      plt.show()
      feature_grabber_test.py 0 → 100644
      View file @ a167f3f3
      from arcgis_terrain import get_terrain_map
      from arcgis.features import FeatureLayer
      from arcgis.gis import GIS
      from arcgis_terrain import lat_lon2meters
      from arcgis_terrain import meters2lat_lon
      import time
      from arcgis.geometry.filters import envelope_intersects
      import arcgis.geometry
      import numpy as np
      import plotly.graph_objects as go
      import plotly.express as px
      from arcgis_terrain import point_rotation
      from scipy import interpolate
      from matplotlib import path
      import matplotlib.pyplot as plt
      import math
      import json
      def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_files = False, plot_data = False):
      roads_url = "https://carto.nationalmap.gov/arcgis/rest/services/transportation/MapServer/30"
      river_url = "https://hydro.nationalmap.gov/arcgis/rest/services/nhd/MapServer/6"
      riverw_url = "https://hydro.nationalmap.gov/arcgis/rest/services/nhd/MapServer/8"
      water_url = "https://hydro.nationalmap.gov/arcgis/rest/services/nhd/MapServer/9"
      powerlines_url = "https://services1.arcgis.com/Hp6G80Pky0om7QvQ/ArcGIS/rest/services/Electric_Power_Transmission_Lines/FeatureServer/0"
      railroads_url = "https://carto.nationalmap.gov/arcgis/rest/services/transportation/MapServer/35"
      # adding water_url twice, once for boundaries and once for linear features
      # the layer named 'lakes' gets boundary treatment
      url_list = [river_url, riverw_url, roads_url, water_url, powerlines_url, railroads_url]
      name_list = ['rivers', 'rivers_bdd', 'roads', 'lakes', 'powerlines', 'railroads']
      inac_layers = ['rivers_bdd', 'lakes']
      gis = GIS(username="larkinheintzman",password="Meepp97#26640") # linked my arcgis pro account
      ap_meters = lat_lon2meters(anchor_point[0], anchor_point[1])
      file_id = str(anchor_point) + "_" + str(extent/1000) + "km"
      scale_factor = 3/20 # factor to get 6.66667m mapping from 1m mapping (1/6.6667)
      scaled_extent = np.ceil(scale_factor*extent).astype(np.int)
      viz_cnt = 0
      viz_map = np.zeros([scaled_extent,scaled_extent,len(name_list)+len(inac_layers)])
      for i,url in enumerate(url_list):
      # binary map, will use feature coords to populate (one per layer)
      bin_map = np.zeros([scaled_extent,scaled_extent])
      inac_bin_map = np.zeros([scaled_extent,scaled_extent])
      geom = arcgis.geometry.Polygon({'spatialReference': {"wkid" : 3857},
      'rings': [[
      [ap_meters[0] - (extent/2), ap_meters[1] - (extent/2)],
      [ap_meters[0] - (extent/2), ap_meters[1] + (extent/2)],
      [ap_meters[0] + (extent/2), ap_meters[1] + (extent/2)],
      [ap_meters[0] + (extent/2), ap_meters[1] - (extent/2)],
      [ap_meters[0] - (extent/2), ap_meters[1] - (extent/2)]
      ]]})
      lyr = FeatureLayer(url = url, gis = gis)
      geom_filter = envelope_intersects(geom, sr=geom['spatialReference'])
      q = []
      query_cnt = 0
      while type(q)==list and query_cnt <= 5: # have to do this because arcgis is sketchy as hell and doesnt always come back
      try:
      print("querying {} layer...".format(name_list[i]))
      q = lyr.query(return_count_only=False, return_ids_only=False, return_geometry=True,
      out_sr='3857', geometry_filter=geom_filter, result_record_count=20)
      except json.decoder.JSONDecodeError as e:
      query_cnt = query_cnt + 1
      print("error on query: {}".format(e))
      print("{} layer failed on query, trying again ...".format(name_list[i]))
      if query_cnt > 5 and not q:
      print("{} layer failed too many times, continuing".format(name_list[i]))
      # if save_files:
      # fn = "map_layers/"+name_list[i]+"_data_"+file_id+".csv"
      # np.savetxt(fn,bin_map,delimiter=",", fmt='%f')
      continue
      print("{} layer sucessfully queried".format(name_list[i]))
      # re-build into list of x-y values
      # feat_points = []
      query_dict = q.to_dict()
      for j,feat in enumerate(query_dict['features']):
      # pull feature points out of query, they have different keys...
      if 'paths' in feat['geometry'].keys():
      x_pts = [pt[0] for pt in feat['geometry']['paths'][0]]
      y_pts = [pt[1] for pt in feat['geometry']['paths'][0]]
      # plot_points = np.array(feat['geometry']['paths'][0])
      else:
      x_pts = [pt[0] for pt in feat['geometry']['rings'][0]] # arcgis is stupid
      y_pts = [pt[1] for pt in feat['geometry']['rings'][0]]
      # plot_points = np.array(feat['geometry']['rings'][0])
      # re-center on 0,0 at left corner
      x_pts = (np.array(x_pts) - (ap_meters[0] - (extent/2)))*scale_factor # reduces number of interpolants
      y_pts = (np.array(y_pts) - (ap_meters[1] - (extent/2)))*scale_factor
      # rotate points about origin to establish heading
      [x_pts, y_pts] = point_rotation(origin = [scaled_extent/2,scaled_extent/2],pt = [x_pts, y_pts],ang = heading)
      # x_uinterp = x_pts # untrimmed uninterpolated points (but scaled)
      # y_uinterp = y_pts
      # trim data to only within extents (because arcgis cant fuckin' do this)
      rm_mask = np.logical_or(x_pts < 0, x_pts > scaled_extent) # mask to remove points (x axis)
      rm_mask = np.logical_or(rm_mask,np.logical_or(y_pts < 0, y_pts > scaled_extent)) # other axis mask
      x_pts = x_pts[np.invert(rm_mask)]
      y_pts = y_pts[np.invert(rm_mask)] # trim interpolated points
      if x_pts.shape[0] > 1: # if there are still points after trimming
      # if data is too short, add some points in the middle
      while x_pts.shape[0] < 4:
      x_pt = (x_pts[0] + x_pts[1])/2 # average between first and second point
      y_pt = (y_pts[0] + y_pts[1])/2
      x_pts = np.insert(x_pts, 1, x_pt)
      y_pts = np.insert(y_pts, 1, y_pt)
      # total length of feature ring/path, for interpolation along features
      total_len = np.sum(np.sqrt(np.sum(np.diff(np.array([x_pts, y_pts]).T, axis=0) ** 2, axis=1)))
      tck, u = interpolate.splprep([x_pts, y_pts], s=0, k=1) # parametric interpolation
      u_new = np.arange(0, 1 + 1 / total_len, 1 / total_len) # scaled discretization
      pts_interp = interpolate.splev(u_new, tck)
      x_pts = pts_interp[0]
      y_pts = pts_interp[1]
      if name_list[i] in inac_layers:
      s_time = time.time()
      # do boundary calculation for binary matrix (slow for large bounaries but whatever)
      ring = path.Path(np.array([x_pts, y_pts]).T)
      # TODO: try cv2 here, test for speeeeeeeeeeeeeeeeeeeeeeed
      # test_pts is the rectangular matrix covering ring for boundary calculation
      x_test, y_test = np.meshgrid(np.arange(np.min(x_pts), np.max(x_pts), 1) , np.arange(np.min(y_pts), np.max(y_pts), 1))
      test_pts = np.array([x_test.flatten(), y_test.flatten()]).T
      mask = ring.contains_points(test_pts, radius=1)
      # instead of filling gaps, we want to save filled in areas separately
      # so we need to re-create the bin_map here but on inac. points
      x_pts_inac = test_pts[mask,0]
      y_pts_inac = test_pts[mask,1]
      pts_inac = np.stack([x_pts_inac,y_pts_inac]).T
      # remove points being used as linear features
      for pt in np.stack([x_pts,y_pts]).T:
      pts_inac = np.delete(pts_inac, np.where(np.equal(pt,pts_inac).all(1)), axis = 0)
      # binarization step
      pts_inac = np.round(pts_inac).astype(np.int)
      # flip y axis
      pts_inac[:,1] = inac_bin_map.shape[1] - pts_inac[:,1]
      # remove any points outside limits of binary map (fixes round versus ceil issues)
      rm_mask = np.logical_or(pts_inac[:,0] < 0, pts_inac[:,0] >= inac_bin_map.shape[1])
      rm_mask = np.logical_or(rm_mask, np.logical_or(pts_inac[:,1] < 0, pts_inac[:,1] >= inac_bin_map.shape[0]))
      pts_inac = pts_inac[np.invert(rm_mask),:]
      inac_bin_map[pts_inac[:,1], pts_inac[:,0]] = 1 # set indices to 1
      print("inac calculation time = {} sec".format(time.time() - s_time))
      # binarization step
      x_pts_idx = np.round(x_pts).astype(np.int)
      y_pts_idx = np.round(y_pts).astype(np.int)
      # flip y axis because indices are fliped
      y_pts_idx = bin_map.shape[1] - y_pts_idx
      # remove any points outside limits of binary map (fixes round versus ceil issues)
      rm_mask = np.logical_or(x_pts_idx < 0, x_pts_idx >= bin_map.shape[1])
      rm_mask = np.logical_or(rm_mask, np.logical_or(y_pts_idx < 0, y_pts_idx >= bin_map.shape[0]))
      x_pts_idx = x_pts_idx[np.invert(rm_mask)]
      y_pts_idx = y_pts_idx[np.invert(rm_mask)]
      bin_map[y_pts_idx, x_pts_idx] = 1 # set indices to 1
      # add to viz map
      if name_list[i] in inac_layers:
      viz_map[:, :, viz_cnt] = inac_bin_map
      viz_cnt = viz_cnt + 1
      if save_files:
      fn = "map_layers/" + name_list[i] + "_inac_data_" + file_id + ".csv"
      np.savetxt(fn, inac_bin_map, delimiter=",", fmt='%f')
      viz_map[:, :, viz_cnt] = bin_map
      viz_cnt = viz_cnt + 1
      if save_files:
      fn = "map_layers/"+name_list[i]+"_data_"+file_id+".csv"
      np.savetxt(fn,bin_map,delimiter=",", fmt='%f')
      # save terrain as csv file (this method is pretty slow, but can compensate with interp)
      [e,x,y,data,ll_pt] = get_terrain_map(lat_lon=anchor_point,
      sample_dist = sample_dist,
      extent = extent,
      heading = -heading) # because flipping
      # flip elevation data up to down to match other layers
      e = np.flipud(e)
      # interpolate terrain to match size/resolution of other layers
      f = interpolate.interp2d(np.arange(0, extent, sample_dist), np.arange(0, extent, sample_dist), e, kind='cubic')
      x_temp = np.linspace(0,extent,scaled_extent) # get correct size of terrain map
      y_temp = np.linspace(0,extent,scaled_extent)
      e_interp = f(x_temp, y_temp)
      elv_filename = "map_layers\\elv_data_"+file_id+".csv"
      if save_files:
      np.savetxt(elv_filename,e_interp,delimiter=",", fmt='%f')
      if plot_data:
      terr_fig = px.imshow(e_interp)
      terr_fig.show()
      plt_list = []
      # fix stupid names
      for nme in inac_layers:
      name_list.insert(name_list.index(nme), nme+' inac')
      for i in range(viz_map.shape[-1]):
      row_idx, col_idx = np.where(viz_map[:,:,i] != 0)
      # flip y values
      col_idx = viz_map.shape[0] - col_idx
      plt_list.append(go.Scatter(x=row_idx, y=col_idx, mode='markers', name=name_list[i]))
      fig = go.Figure(data=plt_list)
      fig.show()
      if __name__ == "__main__":
      # anchor_point = [37.474015, -80.868333] # bozoo
      # anchor_point = [37.304158, -86.206131] # nolin state park
      # anchor_point = [37.615842, -80.905957] # bluestone
      anchor_point = [37.192944, -80.556713] # kentland
      extent = 10000 # used for all layers
      sample_dist = int(extent/100) # only used for terrain (is interpolated anyway, just base sample res)
      # sample_dist = 50 # only used for terrain (is interpolated anyway, just base sample res)
      heading = 0
      start_time = time.time()
      grab_features(anchor_point = anchor_point, extent = extent, sample_dist = sample_dist,
      heading = heading, save_files = True, plot_data = True)
      print("------- %s seconds ------" % (time.time() - start_time))
      # ----------------------- various testing stuffs -----------------------
      # [e,x,y,data,ll_pt] = get_terrain_map(lat_lon=anchor_point,
      # sample_dist = sample_dist,
      # extent = extent,
      # heading = -heading) # because flipping
      # # flip elevation data up to down to match other layers
      # e = np.flipud(e)
      # scale_factor = 3/20 # factor to get 6.66667m mapping from 1m mapping (1/6.6667)
      # # interpolate terrain to match size/resolution of other layers
      # f = interpolate.interp2d(np.arange(0, extent, sample_dist), np.arange(0, extent, sample_dist), e, kind='cubic')
      # x_temp = np.linspace(0,extent,np.ceil(scale_factor*extent).astype(np.int)) # get correct size of terrain map
      # y_temp = np.linspace(0,extent,np.ceil(scale_factor*extent).astype(np.int))
      # e_interp = f(x_temp, y_temp)
      #
      # terr_fig = px.imshow(e_interp)
      # terr_fig.show()
      \ No newline at end of file
      feature_set.py 0 → 100644
      View file @ a167f3f3
      from arcgis_terrain import get_terrain_map
      from arcgis.features import FeatureLayer
      from arcgis.gis import GIS
      from arcgis_terrain import lat_lon2meters
      from arcgis_terrain import meters2lat_lon
      import time
      from arcgis.geometry.filters import envelope_intersects
      import arcgis.geometry
      import numpy as np
      import plotly.graph_objects as go
      import plotly.express as px
      from arcgis_terrain import point_rotation
      from scipy import interpolate
      from matplotlib import path
      import matplotlib.pyplot as plt
      import math
      import json
      import sys
      import csv
      import os
      import matlab.engine
      def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_files = False, file_id = 'temp', plot_data = False):
      roads_url = "https://carto.nationalmap.gov/arcgis/rest/services/transportation/MapServer/30"
      river_url = "https://hydro.nationalmap.gov/arcgis/rest/services/nhd/MapServer/6"
      riverw_url = "https://hydro.nationalmap.gov/arcgis/rest/services/nhd/MapServer/8"
      water_url = "https://hydro.nationalmap.gov/arcgis/rest/services/nhd/MapServer/9"
      powerlines_url = "https://services1.arcgis.com/Hp6G80Pky0om7QvQ/ArcGIS/rest/services/Electric_Power_Transmission_Lines/FeatureServer/0"
      railroads_url = "https://carto.nationalmap.gov/arcgis/rest/services/transportation/MapServer/35"
      # adding water_url twice, once for boundaries and once for linear features
      # the layer named 'lakes' gets boundary treatment
      url_list = [river_url, riverw_url, roads_url, water_url, powerlines_url, railroads_url]
      name_list = ['rivers', 'rivers_bdd', 'roads', 'lakes', 'powerlines', 'railroads']
      inac_layers = ['rivers_bdd', 'lakes']
      gis = GIS(username="larkinheintzman",password="Meepp97#26640") # linked my arcgis pro account
      ap_meters = lat_lon2meters(anchor_point[0], anchor_point[1])
      scale_factor = 3/20 # factor to get 6.66667m mapping from 1m mapping (1/6.6667)
      scaled_extent = np.ceil(scale_factor*extent).astype(np.int)
      viz_cnt = 0
      viz_map = np.zeros([scaled_extent,scaled_extent,len(name_list)+len(inac_layers)])
      for i,url in enumerate(url_list):
      # binary map, will use feature coords to populate (one per layer)
      bin_map = np.zeros([scaled_extent,scaled_extent])
      inac_bin_map = np.zeros([scaled_extent,scaled_extent])
      geom = arcgis.geometry.Polygon({'spatialReference': {"wkid" : 3857},
      'rings': [[
      [ap_meters[0] - (extent/2), ap_meters[1] - (extent/2)],
      [ap_meters[0] - (extent/2), ap_meters[1] + (extent/2)],
      [ap_meters[0] + (extent/2), ap_meters[1] + (extent/2)],
      [ap_meters[0] + (extent/2), ap_meters[1] - (extent/2)],
      [ap_meters[0] - (extent/2), ap_meters[1] - (extent/2)]
      ]]})
      lyr = FeatureLayer(url = url, gis = gis)
      geom_filter = envelope_intersects(geom, sr=geom['spatialReference'])
      q = []
      query_cnt = 0
      while type(q)==list and query_cnt <= 5: # have to do this because arcgis is sketchy as hell and doesnt always come back
      try:
      print("querying {} layer...".format(name_list[i]))
      q = lyr.query(return_count_only=False, return_ids_only=False, return_geometry=True,
      out_sr='3857', geometry_filter=geom_filter)
      except json.decoder.JSONDecodeError as e:
      query_cnt = query_cnt + 1
      print("error on query: {}".format(e))
      print("{} layer failed on query, trying again ...".format(name_list[i]))
      if query_cnt > 5 and not q:
      print("{} layer failed too many times, breaking!".format(name_list[i]))
      continue
      # if save_files:
      # fn = "map_layers/"+name_list[i]+"_data_"+file_id+".csv"
      # np.savetxt(fn,bin_map,delimiter=",", fmt='%f')
      print("{} layer sucessfully queried".format(name_list[i]))
      # re-build into list of x-y values
      # feat_points = []
      query_dict = q.to_dict()
      for j,feat in enumerate(query_dict['features']):
      # pull feature points out of query, they have different keys...
      if 'paths' in feat['geometry'].keys():
      x_pts = [pt[0] for pt in feat['geometry']['paths'][0]]
      y_pts = [pt[1] for pt in feat['geometry']['paths'][0]]
      # plot_points = np.array(feat['geometry']['paths'][0])
      else:
      x_pts = [pt[0] for pt in feat['geometry']['rings'][0]] # arcgis is stupid
      y_pts = [pt[1] for pt in feat['geometry']['rings'][0]]
      # plot_points = np.array(feat['geometry']['rings'][0])
      # re-center on 0,0 at center
      x_pts = (np.array(x_pts) - (ap_meters[0] - (extent/2)))*scale_factor # reduces number of interpolants
      y_pts = (np.array(y_pts) - (ap_meters[1] - (extent/2)))*scale_factor
      # rotate points about origin to establish heading
      [x_pts, y_pts] = point_rotation(origin = [scaled_extent/2,scaled_extent/2],pt = [x_pts, y_pts],ang = heading)
      # x_uinterp = x_pts # untrimmed uninterpolated points (but scaled)
      # y_uinterp = y_pts
      # trim data to only within extents (because arcgis cant fuckin' do this)
      rm_mask = np.logical_or(x_pts < 0, x_pts > scaled_extent) # mask to remove points (x axis)
      rm_mask = np.logical_or(rm_mask,np.logical_or(y_pts < 0, y_pts > scaled_extent)) # other axis mask
      x_pts = x_pts[np.invert(rm_mask)]
      y_pts = y_pts[np.invert(rm_mask)] # trim interpolated points
      if x_pts.shape[0] > 1: # if there are still points after trimming
      # if data is too short, add some points in the middle
      while x_pts.shape[0] < 4:
      x_pt = (x_pts[0] + x_pts[1])/2 # average between first and second point
      y_pt = (y_pts[0] + y_pts[1])/2
      x_pts = np.insert(x_pts, 1, x_pt)
      y_pts = np.insert(y_pts, 1, y_pt)
      # total length of feature ring/path, for interpolation along features
      total_len = np.sum(np.sqrt(np.sum(np.diff(np.array([x_pts, y_pts]).T, axis=0) ** 2, axis=1)))
      tck, u = interpolate.splprep([x_pts, y_pts], s=0, k=1) # parametric interpolation
      u_new = np.arange(0, 1 + 1 / total_len, 1 / total_len) # scaled discretization
      pts_interp = interpolate.splev(u_new, tck)
      x_pts = pts_interp[0]
      y_pts = pts_interp[1]
      if name_list[i] in inac_layers:
      s_time = time.time()
      # do boundary calculation for binary matrix (slow for large bounaries but whatever)
      ring = path.Path(np.array([x_pts, y_pts]).T)
      # TODO: try cv2 here, test for speeeeeeeeeeeeeeeeeeeeeeed
      # test_pts is the rectangular matrix covering ring for boundary calculation
      x_test, y_test = np.meshgrid(np.arange(np.min(x_pts), np.max(x_pts), 1) , np.arange(np.min(y_pts), np.max(y_pts), 1))
      test_pts = np.array([x_test.flatten(), y_test.flatten()]).T
      mask = ring.contains_points(test_pts, radius=1)
      # instead of filling gaps, we want to save filled in areas separately
      # so we need to re-create the bin_map here but on inac. points
      x_pts_inac = test_pts[mask,0]
      y_pts_inac = test_pts[mask,1]
      pts_inac = np.stack([x_pts_inac,y_pts_inac]).T
      # remove points being used as linear features
      for pt in np.stack([x_pts,y_pts]).T:
      pts_inac = np.delete(pts_inac, np.where(np.equal(pt,pts_inac).all(1)), axis = 0)
      # binarization step
      pts_inac = np.round(pts_inac).astype(np.int)
      # flip y axis
      pts_inac[:,1] = inac_bin_map.shape[1] - pts_inac[:,1]
      # remove any points outside limits of binary map (fixes round versus ceil issues)
      rm_mask = np.logical_or(pts_inac[:,0] < 0, pts_inac[:,0] >= inac_bin_map.shape[1])
      rm_mask = np.logical_or(rm_mask, np.logical_or(pts_inac[:,1] < 0, pts_inac[:,1] >= inac_bin_map.shape[0]))
      pts_inac = pts_inac[np.invert(rm_mask),:]
      inac_bin_map[pts_inac[:,1], pts_inac[:,0]] = 1 # set indices to 1
      print("inac calculation time = {} sec".format(time.time() - s_time))
      # binarization step
      x_pts_idx = np.round(x_pts).astype(np.int)
      y_pts_idx = np.round(y_pts).astype(np.int)
      # flip y axis because indices are fliped
      y_pts_idx = bin_map.shape[1] - y_pts_idx
      # remove any points outside limits of binary map (fixes round versus ceil issues)
      rm_mask = np.logical_or(x_pts_idx < 0, x_pts_idx >= bin_map.shape[1])
      rm_mask = np.logical_or(rm_mask, np.logical_or(y_pts_idx < 0, y_pts_idx >= bin_map.shape[0]))
      x_pts_idx = x_pts_idx[np.invert(rm_mask)]
      y_pts_idx = y_pts_idx[np.invert(rm_mask)]
      bin_map[y_pts_idx, x_pts_idx] = 1 # set indices to 1
      # add to viz map
      if name_list[i] in inac_layers:
      viz_map[:, :, viz_cnt] = inac_bin_map
      viz_cnt = viz_cnt + 1
      if save_files:
      fn = "map_layers/" + name_list[i] + "_inac_data_" + file_id + ".csv"
      np.savetxt(fn, inac_bin_map, delimiter=",", fmt='%f')
      viz_map[:, :, viz_cnt] = bin_map
      viz_cnt = viz_cnt + 1
      if save_files:
      fn = "map_layers/"+name_list[i]+"_data_"+file_id+".csv"
      np.savetxt(fn,bin_map,delimiter=",", fmt='%f')
      # save terrain as csv file (this method is pretty slow, but can compensate with interp)
      [e,x,y,data,ll_pt] = get_terrain_map(lat_lon=anchor_point,
      sample_dist = sample_dist,
      extent = extent,
      heading = -heading) # because flipping
      # flip elevation data up to down to match other layers
      e = np.flipud(e)
      # interpolate terrain to match size/resolution of other layers
      f = interpolate.interp2d(np.arange(0, extent, sample_dist), np.arange(0, extent, sample_dist), e, kind='cubic')
      x_temp = np.linspace(0,extent,scaled_extent) # get correct size of terrain map
      y_temp = np.linspace(0,extent,scaled_extent)
      e_interp = f(x_temp, y_temp)
      elv_filename = "map_layers\\elv_data_"+file_id+".csv"
      if save_files:
      np.savetxt(elv_filename,e_interp,delimiter=",", fmt='%f')
      if plot_data:
      terr_fig = px.imshow(e_interp)
      terr_fig.show()
      plt_list = []
      # fix stupid names
      for nme in inac_layers:
      name_list.insert(name_list.index(nme), nme+' inac')
      for i in range(viz_map.shape[-1]):
      row_idx, col_idx = np.where(viz_map[:,:,i] != 0)
      # flip y values
      col_idx = viz_map.shape[0] - col_idx
      plt_list.append(go.Scatter(x=row_idx, y=col_idx, mode='markers', name=name_list[i]))
      fig = go.Figure(data=plt_list)
      fig.show()
      if __name__ == "__main__":
      # read in ics from csv sheet
      # ics = []
      # with open('lp_data/DD.DDD WGS Coordinates_cleaned.xlsx - DD Format.csv') as f:
      # reader = csv.reader(f)
      # for row_num, row in enumerate(reader):
      # if 'Hiker' in row[8]:
      # # check for nearby find point
      # init = lat_lon2meters(float(row[19]), float(row[20]))
      # find = lat_lon2meters(float(row[21]), float(row[22]))
      # if np.linalg.norm(np.array(find) - np.array(init)) <= 5000:
      # ics.append([row[19], row[20], row_num+1])
      # ics = [[36.660460, -81.543921, 'grayson'],
      # [37.197730, -80.585233, 'kentland'],
      # [36.891640, -81.524214, 'hmpark']]
      # BrownMtn-hiker VA hikers -78.65848 38.29288 495 -78.65809 38.29254 2 people
      # DevilsDitch_hikers VA Hikers -78.46993 38.44706 653 -78.43693 38.44311 2 people
      # Punchbowl_hiker VA Hiker -79.33887 37.67752 593 -79.40187 37.6163
      # BiscuitRun_hikers VA Hikers -78.52798 37.99092 55 -78.52562 37.98698 2 people
      ics = [[-78.65848, 38.29288, 'BrownMtn'],
      [-78.46993, 38.44706,'DevilsDitch'],
      [-79.33887, 37.67752, 'Punchbowl'],
      [-78.52798, 37.99092,'BiscuitRun']]
      eng = matlab.engine.start_matlab() # engine for running matlab
      for i,ics_pt in enumerate(ics):
      anchor_point = [float(ics_pt[0]), float(ics_pt[1])]
      extent = 20e3
      save_flag = True
      plot_flag = False
      # file_id = str(anchor_point) + "_" + str(extent/1000) + "km"
      file_extension = 'temp'
      sample_dist = int(extent/100)
      heading = 0
      start_time = time.time()
      grab_features(anchor_point = anchor_point, extent = extent, sample_dist = sample_dist,
      heading = heading, save_files = save_flag, file_id = file_extension, plot_data = plot_flag)
      time.sleep(1) # wait for... files to settle?
      # run matlab
      res = eng.importmap_py(str(ics_pt[2]))
      print("------- total time = {} seconds, iteration {}/{} ------".format(time.time() - start_time,i,len(ics)))
      eng.quit()
      \ No newline at end of file
      importmap_py.m 0 → 100644
      View file @ a167f3f3
      function result = importmap_py(save_filename)
      % import map layers from pycharm generated code
      % whitehorn: [37.474015, -80.868333]
      % nolin state park: [37.304158, -86.206131]
      % kentland: [37.196809, -80.548387]
      % fnameelev = '/Users/ah/PycharmProjects/ags_grabber/map_layers/elv_data_[37.474015, -80.868333]_10.0km.csv';
      % fnameriv = '/Users/ah/PycharmProjects/ags_grabber/map_layers/rivers_data_[37.474015, -80.868333]_10.0km.csv';
      % fnamerivbd = '/Users/ah/PycharmProjects/ags_grabber/map_layers/rivers_bdd_data_[37.474015, -80.868333]_10.0km.csv';
      % fnamerivin = '/Users/ah/PycharmProjects/ags_grabber/map_layers/rivers_bdd_inac_data_[37.474015, -80.868333]_10.0km.csv';
      % fnameroad = '/Users/ah/PycharmProjects/ags_grabber/map_layers/roads_data_[37.474015, -80.868333]_10.0km.csv';
      % fnamerr = '/Users/ah/PycharmProjects/ags_grabber/map_layers/railroads_data_[37.474015, -80.868333]_10.0km.csv';
      % fnamep = '/Users/ah/PycharmProjects/ags_grabber/map_layers/powerlines_data_[37.474015, -80.868333]_10.0km.csv';
      % fnamelbd = '/Users/ah/PycharmProjects/ags_grabber/map_layers/lakes_data_[37.474015, -80.868333]_10.0km.csv';
      % fnamelin = '/Users/ah/PycharmProjects/ags_grabber/map_layers/lakes_inac_data_[37.474015, -80.868333]_10.0km.csv';
      % temp file names to read from
      fnameelev = 'C:\\Users\\Larkin\\ags_grabber\\map_layers\\elv_data_temp.csv';
      fnameriv = 'C:\\Users\\Larkin\\ags_grabber\\map_layers\\rivers_data_temp.csv';
      fnamerivbd = 'C:\\Users\\Larkin\\ags_grabber\\map_layers\\rivers_bdd_data_temp.csv';
      fnamerivin = 'C:\\Users\\Larkin\\ags_grabber\\map_layers\\rivers_bdd_inac_data_temp.csv';
      fnameroad = 'C:\\Users\\Larkin\\ags_grabber\\map_layers\\roads_data_temp.csv';
      fnamerr = 'C:\\Users\\Larkin\\ags_grabber\\map_layers\\railroads_data_temp.csv';
      fnamep = 'C:\\Users\\Larkin\\ags_grabber\\map_layers\\powerlines_data_temp.csv';
      fnamelbd = 'C:\\Users\\Larkin\\ags_grabber\\map_layers\\lakes_data_temp.csv';
      fnamelin = 'C:\\Users\\Larkin\\ags_grabber\\map_layers\\lakes_inac_data_temp.csv';
      Zelev = load(fnameelev);
      BWriver = load(fnameriv);
      BWriverLF = load(fnamerivbd);
      BWriverInac = load(fnamerivin);
      BWroads = load(fnameroad);
      BWrroads = load(fnamerr);
      BWpower = load(fnamep);
      BWlakeLF = load(fnamelbd);
      BWlakeInac = load(fnamelin);
      sZBW = size(Zelev);
      %% elevation smoothing
      %%%%% SMOOTH gradient of elevation
      sZ = size(Zelev);
      sigma = 2;
      sZelev = imgaussfilt(Zelev,sigma);
      sZgrad = imgradient(sZelev,'CentralDifference');
      BWs = edge(sZgrad,'canny',[0.01 0.3]);
      BWelevationGrad = double(BWs);
      % figure, imshow(BWs)
      %% save BW matrices
      BWLF = BWelevationGrad + BWriver + BWriverLF + BWlakeLF + BWroads + BWrroads + BWpower;
      BWInac = BWriverInac + BWlakeInac;
      BWLF(BWLF ~= 0) = 1;
      BWInac(BWInac ~= 0) = 1;
      file_path = 'C:\\Users\\Larkin\\ags_grabber\\matlab_data\\'; % define your own path here!
      % save_filename is just the ics point in question
      save(strjoin({file_path, 'BW_LFandInac_Zelev_',save_filename,'.mat'},''),'BWLF','BWInac','sZelev')
      result = 0
      % clearvars; clc; close all; % try cleaning things up
      end
      \ No newline at end of file
      lp_data/DD.DDD WGS Coordinates_cleaned.xlsx - DD Format.csv 0 → 100644
      View file @ a167f3f3
      Country,State,Key#,County,EcoRegion Domain,EcoRegion Division,Population Density,Terrain,Subject Category,Age,Sex,Number Lost,Weather,Coordinates,LKP Coord. (N/S),LKP Coord. (E/W),Find Coord (N/S),Find Coord (E/W),IPP Precision (m),IPP Coord. DD.D (N/S),IPP Coord. DD.D (E/W),Find Coord DD.D (N/S),Find Coord DD.D (E/W)
      US,AZ,US-AZ0003,COCHISE ,Dry,320,Wilderness,Flat,Mental Retardation,40,M,,,DD.DDD,31.43493334,-110.2323333,31.39535001,-110.1093333,1,31.43493334,-110.2323333,31.39535001,-110.1093333
      US,AZ,US-AZ0004,APACHE ,Dry,m310,Wilderness,Mountainous,Camper,75,M,,,DD.DDD,33.97181664,-109.0950333,34.13733333,-109.066,1,33.97181664,-109.0950333,34.13733333,-109.066
      US,AZ,US-AZ0005,APACHE ,Dry,m310,Wilderness,MOUNTAINOUS,Child,4,M,,,DD.DDD,33.82119999,-109.1491833,33.80291665,-109.1465833,1,33.82119999,-109.1491833,33.80291665,-109.1465833
      US,AZ,US-AZ0008,GILA ,Dry,m310,Wilderness,Flat,Hiker,63,F,,,DD.DDD,34.45878334,-111.2508833,34.44531666,-111.3221,1,34.45878334,-111.2508833,34.44531666,-111.3221
      US,AZ,US-AZ0011,COCHISE ,Dry,320,Wilderness,Flat,Hiker,"57,34",MF,2,,DD.DDD,31.92245,-109.9673167,31.92148336,-110.0335833,1,31.92245,-109.9673167,31.92148336,-110.0335833
      US,AZ,US-AZ0018,COCHISE ,Dry,320,Wilderness,Flat,Hiker,49,M,,,DD.DDD,31.90240002,-109.2784667,31.83949998,-109.2766667,1,31.90240002,-109.2784667,31.83949998,-109.2766667
      US,AZ,US-AZ0019,COCHISE ,Dry,320,Wilderness,Flat,Hiker,"58,20",MM,2,,DD.DDD,31.42903334,-110.2932833,31.42951667,-110.3041167,1,31.42903334,-110.2932833,31.42951667,-110.3041167
      US,AZ,US-AZ0020,YAVAPAI ,Dry,310,Wilderness,Mountainous,Hiker,"26,21",MF,2,,DD.DDD,34.55,-111.6333333,34.55,-111.6166667,1,34.55,-111.6333333,34.55,-111.6166667
      US,AZ,US-AZ0021,YAVAPAI ,Dry,310,Wilderness,Flat,Dementia,83,F,,,DD.DDD,34.65,-112.5,34.65186666,-112.4962333,1,34.65,-112.5,34.65186666,-112.4962333
      US,AZ,US-AZ0022,YAVAPAI ,Dry,310,Wilderness,Flat,Hiker,25,F,,,DD.DDD,34.6,-112.55,34.63333333,-112.6333333,1,34.6,-112.55,34.63333333,-112.6333333
      US,AZ,US-AZ0030,COCHISE ,Dry,320,Wilderness,Flat,Dementia,84,M,,,DD.DDD,31.37999999,-109.8858334,31.3721,-109.9191,1,31.37999999,-109.8858334,31.3721,-109.9191
      US,AZ,US-AZ0031,GILA ,Dry,m310,Wilderness,Flat,Mental Retardation,16,M,,,DD.DDD,34.25450001,-111.3086667,34.26749999,-110.3376667,1,34.25450001,-111.3086667,34.26749999,-110.3376667
      US,AZ,US-AZ0042,YAVAPAI ,Dry,310,Wilderness,Flat,Hiker,"21,20",MF,2,,DD.DDD,34.82166665,-111.8066667,34.81833331,-111.7983334,1,34.82166665,-111.8066667,34.81833331,-111.7983334
      US,AZ,US-AZ0044,PINAL ,Dry,320,Wilderness,Mountainous,Hiker,"32,29",MM,2,,DD.DDD,33.39750001,-111.3478333,33.41566668,-111.365,1,33.39750001,-111.3478333,33.41566668,-111.365
      US,AZ,US-AZ0045,GRAHAM ,Dry,320,Wilderness,Flat,Mental Retardation,18,M,,,DD.DDD,32.64609998,-109.8158167,32.65335,-109.80485,1,32.64609998,-109.8158167,32.65335,-109.80485
      US,AZ,US-AZ0050,GILA ,Dry,m310,Wilderness,MOUNTAINOUS,Hiker,44,M,,,DD.DDD,33.70541668,-111.33805,33.67915001,-111.31695,1,33.70541668,-111.33805,33.67915001,-111.31695
      US,AZ,US-AZ0051,GILA ,Dry,m310,Wilderness,MOUNTAINOUS,Hiker,40,M,,,DD.DDD,33.70541668,-111.33805,33.67915001,-111.31695,1,33.70541668,-111.33805,33.67915001,-111.31695
      US,AZ,US-AZ0054,YAVAPAI ,Dry,310,Wilderness,Flat,Vehicle-4wd,60.37.36,FMF,3,,DD.DDD,34.63499997,-111.5466667,34.63499997,-111.5466667,1,34.63499997,-111.5466667,34.63499997,-111.5466667
      US,AZ,US-AZ0059,MARICOPA ,Dry,320,Wilderness,Mountainous,Vehicle-4wd,"33,39",FM,2,,DD.DDD,33.96816667,-111.9348333,33.96816667,-111.9348333,1,33.96816667,-111.9348333,33.96816667,-111.9348333
      US,AZ,US-AZ0060,SANTA CRUZ ,Dry,320,Wilderness,Flat,Hiker,23,F,,,DD.DDD,31.39708335,-111.2064333,31.40633332,-111.1968333,1,31.39708335,-111.2064333,31.40633332,-111.1968333
      US,AZ,US-AZ0063,COCHISE ,Dry,320,Wilderness,Flat,Despondent,43,F,,,DD.DDD,32.01088333,-109.8509833,32.10883334,-109.8509833,1,32.01088333,-109.8509833,32.10883334,-109.8509833
      US,AZ,US-AZ0066,NAVAJO ,Dry,310,Wilderness,Flat,Hunter,77,M,,,DD.DDD,34.15116666,-109.8896667,34.1517,-109.9133,1,34.15116666,-109.8896667,34.1517,-109.9133
      US,AZ,US-AZ0069,PIMA ,Dry,320,Wilderness,Mountainous,Hiker,72,M,,,DD.DDD,32.40750001,-110.825,32.40750001,-110.825,1,32.40750001,-110.825,32.40750001,-110.825
      US,AZ,US-AZ0077,YAVAPAI ,Dry,310,Wilderness,Mountainous,Hiker,"44,38",MF,2,,DD.DDD,34.89333331,-111.8633333,34.90666669,-111.8760333,1,34.89333331,-111.8633333,34.90666669,-111.8760333
      US,AZ,US-AZ0078,YAVAPAI ,Dry,310,Wilderness,Flat,Hiker,"17,15",MM,2,,DD.DDD,34.94833336,-111.795,34.94666665,-111.795,1,34.94833336,-111.795,34.94666665,-111.795
      US,AZ,US-AZ0080,COCHISE ,Dry,320,Wilderness,Flat,Hiker,"8,8,4,30",FMMF,4,,DD.DDD,31.72261664,-110.1878333,31.74176668,-110.1917833,1,31.72261664,-110.1878333,31.74176668,-110.1917833
      US,AZ,US-AZ0083,PINAL ,Dry,320,Wilderness,Flat,Hiker,27,F,,,DD.DDD,33.39733334,-111.348,33.44166667,-111.3676667,1,33.39733334,-111.348,33.44166667,-111.3676667
      US,AZ,US-AZ0091,YAVAPAI ,Dry,310,Wilderness,Flat,Hiker,"20,20,21",MFM,3,,DD.DDD,34.63041668,-112.5553,34.62723331,-112.5457666,1,34.63041668,-112.5553,34.62723331,-112.5457666
      US,AZ,US-AZ0096,MOHAVE ,Dry,310,Wilderness,Mountainous,Mental Retardation,50,M,,,DD.DDD,35.26088333,-113.9805833,35.18688334,-113.3651,1,35.26088333,-113.9805833,35.18688334,-113.3651
      US,AZ,US-AZ0097,YAVAPAI ,Dry,310,Wilderness,Flat,Vehicle,25,F,,,DD.DDD,34.15995,-112.45995,34.15100001,-112.4788,1,34.15995,-112.45995,34.15100001,-112.4788
      US,AZ,US-AZ0098,GRAHAM ,Dry,320,Wilderness,Flat,Hunter,65,M,,,DD.DDD,32.74816666,-110.387,32.73283335,-110.3853167,1,32.74816666,-110.387,32.73283335,-110.3853167
      US,AZ,US-AZ0103,GRAHAM ,Dry,320,Wilderness,Flat,Hunter,49,M,,,DD.DDD,32.44718332,-109.8538666,32.49103333,-109.82405,1,32.44718332,-109.8538666,32.49103333,-109.82405
      US,AZ,US-AZ0104,PINAL ,Dry,320,Wilderness,Flat,Mental Retardation,49,F,,,DD.DDD,32.56261667,-110.7051333,32.55876668,-110.7036833,1,32.56261667,-110.7051333,32.55876668,-110.7036833
      US,AZ,US-AZ0105,PINAL ,Dry,320,Wilderness,Mountainous,Horseback Rider,"37,62",MM,2,,DD.DDD,33.40944999,-111.2061833,33.34418332,-111.4271667,1,33.40944999,-111.2061833,33.34418332,-111.4271667
      US,AZ,US-AZ0112,YAVAPAI ,Dry,310,Wilderness,Mountainous,Hiker,62,M,,,DD.DDD,34.55976664,-111.65395,34.54006666,-111.7034833,1,34.55976664,-111.65395,34.54006666,-111.7034833
      US,AZ,US-AZ0113,YAVAPAI ,Dry,310,Wilderness,Flat,Hiker,69,M,,,DD.DDD,34.90286668,-111.8131333,34.89183331,-111.8048167,1,34.90286668,-111.8131333,34.89183331,-111.8048167
      US,AZ,US-AZ0114,PIMA ,Dry,320,Wilderness,Flat,Climber,49,M,,,DD.DDD,32.36833334,-110.7166667,32.37233334,-110.7181667,1,32.36833334,-110.7166667,32.37233334,-110.7181667
      US,AZ,US-AZ0118,PIMA ,Dry,320,Wilderness,Mountainous,Mountain Biker,"23,24",MM,2,,DD.DDD,32.44043334,-110.7910667,32.45033334,-110.8168333,1,32.44043334,-110.7910667,32.45033334,-110.8168333
      US,AZ,US-AZ0119,YAVAPAI ,Dry,310,Wilderness,Flat,Vehicle-4wd,"71,76,15",MMM,3,,DD.DDD,34.61666667,-112.8,34.54183331,-112.7815333,1,34.61666667,-112.8,34.54183331,-112.7815333
      US,AZ,US-AZ0120,YAVAPAI ,Dry,310,Wilderness,Flat,Hiker,43,M,,,DD.DDD,34.86666667,-111.8833333,34.86666667,-111.8,1,34.86666667,-111.8833333,34.86666667,-111.8
      US,AZ,US-AZ0121,APACHE ,Dry,m310,Wilderness,Flat,Child,6,M,,,DD.DDD,34.84249999,-109.2758333,34.8197333,-109.3030667,1,34.84249999,-109.2758333,34.8197333,-109.3030667
      US,AZ,US-AZ0125,PIMA ,Dry,320,Suburban,Flat,Autistic,13,M,1,,DD.DDD,32.26666667,-110.7916667,32.26966667,-110.7926667,1,32.26666667,-110.7916667,32.26966667,-110.7926667
      US,AZ,US-AZ0126,PIMA ,Dry,320,Wilderness,Mountainous,Hiker,30,F,,,DD.DDD,32.43543332,-110.7893333,32.42883333,-110.7943333,1,32.43543332,-110.7893333,32.42883333,-110.7943333
      US,AZ,US-AZ0127,PIMA ,Dry,320,Wilderness,Flat,Hiker,24,F,,,DD.DDD,32.40916665,-110.7098333,32.40083332,-110.6923333,1,32.40916665,-110.7098333,32.40083332,-110.6923333
      US,AZ,US-AZ0129,COCHISE ,Dry,320,Wilderness,Flat,Hunter,65,M,,,DD.DDD,32.29733334,-110.2794,32.31533333,-110.3025,1,32.29733334,-110.2794,32.31533333,-110.3025
      US,AZ,US-AZ0131,MOHAVE ,Dry,310,Wilderness,Flat,ATV,"42,14,45,13",FMMM,4,,DD.DDD,34.51823333,-114.2821667,34.51666667,-114.25,1,34.51823333,-114.2821667,34.51666667,-114.25
      US,AZ,US-AZ0132,MOHAVE ,Dry,310,Wilderness,Flat,Motorcycle,"31,30",MM,2,,DD.DDD,35.16326666,-114.3189333,35.11666667,-114.4333333,1,35.16326666,-114.3189333,35.11666667,-114.4333333
      US,AZ,US-AZ0134,COCONINO ,Dry,M310,Wilderness,Flat,Hunter,67,M,,,DD.DDD,35.54000003,-111.9583333,35.54666665,-111.9683333,1,35.54000003,-111.9583333,35.54666665,-111.9683333
      US,AZ,US-AZ0135,COCONINO ,Dry,M310,Wilderness,Flat,Hiker,28,F,,,DD.DDD,35.33068333,-111.7110833,35.33423335,-111.6975,1,35.33068333,-111.7110833,35.33423335,-111.6975
      US,AZ,US-AZ0136,APACHE ,Dry,m310,Wilderness,Flat,Child,12,M,,,DD.DDD,34.18046667,-109.3044667,34.24506667,-109.3257667,1,34.18046667,-109.3044667,34.24506667,-109.3257667
      US,AZ,US-AZ0137,APACHE ,Dry,m310,Wilderness,Flat,Youth,"14,16",MM,2,,DD.DDD,34.18046667,-109.3044667,34.24506667,-109.3257667,1,34.18046667,-109.3044667,34.24506667,-109.3257667
      US,AZ,US-AZ0138,COCONINO ,Dry,M310,Wilderness,Flat,Mental Retardation,23,M,,,DD.DDD,35.0334,-111.2949833,35.58833332,-111.2394167,1,35.0334,-111.2949833,35.58833332,-111.2394167
      US,AZ,US-AZ0139,MOHAVE ,Dry,310,Wilderness,Flat,Worker,68,M,,,DD.DDD,35.9,-114.4,35.91666667,-114.0833333,1,35.9,-114.4,35.91666667,-114.0833333
      US,AZ,US-AZ0142,PIMA ,Dry,320,Suburban,Flat,Autistic,10,M,,,DD.DDD,32.41666667,-110.9333333,32.37083333,-110.9758333,1,32.41666667,-110.9333333,32.37083333,-110.9758333
      US,AZ,US-AZ0144,YAVAPAI ,Dry,310,Wilderness,Flat,Child,10,M,,,DD.DDD,35.09275,-112.4393,35.08423333,-112.4337167,1,35.09275,-112.4393,35.08423333,-112.4337167
      US,AZ,US-AZ0149,MOHAVE ,Dry,310,Wilderness,Flat,ATV,52,M,,,DD.DDD,35.1,-113.55,35.16666667,-113.7,1,35.1,-113.55,35.16666667,-113.7
      US,AZ,US-AZ0150,COCHISE ,Dry,320,Wilderness,Flat,Aircraft,28,MM,2,,DD.DDD,32.0695,-110.0718333,35.16243334,-111.5408833,1,32.0695,-110.0718333,35.16243334,-111.5408833
      US,AZ,US-AZ0151,YAVAPAI ,Dry,310,Wilderness,Flat,Child,11,M,,,DD.DDD,34.40413332,-112.42975,34.3949,-112.4288833,1,34.40413332,-112.42975,34.3949,-112.4288833
      US,AZ,US-AZ0154,COCHISE ,Dry,320,Wilderness,Flat,Hiker,48,M,,,DD.DDD,32.01236667,-109.3157167,31.99934998,-109.3078,1,32.01236667,-109.3157167,31.99934998,-109.3078
      US,AZ,US-AZ0156,COCHISE ,Dry,320,Wilderness,Flat,Hiker,63,M,,,DD.DDD,31.85073331,-109.4219333,31.86200002,-109.338,1,31.85073331,-109.4219333,31.86200002,-109.338
      US,AZ,US-AZ0158,COCONINO ,Dry,M310,Wilderness,Flat,Hiker,59,F,,,DD.DDD,34.88683332,-111.784,34.90266666,-111.7868333,1,34.88683332,-111.784,34.90266666,-111.7868333
      US,AZ,US-AZ0159,MARICOPA ,Dry,320,Wilderness,Flat,Dementia,85,M,,,DD.DDD,33.58235003,-111.2151333,33.59669997,-111.2027833,1,33.58235003,-111.2151333,33.59669997,-111.2027833
      US,AZ,US-AZ0160,PIMA ,Dry,320,Wilderness,Flat,Dementia,91,M,,,DD.DDD,32.32366667,-111.0733333,32.31871665,-111.07295,1,32.32366667,-111.0733333,32.31871665,-111.07295
      US,AZ,US-AZ0161,PIMA ,Dry,320,Wilderness,Flat,Mental Retardation,13,F,,,DD.DDD,31.74216665,-111.0135,31.78933334,-111.0201667,1,31.74216665,-111.0135,31.78933334,-111.0201667
      US,AZ,US-AZ0162,PIMA ,Dry,320,Wilderness,Mountainous,Hiker,53,M,,,DD.DDD,32.41976668,-110.7473333,32.36550001,-110.7758333,1,32.41976668,-110.7473333,32.36550001,-110.7758333
      US,AZ,US-AZ0165,YAVAPAI ,Dry,310,Wilderness,Flat,Vehicle-4wd,,F,,,DD.DDD,34.30129999,-112.10295,34.28333333,-112.05,1,34.30129999,-112.10295,34.28333333,-112.05
      US,AZ,US-AZ0166,YAVAPAI ,Dry,310,Wilderness,Flat,Vehicle-4wd,37,M,,,DD.DDD,34.30129999,-112.10295,34.28333333,-112.05,1,34.30129999,-112.10295,34.28333333,-112.05
      US,AZ,US-AZ0169,MARICOPA ,Dry,320,Wilderness,Flat,Hiker,58,F,,,DD.DDD,33.60398331,-112.5151167,33.5903333,-112.5228333,1,33.60398331,-112.5151167,33.5903333,-112.5228333
      US,AZ,US-AZ0170,COCHISE ,Dry,320,Wilderness,Flat,Mental Retardation,38,F,,,DD.DDD,31.94159997,-109.83255,31.93203335,-109.776,1,31.94159997,-109.83255,31.93203335,-109.776
      US,AZ,US-AZ0176,PINAL ,Dry,320,Wilderness,Mountainous,Mountain Biker,39,M,,,DD.DDD,32.44033333,-110.7910334,32.48291667,-110.8153333,1,32.44033333,-110.7910334,32.48291667,-110.8153333
      US,AZ,US-AZ0178,PINAL ,Dry,320,Wilderness,Mountainous,Hiker,40,M,,,DD.DDD,33.39680001,-111.34805,33.42150002,-111.3605,1,33.39680001,-111.34805,33.42150002,-111.3605
      US,AZ,US-AZ0180,MARICOPA ,Dry,320,Wilderness,Flat,Hiker,"55,45,55,69",MMMM,4,,DD.DDD,33.52603334,-111.3904833,33.52000001,-111.395,1,33.52603334,-111.3904833,33.52000001,-111.395
      US,AZ,US-AZ0181,MARICOPA ,Dry,320,Wilderness,Mountainous,Aircraft,22,F,,,DD.DDD,33.27266668,-112.5923333,33.26836665,-112.5911667,1,33.27266668,-112.5923333,33.26836665,-112.5911667
      US,AZ,US-AZ0185,PIMA ,Dry,320,Wilderness,Mountainous,Hiker,63,M,,,DD.DDD,32.33333333,-110.8528333,32.37150002,-110.8646667,1,32.33333333,-110.8528333,32.37150002,-110.8646667
      US,AZ,US-AZ0186,PIMA ,Dry,320,Wilderness,Mountainous,Hiker,24,M,,,DD.DDD,32.33583333,-110.9101667,32.3665,-110.8801667,1,32.33583333,-110.9101667,32.3665,-110.8801667
      US,AZ,US-AZ0187,PIMA ,Dry,320,Wilderness,Flat,Hiker,"38,51",MM,2,,DD.DDD,32.33699999,-110.9166667,32.35983334,-110.8976667,1,32.33699999,-110.9166667,32.35983334,-110.8976667
      US,AZ,US-AZ0188,LA PAZ ,Dry,320,Wilderness,Flat,Substance Abuse,29,F,,,DD.DDD,33.65670001,-114.4192167,33.61026669,-114.5182333,1,33.65670001,-114.4192167,33.61026669,-114.5182333
      US,AZ,US-AZ0190,MOHAVE ,Dry,310,Wilderness,Mountainous,Dementia,89,M,,,DD.DDD,35.26558334,-114.0267,35.29941667,-114.0836667,1,35.26558334,-114.0267,35.29941667,-114.0836667
      US,AZ,US-AZ0192,COCONINO ,Dry,M310,Wilderness,Flat,Hiker,32,F,,,DD.DDD,35.08133334,-111.0710833,35.08133334,-111.0710833,1,35.08133334,-111.0710833,35.08133334,-111.0710833
      US,AZ,US-AZ0195,YUMA ,Dry,320,Wilderness,Flat,Hiker,51,F,,,DD.DDD,33.25,-113.5093333,33.25,-113.5093333,1,33.25,-113.5093333,33.25,-113.5093333
      US,AZ,US-AZ0196,SANTA CRUZ ,Dry,320,Wilderness,Mountainous,Hiker,68,M,,,DD.DDD,31.50571667,-110.6762167,31.51000001,-110.6533333,1,31.50571667,-110.6762167,31.51000001,-110.6533333
      US,AZ,US-AZ0201,YAVAPAI ,Dry,310,Wilderness,Flat,Hiker,35,M,,,DD.DDD,34.91666667,-111.8,34.96666667,-111.8666667,1,34.91666667,-111.8,34.96666667,-111.8666667
      US,AZ,US-AZ0203,MOHAVE ,Dry,310,Wilderness,Flat,Vehicle-4wd,"19,20,21,25",MFMM,4,,DD.DDD,34.41985,-114.1983333,34.27661667,-114.0408333,1,34.41985,-114.1983333,34.27661667,-114.0408333
      US,AZ,US-AZ0204,MOHAVE ,Dry,310,Wilderness,Flat,Hiker,24,M,,,DD.DDD,35.1938,-114.0570333,35.20871666,-114.13195,1,35.1938,-114.0570333,35.20871666,-114.13195
      US,AZ,US-AZ0205,MOHAVE ,Dry,310,Wilderness,Flat,Worker,35,M,,,DD.DDD,35.73811665,-114.3975833,35.7725167,-114.2564667,1,35.73811665,-114.3975833,35.7725167,-114.2564667
      US,AZ,US-AZ0207,PIMA ,Dry,320,Wilderness,Flat,Hiker,"53,54,56",FMF,3,,DD.DDD,33.39715001,-111.3478833,33.42233334,-111.3514667,1,33.39715001,-111.3478833,33.42233334,-111.3514667
      US,AZ,US-AZ0208,COCONINO ,Dry,M310,Wilderness,Flat,Gatherer,"102,35,37,14,35",MFMMMM,5,,DD.DDD,35.83560003,-112.0263833,35.82865003,-112.0532167,1,35.83560003,-112.0263833,35.82865003,-112.0532167
      US,AZ,US-AZ0209,PIMA ,Dry,320,Wilderness,Flat,Hiker,33,F,1,,DD.DDD,33.39715001,-111.3478833,35.82865003,-112.0532167,1,33.39715001,-111.3478833,35.82865003,-112.0532167
      US,AZ,US-AZ0210,GRAHAM ,Dry,320,Wilderness,Mountainous,Child,12,F,,,DD.DDD,32.85706666,-109.7643667,32.86378333,-109.7708167,1,32.85706666,-109.7643667,32.86378333,-109.7708167
      US,AZ,US-AZ0211,PIMA ,Dry,320,Wilderness,Flat,Hiker,"13,13,57,11,12,12,13,12,13,46,13,31",MMMMMMMMMMMM,12,Snow,DD.DDD,33.37055,-111.1152167,33.37528334,-111.0891833,1,33.37055,-111.1152167,33.37528334,-111.0891833
      US,AZ,US-AZ0219,GILA ,Dry,m310,Wilderness,MOUNTAINOUS,Hiker,"45,78",MM,2,,DD.DDD,34.0927,-111.42465,34.06876667,-111.4359167,1,34.0927,-111.42465,34.06876667,-111.4359167
      US,AZ,US-AZ0221,COCHISE ,Dry,320,Wilderness,Mountainous,Hiker,"10,33,35,9",FFMM,4,Snow,DD.DDD,31.83521665,-110.3567,31.86226667,-110.39655,1,31.83521665,-110.3567,31.86226667,-110.39655
      US,AZ,US-AZ0222,MOHAVE ,Dry,310,Wilderness,Flat,Aircraft,"59,62",MM,2,,DD.DDD,34.87518336,-113.4748333,34.88164997,-113.4667833,1,34.87518336,-113.4748333,34.88164997,-113.4667833
      US,AZ,US-AZ0224,PINAL ,Dry,320,Wilderness,Mountainous,Horseback Rider,"13,32,60,46,32,9",MMMFFF,6,,DD.DDD,33.39720001,-111.3478667,33.47999999,-111.3725,1,33.39720001,-111.3478667,33.47999999,-111.3725
      US,AZ,US-AZ0227,COCONINO ,Dry,M310,Wilderness,Flat,Hunter,39,M,,,DD.DDD,34.89366665,-111.5823333,34.90983334,-111.5875,1,34.89366665,-111.5823333,34.90983334,-111.5875
      US,AZ,US-AZ0228,COCONINO ,Dry,M310,Wilderness,MOUNTAINOUS,Hiker,"17,18",MM,2,,DD.DDD,35.24375,-111.5996667,35.23251667,-111.6015,1,35.24375,-111.5996667,35.23251667,-111.6015
      US,AZ,US-AZ0229,COCONINO ,Dry,M310,Wilderness,Flat,Despondent,59,M,,,DD.DDD,36.9235,-111.4773333,36.9235,-111.4773333,1,36.9235,-111.4773333,36.9235,-111.4773333
      US,AZ,US-AZ0231,MOHAVE ,Dry,310,Wilderness,Flat,Vehicle,"33,33",FM,2,,DD.DDD,34.70491664,-113.6109166,34.86253332,-113.6262167,1,34.70491664,-113.6109166,34.86253332,-113.6262167
      US,AZ,US-AZ0232,GILA ,Dry,m310,Wilderness,Flat,Hunter,41,M,,,DD.DDD,34.35966667,-111.3271667,34.37199999,-111.3353333,1,34.35966667,-111.3271667,34.37199999,-111.3353333
      US,AZ,US-AZ0243,COCONINO ,Dry,M310,urban,Flat,Dementia,72,F,,,DD.DDD,35.09778334,-111.6884167,35.18475,-111.6615667,1,35.09778334,-111.6884167,35.18475,-111.6615667
      US,AZ,US-AZ0250,YAVAPAI ,Dry,310,Wilderness,Flat,Hiker,"44,11",MM,2,,DD.DDD,34.82513332,-111.7874833,34.82195002,-111.7927834,1,34.82513332,-111.7874833,34.82195002,-111.7927834
      US,AZ,US-AZ0251,PINAL ,Dry,320,Wilderness,Mountainous,Hiker,"19,19,18",MMM,3,,DD.DDD,33.39705,-111.3478667,33.40933332,-111.3171667,1,33.39705,-111.3478667,33.40933332,-111.3171667
      US,AZ,US-AZ0253,PIMA ,Dry,320,Wilderness,Flat,Hiker,60,M,,,DD.DDD,33.38884999,-111.36565,33.40120001,-111.3751667,1,33.38884999,-111.36565,33.40120001,-111.3751667
      US,AZ,US-AZ0254,PINAL ,Dry,320,Wilderness,Flat,Hiker,72,F,,,DD.DDD,32.82141666,-111.2021167,32.82466666,-111.2271667,1,32.82141666,-111.2021167,32.82466666,-111.2271667
      US,AZ,US-AZ0255,YAVAPAI ,Dry,310,Wilderness,Flat,Hiker,43,M,,,DD.DDD,34.97868334,-111.8964333,34.94683336,-111.8896667,1,34.97868334,-111.8964333,34.94683336,-111.8896667
      US,AZ,US-AZ0258,COCHISE ,Dry,320,Wilderness,Flat,Mental Retardation,16,M,,,DD.DDD,31.47053334,-110.1897833,31.52950001,-110.1289833,1,31.47053334,-110.1897833,31.52950001,-110.1289833
      US,AZ,US-AZ0260,PINAL ,Dry,320,Wilderness,Mountainous,Hiker,"42,55",FM,2,,DD.DDD,33.47801666,-111.4376667,33.41566668,-111.3647833,1,33.47801666,-111.4376667,33.41566668,-111.3647833
      US,AZ,US-AZ0266,COCONINO ,Dry,M310,Wilderness,MOUNTAINOUS,Hiker,44,F,,,DD.DDD,34.82386665,-111.7751333,34.83166669,-111.743,1,34.82386665,-111.7751333,34.83166669,-111.743
      US,AZ,US-AZ0267,COCONINO ,Dry,M310,Wilderness,MOUNTAINOUS,Hiker,27,M,,,DD.DDD,34.92530003,-111.7341,34.89733334,-111.7411667,1,34.92530003,-111.7341,34.89733334,-111.7411667
      US,AZ,US-AZ0270,GILA ,Dry,m310,Wilderness,MOUNTAINOUS,Hiker,"23,23",FM,2,,DD.DDD,34.09278333,-111.4209833,34.10233333,-111.4903333,1,34.09278333,-111.4209833,34.10233333,-111.4903333
      US,AZ,US-AZ0274,PIMA ,Dry,320,Wilderness,Flat,Despondent,49,M,,,DD.DDD,32.42133334,-110.74,32.41833334,-110.7466667,1,32.42133334,-110.74,32.41833334,-110.7466667
      US,AZ,US-AZ0276,MOHAVE ,Dry,310,Wilderness,Flat,Dementia,80,M,,,DD.DDD,34.75533333,-114.2059,34.82199999,-114.1441167,1,34.75533333,-114.2059,34.82199999,-114.1441167
      US,AZ,US-AZ0277,COCONINO ,Dry,M310,Wilderness,Flat,Hiker,33,F,,,DD.DDD,36.23878333,-112.6892,36.24643334,-112.7000834,1,36.23878333,-112.6892,36.24643334,-112.7000834
      US,AZ,US-AZ0278,COCONINO ,Dry,M310,Wilderness,Hilly,ATV,13,M,,,DD.DDD,35.33245001,-111.4995333,35.33533335,-111.4548333,1,35.33245001,-111.4995333,35.33533335,-111.4548333
      US,AZ,US-AZ0279,COCHISE ,Dry,320,Wilderness,Mountainous,Hiker,34,F,,,DD.DDD,31.33446668,-109.81855,31.41008333,-109.8383333,1,31.33446668,-109.81855,31.41008333,-109.8383333
      US,AZ,US-AZ0284,PINAL ,Dry,320,Wilderness,Flat,Vehicle,"19,27",FM,2,,DD.DDD,33.25333333,-111.4766667,33.24333334,-110.4516667,1,33.25333333,-111.4766667,33.24333334,-110.4516667
      US,NJ,US-NJ0004,Somerset,Temperate,220,Rural,Flat,Child,12,M,1,,UTM,18T 0546398,4500085,18t 0546877,4498251,1,40.65222,-74.45078,40.63567,-74.44526
      US,NM,US-NM0822,7,Dry,M330,Wilderness,Mountainous,Hiker,,,,,DD MM.MM,36N22.484 ,106W40.771,36N22.80 ,106W40.66,3,36.37473,-106.67952,36.38,-106.67767
      US,NY,US-NY2107,,Temperate,m210,Wilderness,Mountainous,Hiker,"34,47",MM,2,Snow,UTM,554883,4652989,553615,4652989,1,42.02893,-74.33659,42.02902,-74.35191
      US,NY,US-NY2109,,Temperate,,Wilderness,Flat,Hunter,63,M,1,Overcast,UTM,549074,4632093,548889,4632529,1,41.84112,-74.40851,41.84506,-74.4107
      US,NY,US-NY2110,,Temperate,220,Wilderness,Hilly,Gatherer,74,F,1,Rain,UTM,51796,4618946,50779,4615221,1,41.59828,-80.37693,41.5643,-80.38629
      US,NY,US-NY2112,,Temperate,m210,Wilderness,Mountainous,Hiker,"57,61",MM,2,Clear,UTM,55318,4613142,55947,4617772,1,41.54819,-80.33056,41.59006,-80.32647
      US,NY,US-NY2113,,Temperate,m210,Wilderness,Mountainous,Hiker,"40,23",MF,2,Rain,UTM,547500,4650800,553300,4653700,1,42.0097,-74.42595,42.03544,-74.35565
      US,NY,US-NY2116,,Temperate,,Wilderness,Flat,Child,4,M,1,Clear,UTM,574500,4508500,574500,4508500,1,40.72596,-74.11741,40.72596,-74.11741
      US,NY,US-NY2117,,Temperate,220,Wilderness,Hilly,Runaway,15,M,1,Clear,UTM,578600,463400,585500,4639000,10,4.19377,-74.29146,41.90022,-73.96881
      US,NY,US-NY2118,,Temperate,,Wilderness,Hilly,Runaway,17,M,1,Rain,UTM,580280,4658557,580280,4658557,1,42.07689,-74.02906,42.07689,-74.02906
      US,NY,US-NY2120,,Temperate,220,Wilderness,Flat,Despondent,72,F,1,Clear,UTM,180587160,4658029,180587240,46580491,1,42.0714,-73.94598,42.07157,-73.94501
      US,NY,US-NY2121,,Temperate,220,Wilderness,Hilly,ATV,28,M,1,Clear,UTM,180589129,4663875,180590010,4672251,1,42.12382,-73.92129,42.19915,-73.90934
      US,NY,US-NY2129,Greene,Temperate,m210,Wilderness,Mountainous,Hiker,"14,26",MMMMM,8,Rain,UTM,564909,4669810,566372,4670245,1,42.17965,-74.21362,42.18345,-74.19585
      US,NY,US-NY2131,,Temperate,,Wilderness,,Worker,70,M,1,Clear,UTM,472846,4708493,476980,4734430,1,42.53026,-75.33023,42.76397,-75.28094
      US,NY,US-NY2132,,Temperate,,Wilderness,Hilly,Hiker,"70,68,64",MFF,3,Clear,UTM,624150,4711875,624149,4711875,1,42.55121,-73.4874,42.55121,-73.48741
      US,NY,US-NY2134,,Temperate,220,Wilderness,Hilly,Dementia,77,M,1,Clear,UTM,606682,4732481,606682,4732481,1,42.73934,-73.69624,42.73934,-73.69624
      US,NY,US-NY2135,,Temperate,m210,Wilderness,Hilly,Dementia,80,F,1,Clear,UTM,185540,4683177,180551,4686933,1,42.23938,-78.81068,42.27111,-78.87307
      US,NY,US-NY2137,,Temperate,m210,Wilderness,Mountainous,Hiker,"19,16,19",MMM,3,Clear,UTM,56920,4664942,57263,4660650,1,42.01362,-80.35002,41.97533,-80.34266
      US,NY,US-NY2139,,Temperate,220,Wilderness,Hilly,Dementia,78,F,1,Rain,UTM,581309,4688329,588221,4684592,1,42.34488,-74.01246,42.31048,-73.92913
      US,NY,US-NY2142,Grafton,Temperate,M210,Wilderness,Flat,Hiker,18,M,1,Rain,UTM,626442,4733189,625782,4734168,1,42.74271,-73.45475,42.75163,-73.46259
      US,NY,US-NY2143,,Temperate,,Wilderness,Hilly,Dementia,74,M,1,Clear,UTM,617168,4650019,617168,4650019,1,41.99538,-73.58493,41.99538,-73.58493
      US,NY,US-NY2145,Knox,Temperate,m210,Wilderness,Mountainous,Despondent,50,M,1,Clear,UTM,578795,4724845,578767,4724845,1,42.67396,-74.03795,42.67396,-74.03829
      US,NY,US-NY2146,Fultonville,Temperate,,Wilderness,Flat,Dementia,83,F,1,Rain,UTM,551244,4755040,552289,4754576,1,42.94819,-74.37142,42.94394,-74.35865
      US,NY,US-NY2148,Fleischmanns,Temperate,,Wilderness,Flat,Child,4,M,1,Clear,UTM,535360,4667270,535360,466727,10,42.15867,-74.57156,4.22413,-74.68106
      US,NY,US-NY2149,Glenford,Temperate,,Wilderness,Hilly,Hiker,31,M,1,Clear,UTM,565627,4667068,565476,4667281,1,42.1549,-74.20523,42.15683,-74.20704
      US,NY,US-NY2150,Oneota,Temperate,,Wilderness,,Dementia,77,M,1,Rain,UTM,494970,4702017,494694,4703461,1,42.4724,-75.06078,42.4854,-75.06415
      US,NY,US-NY2159,,Temperate,,Wilderness,Flat,Hunter,58,M,1,Snow,UTM,522750,4831420,520090,4827300,10,43.63734,-74.71754,43.60032,-74.75067
      US,NY,US-NY2161,Jonesville,Temperate,,Wilderness,Flat,Hiker,66,M,1,Rain,UTM,597546,4750550,597546,4750550,10,42.90324,-73.8047,42.90324,-73.8047
      US,NY,US-NY2162,,Temperate,,Wilderness,Hilly,Hunter,52,M,1,Overcast,UTM,521030,4804670,521010,4804900,10,43.39653,-74.7399,43.3986,-74.74013
      US,NY,US-NY2163,,Temperate,,Wilderness,Hilly,Hunter,37,M,1,Rain,UTM,57500,4864830,573520,4864670,10,43.80591,-74.50039,43.9334,-74.0836
      US,NY,US-NY2165,,Temperate,m210,Wilderness,Mountainous,Youth,14,M,1,Clear,UTM,180602,4890150,180600,4888550,10,44.09674,-72.98959,44.08237,-72.98864
      US,NY,US-NY2168,,Temperate,m210,Wilderness,Mountainous,Hiker,50,F,1,Snow,UTM,591481,4890139,591173,4889474,1,44.16065,-73.85545,44.1547,-73.85942
      US,NY,US-NY2170,Dickersn Center,Temperate,,Wilderness,Flat,Child,3,M,1,Clear,UTM,53304,4950780,53304,4950780,10,44.57354,-74.62532,44.57354,-74.62532
      US,NY,US-NY2172,,Temperate,m210,Wilderness,Mountainous,Hiker,18,FF,3,Snow,UTM,58940,4822500,58900,4824300,10,43.42736,-74.4481,43.44347,-74.45004
      US,NY,US-NY2175,,Temperate,,Wilderness,Flat,Youth,"12,12,12,13",FFFF,3,Clear,UTM,551268,907735,550906,906500,10,8.21342,-74.53422,8.20225,-74.53752
      US,NY,US-NY2183,Keene,Temperate,m210,Wilderness,Mountainous,Hiker,"21,16",MMM,3,Snow,UTM,180594,4899421,180594,4899421,1,44.18,-72.99531,44.18,-72.99531
      US,NY,US-NY2187,,Temperate,m210,Wilderness,Mountainous,Hiker,62,M,1,Clear,UTM,613433,4819331,613088,4819590,1,43.52022,-73.59601,43.5226,-73.60022
      US,NY,US-NY2192,,Temperate,m210,Wilderness,Mountainous,Hiker,"49,41",FM,2,Rain,UTM,597064,4886293,596856,4886894,1,44.12531,-73.78635,44.13075,-73.78884
      US,NY,US-NY2196,Indian Lake,Temperate,,Wilderness,Hilly,Hiker,48,M,1,Clear,UTM,559900,4842400,557700,4842700,10,43.73413,-74.25577,43.73701,-74.28305
      US,NY,US-NY2197,,Temperate,,Wilderness,Hilly,Hunter,57,M,1,Overcast,UTM,546600,4841300,544300,4835500,10,43.72519,-74.42101,43.67311,-74.45004
      US,NY,US-NY2198,,Temperate,,Wilderness,Hilly,Hunter,35,M,1,Clear,UTM,555525,4847930,554566,4847980,10,43.78426,-74.30952,43.78478,-74.32143
      US,NY,US-NY2200,,Temperate,,Wilderness,Hilly,Hunter,45,M,1,Overcast,UTM,52567,4787280,52498,4788120,10,43.10791,-74.4979,43.1154,-74.49942
      US,NY,US-NY2203,,Temperate,,Wilderness,Hilly,Hiker,"43,16",FM,2,Clear,UTM,53349,4782890,53319,4781300,10,43.06902,-74.48481,43.05475,-74.4839
      US,NY,US-NY2204,Indian Lake,Temperate,,Wilderness,Hilly,Hiker,,MM,2,Clear,UTM,545700,4858000,550200,4862000,10,43.8756,-74.43076,43.91132,-74.37437
      US,NY,US-NY2206,,Temperate,,Wilderness,Hilly,Hunter,45,M,1,Snow,UTM,610152,4875706,606162,8741020,10,44.02816,-73.62502,78.70308,-76.13927
      US,NY,US-NY2207,Newcomb,Temperate,,Wilderness,Hilly,Hunter,77,M,1,Overcast,UTM,570500,4866900,568700,4864800,10,43.95377,-74.12092,43.93503,-74.14363
      US,NY,US-NY2216,Inlet,Temperate,,Wilderness,Hilly,Dementia,72,M,1,Clear,UTM,51860,4841640,52190,4841550,10,43.5947,-74.55091,43.59409,-74.54677
      US,NY,US-NY2217,Ft Ann,Temperate,m210,Wilderness,Mountainous,Hiker,18,MF,5,Clear,UTM,61550,4821000,61550,4821000,10,43.41544,-74.4148,43.41544,-74.4148
      US,NY,US-NY2218,,Temperate,m210,Wilderness,Mountainous,Mountain Biker,26,F,1,Overcast,UTM,180614,4830350,180614,4830350,10,43.55961,-72.95376,43.55963,-78.95397
      US,NY,US-NY2229,,Temperate,,Wilderness,Hilly,Despondent,26,M,1,Foggy,UTM,58730,4820800,587300,4820800,10,43.412,-74.44931,43.53696,-73.91907
      US,NY,US-NY2235,,Temperate,,Wilderness,Hilly,Hiker,"48,49",MF,2,Rain,UTM,602597,4808550,601965,4808990,10,43.42473,-73.73209,43.42878,-73.73981
      US,NY,US-NY2236,,Temperate,,Wilderness,Hilly,Hiker,"40,14",MF,2,Clear,UTM,51671,4841997,51694,4842097,1,43.59779,-74.55354,43.5987,-74.55334
      US,NY,US-NY2237,,Temperate,,Wilderness,Hilly,Hiker,36,M,1,Rain,UTM,62265,4824240,62278,4823030,10,43.4449,-74.4086,43.43407,-74.40747
      US,NY,US-NY2238,WASHINGTON,Temperate,220,Wilderness,Hilly,Hiker,40,FF,1,Rain,UTM,61716,4822970,61818,4825600,10,43.4332,-74.41433,43.45684,-74.41519
      US,NY,US-NY2239,WASHINGTON,Temperate,m210,Wilderness,Mountainous,Camper,46,M,1,Overcast,UTM,61607,4822020,61700,4822080,10,43.42462,-74.41491,43.42521,-74.41382
      US,NY,US-NY2240,,Temperate,,Wilderness,Hilly,Hiker,18,M,1,Clear,UTM,53058,4825850,53369,4818440,10,43.4539,-74.52317,43.38768,-74.5133
      US,NY,US-NY2242,SARATOGA,Temperate,220,Wilderness,Flat,Fisher,44,M,1,Clear,UTM,57160,4780500,57160,4780400,10,43.04983,-74.43631,43.04893,-74.43623
      US,NY,US-NY2245,,Temperate,,Wilderness,Hilly,Child,10,M,1,Clear,UTM,63020,4812840,62946,4813340,10,43.34315,-74.39022,43.34759,-74.39152
      US,NY,US-NY2249,HAMILTON,Temperate,m210,Wilderness,Hilly,Hiker,77,M,1,Rain,UTM,51475,4846000,51591,4846630,10,43.63354,-74.55927,43.63926,-74.55836
      US,NY,US-NY2250,,Temperate,m210,Wilderness,Mountainous,Hiker,39,FF,1,Rain,UTM,558840,4893253,56305,4892900,1,44.19204,-74.26329,44.05675,-74.53854
      US,NY,US-NY2253,,Temperate,,Wilderness,Hilly,Dementia,79,M,1,Clear,UTM,63118,4764670,62948,4758250,10,42.91135,-74.35112,42.85369,-74.34821
      US,NY,US-NY2254,,Temperate,,Wilderness,Flat,Snowmobile,29,M,1,Overcast,UTM,5526,4834500,5572,4843700,100,43.50153,-75.11533,43.58393,-75.12313
      US,NY,US-NY2258,,Temperate,,Wilderness,Hilly,Hiker,"19,17, 15, 9",MMMM,4,Rain,UTM,55869,4921580,55634,4921300,10,44.31349,-74.56818,44.31083,-74.57087
      US,NY,US-NY2259,WASHINGTON,Temperate,220,Wilderness,Hilly,Hiker,"19,54,9",MMM,3,Clear,UTM,61606,4822060,61602,4821050,10,43.42498,-74.41496,43.41592,-74.4142
      US,NY,US-NY2260,,Temperate,,Wilderness,Hilly,Walkaway,56,F,1,Snow,UTM,53049,4774310,53320,4774160,10,42.99194,-74.48161,42.99075,-74.47818
      US,NY,US-NY2261,,Temperate,,Wilderness,Hilly,Runner,17,M,1,Overcast,UTM,587733,4793553,588171,4793583,1,43.29161,-73.91809,43.29182,-73.91268
      US,NY,US-NY2268,HAMILTON,Temperate,m210,Wilderness,Hilly,Hiker,64,M,1,Rain,UTM,54771,4812130,54651,4802360,10,43.33195,-74.49095,43.24431,-74.48453
      US,NY,US-NY2274,,Temperate,m210,Wilderness,Mountainous,Hiker,"17,35,35",MMM,3,Snow,UTM,5865,4885000,5909,4889900,100,43.95385,-75.15748,43.99774,-75.16148
      US,NY,US-NY2275,,Temperate,m210,Wilderness,Mountainous,Skier-Nordic,"28,26",MM,2,Snow,UTM,58613,4885378,58431,4885378,1,43.99073,-74.5036,43.99062,-74.50586
      US,NY,US-NY2277,,Temperate,,Wilderness,Flat,Hunter,42,M,1,Rain,UTM,444310,4825871,444764,4826004,1,43.58565,-75.68942,43.58688,-75.68381
      US,NY,US-NY2280,ONEIDA,Temperate,210,Wilderness,Flat,Hunter,29,M,1,Overcast,UTM,431536,4788933,431597,4789346,1,43.25201,-75.84301,43.25574,-75.84231
      US,NY,US-NY2281,,Temperate,,Wilderness,Flat,Hunter,20,M,1,Overcast,UTM,477031,4833630,477874,4833749,1,43.65724,-75.28444,43.65833,-75.27399
      US,NY,US-NY2282,,Temperate,,Wilderness,Hilly,Hunter,43,M,1,Rain,UTM,42558,4843240,42558,4843240,10,43.60339,-74.66695,43.60339,-74.66695
      US,NY,US-NY2285,,Temperate,,Wilderness,Hilly,Hunter,57,M,1,Snow,UTM,443905,4804936,444117,4805730,1,43.39713,-75.69228,43.40429,-75.68974
      US,NY,US-NY2286,,Temperate,,Wilderness,Hilly,Hunter,28,M,1,Rain,UTM,447345,4809462,450052,4807025,1,43.43813,-75.65024,43.41637,-75.61657
      US,NY,US-NY2290,,Temperate,,Wilderness,Hilly,Child,12,MM,2,Clear,UTM,497574,4889111,498314,4888958,1,44.15712,-75.02992,44.15575,-75.02066
      US,NY,US-NY2291,,Temperate,,Wilderness,Flat,Hiker,13,M,1,Clear,UTM,52861,4901300,52701,4892980,10,44.12993,-74.58844,44.05528,-74.58339
      US,NY,US-NY2293,,Temperate,,Wilderness,Hilly,Hunter,31,F,1,Clear,UTM,458048,4804390,456158,4803167,1,43.39313,-75.5176,43.38201,-75.54084
      US,NY,US-NY2294,,Temperate,,Wilderness,Hilly,Youth,"16,14,12",MMF,3,Clear,UTM,50136,4836800,99439,4834109,1,43.55029,-74.56816,43.55422,-73.95825
      US,NY,US-NY2295,,Temperate,m210,Wilderness,Mountainous,Hiker,43,M,1,Clear,UTM,51986,4918840,52980,4919070,10,44.28656,-74.61429,44.28923,-74.60208
      US,NY,US-NY2297,,Temperate,,Wilderness,Hilly,Youth,"14,13",FF,2,Clear,UTM,477076,4840718,476387,4840231,1,43.72106,-75.28418,43.71665,-75.29271
      US,NY,US-NY2298,,Temperate,,Water,Water,Boater,68,M,1,Clear,UTM,509116,4903639,503578,4903898,1,44.28787,-74.88532,44.29025,-74.95473
      US,NY,US-NY2300,,Temperate,,Wilderness,Hilly,Worker,54,M,1,Clear,UTM,46200,4850300,46200,4850300,10,43.66888,-74.62796,43.66888,-74.62796
      US,NY,US-NY2301,,Temperate,,Wilderness,Hilly,Hiker,"17,18,18",FFF,3,Clear,UTM,49374,4832426,49700,4834581,1,43.51063,-74.57393,43.53014,-74.57169
      US,NY,US-NY2302,,Temperate,,Wilderness,Mountainous,Hiker,20,F,1,Overcast,UTM,514882,4892794,516175,4892529,1,44.19013,-74.81336,44.18772,-74.79719
      US,NY,US-NY2306,,Temperate,,Wilderness,Flat,Snowmobile,24,M,1,Overcast,UTM,51450,4845931,51362,4845613,1,43.63291,-74.55952,43.63001,-74.56034
      US,NY,US-NY2309,OSWEGO,Temperate,220,Wilderness,Flat,Hunter,20,M,1,Overcast,UTM,418348,4812242,416565,4810368,1,43.46056,-76.00892,43.44349,-76.03067
      US,NY,US-NY2310,OSWEGO,Temperate,220,Wilderness,Flat,Hunter,"18,24",MM,2,Rain,UTM,414117,4788217,414117,4788217,1,43.24379,-76.05745,43.24379,-76.05745
      US,NY,US-NY2312,,Temperate,,Wilderness,Flat,Runaway,13,M,1,Clear,UTM,416367,4696446,416367,4696446,1,42.41774,-76.01611,42.41774,-76.01611
      US,NY,US-NY2313,,Temperate,,Wilderness,Hilly,Hiker,76,M,1,Overcast,UTM,419316,4833994,418403,4833913,1,43.65649,-76.00019,43.65566,-76.0115
      US,NY,US-NY2316,,Temperate,,Wilderness,Hilly,Walkaway,17,M,1,Clear,UTM,436670,4818716,436322,4818984,1,43.52062,-75.78321,43.523,-75.78755
      US,NY,US-NY2317,Greene,Temperate,220,Wilderness,Hilly,Child,4,M,1,Overcast,UTM,441291,4684485,441396,4684332,1,42.31231,-75.71198,42.31094,-75.71069
      US,NY,US-NY2318,Fulton,Temperate,210,Wilderness,Flat,,72,M,1,Clear,UTM,388837,4808262,388898,4808268,1,43.42093,-76.37282,43.42099,-76.37207
      US,NY,US-NY2319,,Temperate,,Wilderness,Mountainous,Hiker,18,M,1,Clear,UTM,378644,4697807,378642,4697813,1,42.42501,-76.47478,42.42506,-76.47481
      US,NY,US-NY2320,Greene,Temperate,220,urban,,Child,3,F,1,Clear,UTM,433109,4691863,433109,461863,1,42.37809,-75.81211,4.17996,-75.60242
      US,NY,US-NY2321,,Temperate,,Wilderness,Flat,Despondent,45,M,1,Rain,UTM,416250,4709900,416250,4709900,1,42.53887,-76.0195,42.53887,-76.0195
      US,NY,US-NY2322,,Temperate,,Wilderness,Hilly,Dementia,78,M,1,Clear,UTM,465598,4707305,466936,4706919,1,42.51927,-75.41841,42.51585,-75.4021
      US,NY,US-NY2324,,Temperate,,Wilderness,Flat,Child,11,F,1,Rain,UTM,371842,4650936,378590,4650626,1,42.00193,-76.5471,42.00021,-76.46558
      US,NY,US-NY2327,TOMPKINS,Temperate,,Wilderness,Hilly,Hiker,"26,23",FM,2,Clear,UTM,378179,4685857,377174,4684249,1,42.31735,-76.47791,42.30271,-76.48976
      US,NY,US-NY2328,BROOME,Temperate,210,Wilderness,Hilly,Despondent,19,M,1,Rain,UTM,422610,4691201,422612,4691158,1,42.37115,-75.93954,42.37077,-75.93951
      US,NY,US-NY2329,,Temperate,,Wilderness,Hilly,Despondent,14,M,1,Overcast,UTM,374793,4698950,375384,4699633,1,42.43469,-76.52183,42.44093,-76.51479
      US,NY,US-NY2334,ONTARIO,Temperate,210,Wilderness,Flat,Child,"4,2",FM,2,Overcast,UTM,29521,4728204,29527,4728313,1,42.56466,-74.73046,42.56564,-74.73048
      US,NY,US-NY2335,,Temperate,,Wilderness,Flat,Dementia,68,F,1,Clear,UTM,282350,4726866,282180,4727825,1,42.66541,-77.65544,42.67399,-77.65788
      US,NY,US-NY2336,,Temperate,,Wilderness,Flat,Elderly,79,M,1,Clear,UTM,324390,4667757,324088,4668173,1,42.14418,-77.1248,42.14786,-77.12858
      US,NY,US-NY2337,,Temperate,,Wilderness,Flat,Hiker,39,M,1,Rain,UTM,295983,4690764,295250,4692614,1,42.34432,-77.47638,42.36077,-77.48593
      US,NY,US-NY2338,,Temperate,,Wilderness,Flat,Dementia,74,M,1,Rain,UTM,306753,4690865,307162,4690565,1,42.34797,-77.34577,42.34538,-77.34071
      US,NY,US-NY2342,,Temperate,,Wilderness,Hilly,Psychotic,48,F,1,Clear,UTM,735062,4684199,735154,4685099,1,42.27649,-72.14901,42.28456,-72.14753
      US,NY,US-NY2343,CATTAREUGUS,Temperate,,Wilderness,Hilly,Animal,77,M,1,Rain,UTM,1707013,4670873,1707056,4669392,1,42.03712,-74.95446,41.26768,-72.60778
      US,NY,US-NY2344,,Temperate,,Wilderness,Flat,Hiker,21,M,1,Snow,UTM,269837,4681777,269819,4681811,1,42.25618,-77.78988,42.25648,-77.79011
      US,NY,US-NY2348,,Temperate,,Wilderness,Flat,Hiker,"19,18",FF,2,Overcast,UTM,673888,4695952,673888,4695952,1,42.39831,-72.88675,42.39831,-72.88675
      US,NY,US-NY2350,,Temperate,,Wilderness,Flat,Hunter,37,M,1,Clear,UTM,664286,4672902,662299,4671881,1,42.19293,-73.00989,42.18416,-73.03423
      US,WA,US-WA0072,Olympia PD,Temperate,m240,Suburban,Flat,Dementia,81,M,1,Clear,,47 02 57,122 50 37,47 07 19,122 52 28,30,47.0492,-122.8436,47.1219,-122.8744
      US,WA,US-WA0219,Snohomish ,Temperate,m240,Wilderness,Mountainous,Hiker,27,F,1,Partly cloudy,,48 09.615,121 29.499,48 08.517,121 28.433,1.8,48.1103,-121.4917,48.142,-121.4739
      US,WA,US-WA0371,Whatm ,Temperate,m240,Wilderness,Mountainous,Hiker,"22,28",FM ,2,Rain,,"48 38'53""","122 26'29""","048 39'45.4""","122 24'57.2""",3,48.64606,-122.42472,48.75667,-122.41589
      \ No newline at end of file
      requirements.txt 0 → 100644
      View file @ a167f3f3
      arcgis==1.8.2
      argon2-cffi @ file:///C:/ci/argon2-cffi_1596828549974/work
      attrs @ file:///tmp/build/80754af9/attrs_1600298409949/work
      backcall==0.2.0
      bleach @ file:///tmp/build/80754af9/bleach_1600439572647/work
      blinker==1.4
      brotlipy==0.7.0
      certifi==2020.6.20
      cffi @ file:///C:/ci/cffi_1600699250966/work
      chardet==3.0.4
      colorama==0.4.3
      cryptography==2.8
      cycler==0.10.0
      decorator==4.4.2
      defusedxml==0.6.0
      entrypoints==0.3
      idna @ file:///tmp/build/80754af9/idna_1593446292537/work
      importlib-metadata @ file:///C:/ci/importlib-metadata_1593446525189/work
      ipykernel @ file:///C:/ci/ipykernel_1596208728219/work/dist/ipykernel-5.3.4-py3-none-any.whl
      ipython @ file:///C:/ci/ipython_1598883894321/work
      ipython-genutils==0.2.0
      ipywidgets==7.5.1
      jedi==0.16.0
      Jinja2==2.11.2
      jsonschema @ file:///C:/ci/jsonschema_1594363671836/work
      jupyter-client==6.1.2
      jupyter-core @ file:///C:/Users/bri11091/conda-bld/jupyter_core_1595863163509/work
      keyring==21.2.0
      kiwisolver==1.2.0
      lerc @ file:///Volumes/JetDrive/virtualenv/miniconda/conda-bld/lerc_1594078116229/work
      MarkupSafe @ file:///C:/ci/markupsafe_1594405949945/work
      matplotlib @ file:///C:/ci/matplotlib-base_1592846084747/work
      mistune @ file:///C:/ci/mistune_1594373272338/work
      mkl-fft==1.2.0
      mkl-random==1.1.1
      mkl-service==2.3.0
      nbconvert==5.6.1
      nbformat==5.0.7
      notebook @ file:///C:/Users/bri11091/conda-bld/notebook_1599601693431/work
      ntlm-auth==1.4.0
      numpy @ file:///C:/ci/numpy_and_numpy_base_1596233945180/work
      oauthlib==3.1.0
      packaging==20.4
      pandas @ file:///C:/ci/pandas_1598371525254/work
      pandocfilters==1.4.2
      parso @ file:///tmp/build/80754af9/parso_1596826841367/work
      pickleshare @ file:///C:/ci/pickleshare_1594374056827/work
      plotly @ file:///tmp/build/80754af9/plotly_1599764639247/work
      prometheus-client==0.7.1
      prompt-toolkit @ file:///tmp/build/80754af9/prompt-toolkit_1598885458782/work
      pycparser @ file:///tmp/build/80754af9/pycparser_1594388511720/work
      Pygments==2.6.1
      PyJWT==1.7.1
      pyOpenSSL @ file:///tmp/build/80754af9/pyopenssl_1594392929924/work
      pyparsing==2.4.7
      pyproj==2.6.1.post1
      pyrsistent @ file:///C:/ci/pyrsistent_1600123688363/work
      pyshp @ file:///tmp/build/80754af9/pyshp_1600141782871/work
      PySocks @ file:///C:/ci/pysocks_1594394709107/work
      python-dateutil==2.8.1
      pytz==2019.3
      pywin32-ctypes==0.2.0
      pywinpty==0.5.7
      pyzmq==19.0.0
      requests @ file:///tmp/build/80754af9/requests_1592841827918/work
      requests-kerberos==0.12.0
      requests-ntlm==1.1.0
      requests-oauthlib==1.3.0
      requests-toolbelt==0.9.1
      retrying==1.3.3
      scipy @ file:///C:/ci/scipy_1597686737426/work
      Send2Trash==1.5.0
      six==1.15.0
      terminado==0.8.3
      testpath==0.4.4
      tornado==6.0.4
      traitlets==4.3.3
      urllib3 @ file:///tmp/build/80754af9/urllib3_1597086586889/work
      wcwidth @ file:///tmp/build/80754af9/wcwidth_1593447189090/work
      webencodings==0.5.1
      widgetsnbextension==3.5.1
      win-inet-pton==1.1.0
      wincertstore==0.2
      winkerberos==0.7.0
      zipp==3.1.0
      test_engine.m 0 → 100644
      View file @ a167f3f3
      function temp = test_engine(input_string)
      temp = input_string;
      temp
      end
      \ No newline at end of file
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