In this example I show how we can use the H3 API to do a simple workflow whereby we create a series of hex indexes along the boundary of a polygon and then calculate the distance of a point to all of the polygon boundary indices.
First we need to import h3 and pandas which will provide all the data structures and processing capabilities we need:
import h3
import pandasNow we set two variables:
- The zoom level that the H3 indexes should be taken from
- A polygon boundary for the breede river district in South Africa
zoom_level = 12
breede = {
"type": "Polygon",
"coordinates": [ [
[ 19.413943058300504, -33.690263063631619 ],
[ 19.414477991361053, -33.690227021447981 ],
[ 19.414438931692246, -33.689170063457794 ],
[ 19.414936984378812, -33.688270014695007 ],
[ 19.416262917641831, -33.687189000642718 ],
[ 19.417801918883526, -33.686549042335429 ],
[ 19.419035986487756, -33.686122068187757 ],
[ 19.420098476534037, -33.685790983012424 ],
[ 19.42046585535013, -33.685977731815115 ],
[ 19.420627793719461, -33.685591661354948 ],
[ 19.420806244438012, -33.685239034688436 ],
[ 19.420883022671092, -33.685087322241003 ],
[ 19.421172449787662, -33.684596226534069 ],
[ 19.421499595468532, -33.684041260724939 ],
[ 19.422032181596165, -33.683136266639309 ],
[ 19.42340346095537, -33.680821771716012 ],
[ 19.423931269398196, -33.679928260837698 ],
[ 19.42426788662965, -33.679296600614549 ],
[ 19.42443409976957, -33.678947578166401 ],
[ 19.424567036753899, -33.678602662850807 ],
[ 19.424679270437395, -33.678270152751907 ],
[ 19.42471665372554, -33.678133024815992 ],
[ 19.424784044227067, -33.677888105605263 ],
[ 19.424841376444789, -33.677680066768517 ],
[ 19.425115632316619, -33.676562172342244 ],
[ 19.425364993936029, -33.675556427780464 ],
[ 19.425984165123452, -33.673071361127569 ],
[ 19.426752953282517, -33.670008613707978 ],
[ 19.426894272170045, -33.669447612928565 ],
[ 19.426942132837155, -33.66925382332721 ],
[ 19.426989825866201, -33.66906112337324 ],
[ 19.427071381784089, -33.669073109494775 ],
[ 19.427454350940081, -33.66912960352218 ],
[ 19.429079434327377, -33.669394220205362 ],
[ 19.429167695767774, -33.669524223523581 ],
[ 19.429659797303088, -33.669180733131526 ],
[ 19.429645883343824, -33.669292128624704 ],
[ 19.429678321309126, -33.66947326155227 ],
[ 19.430769477464253, -33.668883510845006 ],
[ 19.43381981966715, -33.667230683358213 ],
[ 19.433963820763665, -33.667439560385304 ],
[ 19.434706625022898, -33.667035636471354 ],
[ 19.438839992934874, -33.664780066327381 ],
[ 19.438878717327526, -33.664835470707345 ],
[ 19.439236121678846, -33.664599352494946 ],
[ 19.440985257233081, -33.667210147695442 ],
[ 19.441022891978349, -33.667251638116142 ],
[ 19.441333692948007, -33.667595463784323 ],
[ 19.44171423135208, -33.668016402961683 ],
[ 19.442001479173854, -33.668270458446898 ],
[ 19.442321751694095, -33.668546642156457 ],
[ 19.442586871291496, -33.66877856941727 ], [ 19.443161199296924, -33.669136728139875 ], [ 19.443868129010582, -33.669518607648456 ], [ 19.444762897174371, -33.669894200729658 ], [ 19.446872538384099, -33.670700539814959 ], [ 19.450053722095561, -33.671805023196129 ], [ 19.448206685912226, -33.674534254688439 ], [ 19.447434964087051, -33.676994007993756 ], [ 19.450700469744419, -33.680844402854575 ], [ 19.452771973294716, -33.68323265852581 ], [ 19.454185329807842, -33.684816670587537 ], [ 19.454526557086041, -33.685523432663132 ], [ 19.455062663613006, -33.686254502257924 ], [ 19.45535519203375, -33.686546946859636 ], [ 19.456061870290313, -33.68586457612227 ], [ 19.456689591019, -33.685165860673749 ], [ 19.457311863510597, -33.684543504363091 ], [ 19.457830451859934, -33.683995244076527 ], [ 19.458719436510506, -33.683165519481378 ], [ 19.458823120652767, -33.68303216340189 ], [ 19.459697353153757, -33.68371377976797 ], [ 19.460408641457065, -33.684232368117307 ], [ 19.460690189584682, -33.68429162817273 ], [ 19.461030997767722, -33.684202779999111 ], [ 19.461445818155767, -33.684187860211438 ], [ 19.462216366514497, -33.68429162817273 ], [ 19.463357227355459, -33.684336052259567 ], [ 19.465076020420213, -33.684232368117307 ], [ 19.466972677470324, -33.684143436124629 ], [ 19.467239389629299, -33.683891559934267 ], [ 19.470727434815927, -33.685084556212956 ], [ 19.47046793109368, -33.685969014635816 ], [ 19.470282942490627, -33.686600004306712 ], [ 19.467810951606708, -33.691662003271261 ], [ 19.467081977487709, -33.693154065855197 ], [ 19.466663971976431, -33.694046067990911 ], [ 19.466162985623754, -33.695037060403109 ], [ 19.465905996472458, -33.695565036484027 ], [ 19.463880928665731, -33.699710055241383 ], [ 19.463355970069983, -33.700780005181485 ], [ 19.455147991387321, -33.698647062280912 ], [ 19.455030141828701, -33.698613534668226 ], [ 19.45487951902868, -33.698901117766098 ], [ 19.454713473526795, -33.69923320876984 ], [ 19.454235956502998, -33.699482402751187 ], [ 19.453509245497855, -33.699607041651376 ], [ 19.452658063230587, -33.699710809612668 ], [ 19.451952139345309, -33.699939132655118 ], [ 19.451536983681137, -33.700333668837487 ], [ 19.451200953182905, -33.700982009047976 ], [ 19.451165916827648, -33.701073036516419 ], [ 19.451069944036306, -33.701323068688112 ], [ 19.450979922396215, -33.701668067822709 ], [ 19.450967936274679, -33.701712072814388 ], [ 19.450967936274679, -33.7021130630622 ], [ 19.449808886703863, -33.702325460488623 ], [ 19.449619623330193, -33.700995000997892 ], [ 19.448749581780788, -33.698674638742375 ], [ 19.447734365668396, -33.697224318036263 ], [ 19.445268828849919, -33.696281773024396 ], [ 19.442875963131939, -33.695556570761823 ], [ 19.44055551705739, -33.695701577686719 ], [ 19.438452665189203, -33.695846668430647 ], [ 19.436204638758085, -33.695701577686719 ], [ 19.434101870708929, -33.695484067299361 ], [ 19.431708837352886, -33.694396347724535 ], [ 19.43062111777806, -33.693598725818532 ], [ 19.429098377428545, -33.69309111776235 ], [ 19.428228252060109, -33.692583509706168 ], [ 19.42685043481643, -33.693018530480884 ], [ 19.424965177154661, -33.693163621224812 ], [ 19.421194494193003, -33.693163621224812 ], [ 19.418728957374526, -33.693018530480884 ], [ 19.416771028612231, -33.692365915499778 ], [ 19.415393379006616, -33.691423370487911 ],
[ 19.413943058300504, -33.690263063631619 ] ] ]
}Next we fill the polygon wht Hex indices at the given zoom level:
hexes = h3.polyfill(breede, zoom_level, geo_json_conformant=True)And then convert the list of indices to a pandas dataframe.
df = pandas.DataFrame(hexes)
df.sort_values(by=[0], inplace=True)
<style scoped>
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vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
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text-align: right;
}
| 0 | |
|---|---|
| 8232 | 8cbc6b2800801ff |
| 9101 | 8cbc6b2800803ff |
| 27700 | 8cbc6b2800805ff |
| 17144 | 8cbc6b2800807ff |
| 15034 | 8cbc6b2800809ff |
| ... | ... |
| 10468 | 8cbc6b29d9a69ff |
| 34051 | 8cbc6b29d9a6bff |
| 21212 | 8cbc6b29d9a6dff |
| 3265 | 8cbc6b29dd249ff |
| 13653 | 8cbc6b29dd24dff |
35645 rows × 1 columns
Now we so some work to find the number of neighbours our indices each have. Any index that has less than 7 neighbours can be considered an edge index so we will keep it in our edge set:
%time edge_hexes = []
for index in hexes:
neighbours = h3.k_ring(index,1)
count = len(list(filter(hexes.__contains__, neighbours)))
# If we get 7 it is the central hex + its 6 neighbours
# to find the edge hexes we include only the ones less than 7
#print (index, count)
if count < 7:
edge_hexes.append(index)
df_edges = pandas.DataFrame(edge_hexes, columns=['h3_index'])Next we calculate the centroids of all edge indices and add them to our dataframe, then make a little plot of the result.
df_edges['lat'] = df_edges['h3_index'].apply(lambda row: h3.h3_to_geo(row)[0])
df_edges['lng'] = df_edges['h3_index'].apply(lambda row: h3.h3_to_geo(row)[1])
df_edges.plot(kind='hexbin', x='lat', y='lng', figsize=(17,15))
CPU times: user 2.84 ms, sys: 0 ns, total: 2.84 ms
Wall time: 2.73 ms
Now we get the location of a cell somewhere outside the polygon and calculate the distance to any point on the edge.
# Example origin location
index = "87bc4ed9affffff"
location = h3.h3_to_geo(index)Now we compute the distance from our location to the each edge cell.
df_edges['distance'] = df_edges['h3_index'].apply(
lambda h3_index: h3.point_dist(h3.h3_to_geo(h3_index), location, unit='km'))
df_edges.sort_values(by=['distance'], inplace=True)The results are sorted by disance as we cam see in the table below:
print(df_edges) h3_index lat lng distance
532 8cbc6b282ca51ff -33.685098 19.470601 264.585027
234 8cbc6b282ca59ff -33.685260 19.470622 264.592333
799 8cbc6b282ca53ff -33.685023 19.470415 264.595815
916 8cbc6b282c125ff -33.684948 19.470229 264.606604
1 8cbc6b282c165ff -33.685346 19.470456 264.610426
.. ... ... ... ...
321 8cbc6b2817631ff -33.690697 19.414593 269.398859
624 8cbc6b2817459ff -33.690460 19.414386 269.402592
374 8cbc6b2817633ff -33.690622 19.414406 269.409765
446 8cbc6b281745bff -33.690385 19.414200 269.413499
154 8cbc6b28176adff -33.690310 19.414014 269.424407
[991 rows x 4 columns]
Lastly we can make a visualisation of the distance of each part of the edge to the origin point.
df_edges.plot.hexbin(
x='lat',
y='lng',
C='distance',
gridsize=20,
cmap="viridis",
figsize=(17,15))