座標系の可視化

・State Plane Coordinate Systemが初見だったので、緯度経度変換を試みましたのでメモです。
・とりあえずfolium上に点を打ちたかったのでメモです。

import pandas as pd
pd.options.display.max_columns = 100
pd.options.display.max_rows = 999
pd.options.display.float_format = '{:.6f}'.format

import warnings
warnings.filterwarnings('ignore')

from pyproj import Proj, transform
import folium

from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).

path = "/content/drive/MyDrive/tmp/7_probspace/3_taxi/"
zone = pd.read_csv(path+'data/taxi_zones.csv')

# 緯度経度に変換
def sp2latlong(row):
  inProj = Proj(init='epsg:2263', preserve_units=True)
  outProj = Proj(init='epsg:4326')
  return transform(inProj, outProj, row["X"], row["Y"])

zone[["long","lat"]] = zone.apply(lambda x:sp2latlong(x), axis=1, result_type='expand')
zone

	LocationID	X	Y	long	lat
0	0	990618.795051	203105.532318	-73.977023	40.724152
1	1	1005999.211465	216570.160207	-73.921491	40.761085
2	2	979781.103303	198536.380519	-74.016120	40.711612
3	3	993782.574750	231481.048878	-73.965569	40.802033
4	4	988005.473266	189064.601119	-73.986459	40.685615
5	5	985545.308060	192934.884487	-73.995329	40.696238
6	6	985409.939590	186473.826450	-73.995818	40.678504
7	7	997520.588883	232274.162568	-73.952065	40.804205
8	8	1000990.471500	238412.229126	-73.939516	40.821046
9	9	993784.366691	224349.916883	-73.965572	40.782460
10	10	984734.419691	199062.702717	-73.998253	40.713058
11	11	987059.930613	216980.297541	-73.989857	40.762237
12	12	985939.886284	218603.522675	-73.993899	40.766692
13	13	985103.511142	189424.994278	-73.996922	40.686605
14	14	988251.145129	192619.772936	-73.985571	40.695373
15	15	988025.704896	194936.823442	-73.986383	40.701732
16	16	984266.581407	211937.944079	-73.999940	40.748397
17	17	1001328.229336	232772.309614	-73.938310	40.805566
18	18	999491.706781	227336.882497	-73.944957	40.790650
19	19	988348.121326	204486.635308	-73.985214	40.727944
20	20	982084.066286	196731.733219	-74.007812	40.706660
21	21	981114.731618	195542.004470	-74.011308	40.703394
22	22	985142.963791	209806.230709	-73.996778	40.742546
23	23	991023.099286	190887.289491	-73.975577	40.690616
24	24	987356.960940	213802.382670	-73.988786	40.753514
25	25	988875.126508	207912.937312	-73.983310	40.737348
26	26	985738.766591	206141.008242	-73.994628	40.732486
27	27	984616.422735	204729.465979	-73.998678	40.728612
28	28	998816.051812	240775.021260	-73.947368	40.827535
29	29	982261.075534	203550.791285	-74.007176	40.725376
30	30	1015442.387384	215841.865002	-73.887406	40.759057
31	31	1045786.102387	173472.416535	-73.778264	40.642605
32	32	990590.519000	209010.276122	-73.977119	40.740359
33	33	1019479.471765	221607.471802	-73.872804	40.774867
34	34	996835.032220	218174.790003	-73.954568	40.765507
35	35	995409.701765	218659.383729	-73.959713	40.766839
36	36	989414.886084	221232.172168	-73.981352	40.773906
37	37	987581.075606	221911.050507	-73.987973	40.775770
38	38	984968.854761	201803.794503	-73.997407	40.720581
39	39	998464.788931	211283.664795	-73.948699	40.746590
40	40	1002636.940025	214178.874402	-73.933634	40.754529
41	41	986822.878438	201304.868259	-73.990718	40.719212
42	42	993163.012434	229962.809716	-73.967808	40.797866
43	43	996892.499732	237145.858731	-73.954325	40.817577
44	44	981926.019728	207147.398964	-74.008386	40.735248
45	45	990502.253134	215519.865935	-73.977432	40.758226
46	46	991966.940592	215006.459745	-73.972145	40.756816
47	47	990242.622713	217778.289482	-73.978367	40.764425
48	48	988148.815733	212087.794423	-73.985929	40.748808
49	49	994820.493140	234227.640382	-73.961815	40.809570
50	50	990638.973215	211668.106693	-73.976942	40.747654
51	51	1004521.785696	220336.309601	-73.926812	40.771425
52	52	990062.886973	184111.792889	-73.979045	40.672019
53	53	986340.471812	211966.866225	-73.992455	40.748476
54	54	1000792.132152	216799.116340	-73.940286	40.761724
55	55	983595.776972	197398.396504	-74.002360	40.708490
56	56	983868.950387	203012.498007	-74.001375	40.723899
57	57	1010097.755219	222807.414929	-73.906672	40.778193
58	58	990391.043675	205865.651703	-73.977842	40.731728
59	59	1003837.481998	207164.423377	-73.929321	40.735273
60	60	993898.329957	214924.499415	-73.965174	40.756589
61	61	988633.708586	216109.041064	-73.984176	40.759845
62	62	982389.517117	201116.796517	-74.006712	40.718696
63	63	989154.879219	199911.447906	-73.982307	40.715386
64	64	992214.287741	212221.562058	-73.971256	40.749172
65	65	986888.777072	208992.915596	-73.990478	40.740313
66	66	996166.327458	223633.864977	-73.956972	40.780491
67	67	993753.310704	219279.162682	-73.965691	40.768542
68	68	991777.637057	227739.890845	-73.972815	40.791766
69	69	990266.785345	224949.075601	-73.978273	40.784107
70	70	1000390.410604	245761.392287	-73.941667	40.841219
71	71	982999.734449	213409.888398	-74.004513	40.752437
72	72	983558.014551	206915.396729	-74.002497	40.734612
73	73	996132.623796	201170.111116	-73.957134	40.718834
74	74	995586.732453	198307.282684	-73.959108	40.710977
75	75	1010929.715155	211369.883417	-73.903713	40.746798
76	76	980668.187168	197575.689861	-74.012919	40.708976
77	77	999253.108568	222193.818758	-73.945830	40.776534
78	78	997763.158272	222907.634704	-73.951208	40.778496

map = folium.Map(location=[zone["lat"].mean(), zone["long"].mean()],  tiles='cartodbpositron',zoom_start=11)

for i, d in zone.iterrows():
  folium.Marker(location=[d["lat"], d["long"]]).add_to(map)

map

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タクシー需要予測

【EDA】座標系の緯度経度変換とfoliumによる可視化

座標系の可視化

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