intersect¶

In [ ]:

intersect(vdf: vDataFrame, 
          index: str, 
          gid: str, 
          g: str = "", 
          x: str = "", 
          y: str = "",)

Spatially intersects a point or points with a set of polygons.

Parameters¶

Name	Type	Optional	Description
vdf	vDataFrame	❌	vDataFrame to use to compute the spatial join.
index	str	❌	Name of the index.
gid	str	❌	An integer column or integer that uniquely identifies the spatial object(s) of g or x and y.
g	str	✓	A geometry or geography (WGS84) column that contains points. The g column can contain only point geometries or geographies.
x	str	✓	x-coordinate or longitude.
y	str	✓	y-coordinate or latitude.

Returns¶

vDataFrame : object containing the result of the intersection.

Example¶

In [38]:

from verticapy.geo import *
from verticapy.datasets import load_world, load_cities

world = load_world()
world["id"] = "ROW_NUMBER() OVER(ORDER BY country, pop_est)"
display(world)

cities = load_cities()
cities["id"] = "ROW_NUMBER() OVER (ORDER BY city)"
cities["lat"] = "ST_X(geometry)"
cities["lon"] = "ST_Y(geometry)"
display(cities)

	123 pop_est Int	Abc continent Varchar(32)	Abc country Varchar(82)	🌎 Geometry(1048576)	123 id Integer
1	34124811	Asia	Afghanistan		1
2	3047987	Europe	Albania		2
3	40969443	Africa	Algeria		3
4	29310273	Africa	Angola		4
5	4050	Antarctica	Antarctica		5
6	44293293	South America	Argentina		6
7	3045191	Asia	Armenia		7
8	23232413	Oceania	Australia		8
9	8754413	Europe	Austria		9
10	9961396	Asia	Azerbaijan		10
11	329988	North America	Bahamas		11
12	157826578	Asia	Bangladesh		12
13	9549747	Europe	Belarus		13
14	11491346	Europe	Belgium		14
15	360346	North America	Belize		15
16	11038805	Africa	Benin		16
17	758288	Asia	Bhutan		17
18	11138234	South America	Bolivia		18
19	3856181	Europe	Bosnia and Herz.		19
20	2214858	Africa	Botswana		20
21	207353391	South America	Brazil		21
22	443593	Asia	Brunei		22
23	7101510	Europe	Bulgaria		23
24	20107509	Africa	Burkina Faso		24
25	11466756	Africa	Burundi		25
26	16204486	Asia	Cambodia		26
27	24994885	Africa	Cameroon		27
28	35623680	North America	Canada		28
29	5625118	Africa	Central African Rep.		29
30	12075985	Africa	Chad		30
31	17789267	South America	Chile		31
32	1379302771	Asia	China		32
33	47698524	South America	Colombia		33
34	4954674	Africa	Congo		34
35	4930258	North America	Costa Rica		35
36	4292095	Europe	Croatia		36
37	11147407	North America	Cuba		37
38	1221549	Asia	Cyprus		38
39	10674723	Europe	Czechia		39
40	24184810	Africa	Côte d'Ivoire		40
41	83301151	Africa	Dem. Rep. Congo		41
42	5605948	Europe	Denmark		42
43	865267	Africa	Djibouti		43
44	10734247	North America	Dominican Rep.		44
45	16290913	South America	Ecuador		45
46	97041072	Africa	Egypt		46
47	6172011	North America	El Salvador		47
48	778358	Africa	Eq. Guinea		48
49	5918919	Africa	Eritrea		49
50	1251581	Europe	Estonia		50
51	105350020	Africa	Ethiopia		51
52	2931	South America	Falkland Is.		52
53	920938	Oceania	Fiji		53
54	5491218	Europe	Finland		54
55	140	Seven seas (open ocean)	Fr. S. Antarctic Lands		55
56	67106161	Europe	France		56
57	1772255	Africa	Gabon		57
58	2051363	Africa	Gambia		58
59	4926330	Asia	Georgia		59
60	80594017	Europe	Germany		60
61	27499924	Africa	Ghana		61
62	10768477	Europe	Greece		62
63	57713	North America	Greenland		63
64	15460732	North America	Guatemala		64
65	12413867	Africa	Guinea		65
66	1792338	Africa	Guinea-Bissau		66
67	737718	South America	Guyana		67
68	10646714	North America	Haiti		68
69	9038741	North America	Honduras		69
70	9850845	Europe	Hungary		70
71	339747	Europe	Iceland		71
72	1281935911	Asia	India		72
73	260580739	Asia	Indonesia		73
74	82021564	Asia	Iran		74
75	39192111	Asia	Iraq		75
76	5011102	Europe	Ireland		76
77	8299706	Asia	Israel		77
78	62137802	Europe	Italy		78
79	2990561	North America	Jamaica		79
80	126451398	Asia	Japan		80
81	10248069	Asia	Jordan		81
82	18556698	Asia	Kazakhstan		82
83	47615739	Africa	Kenya		83
84	1895250	Europe	Kosovo		84
85	2875422	Asia	Kuwait		85
86	5789122	Asia	Kyrgyzstan		86
87	7126706	Asia	Laos		87
88	1944643	Europe	Latvia		88
89	6229794	Asia	Lebanon		89
90	1958042	Africa	Lesotho		90
91	4689021	Africa	Liberia		91
92	6653210	Africa	Libya		92
93	2823859	Europe	Lithuania		93
94	594130	Europe	Luxembourg		94
95	2103721	Europe	Macedonia		95
96	25054161	Africa	Madagascar		96
97	19196246	Africa	Malawi		97
98	31381992	Asia	Malaysia		98
99	17885245	Africa	Mali		99
100	3758571	Africa	Mauritania		100

Rows: 1-100 | Columns: 5

	Abc city Varchar(82)	🌎 Geometry(1048576)	123 id Integer	🌎 lat Float	🌎 lon Float
1	Abidjan		1	-4.04199411850709	5.32194282609856
2	Abu Dhabi		2	54.366593382592	24.4666835723799
3	Abuja		3	7.53138214293233	9.0852790077542
4	Accra		4	-0.218661598960694	5.55198046444593
5	Addis Ababa		5	38.6980585753487	9.03525622129575
6	Algiers		6	3.04860667090924	36.7650106566281
7	Amman		7	35.9313540668741	31.9519711058275
8	Amsterdam		8	4.91469431740097	52.3519145466644
9	Andorra		9	1.51648596050552	42.5000014435459
10	Ankara		10	32.8624457823566	39.9291844440755
11	Antananarivo		11	47.5146780415299	-18.9146914920322
12	Apia		12	-171.738641608603	-13.8415450424484
13	Ashgabat		13	58.3832991117746	37.949994933111
14	Asmara		14	38.9333235257593	15.3333392526819
15	Astana		15	71.427774209483	51.1811253042576
16	Asuncion		16	-57.6434510279013	-25.2944571170577
17	Athens		17	23.7313752256794	37.9852720905523
18	Baghdad		18	44.3919229145641	33.3405943561586
19	Baku		19	49.8602713032578	40.397217891343
20	Bamako		20	-8.0019849632497	12.6519605263233
21	Bandar Seri Begawan		21	114.933284056662	4.88333111461924
22	Bangkok		22	100.514698793695	13.751945064088
23	Bangui		23	18.5582881252873	4.36664430634909
24	Banjul		24	-16.5917014892126	13.4538764603159
25	Basseterre		25	-62.7170093196993	17.3020304554894
26	Beijing		26	116.386339825659	39.9308380899091
27	Beirut		27	35.5077623513777	33.8739209756269
28	Belgrade		28	20.4660448220205	44.8205913044467
29	Belmopan		29	-88.7670729998165	17.2520335072469
30	Berlin		30	13.3996027647005	52.5237645222512
31	Bern		31	7.46697546248242	46.9166827586677
32	Bishkek		32	74.5832583639037	42.8750253050901
33	Bissau		33	-15.5983608413207	11.8650238229806
34	Bloemfontein		34	26.2299128811774	-29.1199938773787
35	Bogota		35	-74.0852898137744	4.59836942114782
36	Brasilia		36	-47.9179981470031	-15.781394372879
37	Bratislava		37	17.1169807522346	48.1500183299617
38	Brazzaville		38	15.2827436338487	-4.25723991319751
39	Bridgetown		39	-59.6165267350516	13.1020025827511
40	Brussels		40	4.33137074969045	50.8352629353303
41	Bucharest		41	26.0980007953504	44.4353176634946
42	Budapest		42	19.0813748187597	47.5019521849914
43	Buenos Aires		43	-58.3994772323314	-34.6005557499074
44	Bujumbura		44	29.3600060615284	-3.37608722037464
45	Cairo		45	31.2480223611261	30.0519062051037
46	Canberra		46	149.129026244299	-35.2830285453721
47	Cape Town		47	18.433042299226	-33.9180651086288
48	Caracas		48	-66.9189830510504	10.5029444130333
49	Castries		49	-61.0000081803695	14.0019734893303
50	Chisinau		50	28.8577111396514	47.0050236196706
51	Colombo		51	79.8577506092564	6.93196575818212
52	Conakry		52	-13.6821808861239	9.53346870502179
53	Cotonou		53	2.5180447405686	6.40195442278247
54	Dakar		54	-17.4750759870506	14.7177775836233
55	Damascus		55	36.2980500304171	33.5019798542061
56	Dar es Salaam		56	39.2663959776946	-6.79806673612438
57	Dhaka		57	90.4066336081075	23.7250055703128
58	Dili		58	125.579455931705	-8.55938840854645
59	Djibouti		59	43.1480016670523	11.5950144642555
60	Doha		60	51.5329678942993	25.2865560089066
61	Dublin		61	-6.25085154039107	53.3350069945849
62	Dushanbe		62	68.7738793527017	38.5600352163166
63	Freetown		63	-13.2361615990127	8.47195727109818
64	Funafuti		64	179.216647094029	-8.51665199904107
65	Gaborone		65	25.9119477932854	-24.6463134574389
66	Georgetown		66	-58.1670286474806	6.80197369275203
67	Guatemala		67	-90.5289114365615	14.6230805214482
68	Hanoi		68	105.848068341242	21.0352731077371
69	Harare		69	31.0427635720628	-17.8158438357778
70	Hargeysa		70	44.0653100166654	9.56002239881775
71	Havana		71	-82.3661280299533	23.1339046995422
72	Helsinki		72	24.9321804828456	60.1775092325681
73	Honiara		73	159.949765733606	-9.4379942950896
74	Islamabad		74	73.1646886210596	33.7019418089596
75	Jakarta		75	106.82749176247	-6.17247184679889
76	Jerusalem		76	35.2066259345987	31.7784078155733
77	Johannesburg		77	28.0280638650195	-26.1680988813841
78	Juba		78	31.5800255927873	4.82997519827796
79	Kabul		79	69.1813141907051	34.5186361449003
80	Kampala		80	32.5813776671211	0.318604813383331
81	Kathmandu		81	85.3146963522279	27.7186377724772
82	Khartoum		82	32.5322333800116	15.5900240842777
83	Kiev		83	30.5146821104722	50.4353131876072
84	Kigali		84	30.0585859190641	-1.95164421006325
85	Kingston		85	-76.7674337136691	17.9770766238309
86	Kingstown		86	-61.2120624202793	13.1482788278684
87	Kinshasa		87	15.3130260231717	-4.32777824327599
88	Kuala Lumpur		88	101.698037416746	3.16861173071237
89	Kuwait		89	47.9763552876253	29.3716634886296
90	København		90	12.5615398887033	55.6805100490259
91	La Paz		91	-68.1519310491022	-16.4960277550434
92	Libreville		92	9.4579650458237	0.385388609718518
93	Lilongwe		93	33.7833019599835	-13.9832950654692
94	Lima		94	-77.0520079534347	-12.0460668175256
95	Lisbon		95	-9.14681216410213	38.7246687364878
96	Ljubljana		96	14.5149690334741	46.0552883087945
97	Lome		97	1.22081126074562	6.13388293026838
98	London		98	-0.118667702475932	51.5019405883275
99	Luanda		99	13.2324811826686	-8.83634025501266
100	Lusaka		100	28.2813817361143	-15.4146984093359

Rows: 1-100 | Columns: 5

In [39]:

# Creating Index
create_index(world, "id", "geometry", "world_polygons", True)

Out[39]:

	Abc type Varchar(20)	123 polygons Integer	123 SRID Integer	123 min_x Float	123 min_y Float	123 max_x Float	123 max_y Float	Abc info Varchar(500)
1	GEOMETRY	177	0	-180.0	-90.0	180.0	83.64513

Rows: 1-1 | Columns: 8

In [40]:

# Intersect using Geometry
intersect(cities, "world_polygons", "id", "geometry")

Out[40]:

	123 point_id Int	123 polygon_gid Int
1	1	124
2	2	69
3	3	171
4	4	129
5	5	166
6	6	145
7	7	90
8	8	112
9	9	157
10	10	160
11	11	126
12	13	61
13	14	67
14	15	116
15	16	75
16	17	94
17	18	143
18	19	89
19	20	114
20	21	11
21	22	158
22	23	65
23	24	33
24	26	177
25	27	72
26	28	77
27	29	10
28	30	159
29	31	81
30	32	66
31	33	28
32	34	153
33	35	149
34	36	173
35	37	62
36	38	162
37	40	101
38	41	120
39	42	87
40	43	147
41	44	100
42	45	164
43	46	122
44	47	153
45	48	136
46	50	47
47	51	121
48	52	104
49	53	96
50	54	107
51	55	115
52	56	152
53	57	170
54	58	25
55	60	36
56	61	59
57	62	83
58	65	35
59	66	17
60	67	108
61	68	163
62	69	106
63	70	48
64	71	98
65	72	63
66	73	16
67	74	172
68	75	174
69	76	54
70	77	153
71	78	105
72	79	139
73	80	144
74	81	133
75	82	141
76	83	146
77	84	102
78	85	40
79	87	162
80	88	137
81	89	39
82	90	64
83	91	97
84	92	27
85	93	117
86	94	135
87	95	95
88	96	32
89	97	80
90	98	156
91	99	132
92	100	109
93	101	13
94	102	150
95	106	68
96	108	165
97	109	128
98	110	31
99	111	26
100	113	167

Rows: 1-100 | Columns: 2

In [41]:

# Intersect using Latitude and Longitude
intersect(cities, "world_polygons", "id", x="lat", y="lon")

Out[41]:

	123 point_id Int	123 polygon_gid Int
1	1	124
2	2	69
3	3	171
4	4	129
5	5	166
6	6	145
7	7	90
8	8	112
9	9	157
10	10	160
11	11	126
12	13	61
13	14	67
14	15	116
15	16	75
16	17	94
17	18	143
18	19	89
19	20	114
20	21	11
21	22	158
22	23	65
23	24	33
24	26	177
25	27	72
26	28	77
27	29	10
28	30	159
29	31	81
30	32	66
31	33	28
32	34	153
33	35	149
34	36	173
35	37	62
36	38	162
37	40	101
38	41	120
39	42	87
40	43	147
41	44	100
42	45	164
43	46	122
44	47	153
45	48	136
46	50	47
47	51	121
48	52	104
49	53	96
50	54	107
51	55	115
52	56	152
53	57	170
54	58	25
55	60	36
56	61	59
57	62	83
58	65	35
59	66	17
60	67	108
61	68	163
62	69	106
63	70	48
64	71	98
65	72	63
66	73	16
67	74	172
68	75	174
69	76	54
70	77	153
71	78	105
72	79	139
73	80	144
74	81	133
75	82	141
76	83	146
77	84	102