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How to join point with polygon in geopandas - python

I have the polygon combination of lat-long1,lat2-long2 ..... and point like Lat - Long .
I have used GeoPandas library to get the result if there is any point is exist within polygon.
Sample Data of Polygon saved in csv file:
POLYGON((28.56056 77.36535,28.564635293716776
77.3675137204626,28.56871055311656 77.36967760850214,28.572785778190855 77.3718416641586,28.576860968931193 77.37400588747194,28.580936125329096 77.3761702784821,28.585011247376094 77.37833483722912,28.58908633506372 77.38049956375293,28.593161388383457 77.38266445809356,28.59723640732686 77.38482952029099,28.60131139188541 77.38699475038526,28.605386342050664 77.38916014841635,28.60946125781409 77.39132571442434,28.613536139167238 77.39349144844923,28.61761098610158 77.39565735053108,28.62168579860863 77.39782342070995,28.62576057667991 77.39998965902589,28.62983532030691 77.402156065519,28.633910029481108 77.40432264022931,28.637984704194054 77.40648938319696,28.642059344437207 77.408656294462,28.64068221074683 77.41187044231611,28.63920739580329 77.41502778244606,28.63763670052024 77.41812446187686,28.635972042808007 77.42115670220443,28.634215455216115 77.42412080422613,28.63236908243526 77.42701315247152,28.630435178662026 77.42983021962735,28.628416104829583 77.43256857085188,28.626314325707924 77.43522486797251,28.624132406877322 77.437795873562,28.621873011578572 77.44027845488824,28.619538897444272 77.4426695877325,28.617132913115164 77.44496636007166,28.614657994745563 77.44716597562005,28.612117162402576 77.44926575722634,28.609513516363293 77.45126315012166,28.606850233314923 77.45315572501488,28.604130562462267 77.45494118103147,28.60135782154758 77.45661734849246,28.598535392787774 77.45818219153013,28.595666718733966 77.45963381053753,28.592755298058414 77.46097044444889,28.589804681274302 77.46219047284835,28.586818466393503 77.46329241790465,28.583800294527727 77.46427494612952,28.58075384543836 77.46513686995802,28.57768283304089 77.46587714914885,28.574591000868892 77.4664948920035,28.571482117503592 77.46698935640259,28.568359971974488 77.46735995065883,28.565228369136484 77.46760623418534,28.56209112502966 77.4677279179792,28.558952062226695 77.4677248649196,28.55581500517431 77.46759708988064,28.552683775533943 77.46734475965891,28.552683775533943 77.46734475965891,28.553079397193876 77.4622453846313,28.553474828308865 77.45714597129259,28.55387006887434 77.4520465196603,28.554265118885752 77.44694702975198,28.554659978338513 77.4418475015852,28.555054647228083 77.43674793517746,28.555449125549913 77.43164833054634,28.555843413299442 77.42654868770937,28.55623751047213 77.42144900668411,28.556631417063407 77.41634928748812,28.55702513306874 77.41124953013893,28.55741865848359 77.40614973465412,28.557811993303396 77.40104990105122,28.55820513752363 77.39595002934782,28.558598091139757 77.39085011956145,28.558990854147225 77.38575017170969,28.559383426541523 77.3806501858101,28.559775808318093 77.37555016188024,28.560167999472434 77.37045009993768,28.56056 77.36535))
and second dataset is for LAT and LONG as 28.56282, 77.36824 respectively saved in csv file .
I have used below Python code to join both data set based on condition if point exist within polygon. like below
import pandas as pd
import shapely.geometry
from shapely.geometry import Point
import geopandas as gpd
site_df = pd.read_csv (r'lat_long_file.csv') # load lat and long file
site_df['geometry'] = pd.DataFrame(site_df).apply(lambda x: Point(x.LAT,x.LONG), axis='columns') # convert lat and long to point
gdf = gpd.GeoDataFrame(site_df, geometry = site_df.geometry,crs='EPSG:4326') #creating geo pandas data frame for point
from shapely import wkt
polygon_df = pd.read_csv (r'polygon_csv_file') #reading polygon sample raw string file
polygon_df['geometry'] = pd.DataFrame(polygon_df).apply(lambda row: shapely.wkt.loads(row.polygon), axis='columns') #converting string polygon to geometory
gd_polygon = gpd.GeoDataFrame(polygon_df, geometry = polygon_df.geometry,crs='EPSG:4326') #create geopandas dataframe
import shapely.speedups
shapely.speedups.enable() # this makes some spatial queries run faster
join_data = gpd.sjoin(gdf, gd_polygon, how="inner", op="within") //actual join condition
But that query does not retun anything . But point is exist within polygon. as we can see in below diagram
Green Location marker is point Lat and long which is exist within polygon.

I would check the axis order - WKT usually interpreted as longitude first, latitude second order, while the point you construct uses latitude:longitude order.
You can try removing the CRS identifier to see if it changes the result.
Also see
https://gis.stackexchange.com/questions/376751/shapely-flips-lat-long-coordinate
and
https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6

your sample data is unusable as it's an image
have sourced a polygon - a county boundary in UK
constructed a geopandas data frame of a point that is within this county
have used plotly to demonstrate visually the data
have used your code fragment gpd.sjoin(gdf, gd_polygon, how="inner", op="within") to do spatial join and it correctly joins point to polygon
import requests, json
import geopandas as gpd
import plotly.express as px
import shapely.geometry
# fmt: off
# get a polygon and construct a point
res = requests.get("https://opendata.arcgis.com/datasets/69dc11c7386943b4ad8893c45648b1e1_0.geojson")
gd_polygon = gpd.GeoDataFrame.from_features(res.json()).loc[lambda d: d["LAD20NM"].str.contains("Hereford")]
gdf = gpd.GeoDataFrame(geometry=gd_polygon.loc[:,["LONG","LAT"]].apply(shapely.geometry.Point, axis=1)).reset_index(drop=True)
# fmt: on
# plot to show point is within polygon
px.scatter_mapbox(gd_polygon, lon="LONG", lat="LAT").update_traces(
name="gd_polygon"
).add_traces(
px.scatter_mapbox(gdf, lat=gdf2.geometry.y, lon=gdf2.geometry.x)
.update_traces(name="gdf", marker_color="red")
.data
).update_traces(
showlegend=True
).update_layout(
mapbox={
"style": "carto-positron",
"layers": [
{"source": json.loads(gd_polygon.geometry.to_json()), "type": "line"}
],
}
).show()
# spatial join, all good :-)
gpd.sjoin(gdf, gd_polygon, how="inner", op="within")
output
spatial join has worked, point is within polygon
geometry
index_right
OBJECTID
LAD20CD
LAD20NM
LAD20NMW
BNG_E
BNG_N
LONG
LAT
Shape__Area
Shape__Length
0
POINT (-2.73931 52.081539)
18
19
E06000019
Herefordshire, County of
349434
242834
-2.73931
52.0815
2.18054e+09
285427

Related

I am trying to replicate the output from ArcGIS Dissolve on a set of stream flow lines using geopandas. Essentially the df/stream_0 layer is a stream network extracted from a DEM using pysheds. That output has some randomly overlapping reaches which I am trying to remove. Running Dissolve through ArcGIS Pro does this well, but I would prefer not to have to deal with ArcGIS/ArcPy to resolve this.
Stream Network
ArcGIS Dissolve Setting
#streams_0.geojson = df.shp = streams_0.shp from Dissolve Setting image
#~~~~~~~~~~~~~~~~~~~~
import geopandas as gpd
df = gpd.read_file('streams_0.geojson')
df.head()
Out[3]:
geometry
0 LINESTRING (400017.781 3000019.250, 400017.781...
1 LINESTRING (400027.781 3000039.250, 400027.781...
2 LINESTRING (400027.781 3000039.250, 400037.781...
3 LINESTRING (400027.781 3000029.250, 400037.781...
4 LINESTRING (400047.781 3000079.250, 400047.781...
I have tried using gpd.dissolve() using a filler column with no luck.
df['dissolvefield'] = 1;
df2 = df.dissolve(by='dissolvefield')
df3 = gpd.geoseries.GeoSeries([geom for geom in df2.geometry.iloc[0].geoms])
Similarly tried to use unary_union in shapely with no luck.
import fiona
shape1 = fiona.open("df.shp")
first = shape1.next()
from shapely.geometry import shape
shp_geom = shape(first['geometry'])
from shapely.ops import unary_union
shape2 = unary_union(shp_geom)
Seems like an easy solution, wondering why I am running into so many issues. My GeoDataFrame only consists of the line geometry, so there is not necessarily another attribute I can aggregate based on. I am essentially just trying keep the geometry of the lines unchanged, but remove any overlapping features that may be there. I don't want to split the lines, and I don't want to aggregate them into multipart features.

i use the unary_union, but no need to read it as shapely feature.
after reading the file and put it in GPD (you can do it straight from the *.shp file):
df = gpd.read_file('streams_0.geojson')
try to plot it to see the if the output is correct
df.plot()
than use the unary_union like this, and plot again:
shape2 = df.unary_union
shape2
and the last step (if necessary), is to set as geopandas again:
# transform Geometry Collection to shapely multilinestirng
segments = [feature for feature in shape2]
# set back as geopandas
gdf = gpd.GeoDataFrame(list(range(len(segments))), geometry=segments,
crs=crs)
gdf .columns = ['index', 'geometry']

I have the Polygon data from the States from the USA from the website
arcgis
and I also have an excel file with coordinates of citys. I have converted the coordinates to geometry data (Points).
Now I want to test if the Points are in the USA.
Both are dtype: geometry. I thought with this I can easily compare, but when I use my code I get for every Point the answer false. Even if there are Points that are in the USA.
The code is:
import geopandas as gp
import pandas as pd
import xlsxwriter
import xlrd
from shapely.geometry import Point, Polygon
df1 = pd.read_excel('PATH')
gdf = gp.GeoDataFrame(df1, geometry= gp.points_from_xy(df1.longitude, df1.latitude))
US = gp.read_file('PATH')
print(gdf['geometry'].contains(US['geometry']))
Does anybody know what I do wrong?

contains in GeoPandas currently work on a pairwise basis 1-to-1, not 1-to-many. For this purpose, use sjoin.
points_within = gp.sjoin(gdf, US, op='within')
That will return only those points within the US. Alternatively, you can filter polygons which contain points.
polygons_contains = gp.sjoin(US, gdf, op='contains')

I have written Code to establish Point in Polygon in Python, the program uses a shapefile that I read in as the Polygons.
I now have a dataframe I read in with a column containing the Polygon e.g [[28.050815,-26.242253],[28.050085,-26.25938],[28.011934,-26.25888],[28.020216,-26.230127],[28.049828,-26.230704],[28.050815,-26.242253]].
I want to transform this column into a polygon in order to perform Point in Polygon, but all the examples use geometry = [Point(xy) for xy in zip(dataPoints['Long'], dataPoints['Lat'])] but mine is already zip?
How would I go about achieving this?
Thanks

taking your example above you could do the following:
list_coords = [[28.050815,-26.242253],[28.050085,-26.25938],[28.011934,-26.25888],[28.020216,-26.230127],[28.049828,-26.230704],[28.050815,-26.242253]]
from shapely.geometry import Point, Polygon
# Create a list of point objects using list comprehension
point_list = [Point(x,y) for [x,y] in list_coords]
# Create a polygon object from the list of Point objects
polygon_feature = Polygon([[poly.x, poly.y] for poly in point_list])
And if you would like to apply it to a dataframe you could do the following:
import pandas as pd
import geopandas as gpd
df = pd.DataFrame({'coords': [list_coords]})
def get_polygon(list_coords):
point_list = [Point(x,y) for [x,y] in list_coords]
polygon_feature = Polygon([[poly.x, poly.y] for poly in point_list])
return polygon_feature
df['geom'] = df['coords'].apply(get_polygon)
However, there might be geopandas built-in functions in order to avoid "reinventing the wheel", so let's see if anyone else has a suggestion :)

I have a list of customers lat and long and I want to define which ones are within a given polygon.
But the results I got are none of them in that polygon and it is not correct.
Could you please help? Thanks!
from shapely.geometry import Polygon
from shapely.geometry import Point
import pandas as pd
import geopandas as gpd
df=pd.read_csv("C:\\Users\\n.nguyen.2\\Documents\\order from May 1.csv")
geometry=[Point(xy) for xy in zip(df['customer_lat'],df['customer_lng'])]
crs={'init':'epsg:4326'}
gdf=gpd.GeoDataFrame(df,crs=crs,geometry=geometry)
gdf.head()
polygon= Polygon ([(103.85362669999994, 1.4090082), (103.8477709, 1.4051988), (103.84821190000002, 1.4029509), (103.84933950000004, 1.4012179), (103.85182859999998, 1.4001453), (103.85393150000004, 1.3986867), (103.85745050000001, 1.3962412), (103.85809410000002, 1.3925516), (103.85843750000004, 1.3901491), (103.8583946, 1.3870601), (103.8585663, 1.3838853), (103.8582659, 1.3812682), (103.85822299999997, 1.3792946), (103.85843750000004, 1.3777931), (103.85882370000002, 1.3748757), (103.86015410000005, 1.3719582), (103.8607978, 1.3700276), (103.86092659999998, 1.368097), (103.86036880000006, 1.3657372), (103.8593174, 1.3633562), (103.85852339999995, 1.3607605), (103.85745050000001, 1.3581005), (103.8571071, 1.355655), (103.85736459999998, 1.3520941), (103.85873790000007, 1.3483615), (103.86187100000006, 1.3456583), (103.86488409999993, 1.340689), (103.87096889999998, 1.3378933), (103.87519599999996, 1.3373354), (103.88178349999998, 1.3408963), (103.88508790000004, 1.3433418), (103.89186870000005, 1.3436426), (103.89742610000008, 1.342355), (103.91813279999997, 1.3805388), (103.91824964404806, 1.3813377489306), (103.91433759243228, 1.38607494841128), (103.91607279999994, 1.3895484), (103.91942029999996, 1.3940104), (103.92903330000001, 1.4009604), (103.9342689, 1.402076), (103.93289559999994, 1.4075675), (103.92534249999994, 1.4146035), (103.92517090000003, 1.4211246), (103.90972139999997, 1.4238704), (103.89942169999993, 1.4202666), (103.89744760000008, 1.4224117), (103.89315599999998, 1.425758), (103.88740540000003, 1.4285896), (103.88148309999995, 1.4328798), (103.87478829999998, 1.4331372), (103.85918850000007, 1.4249644), (103.85401679999995, 1.4114284), (103.85362669999994, 1.4090082)])
gdf['answer']=gdf['geometry'].within(polygon)
writer = pd.ExcelWriter("C:\\Users\\n.nguyen.2\\Documents\\order may define1.xlsx")
gdf.to_excel(writer, 'Sheet1', index=False)
writer.save()
The results are all false.
Raw data:
Result:

Adding my comments as an answer for future reference.
You have switched longitude and latitude in the order of coordinates. Look at coordinates of your polygon and those of points. Coordinates of points you have generated are (Lat, Lon), while your polygon (Lon, Lat). So these points are not within this polygon. Do
geometry=[Point(xy) for xy in zip(df['customer_lng'],df['customer_lat'])]
instead and it will work.
To make your life easier, geopandas has helper function for creating points from polygons points_from_xy() (http://geopandas.org/gallery/create_geopandas_from_pandas.html?highlight=points_from_xy)

I have a dataframe with earthquake data called eq that has columns listing latitude and longitude. using geopandas I created a point column with the following:
from geopandas import GeoSeries, GeoDataFrame
from shapely.geometry import Point
s = GeoSeries([Point(x,y) for x, y in zip(df['longitude'], df['latitude'])])
eq['geometry'] = s
eq.crs = {'init': 'epsg:4326', 'no_defs': True}
eq
Now I have a geometry column with lat lon coordinates but I want to change the projection to UTM. Can anyone help with the transformation?

Latitude/longitude aren't really a projection, but sort of a default "unprojection". See this page for more details, but it probably means your data uses WGS84 or epsg:4326.
Let's build a dataset and, before we do any reprojection, we'll define the crs as epsg:4326
import geopandas as gpd
import pandas as pd
from shapely.geometry import Point
df = pd.DataFrame({'id': [1, 2, 3], 'population' : [2, 3, 10], 'longitude': [-80.2, -80.11, -81.0], 'latitude': [11.1, 11.1345, 11.2]})
s = gpd.GeoSeries([Point(x,y) for x, y in zip(df['longitude'], df['latitude'])])
geo_df = gpd.GeoDataFrame(df[['id', 'population']], geometry=s)
# Define crs for our geodataframe:
geo_df.crs = {'init': 'epsg:4326'}
I'm not sure what you mean by "UTM projection". From the wikipedia page I see there are 60 different UTM projections depending on the area of the world. You can find the appropriate epsg code online, but I'll just give you an example with a random epsgcode. This is the one for zone 33N for example
How do you do the reprojection? You can easily get this info from the geopandas docs on projection. It's just one line:
geo_df = geo_df.to_crs({'init': 'epsg:3395'})
and the geometry isn't coded as latitude/longitude anymore:
id population geometry
0 1 2 POINT (-8927823.161620541 1235228.11420853)
1 2 3 POINT (-8917804.407449147 1239116.84994171)
2 3 10 POINT (-9016878.754255159 1246501.097746004)