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This article mainly introduces GeoPandas how to achieve the coordinate reference system, has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, the following let the editor take you to understand it.

02-coordinate reference system

Please see the source code here.

% matplotlib inlineimport pandas as pdimport geopandascountries = geopandas.read_file ("zip://data/ne_110m_admin_0_countries.zip") cities = geopandas.read_file ("zip://data/ne_110m_populated_places.zip") rivers = geopandas.read_file ("zip://data/ne_50m_rivers_lake_centerlines.zip") 2.1coordinate reference system

So far, we have used geometric data with specific coordinates without further wondering what these coordinates mean or how they are expressed.

* * coordinate reference system (CRS) * * Associates coordinates with specific locations on Earth

Detailed explanation of coordinate reference system: https://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/coordinate_reference_systems.html

2.1.1 Geographic coordinate system

Latitude and longitude

For example, 48 °51 °N, 2 °17 °E

The most common types of coordinates are geographical coordinates, latitude and longitude to define the position of the earth relative to the equator and the prime meridian

With this coordinate system, we can easily specify any location on Earth, for example, in the Global Positioning system, if you look at the coordinates of a location on Google Maps, you will see latitude and longitude.

Note the use of (latitude, longitude) (lon,lat) in Python to represent geographic coordinates

Longitude: [- 180,180]

Latitude: [- 90,90]

2.1.2 projection coordinate system

The coordinates are usually in meters or feet.

Although the earth is a sphere, in fact we usually represent it on a plane, such as a map of life, or a map we make on a computer screen with Python

From three-dimensional earth to plane map is what we call projection.

We project the surface of the earth onto a 2D plane so that we can represent the position on the plane with Cartesian x and y coordinates. On this plane, we usually use units of length, such as meters, rather than degrees, which makes the analysis more convenient and effective.

However, it is important that the three-dimensional earth can never be rendered perfectly on a two-dimensional map, so the projection will inevitably introduce distortion. To minimize this error, there are different projection methods, each of which has specific advantages and disadvantages.

Some projection systems will try to maintain the area size of the geometry, such as Ebers area projection (Albers Equal Area). Other projection systems try to preserve the angle, such as the Mercator projection, but will see large deformations in the area. Every projection system always has some distortion of area, angle or distance.

AlbersEqualArea

Mercator

Robinson

Comparison between projection size and actual size

2.2 coordinate reference system in Python and GeoPandas

GeoDataFrame or GeoSeries has a .CRS property that holds (optional) the description of the geometric reference system:

Countries.crsName: WGS 84Axis Info [ellipsoidal]:-Lat [north]: Geodetic latitude (degree)-Lon [east]: Geodetic longitude (degree) Area of Use:- name: World- bounds: (- 180.0,-90.0, 180.0, 90.0) Datum: World Geodetic System 1984-Ellipsoid: WGS 84-Prime Meridian: Greenwich

As you can see, for the countries data frame, the EPSG 4326 / WGS84 lon/lat reference system is used, which is one of the most commonly used systems in the geographic coordinate system.

It uses coordinates as the degrees of latitude and longitude, as can be seen from the xUnix y label on the graph:

Countries.plot ()

The return value of the .crs property is a dictionary type, in which case it only indicates the EPSG code, but it can also contain the full proj4 string (in dictionary form)

Possible CRS expressions:

Proj4 string

For example: + proj=longlat + datum=WGS84 + no_defs or {'proj':' longlat', 'datum':' WGS84', 'no_defs': True}

EPSG code

For example: EPSG:4326 = WGS84 Geographic coordinate system (longitude, latitude)

Rich text (Well-Know-Text,WKT) representation (PROJ6 provides better support in the next version of GeoPandas)

For example: https://epsg.io/4326

On the backend, GeoPandas uses the pyproj/PROJ library to handle reprojection

More information: http://geopandas.readthedocs.io/en/latest/projections.html

2.3 Transformation of coordinate system

In GeoDataFrame, to_crs function is used to transform coordinate system.

For example, change the countries to the Mercator projection (http://epsg.io/3395):

# because Mercator projection cannot handle poles, remove Antarctic countries=countries [(countries ['name']! = "Antarctica")] countries_mercator=countries.to_crs (epsg=3395) # or use .to _ crs ({' init': 'epsg:3395'}) countries_mercator.plot ()

Pay attention to comparing the difference between the x and y axis scale here and the projection in the geographic coordinate system

2.4 reasons for using different CRS

Different source data with different CRS-> need to be converted to the same CRS

Df1 = geopandas.read_file (...) df2 = geopandas.read_file (...) df2 = df2.to_crs (df1.crs)

Map mapping (due to distortion of shape and distance)

Distance / area-based calculations-> ensure that the appropriate projection coordinate system is used in meaningful units, such as meters or feet (not degrees)

Note:

All calculations that occur on the surface assume that the data are on a 2D Cartesian plane, so the results of these calculations are correct only if the data are projected correctly.

2.5 practice

Using the Paris dataset again, so far, we have provided a suitable dataset in projection CRS for the exercise.

But the original data are actually geographic coordinates.

In the following exercise, we will start with the raw data

The original dataset, now available in the GeoJSON ("data/paris_districts.geojson") file of the geographic coordinate system

In order to convert to projection coordinates, we will use the French standard projection coordinate system, the RGF93 / Lambert-93 reference system, with the reference number EPSG:2154 (Belgium is Lambert 72 and EPSG is 31370).

2.5.1 exercise 1: project the GeoDataFrame

Read the regional dataset ("data/paris_districts.geojson") into the GeoDataFrame named districts

View the CRS properties of districts

Simple drawing of districts data sets

Calculate the area of all areas

Project the districts (EPSG:2154 is used in France) and call the new dataset districts_RGF93

Draw a similar districts_RGF93 diagram

Calculate the area of all areas again for districts_RGF93 (the result is now represented by m ²)

Districts=geopandas.read_file ("data/paris_districts.geojson") districts.crsName: WGS 84Axis Info [ellipsoidal]:-Lat [north]: Geodetic latitude (degree)-Lon [east]: Geodetic longitude (degree) Area of Use:- name: World- bounds: (- 180.0,-90.0, 180.0, 90.0) Datum: World Geodetic System 1984-Ellipsoid: WGS 84-Prime Meridian: Greenwichdistricts.plot (figsize=

Districts.geometry.area0 0.0001071 0.0000512 0.0000343 0.0000334 0.000023... 75 0.00015976 0.00009977 0.00018278 0.00019679 0.000256Length: 80, dtype: float64districts_RGF93=districts.to_crs (epsg=2154) districts_RGF93.plot (figsize= (12Co6))

Districts_RGF93.geometry.area0 8.690007e+051 4.124585e+052 2.736968e+053 2.694568e+054 1.880122e+05... 75 1.294988e+0676 8.065686e+0577 1.486971e+0678 1.599002e+0679 2.090904e+06Length: 80, dtype: float64

You can see that the area values of the same area are different under different coordinate systems.

In the 01-Geographic data introduction, we did an exercise to draw the location of the Paris cycling station and add a background map to it using the contextily package

Contextily "assumes that the data coordinate system is the network Mercator projection (Web Mercator projection), which is the system used by most web mapping services. In the first exercise, we provided the data in the appropriate CRS, so we don't need to care about this.

However, in general, the reference coordinate system of the data will not be the network Mercator projection (Web Mercator projection), so we must align them with the network background map ourselves.

2.5.2 exercise 2: project GeoDataFrame

Read the bicycle station data set (data/paris_bike_stations.geojson) into the GeoDataFrame named stations

Convert a stations dataset to a network Mercator projection (EPSG:3857) named stations_webmercator

Draw a map of the projected dataset (specify a marker size of 5) and add a background map using contextily

Stations=geopandas.read_file ('data/paris_bike_stations.geojson') stations_webmercator=stations.to_crs (epsg=3857) stations_webmercator.head ()

.dataframe tbody tr th:only-of-type {vertical-align: middle;}

.dataframe tbody tr th {vertical-align: top;}. Dataframe thead th {text-align: right;}

Name bike_stands available_bikes geometry 0 14002-RASPAIL QUINET 44 4 POINT (259324.887 6247620.771) 1 20503-COURS DE VINCENNES PYR É N É ES 21 3 POINT (267824.377 6249062.894) 2 20011-PYR É N ES-DAGORNO 21 0 POINT (267742.135 6250378.469) 3 31008-VINCENNES (MONTREUIL) 56 0 POINT (271326.638 6250750.824) 4 43006-MINIMES (VINCENNES) 28 27 POINT (270594.689 6248007.705)

Import contextily as ctxax = stations_webmercator.plot (figsize= (10, 10), markersize=5, alpha=0.5, edgecolor='k') ctx.add_basemap (ax, source=ctx.providers.Stamen.TonerLite) Thank you for reading this article carefully. I hope the article "how to implement the coordinate reference system in GeoPandas" shared by the editor will be helpful to everyone. At the same time, I also hope that you will support and pay attention to the industry information channel. More related knowledge is waiting for you to learn!

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