{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Map projections\n", "\n", "Coordinate reference systems (CRS) are important because the geometric shapes in a GeoDataFrame are simply a collection of coordinates in an arbitrary space. A CRS tells Python how those coordinates are related to places on the Earth. A map projection (or a projected coordinate system) is a systematic transformation of the latitudes and longitudes into a plain surface where units are quite commonly represented as meters (instead of decimal degrees). This transformation is used to represent the three dimensional earth on a flat, two dimensional map.\n", "\n", "As the CRS in different spatial datasets differ fairly often (i.e. one might have coordinates defined in decimal degrees while the other one has them in meters), it is a common procedure to redefine (or reproject) the CRS to be identical in both layers. It is important that the layers have the same coordinate reference system as it makes it possible to analyze the spatial relationships between the layers, such as conduct a Point in Polygon -query.\n", "\n", "Choosing an appropriate projection for your map is not always straightforward because it depends on what you actually want to represent with your map, and what is the spatial scale of your data. In fact, there does not exist a \"perfect projection\" since each one of them has some strengths and weaknesses, and you should choose such projection that fits best for your needs. In fact, the projection you choose might even tell something about you:\n", " \n", "![](img/Map-projections.png)\n", "*Source: XKCD, See a full comic about [\"What your favorite map projection tells about you\"](https://xkcd.com/977/)*.\n", "\n", "For those of you who want a bit more analytical approach for choosing the projection, you can get a good overview from [georeference.org](http://www.georeference.org/doc/guide_to_selecting_map_projections.htm), or from this blog post introducing [the strengths and weaknesses of a few commonly used projections](http://usersguidetotheuniverse.com/index.php/2011/03/03/whats-the-best-map-projection/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Coordinate reference system (CRS) in Geopandas\n", "\n", "Luckily, defining and changing projections is easy in Geopandas. In this tutorial we will see how to retrieve the\n", "coordinate reference system information from the data, and how to change it. We will re-project a data file from\n", "WGS84 (lat, lon coordinates) into a Lambert Azimuthal Equal Area projection which is the [recommended projection for\n", "Europe](http://mapref.org/LinkedDocuments/MapProjectionsForEurope-EUR-20120.pdf) by European Commission.\n", "\n", "For this tutorial we will be using a Shapefile called `Europe_borders.shp` representing the country borders in Europe, that you already should have [downloaded during the previous tutorial](geopandas-basics.ipynb). \n", "\n", "Shapefile should always contain information about the coordinate reference system that is stored in `.prj` -file (at least if the data has been appropriately produced). When reading the data into `GeoDataFrame` with Geopandas this information is automatically stored into `.crs` attribute of the GeoDataFrame.\n", "\n", "- Let's start by reading the data from the `Europe_borders.shp` file and checking the `crs`:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'init': 'epsg:4326'}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import necessary packages\n", "import geopandas as gpd\n", "\n", "# Read the file\n", "fp = \"L2_data/Europe_borders.shp\"\n", "data = gpd.read_file(fp)\n", "\n", "# Check the coordinate reference system\n", "data.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, here, the `crs` is a Python dictionary with a key `init` that has a value `epsg:4326`. This is a very typical way how CRS is stored in GeoDataFrames. There is also another typical way of representing the coordinate reference system, namely storing that information in [Proj4-string](https://proj4.org/usage/quickstart.html) format (we will come back to this later). \n", "\n", "The EPSG number (*\"European Petroleum Survey Group\"*) is a code that tells about the coordinate system of the dataset. \"[EPSG Geodetic Parameter Dataset](http://www.epsg.org/) is a collection of definitions of coordinate reference systems and coordinate transformations which may be global, regional, national or local in application\". EPSG code `4326` that we have here, belongs to the WGS84 coordinate system (i.e. coordinates are in decimal degrees: latitudes and longitudes).\n", "\n", "You can find a lot of information and lists of available coordinate reference systems from:\n", "\n", " - [www.spatialreference.org](http://spatialreference.org/)\n", " - [www.proj4.org](https://proj4.org/operations/projections/)\n", " - [www.mapref.org](http://mapref.org/CollectionofCRSinEurope.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's continue by checking the values in our `geometry` -column to verify that the CRS of our GeoDataFrame seems correct:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 POLYGON ((8.457777976989746 54.56236267089844,...\n", "1 POLYGON ((8.71992015838623 47.69664382934571, ...\n", "2 POLYGON ((6.733166694641113 53.5740852355957, ...\n", "3 POLYGON ((6.858222007751465 53.59411239624024,...\n", "4 POLYGON ((6.89894437789917 53.6256103515625, 6...\n", "Name: geometry, dtype: object" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['geometry'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the coordinate values of the Polygons indeed look like latitude and longitude values, so everything seems to be in order (as in most cases).\n", "\n", "WGS84 projection is not really a good one for representing European borders, so let's convert those geometries into Lambert Azimuthal Equal Area projection ([EPSG: 3035](http://spatialreference.org/ref/epsg/etrs89-etrs-laea/)) which is the recommended projection by European Comission.\n", "\n", "Changing the projection is simple to [do in Geopandas](http://geopandas.org/projections.html#re-projecting) with `.to_crs()` -function which is a built-in function of the GeoDataFrame. The function has two alternative parameters 1) `crs` and 2) `epgs` that can be used to make the coordinate transformation and re-project the data into the CRS that you want to use. \n", "\n", "- Let's re-project our data into `EPSG 3035` using `epsg` -parameter:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 POLYGON ((4221214.558088431 3496203.404338956,...\n", "1 POLYGON ((4224860.478308966 2732279.319617757,...\n", "2 POLYGON ((4104652.175545862 3390034.953002084,...\n", "3 POLYGON ((4113025.664284974 3391895.755505159,...\n", "4 POLYGON ((4115871.227627173 3395282.099288368,...\n", "Name: geometry, dtype: object\n" ] } ], "source": [ "# Let's make a copy of our data\n", "orig = data.copy()\n", "\n", "# Reproject the data\n", "data = data.to_crs(epsg=3035)\n", "\n", "# Check the new geometry values\n", "print(data['geometry'].head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here we go, the coordinate values in the geometries have changed! Now we have successfully changed the projection of our layer into a new one, i.e. to `ETRS-LAEA` projection. \n", "\n", "To really understand what is going on, it is good to explore our data visually. Hence, let's compare the datasets by making\n", "maps out of them.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BU06B5OSwPC3eey8855EDowRLxFuET6jqBwacArwNfNakbT3QB/gBMMM5N8c5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\nIiIiIg1YdjZw0kkMIpYvj3Zrosd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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "# Make subplots that are next to each other\n", "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 8))\n", "\n", "# Plot the data in WGS84 CRS\n", "orig.plot(ax=ax1, facecolor='gray');\n", "\n", "# Add title\n", "ax1.set_title(\"WGS84\");\n", "\n", "# Plot the one with ETRS-LAEA projection\n", "data.plot(ax=ax2, facecolor='blue');\n", "\n", "# Add title\n", "ax2.set_title(\"ETRS Lambert Azimuthal Equal Area projection\");\n", "\n", "# Remove empty white space around the plot\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indeed, the maps look quite different, and the re-projected one looks much better in Europe as the areas especially in the north are more realistic and not so stretched as in WGS84.\n", "\n", "- Finally, let's save our projected layer into a Shapefile so that we can use it later." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ouput filepath\n", "outfp = \"L2_data/Europe_borders_epsg3035.shp\"\n", " \n", "# Save to disk\n", "data.to_file(outfp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTICE:** On Windows, the `.prj` -file might **NOT** update with the new CRS value when saving to Shapefile. If this happens it is possible to fix the `prj` by passing the coordinate reference information as proj4-string. For this purpose a library called [PyCRS](https://github.com/karimbahgat/PyCRS) is a handy tool that makes passing the proj4-strings easy. PyCRS has functions `pycrs.parser.from_epsg_code()` and `.to_proj4()` that can be used to define the CRS as proj4-string format:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'+proj=laea +ellps=GRS80 +a=6378137.0 +f=298.257222101 +pm=0 +lon_0=10 +x_0=4321000 +y_0=3210000 +lat_0=52 +units=m +axis=enu +no_defs'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import pycrs\n", "import pycrs\n", "\n", "# Define the crs for the GeoDataFrame as proj4-string\n", "epsg_code = 3035\n", "data.crs = pycrs.parser.from_epsg_code(epsg_code).to_proj4()\n", "\n", "# Let's see what we have now\n", "data.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, now the CRS of the GeoDataFrame is represented as [Proj4-string](https://proj4.org/usage/quickstart.html) that will be saved into the `.prj` -file when saving the GeoDataFrame into Shapefile. \n", "\n", "- Let's save the file once more:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ouput filepath\n", "outfp = \"L2_data/Europe_borders_epsg3035.shp\"\n", " \n", "# Save to disk\n", "data.to_file(outfp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [PyCRS](https://github.com/karimbahgat/PyCRS) library is really useful as it contains information and supports many different coordinate reference definitions, such as OGC WKT (v1), ESRI WKT, Proj4, and any EPSG, ESRI, or SR-ORG code available from spatialreference.org." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating distances" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will conduct a practical example, and continue working with the `Europe_borders.shp` -file. Our aim is to find the Euclidean distances from the centroids (midpoints) of all European countries to Helsinki, Finland. We will calculate the distance between Helsinki and other European countries using a metric projection ([Azimuthal Equidistant -projection](https://proj4.org/operations/projections/aeqd.html)) that gives us the distance in meters. Notice, that this projection is slightly less commonly used, but still useful to know. \n", "\n", "- Let's first create a GeoDataFrame that contains a single Point representing the location of Helsinki, Finland:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " geometry\n", "0 POINT (24.9417 60.1666)\n" ] } ], "source": [ "# Import necessary modules\n", "from shapely.geometry import Point\n", "import pycrs\n", "\n", "# Create the point representing Helsinki (in WGS84)\n", "hki_lon = 24.9417\n", "hki_lat = 60.1666\n", "\n", "# Create GeoDataFrame\n", "helsinki = gpd.GeoDataFrame([[Point(hki_lon, hki_lat)]], geometry='geometry', crs={'init': 'epsg:4326'}, columns=['geometry'])\n", "\n", "# Print \n", "print(helsinki)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, it is possible to create a GeoDataFrame directly with one line of code. Notice that, here, we specified the CRS directly by passing the crs as Python dictionary `{'init': 'epsg:4326'}` which is one alternative way to define the CRS. We also told that the `geometry` information is stored in column called `'geometry'` that we actually define with parameter `columns=['geometry']`. \n", "\n", "Next, we need to convert this `GeoDataFrame` to \"Azimuthal Equidistant\" -projection that has useful properties because all points on the map in that projection are at proportionately correct distances from the center point (defined with parameters `lat_0` and `lon_0`), and all points on the map are at the correct direction from the center point. \n", "\n", "To conduct the transformation, we are going to utilize again a Proj4-string that we can obtain using another library concentrated for coordinate reference systems, called [pyproj](https://github.com/jswhit/pyproj). This package is useful when dealing with \"special\" projections such as the one demonstrated here.\n", "\n", " - We will create our Proj4-string by passing specific parameters to `Proj()` -object that are needed to construct the [Azimuthal Equidistant projection](https://proj4.org/operations/projections/aeqd.html):\n", " \n", " - `proj='aeqd'` refers to *projection specifier* that we determine to be Azimuthal Equidistant ('aeqd')\n", " - `ellps='WGS84'` refers to the [reference ellipsoid](https://en.wikipedia.org/wiki/Reference_ellipsoid) that is a mathematically modelled (based on measurements) surface that approximates the true shape of the world. World Geodetic System (WGS) was established in 1984, hence the name. \n", " - `datum='WGS84'` refers to the [Geodetic datum](https://en.wikipedia.org/wiki/Geodetic_datum) that is a coordinate system constituted with a set of reference points that can be used to locate places on Earth.\n", " - `lat_0` is the latitude coordinate of the center point in the projection\n", " - `lon_0` is the longitude coordinate of the center point in the projection" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " geometry\n", "0 POINT (0 0)\n", "\n", "CRS:\n", " +units=m +proj=aeqd +ellps=WGS84 +datum=WGS84 +lat_0=60.1666 +lon_0=24.9417 \n" ] } ], "source": [ "# Import pyproj\n", "import pyproj\n", "\n", "# Define the projection using the coordinates of our Helsinki point (hki_lat, hki_lon) as the center point\n", "# The .srs here returns the Proj4-string presentation of the projection\n", "aeqd = pyproj.Proj(proj='aeqd', ellps='WGS84', datum='WGS84', lat_0=hki_lat, lon_0=hki_lon).srs\n", "\n", "# Reproject to aeqd projection using Proj4-string\n", "helsinki = helsinki.to_crs(crs=aeqd)\n", "\n", "# Print the data\n", "print(helsinki)\n", "\n", "# Print the crs\n", "print('\\nCRS:\\n', helsinki.crs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see the projection is indeed centered to Helsinki as the 0-position (in meters) in both x and y is defined now directly into the location where we defined Helsinki to be located (you'll understand soon better when seeing the map). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we want to transform the `Europe_borders.shp` data into the desired projection. \n", "\n", "- Let's create a new copy of our GeoDataFrame into a variable called `europe_borders_aeqd`: " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a copy\n", "europe_borders_aeqd = data.copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's now reproject our Europer borders data into the Azimuthal Equidistant projection that was centered into Helsinki:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TZID geometry\n", "0 Europe/Berlin POLYGON ((-1057542.597130521 -493724.801682817...\n", "1 Europe/Berlin POLYGON ((-1216418.435359255 -1243831.63520634...\n" ] } ], "source": [ "# Reproject to aeqd projection that we defined earlier\n", "europe_borders_aeqd = europe_borders_aeqd.to_crs(crs=aeqd)\n", "\n", "# Print \n", "print(europe_borders_aeqd.head(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, now we can see that the coordinates in `geometry` column are fairly large numbers as they represents the distance in meters from Helsinki to different directions. \n", "\n", "- Let's plot the Europe borders and the location of Helsinki to get a better understanding how our projection has worked out:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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tDextHqTf5ccXDHGkw87W2l4A7J4A1/3qbb799AEu/Z9NVHfaJ2rSrJdkNY67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KfWqucLT6aHlIbjKZ69MJHZ9lVbatnp0tE56OO7B/qQZXj71w4AYNSq2Nvs4PAcRnjTVJp1fPWd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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "fig, ax = plt.subplots(figsize=(10,10))\n", "\n", "# Plot the country borders\n", "europe_borders_aeqd.plot(ax=ax)\n", "\n", "# Plot the Helsinki point on top of the borders using the same axis\n", "helsinki.plot(ax=ax, color='black', markersize=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see from the map, the projection is indeed centered to Helsinki as the 0-position of the x and y axis is located where Helsinki is positioned. Now the coordinate values are showing the distance from Helsinki (black point) to different directions (South, North, East and West) in meters. \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, our goal is to calculate the distance from all countries to Helsinki. To be able to do that, we need to calculate the centroids for all the Polygons representing the boundaries of European countries. \n", "\n", "- This can be done easily in Geopandas by using the `centroid` attribute:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TZID geometry \\\n", "0 Europe/Berlin POLYGON ((-1057542.597130521 -493724.801682817... \n", "1 Europe/Berlin POLYGON ((-1216418.435359255 -1243831.63520634... \n", "\n", " centroid \n", "0 POINT (-1057718.135423443 -492420.5658204998) \n", "1 POINT (-1218235.216971495 -1242668.589667922) \n" ] } ], "source": [ "europe_borders_aeqd['centroid'] = europe_borders_aeqd.centroid\n", "print(europe_borders_aeqd.head(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have created a new column called `centroid` that has the Point geometries representing the centroids of each Polygon (in Azimuthal Equidistant projection).\n", "\n", "Next, we will calculate the distances between the country centroids and Helsinki. For doing this, we could use `iterrows()` -function that we have used earlier, but here we will demonstrate a more efficient (faster) technique to go through all rows in (Geo)DataFrame by using `apply()` -function. \n", "\n", "The [apply()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html) -function can give a big boost in performance over the `iterrows()` and it is the recommendable way of iterating over the rows in (Geo)DataFrames. Here, we will see how to use that to calculate the distance between the centroids and Helsinki. \n", "\n", " - First, we will create a dedicated function for calculating the distances called `calculate_distance()`:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def calculate_distance(row, dest_geom, src_col='geometry', target_col='distance'):\n", " \"\"\"\n", " Calculates the distance between Point geometries.\n", "\n", " Parameters\n", " ----------\n", " dest_geom : shapely.Point\n", " A single Shapely Point geometry to which the distances will be calculated to.\n", " src_col : str\n", " A name of the column that has the Shapely Point objects from where the distances will be calculated from.\n", " target_col : str\n", " A name of the target column where the result will be stored.\n", "\n", " Returns\n", " -------\n", " \n", " Distance in kilometers that will be stored in 'target_col'.\n", " \"\"\"\n", " \n", " # Calculate the distances\n", " dist = row[src_col].distance(dest_geom)\n", "\n", " # Convert into kilometers\n", " dist_km = dist / 1000\n", "\n", " # Assign the distance to the original data\n", " row[target_col] = dist_km\n", " return row" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, the parameter `row` is used to pass the data from each row of our GeoDataFrame into the function. Other paramaters are used for passing other necessary information for using our function.\n", "\n", "- Before using our function and calculating the distances between Helsinki and centroids, we need to get the Shapely point geometry from the re-projected Helsinki center point that we can pass to our function (into the `dest_geom` -parameter. We can use the `loc` -functionality to retrieve the value from specific index and column:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "POINT (0 0)\n" ] } ], "source": [ "# Retrieve the geometry from Helsinki GeoDataFrame\n", "helsinki_geom = helsinki.loc[0, 'geometry']\n", "print(helsinki_geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to use our function with `apply()`. When using the function, it is important to specify the direction of iteration that should be in our case specified with `axis=1`. This ensures that the calculations are done row by row (instead of column-wise).\n", " \n", " - When iterating over a DataFrame or GeoDataFrame, apply function is used by following the format `GeoDataFrame.apply(name_of_your_function, param1, param2, param3, axis=1)`\n", " \n", " - Notice that the first parameter is always the name of the function that you want to use **WITHOUT** the parentheses. This will start the iteration using the function you have created, and the values of the row will be inserted into the `row` parameter / attribute inside the function (see above). " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TZID geometry \\\n", "0 Europe/Berlin POLYGON ((-1057542.597130521 -493724.801682817... \n", "1 Europe/Berlin POLYGON ((-1216418.435359255 -1243831.63520634... \n", "2 Europe/Berlin POLYGON ((-1194521.638643212 -571726.459328880... \n", "3 Europe/Berlin POLYGON ((-1185933.276237431 -571780.052772927... \n", "4 Europe/Berlin POLYGON ((-1182416.219837205 -569097.571220089... \n", "5 Europe/Berlin POLYGON ((-1172799.400650826 -565749.438722840... \n", "6 Europe/Berlin POLYGON ((-1162805.427517967 -563558.434197176... \n", "7 Europe/Berlin POLYGON ((-1129053.540904054 -568388.469960322... \n", "8 Europe/Berlin POLYGON ((-1109126.532709598 -570899.989413469... \n", "9 Europe/Berlin POLYGON ((-703490.1465879162 -664009.791857452... \n", "\n", " centroid dist_to_Hki \n", "0 POINT (-1057718.135423443 -492420.5658204998) 1166.724332 \n", "1 POINT (-1218235.216971495 -1242668.589667922) 1740.207536 \n", "2 POINT (-1194210.78929945 -568987.1532380249) 1322.832487 \n", "3 POINT (-1185320.605845876 -571340.3134827727) 1315.832319 \n", "4 POINT (-1182191.163363772 -567293.7639830824) 1311.258236 \n", "5 POINT (-1175758.089721245 -564846.4759828373) 1304.399719 \n", "6 POINT (-1157868.191291224 -565162.3607219104) 1288.435968 \n", "7 POINT (-1124883.959682356 -567850.4028534379) 1260.086506 \n", "8 POINT (-1113376.309723986 -569037.768865205) 1250.364263 \n", "9 POINT (-703970.5591804661 -663710.575604432) 967.515517 \n" ] } ], "source": [ "# Calculate the distances using our custom function called 'calculate_distance'\n", "europe_borders_aeqd = europe_borders_aeqd.apply(calculate_distance, dest_geom=helsinki_geom, src_col='centroid', target_col='dist_to_Hki', axis=1)\n", "print(europe_borders_aeqd.head(10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Now we have successfully calculated the distances between the Polygon centroids and Helsinki. 😎" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's check what is the longest and mean distance to Helsinki from the centroids of other European countries:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum distance to Helsinki is 3470 km, and the mean distance is 1177 km.\n" ] } ], "source": [ "# Calculat the maximum and average distance\n", "max_dist = europe_borders_aeqd['dist_to_Hki'].max()\n", "mean_dist = europe_borders_aeqd['dist_to_Hki'].mean()\n", "\n", "print(\"Maximum distance to Helsinki is %.0f km, and the mean distance is %.0f km.\" % (max_dist, mean_dist))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the finns living in the North are fairly far away from all other European countries, as the mean distance to other countries is 1185 kilometers. \n", "\n", "Notice: If you would like to calculate distances between multiple locations across the globe, it is recommended to use [Haversine formula](https://en.wikipedia.org/wiki/Haversine_formula) to do the calculations. [Haversine](https://github.com/mapado/haversine) package in Python provides an easy-to-use function for calculating these\n", " based on latitude and longitude values.\n", "\n", "## Summary \n", "\n", "During this tutorial we have seen how to \n", "\n", "**1)** define the coordinate reference system with a few different approaches, mainly by:\n", "\n", " - specifying the coordinate reference system with crs dictionary e.g. `{'init': 'epsg:4326'}`\n", " - specifying CRS with Proj4-string using PyCRS library: `pycrs.parser.from_epsg_code(4326).to_proj4()`\n", " - specifying slightly less common CRS (Azimuthal Equidistant) with Proj4-string using pyproj library: `pyproj.Proj(proj='aeqd', ellps='WGS84', datum='WGS84', lat_0=hki_lat, lon_0=hki_lon).srs`\n", " - A fourth option that was not covered in the materials is to use a library called `fiona` and its function `from_epsg()`. See documentation about this from [here](http://toblerity.org/fiona/manual.html#format-drivers-crs-bounds-and-schema) if you are interested. We also quickly introduced this in the [previous tutorial](geopandas-basics.ipynb).\n", " \n", "**2)** reproject (transform) the geometries from crs to another using the `to_crs()` -function in GeoPandas\n", "\n", "**3)** calculate distances between locations and using `apply()` -function to iterate over rows more efficiently than using `iterrows()`. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }