{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating distances" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, we will calculate distances with projected layers. **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", "- First, let's import necessary modules and continue working with the European borders:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import necessary modules\n", "import geopandas as gpd\n", "from pyproj import CRS\n", "from shapely.geometry import Point\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "# Input file that we created on the previous page\n", "fp = r\"L2_data/Europe_borders_epsg3035.shp\"\n", " \n", "# Save to disk\n", "data = gpd.read_file(fp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's create a GeoDataFrame that contains a single Point representing the location of Helsinki, Finland:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " geometry\n", "0 POINT (24.94170 60.16660)\n" ] } ], "source": [ "# 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 [pyproj](https://pyproj4.github.io/pyproj) library which is also good at dealing with \"special\" projections such as the one demonstrated here.\n", "\n", " - We will create a CRS 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": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " geometry\n", "0 POINT (0.00000 0.00000)\n", "\n", "CRS:\n", " +proj=aeqd +ellps=WGS84 +datum=WGS84 +lat_0=60.1666 +lon_0=24.9417 +type=crs\n" ] } ], "source": [ "# Define the projection using the coordinates of our Helsinki point (hki_lat, hki_lon) as the center point\n", "# The .srs here returns the text presentation of the projection\n", "aeqd = CRS(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": 4, "metadata": {}, "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": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TZID geometry\n", "0 Europe/Berlin POLYGON ((-1057542.597 -493724.802, -1058052.5...\n", "1 Europe/Berlin POLYGON ((-1216418.435 -1243831.635, -1216378....\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": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TZID geometry \\\n", "0 Europe/Berlin POLYGON ((-1057542.597 -493724.802, -1058052.5... \n", "1 Europe/Berlin POLYGON ((-1216418.435 -1243831.635, -1216378.... \n", "\n", " centroid \n", "0 POINT (-1057718.135 -492420.566) \n", "1 POINT (-1218235.217 -1242668.590) \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": 8, "metadata": {}, "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": 9, "metadata": {}, "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": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TZID geometry \\\n", "0 Europe/Berlin POLYGON ((-1057542.597 -493724.802, -1058052.5... \n", "1 Europe/Berlin POLYGON ((-1216418.435 -1243831.635, -1216378.... \n", "2 Europe/Berlin POLYGON ((-1194521.639 -571726.459, -1194674.9... \n", "3 Europe/Berlin POLYGON ((-1185933.276 -571780.053, -1186040.7... \n", "4 Europe/Berlin POLYGON ((-1182416.220 -569097.571, -1183274.4... \n", "5 Europe/Berlin POLYGON ((-1172799.401 -565749.439, -1175327.7... \n", "6 Europe/Berlin POLYGON ((-1162805.428 -563558.434, -1161240.8... \n", "7 Europe/Berlin POLYGON ((-1129053.541 -568388.470, -1129252.5... \n", "8 Europe/Berlin POLYGON ((-1109126.533 -570899.989, -1109690.5... \n", "9 Europe/Berlin POLYGON ((-703490.147 -664009.792, -703842.631... \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 -568987.1532380251) 1322.832487 \n", "3 POINT (-1185320.605845876 -571340.3134827728) 1315.832319 \n", "4 POINT (-1182191.163363772 -567293.7639830827) 1311.258236 \n", "5 POINT (-1175758.089721244 -564846.4759828376) 1304.399719 \n", "6 POINT (-1157868.191291224 -565162.3607219103) 1288.435968 \n", "7 POINT (-1124883.959682355 -567850.4028534387) 1260.086506 \n", "8 POINT (-1113376.309723986 -569037.768865205) 1250.364263 \n", "9 POINT (-703970.5591804663 -663710.5756044327) 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": 11, "metadata": {}, "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", "\n", "That's it! During this tutorial we have seen how to 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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }