Exercises

Exercises#

Exercise 6.1 - Geometric Objects#

Refresh your memory about shapely objects and

  1. Create a LineString that goes trough at least three points. What is the length of your line?

  2. Create a shapely Point and create a buffer around it. What is the area of the buffer?

Exercise 6.2 - From text file to GeoDataFrame#

The goal of this exercise is to create geometries based on text file input in Python. You will combine basic knowledge of the pandas module (see Part I) with geopandas methods introduced in this chapter.

Input data for this exercise contains information about the public transport travel times to the main railway station in Helsinki, Finland. The input file travelTimes-2015-Helsinki.txt contains travel times information from multiple origin locations to the railway station.

This file is an extract of the Helsinki Region Travel Time Matrix dataset - an open data set that contains multimodal travel time information across the Helsinki Region in Finland. In this exercise, we are interested in the columns listed in Table 6.1.

: Table 6.1. Column names and descriptions for the travel time data.

Column

Description

from_x

x-coordinate of the origin location (longitude)

from_y

y-coordinate of the origin location (latitude)

to_x

x-coordinate of the destination location (longitude)

to_y

y-coordinate of the destination location (latitude)

total_route_time

Travel time with public transportation at the route

Your task is to read in the file and create two new columns with Point objects representing the origin and destination points. Problems 1-3 will guide you through the necessary steps. Before starting, check the contents of the input file in a text editor and familiarize yourself with the data structure.

Problem 1: Read the file#

  • Import required modules.

  • Read in the text file using pandas into a variable called data and subset the DataFrame to contain only those columns we are interested in

  • Check that data looks ok

Problem 2: Create Point geometries#

Continue working with the DataFrame:

  • Create new columns from_geom and to_geom

  • Populate these columns with point objects that represent the origin and destination geometries using the points_from_xy() function in geopandas:

    • from_geom based on columns from_x and from_y

    • to_geom based on columns to_x and to_y

Problem 3: Create LineString geometries#

Continue working with the DataFrame and create LineString shapely objects based on the origin and destination point coordinates:

  • Create new column route_geom

  • Populate the new columns with LineString objects that represent a straight line between each origin (from_geom) and destination (to_geom) coordinates.

  • You can achieve this at least in two different ways:

    • Option 1: Apply a lambda-function on the DataFrame that creates a LineString based on from_geom and to_geom columns for each row (axis=1).

    • Option 2: Use zip() and a for-loop to create the LineString for each coorinate pair. You can store the LineStrings in a list inside the loop and outside the loop insert the list into the route_geom column.

Problem 4: Convert DataFrame into a GeoDataFrame#

Convert the DataFrame into a GeoDataFrame. While doing this, you should:

  • Define route_geom as the geometry column.

  • Set a coordinate reference system for the data. Think carefully what is the correct crs definition for the input coordinates.

Check the result.

Problem 5: Re-project the data#

Our data is located in the Helsinki Region, Finland and the data should be converted into a projected coordinate reference system if we want to draw maps or measure distances.

  • Check the crs definition of the data.

  • Re-project the data into ETRS89 / TM35FIN (EPSG:3067).

  • Check the result. You should notice that the route_geom coordinate values have changed.

Problem 6: Calculate average trip distance#

Now that we have the GeoDataFrame in a projected coordinate reference system we can calculate some distance metrics based on the line objects.

  • Calculate the lenght of each route based on the route_geom column.

  • Add the length information into a new column route_length.

  • Generate descriptive statistics of route lengths. What is the median route length?

Exercise 6.3 - Buffer and spatial join#

In this exercise your task is to find out how many people live within a 500 meter buffer zone from the nearest transit station in Helsinki. We will re-use data from chapter six. Locations of transit stations is available in the file data/Helsinki/addresses.shp. Population information is available in the file data/Helsinki/Population_grid_2021_HSY.gpkg.

You should join the buffers with the population information based on the spatial intersection of the buffer polygons and the population grid polygon centroids. Follow these steps to achieve the wanted result:

  1. Create a 500 meter buffer around each transit station

  2. Convert the population grid geometries (polygons) into points based on the grid centroid

  3. Codunct a spatial join where you find out which points are located within each buffer and join information about the buffer into each point

  4. Aggregate the results for each buffer so that you get population sum per buffer

  5. Sum up the results to get total number of people living within 500 meters from the nearest transit station.

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