Exercise 1

Working with Geometric Objects

This lesson we will practice how to create geometric objects using Shapely module and how to find out different useful attributes from those geometries. We will also take advantage of what we have learned earlier, specifically functions, that you should use for making different GIS operations easier to use in the future. We will also use Pandas to read data from a file.

Write all your codes for all the challenges here into a single exercise1.ipynb -file, **submit for grading in Moodle Exercise 1** by Tuesday, 5 November 2019 , 14:00 pm (EET Tartu time) Use common sense to structure your Jupyter Notebook file with several cells according to solving the problems from this exercise. Include comments in order to document your functions and codes.

Sections

Problem 1: Creating basic geometries (3 Points)

1. Create a function called createPointGeom() that has two parameters (x_coord, y_coord). Function should create a shapely Point geometry object and return that. Demonstrate the usage of the function by creating 3 Point -objects with the function.

2. Create a function called createLineGeom() that takes a list of Shapely Point objects as parameter and returns a LineString object of those input points. Ideally, the function should try to check that the input list really contains Shapely Point(s). Demonstrate the usage of the function by creating 2 LineString -objects with the function (one with coordinate tuples, and one with list of shapely Points).

3. Create a function called createPolyGeom() that takes a list of coordinate tuples OR a list of Shapely Point objects and creates/returns a Polygon object of the input data. Both ways of passing the data to the function should be working. Demonstrate the usage of the function by passing data first with coordinate-tuples and then with Point -objects.

Problem 2: Attributes of geometries (3 Points)

  1. Create a function called getCentroid() that takes any kind of Shapely’s geometric -object as input and returns a centroid of that geometry. Demonstrate the usage of the function.

  2. Create a function called getArea() that takes a Shapely’s Polygon -object as input and returns the area of that geometry. Demonstrate the usage of the function.

3. Create a function called getLength() takes either a Shapely’s LineString or Polygon -object as input. Function should check the type of the input and returns the length of the line if input is LineString and length of the exterior ring if input is Polygon. If something else is passed to the function, it should tell the user –> "Error: LineString or Polygon geometries required!". Demonstrate the usage of the function.

Problem 3: Reading coordinates from a file and creating a geometries (4 Points)

One of the “classical” problems in GIS is the situation where you have a set of coordinates in a file and you need to get them into a map (or into a GIS-software). Python is a really handy tool to solve this problem as with Python it is basically possible to read data from any kind of input datafile (such as csv-, txt-, excel-, or gpx-files (gps data) or from different databases). So far, I haven’t faced any kind of data or file that would be impossible to read with Python.

Thus, let’s see how we can read data from a file and create Point -objects from them that can be saved e.g. as a new Shapefile (we will learn this next lesson). Our dataset ** Years.2015-2017.ibtracs_wmo.storms.csv ** consist of tracked paths of tropical storms, hurricanes etc in the years 2015-2017. The first four rows of our data looks like this:

Name,Serial_Num,year,Basin,Sub_basin,Num,Latitude_first,Longitude_first,Latitude_last,Longitude_last,ISO_time_first,ISO_time_last,Nature
ADJALI,2014319S06066,2015, SI, MM,1,-6.7,66.4,-11.9,51.4,2014-11-15 06:00:00,2014-11-24 06:00:00, TS
0220142015:TWO,2014327S08077,2015, SI, MM,2,-8.0,77.3,-28.9,62.5,2014-11-23 06:00:00,2014-12-02 00:00:00, TS
KATE,2014356S08101,2015, SI, WA,4,-7.5,100.5,-30.0,89.6,2014-12-21 15:00:00,2015-01-04 12:00:00, NR

Thus, we have many columns of data, but the few important ones are:

Column

Description

Longitude_first

Longitude-coordinate of the first time of tracking

Latitude_first

Latitude-coordinate of the first time of tracking

Longitude_last

Longitude-coordinate of the last time of tracking

Latitude_last

Latitude-coordinate of the last time of tracking

Tasks

  1. Save the Years.2015-2017.ibtracs_wmo.storms.csv into your computer.

  2. We will use only 4 columns, i.e. ‘Longitude_first’ (x), ‘Latitude_first’ (y), ‘Longitude_last’, ‘Latitude_last’ from the data in.

  3. Iterate over the rows of your DataFrame and create Shapely Point -objects for orig_points and dest_points representing the origin locations and destination locations of the storm paths rows, accordingly. Therefore, create two additional columns called orig_points and dest_points by applying a function that creates shapely points from the coordinates. Think through, how to make a step-by-step approach based on the lecture and hints provided.

Problem 4: Creating LineStrings that represent the movements (optional task for advanced students, additional max 3 points)

This is an optional extra task for those who likes to learn even more.

  1. Create an additional column called lines: Iterate over the dataframe again, row by row, and use the origin and destination fields from above and create a Shapely LineString -object between the origin and destination point and add as a new column to your dataframe

  2. Find out what is the average (Euclidian) distance of all the origin-destination LineStrings that we just created, and print it; see Pandas calculate mean for a dataframe.

Consider: To make things more reusable: write creation of the LineString and calculating the average distance into dedicated functions and use them.