Python代写: COMP1040 Assignment 2

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COMP1040 Assignment 2

Semester 2, 2016

This README.md document describes the assignment and method of assessment. Please read through the entire document carefully.

This assignment is worth 20% of your course grade. It is due at 11:59pm on Friday, 19 August 2016.

Overview

In this assignment you will be developing a rudimentary system for visualising traffic incidents in the Canberra region. You will do this by parsing tweets from the ACTPol_Traffictwitter account to extract the location (street and suburb) for incidents reported in each tweet. You will then plot these locations on a map of Canberra such as the one shown below. All data and starter code is provided.

Getting and Submitting the Assignment

The assignment is entirely managed from within the School’s GitLab server. You will fork and checkout the assignment to work on it and then commit your changes and push them back to the GitLab server to submit your solutions. You should push back often. The latest version of your code available at the due date will be considered as your assignment submission.

Forking and Cloning the Data and Code

Before working on the assignment you must first fork a copy from the COMP1040 account here: https://gitlab.cecs.anu.edu.au/comp1040/comp1040-2016-a2

Once you have forked the project, you will need to clone the project repository using PyCharm IDE as you did in the first assignment.

Important: Make sure your fork of the assignment is private before you start committing work to it, otherwise everyone using GitLab will be able to see your work. This should happen automatically but it is always good practice to check. You can set a Project to private via the GitLab interface:

  1. Go to the version of the assignment that you’ve forked from your Profile page.
  2. Click on the Gear drop-down box in the top right and choose “Edit Project”.
  3. Under “Visibility Level”, select “Private”.
  4. Click on the “Save changes” button

Description of the Data

This section describes how we obtained the data for the assignment. It is provided for your information. You do not need to implement the steps outlined in this section. All data is provided in the data directory.

The ACTPol_Traffic file was obtained by dumping tweets posted from the ACTPol_Traffic Twitter account. We did this using the ‘twitter-archiver’ application, which is available in the Python ‘twitter’ package, as follows:

sudo easy_install twitter
twitter-archiver -o ACTPol_Traffic

We repeat, this is for your information only. You do not need to execute the steps above.

The file has one tweet per line. The start of the line contains the following space-separated fields, which are then followed by the tweet (until the end of the line)

  1. Unique tweet identifier
  2. Date
  3. Time
  4. Timezone (AEST)
  5. Twitter account in angled brackets

There are two versions of the map of Canberra obtained from Open Street Map,http://www.openstreetmap.org/export#map=12/-35.3283/149.2155. Associated with each map is a text file indicating the latitude and longitude range covered by the map. The format of the lat/lon files is:

lat_max lat_min
lon_min lon_max

Canberra street locations were obtained via Google’s geocode API. The file format includes the query address, returned address, latitude, longitude, location type (from Google), and number of results found. The fields are delimited by a vertical bar.

Tasks

You are provided with:

  • a map of Canberra (and longitude/latitude bounds on the map)
  • a file containing street and suburb names and their locations
  • a file of tweets obtained from the ACTPol_Traffic twitter account
  • and starter Python code and test cases

You should complete the tasks listed below in the Python 3 programming language. You are free to use additional Python libraries to complete the task as long as they are bundled with the Anaconda 3 distribution. Each task is associated with a detailed GitLab issue which you must close when completing the task. A break down of marks is given next to each task.

Part A. Text Processing (40%)

  1. parse and normalise tweets (15%)
  2. match tweets against some street names (10%)
  3. compute histogram of suburb mentions (15%)
    • Note: the histogram could be slightly inaccurate since suburb can be same as street names

Part B. Visualization (55%)

  1. parse street locations from file (10%)
  2. read and display the map (5%)
  3. compute lat/long map coordinates (and test) (15%)
  4. animate tweet events on map (15%)
  5. show heatmap of events such as shown below (bonus) (10%)

Miscellaneous (5%)

  1. demonstrated use of GitLab (5%)
    • inclusion of NOTES.md file
    • commit history and issue tracking

Copying & Plagiarism

The ANU takes plagiarism (i.e., the unattributed copying of another’s work) very seriously (see Academic Honesty & Plagiarism).

While we fully expect you to discuss approaches to questions with your classmates you must never copy a complete or partial solution from someone else and claim it as your own.

Make sure the code you write is your own. If you do get help from someone or somewhere on the web, you must make a note (e.g., in the code comments or a NOTES.md file) as to the nature of that help.

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