代做158.741 Location Data: Mapping, Analysis and Visualisation代做Python编程

158.741

Location Data: Mapping, Analysis and Visualisation

Course and Assessment Guide

School of Mathematical and Computational Sciences

2025

The Course

Prescription

This course will develop knowledge and skills in the processing, analysis and visualisation of data that has a location on the earth.  Location data is more and more readily available (e.g. through georeferencing of social media posts and online mapping tools), and students will learn how to transform. and integrate data from multiple sources, consider the impact of data uncertainty and privacy, and perform. appropriate analysis for environmental, social and economic applications.  Different data collection methods (e.g. crowdsourcing, sensors) will be discussed, and a range of open source tools will be used.

Pre/co requisites

None.

Learning outcomes

Students who successfully complete this course should be able to:

1. Critically evaluate the appropriateness and relevance of different methods for analysing and visualising location data in a range of real world contexts.

2. Choose and apply appropriate analysis and visualisation methods to different kinds of location data.

3. Explain and discuss the importance of data uncertainty, quality and privacy issues in the context of location data.

4. Synthesise different kinds of location and non-location data to produce appropriate visualisations for a given project and purpose.

5. Construct an analysis and visualisation workflow for a particular type of location data.

Topics

Week	Content
1	Course Outline and Assessment Introduction to Location Data
2	The Nature of Geographic Data
3	Georeferencing
4	Representing Geographic Data (Raster, Vector, Geometry Types) Data Formats (Shape Files, kml, etc.) Querying of spatial data using QGIS.
5	Data Uncertainty and Quality
6	Cartography
7	Geovisualisation
8	Spatial Analysis Methods: · Spatial join, union, difference · Buffer · Multi-criteria Evaluation for Selection
9	Spatial Analysis Methods: · Network analysis, network allocation and cost models · Spatial interaction modelling – gravity models and location-allocation
10	Spatial Analysis Methods: Terrain options: DEMs, DTMs, intervisibility analysis, slope and aspect modelling, watersheds, channels
11	Spatial analysis using machine learning, R and python (maybe the final week)
12	VGI and Citizen Science Privacy and Ethics

Course content may be adjusted to suit the interests and background of course participants.

Study resources

The study resources for this course include the lecture slides, supplementary readings and a text book.

The slides for the lectures and supplementary material will be uploaded onto Stream each week and may be slightly edited prior to the lecture (mainly to update the material).

Supplementary resources may include links to web sites, uploaded documents and chapters from other textbooks that can be accessed online or through Stream.   They are often of current relevance and may be uploaded as the course proceeds.

Textbooks

The course has a highly recommended textbook that students will find very helpful.

Geographic Information Science And Systems
Author: Paul A. Longley, Michael F. Goodchild, David J. Maguire, David W. Rhind
ISBN: 978-1-119-03130-7
Edition: 4th
Publisher: WILEY

The textbook is available through the library, and can be purchased online.

Staff

The course coordinator is Niloofar Aflaki ([email protected])

Using Stream and Email

Accessing Stream helps you do well in the course in three ways:

1. Lecturer-to-Student Communication: Important notices will be posted through the ‘Course Announcements’ message board on Stream, and lecture slides, instructions and additional readings will be posted on the page and updated regularly.  By checking Stream often you will always know ‘what’s going on’.

2. Student-to-Lecturer Communication: The best way to communicate with staff if you have any questions is through a direct email.  If your question is relevant to the whole class, we may post a message on the forum for all students.

3. Student-to-Student Communication: Stream allows you to communicate with other students via the ‘Student Discussion’ forum.  Post a message introducing yourself to the class.

Please note that we expect professional, respectful communication on Stream and via email, and abuse or rudeness towards staff or other students will not be tolerated.

Contact course

This course requires attendance at a two hour lecture/tutorial session each week.

How to approach your study

At postgraduate level, some level of independent learning is required, and this course takes an active learning approach, in which students undertake problem solving and investigations themselves, guided by the course coordinator.

Suggested study schedule

For a 15 credit course like this one, an average student is expected to spend 150 hours of study across the semester, in order to gain a passing grade (e.g. C).  Although the workload varies a little from week to week, you should allocate about 10 hours a week over the semester.  If you find the subject difficult or would like to obtain a higher grade, you may need to spend more time.

Assessment

The assessment for the course is as follows.

Assessment	Due Date	Word Limit (or no. hours for tests or exam)	Weighting
At-home Activities	Multiple dates (see below)	5 individual activities (see below for length limits)	25%
Spatial Analysis Using AI and QGIS	4 May 2025, midnight	2500 word report	25%
Spatial Analysis Workflow	1 June 2025, midnight	Portfolio (4000 word plus images) and A0 poster.	50%