Assignment Method In Quantitative Techniques In Geography

About the class

Geographers study big things, cities, forests, snowpack, climate. The systems geographers study are usually too big to directly observe. Geographers rely on data from samples, sensors, surveys, and satellites. Statistics is the art and science of building knowledge from data. This class teaches you how to work with data and how to use it to gain insight into geographic systems. Often geographic phenomena are characterized by complex interactions accross space, time, and scale. This is a second course in statistics and it assumes some knowledge of basic statistical concepts.

Geography is a diverse discipline but in spite this diversity there are set of core analytical techniques that are used across the discipline. This class will focus on these core analytical methods. We will, inevitably, cover subject matter that is not specific to your current area of study and we may miss some things that are central to your sub-field. However, the broad exposure to analytical methods provided by this class will (hopefully) provide a platform for you to learn discipline specific methods in the future.

In this class we focus on developing an intuitive understanding of statistical methods, generally we do this via discussion and example, not formal proofs. On the one hand, I do not want statistical methods to be "black-boxes", on the other hand, I don't want to spend weeks of class time on mathemetical detals. Therefore, this course emphasizes applied analysis through hands-on experiences that aim to provide practical understanding of methods. You should leave this course with confidence in the methods we have discussed and an appreciation for how these statistical methods are applied in "real" geographic research. I will emphasize conceptual understanding, how to implement statistical tests in R and interpret the output.

This is a course for geographers. You do not have to be a geography student to benefit from this class. However, this class assumes an interest in spatial problems and spatial data. Many of the techniques we will learn are not explicitly spatial, however we will, whenever possible, discuss the application of these techniques to spatial problems.

By the end of the course, I want you to know how to select the appropriate statistical method to answer a research question, analyze data, and correctly interpret and write-up the results of your analysis. The course objectives are:

  • To develop "statistical literacy," a working understanding of statistics that can help in critically evaluating data-driven results in the discipline of geography (or ecology, etc...).
  • To obtain a rich set of statistical tools for data analysis, with an understanding of the how to choose appropriate tools and implement them in statistical software.
  • To enable you to confidently and carefully interpret the results of data analyses and clearly communicate those results.
  • To provide practical experience in using real sets of data addressing meaningful research questions.
This class depends heavily on the R programming language, you can expect to spend several hours each week programming in R.

Teaching Philosophy

I am here to organize the course and introduce you to the topics and readings we will examine. I don't have all the answers and I don't pretend to have all the answers, but I will share with you what I know. I will do my best to make the course interesting, relevant, and challenging. That being said, it's important that you understand that you have the most important role in making GEOG 5023 a success. You will determine how much you actually get out of this course. Doing the readings outlined, and coming to class and labs ready to think and participate in group discussions puts you in the best position to benefit from what this course offers. I encourage you to make full use of the learning opportunities that this class presents I am very open to feedback and I would like to help you overcome any problems. If there is ever anything you don't understand please get in touch. If you'd like more detail on something, just ask.

Communications

I request that you post all questions related to lectures, labs, exams, and the final project on the class Piazza site . The reason for this is that it allows others to benefit from your question, allows students to answer, and prevents duplication of questions. Petra and I will post responses to Piazza questions. If you have a question or concern that you'd like to keep private please don't hesitate to contact the instructors directly.

Prerequisites

Students enrolled in this course must have completed an introductory statistics course (e.g. GEOG 3023, APPM 4570, ECON 3818, PSYC 3101, SOCY 4061, EDUC 5716). This course satisfies the requirement for quantitative methods for MA and PhD students in Geography.

Class Materials

We will be using quite a few books this semester making purchasing them all is an expernsive proposition. Assigned readings will be available as reserves. I think, R for Everyone: Advanced Analytics and Graphics, a small book might be a useful desk reference, especially for those new to programming in R. Lab materials are available here.

Grading

Labs8-10 Labs70% of Total Course Grade
Article Commentaries (GRAD STUDENTS ONLY)3 Presentations10% of Total Course Grade
Exams2 Exams15% of Total Course Grade
ParticipationOnline and in class5% of Total Course Grade (GRADS), 15% for Undergrads

Labs

Labs will vary in scope. Some will take one week (short labs) to complete, others will take two weeks (long labs). Generally, short lab assignments provide more direction than the two week long labs. Short labs will generally include code to get you started, long labs will not. For both short and long labs I will provide you a dataset, a set of questions, and some programming advice. To complete the lab you will have to figure out how to use the statistical techniques and the software we've covered in class to answer a set of broad questions. Labs are open ended and designed to allow you significant latitude and room for creativity. Lab write-ups are expected to look very similar to a journal article's results and discussion section. I will pass out a grading rubric and an example of a good lab before the first lab so you understand how labs will be graded. You may work on your lab in groups but you should submit individual assignments. When a lab has been completed collaboratively identify your collaborators and how they contributed to the final product. Feel free to use any online or offline resources that you find helpful. Feel free to share ideas and/or ask questions on the Piazza website. The only way you can cheat on a lab is taking a classmates code/ideas without their consent. Anything online is fair game.

Article Commentaries and Disscussant

Grad students are expected to write and present three article commentaries. You may select articles yourself. Articles should employ a methodological approch covered in the class or one that you feel comfortable explaining to the class. Your commentaries should address the questions in the "reading scientifc articles worksheet." You must prepare a brief summary of the article and a 5-10 minute presentation. Each commentary will be assigned a discussant. The discussant role is to ask critical questions about your commentary and to kick-off class discussion. You must give the discussant your commentary in the class session before it is presented. Commentaries and discussions should be a critical evaluation of the research methods used in the article. The idea here is to have discussions that provide insight into how methods are used in the scientifc literature.

Exams

There will be two exams. Exams will focus on the interepretation of model output.

Final Project

Historically, this class has included an individual final project. In spring 2015 we'll be trying a new approach, inspired by kaggle.com. We will, via some democratic mechanism, select a class project. The class will be divided into teams, each team will complete the project to the best of their ability. Final projects will be graded as two labs; One lab will be for the analysis (code and methods), one for the presentation of results.

Late Policy

Late assignments (labs, commentaries) up to 1 week late will be downgraded 20%, 100% thereafter. Students must complete all lab assignments to receive a passing grade, even if they are submitted too late to receive any points. Exams must be entirely your own work.

DRPS : Course Catalogue : School of Geosciences : Geography


Undergraduate Course: Quantitative Methods in Geography (GEGR09004)

SchoolSchool of GeosciencesCollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 9 (Year 3 Undergraduate)AvailabilityAvailable to all students
SCQF Credits10ECTS Credits5
SummaryThis course provides a further introduction to statistical methods in Geography using relevant example from across the discipline. Course work is designed to give students experience in using the methods to analyse real world data and thereby gain insights into their value and limitations.

Please note this is a core course for students on the Geography Degree Programmes, and Sustainable Development (Geography Pathway). This course is open to all university students, however priority will be given to the degree programmes listed here.
Course description This course is intended to provide a broad introduction to the types of quantitative methods (principally statistical) used in both physical and human geography, with the goal of readying students for the use of these methods in their dissertation (and other) research. Material will be presented through both lectures and practicals, in which the practical session will build on the material introduced in lecture and instruct in how to apply the methods to actual data. Software tools to aid statistical analysis will be introduced through these practicals.

Topics introduced will include types of data, data presentation, correlation and regression, probability, significance and hypothesis testing, and nonparametric statistics (such as logistic regression).

Students� grades will be determined entirely through a written coursework assessment which will be due before the exam diet.
Pre-requisitesCo-requisites
Prohibited CombinationsOther requirements None
Pre-requisitesNone
High Demand Course?Yes
Academic year 2016/17, Available to all students (SV1) Quota:  None
Course StartBlock 2 (Sem 1)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 10, Supervised Practical/Workshop/Studio Hours 6, Feedback/Feedforward Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 80 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment)Written Exam: 0%, Course Work: 100 %, Practical Exam: 0%.

The coursework assignment will be for the most part numerical in nature, with short-form (non-essay) answers. (See Assessment deadlines for the deadline.) The assessment will be released on LEARN with detailed instructions, and submission and feedback will be via the TurnItIn facility. Students will work with similar but unique data sets, so each student will be required to download their own data to complete the assessment.

To provide students with a chance to engage with the course materials early on, a non-assessed (formative) assignment will be posted on LEARN approximately halfway through the course, with model answers posted one week later. You will then receive feedback through anonymous peer assessment. Students must carry out the formative assignment and peer assessment steps before being receiving their data for the assessment.
FeedbackThe practicals will take you through computer-based exercises that will instruct you in the methods required for assessment, with instructors and demonstrators on hand

The course organisers are available for contact by email regarding questions about course and assessment material (for detailed questions, scheduled meetings may be more appropriate)

There will be a formative feedback assignment that will test your competence in the methods introduced (methods which will also be required for summative assessment). A model answer will be provided and the assessment will be peer-reviewed.
No Exam Information
On completion of this course, the student will be able to:
  1. understand differences between types of quantitative data (categorical, ordinal and scale) and when each is applicable
  2. comprehend, generate, and critically discuss presentations of quantitative data (both descriptive statistics and graphical presentations)
  3. carry out tests of relationships between different variables and determine which tests are most appropriate for a given set of data
  4. carry out formal statistical testing (e.g. differences of means) and be able to critique the test in terms of its results and assumptions
  5. demonstrate a broad, integrated knowledge and understanding of quantitative methods, their principles and appropriate application within Geography
Most of the suggested readings will be from the Online Stats Book (http://onlinestatbook.com/2/index.html). This resource contains discussions of a number of statistical subjects at all levels. A particularly valuable feature is the MCQ quiz sections that appear at the end of certain sections. In your review you are encouraged to attempt these questions to test your own knowledge.

We also recommend the following as a �numbers free� gentle approach to statistics:
Wheelan, Charles. Naked Statistics: Stripping the Dread from Data (New York, NY, W. W. Norton & Company, 2014). 282 pp. ISBN 978-0-393-07195-5

Graduate Attributes and SkillsStudents will be able to demonstrate skills in the use of statistical methods and basic theory in Geography, as well as using SPSS software.
Students will also be able to demonstrate an ability to acquire and apply specialist knowledge.
Finally, students will be able to communicate effectively both orally and in writing.
KeywordsGEGR09004
Course organiserDr Daniel Goldberg
Tel: (0131 6)50 2561
Email: Dan.Goldberg@ed.ac.uk
Course secretaryMiss Kirsty Allan
Tel: (0131 6)50 9847
Email: Kirsty.Allan@ed.ac.uk

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