Statistical Problem Solving in Geography

Name: Statistical Problem Solving in Geography
Rating: 4.4 (336 reviews)

A college level course on how to apply statistics in geography, GIS and environmental science

Created byArthur Lembo

Last updated 8/2020

English

What you'll learn

perform statistical analysis with geographic data
understand descriptive and inferential statistics
correctly interpret statistical results

Course content

6 sections • 47 lectures • 11h 27m total length

Chapter 1: Introduction to Statistics and Geography17:02
Chapter 1: Examples of hypotheses17:12
Chapter 2: Geographic Data - Introduction14:12
This lecture is an introduction to the terms and concepts of geographic data. You will learn about primary and secondary data sources, qualitative and quantitative data, and discreet and continuous variables.
Chapter 2: Data Types15:26
Chapter 2: Classification17:37
Chapter 2: Classification Map Examples12:17

Chapter 3: Measures of Central Tendency18:51
Chapter 3 - Measures of Dispersion17:38
Chapter 3: Shape and Relative Position4:37
Chapter 3: Considerations for Spatial Data and Descriptive Statistics19:01
Chapter 4: Descriptive Spatial Statistics - Central Tendency17:53
Chapter 4: Spatial Dispersion10:12
This lecture concludes our discussion of spatial descriptive statistics by looking at measures of spatial dispersion.

Chapter 5: Probability - Terms and Definitions17:02
Chapter 5: Probability - Probability Rules11:46
Chapter 5: Probability - Binomial Distribution13:45
Chapter 5: Probability - Geometric Distribution7:26
Chapter 5: Probability - Poisson15:02
Chapter 5: Probability - Poisson Spatial6:26
Chapter 6: The Normal Distribution - Introduction10:25
Chapter 6: The Normal Distribution - Calculation8:03
Chapter 6: The Normal Distribution - Last Spring Frost Example8:00
Chapter 8: Estimation in Sampling - Introduction9:50
Chapter 8: Estimation in Sampling - Central Limit Theorem18:18
Chapter 8: Estimation in Sampling - Confidence Intervals15:22
Chapter 8: Estimation in Sampling - Examples15:17

Chapter 9: Elements of Inferential Statistics - Terms and Concepts19:20
Chapter 9: Elements of Inferential Statistics - one sample difference of means12:31
Chapter 10: Two Sample Difference Tests - Introduction11:22
In this lecture you will learn how to perform two-sample difference tests. These include two-sample difference of means and proportions. You will also learn about a special case of the two sample difference test: the matched pairs test for dependent samples. Each test will include geographic examples for both the parametric and non-parametric cases.
Chapter 10: Two sample difference of means - calculation14:28
In this lecture you will learn how to calculate and interpret a two-sample difference of means test. This will include both the parametric and non parametric tests.
Chapter 10: Difference of Proportions - calculation12:31
Chapter 10: Matched Pairs Test15:44
Chapter 11: ANOVA - Introduction10:53
In this lecture you will learn how to perform a three or more sample difference test (ANOVA). The first lecture in this series will explain what ANOVA is, and what it does.
Chapter 11: ANOVA - Calculation9:14
In this lecture you will learn how to calculate the ANOVA formulas. In learning the calculation methods, you will better understand how ANOVA works, and will then be ready to interpret the results of an ANOVA analysis.
Chapter 11: ANOVA - Examples17:48
In this lecture, you will perform an ANOVA test and interpret the results for numerous geographical examples. You'll also learn how to use Excel to calculate and interpret an ANOVA table.

Chapter 13: General Issues in Inferential Spatial Statistics19:55
In this lecture you will learn about the unique characteristics of spatial data in statistical analysis and will be introduced to the concept of spatial autocorrelation and how to interpret variograms.
Chapter 14: Point Pattern Analysis - Nearest Neighbor18:35
In this lecture you will learn a technique of point pattern analysis called nearest neighbor analysis. You'll learn what nearest neighbor analysis is, how to calculate it, and how to interpret the results. The lecture will also perform a nearest analysis on geographic data and interpret the results.
Chapter 14: Point Pattern Analysis - Quadrat Analysis19:09
In this lecture you will learn a technique of point pattern analysis called quadrat analysis. You'll learn what quadrat analysis is, how to calculate it, and how to interpret the results. The lecture will also perform a quadrat analysis on geographic data and interpret the results.
Chapter 15: Area Pattern Analysis - Join Count18:46
In this lecture you will learn a technique of area pattern analysis called join count analysis. You'll learn what join count analysis is, how to calculate it, and how to interpret the results. The lecture will also perform a join count analysis on geographic data and interpret the results.
Chapter 15: Area Pattern Analysis - Moran's I (Introduction)15:50
In this lecture you will learn a technique of area pattern analysis called Moran's I Coefficient. This is the most common method of measuring spatial autocorrelation in a data set. You'll learn what Moran's I is, how to calculate it, and how to interpret the results. The lecture will also perform a Moran's I analysis on geographic data and interpret the results.
Chapter 15: Area Pattern Analysis - Moran's I (conclusion)17:51
In this lecture you will continue to explore the concept of Moran's I analysis, by exploring a a geographic dataset. In addition, you will perform a Moran's I analysis to test for both global and local spatial autocorrelation.

Chapter 16: Correlation - Introduction10:27
In this lecture you will be introduced to the concept of correlation. This first lecture in a series will introduce you to what correlation is, a how it is used with geographic data.
Chapter 16: Correlation - Pearson15:09
In this lecture you will learn how to perform a Pearson's Correlation (the most common form of correlation) on a set of geographic data. You will learn how to calculate the Pearson Correlation component and interpret the results for a geographic data set.
Chapter 16: Correlation - Spearman17:13
In this lecture you will learn how to perform a non parametric test of correlation, using the Spearman Rank Correlation coefficient. You will learn how to calculate the Spearman Correlation component and interpret the results for a geographic data set.
Chapter 17: Simple Linear Regression - Introduction16:47
Now it gets interesting. In this lecture you will learn how to perform simple linear regression. Regression is the most common method of performing statistical analysis, and is the basis for statistical modeling of geographic data. You will learn what regression is, how to interpret regression results, and how to make predications based on your analysis.
Chapter 17: Simple Linear Regression - Calculation19:14
This lecture will show you the nitty-gritty of how simple regression is calculated.
Chapter 17: Simple Linear Regression - Interpretation16:49
In this lecture, you will analyze different geographic data sets, perform simple linear regression, interpret the results, and make predictions based on the results. When you complete this lecture, you will learn why regression is such a powerful statistical tool for any geographer.
Chapter 18: Multiple Regression - Introduction19:20
I've saved the best for last. A geographer who knows how to perform multi-variate regression can command higher salaries and engage in more interesting and rewarding work. Multi-variate regression is one of the most powerful tools in a geographers toolbox. Unfortunately, most geographers do not know how to apply regression to real world scenarios. In this lecture you will conduct multivariate regression analysis on geographic data, correct for problems of multicollinearity and non significant predictors, and learn how to choose the best variables that explain a geographic phenomenon. In short, when you are done with this lecture, you are truly engaging in meaningful geographic research (not to say that everything else we've done here isn't meaningful!!).

Requirements

Students should have some background with basic arithmetic and spreadsheet software
Students should obtain the book An Introduction to Statistical Problem Solving, although any introductory book on statistics and geography should be fine

Description

Do you struggle with statistics? Do you want to obtain a more quantitative background in the use of statistics in geography, environmental science, and GIS. Or, are you a student who is taking a course in statistics and geography but feel intimidated by the complexities of the subject? No worries. I created this class for you.

This class will walk you through each chapter of my textbook An Introduction to Statistical Problem Solving in Geography, along with the lecture notes I use in my course. It is designed specifically for geographers. So, the course isn't really a math course, but an applied course in statistics for geographers.

You can also think of this course as a personal tutoring session. I will not only go over each chapter, teaching you statistics, but will also work side-by-side with you to use statistical software to recreate examples in the book so that you know how to actually perform the statistical analysis.

At the end of this course you will know how to apply statistics in the field of geography and GIS. And many of my students who were initially intimidated by statistics, find they actually love this subject, and have chosen to refocus their career on quantitative geography.

Who this course is for:

This course is designed for students taking a general or geography based introductory statistics class
Students using the textbook An Introduction to Statistical Problem Solving in Geography by McGrew, Lembo, and Monroe
Geographers and GIS professionals wanting obtain quantitative skills for their daily work

Statistical Problem Solving in Geography

What you'll learn

Explore related topics

Course content

Basic Statistical Concepts in Geography6 lectures • 1hr 34min

Descriptive Problem Solving in Geography6 lectures • 1hr 28min

The Transition to Inferential Problem Solving13 lectures • 2hr 37min

Inferential Problem Solving in Geography9 lectures • 2hr 4min

Inferential Spatial Statistics6 lectures • 1hr 50min

Statistical Relationships Between Variables7 lectures • 1hr 55min

Requirements

Description

Who this course is for: