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Statistical Problem Solving in Geography

A college level course on how to apply statistics in geography, GIS and environmental science
4.7 (56 ratings)
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379 students enrolled
Last updated 3/2016
English
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Includes:
  • 11.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
perform statistical analysis with geographic data
understand descriptive and inferential statistics
correctly interpret statistical results
View Curriculum
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 is the target audience?
  • 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
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Curriculum For This Course
Expand All 48 Lectures Collapse All 48 Lectures 11:28:31
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Basic Statistical Concepts in Geography
7 Lectures 01:34:41

Chapter 1: Introduction to Statistics and Geography
14:34

Chapter 1: Examples of hypotheses
14:19

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: Geographic Data - Introduction
14:12

Chapter 2: Data Types
15:26

Chapter 2: Classification
17:37

Chapter 2: Classification Map Examples
12:17
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Descriptive Problem Solving in Geography
6 Lectures 01:28:12
Chapter 3: Measures of Central Tendency
18:51

Chapter 3 - Measures of Dispersion
17:38

Chapter 3: Shape and Relative Position
04:37

Chapter 3: Considerations for Spatial Data and Descriptive Statistics
19:01

Chapter 4: Descriptive Spatial Statistics - Central Tendency
17:53

This lecture concludes our discussion of spatial descriptive statistics by looking at measures of spatial dispersion.

Chapter 4: Spatial Dispersion
10:12
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The Transition to Inferential Problem Solving
13 Lectures 02:36:42
Chapter 5: Probability - Terms and Definitions
17:02

Chapter 5: Probability - Probability Rules
11:46


Chapter 5: Probability - Geometric Distribution
07:26

Chapter 5: Probability - Poisson
15:02

Chapter 5: Probability - Poisson Spatial
06:26

Chapter 6: The Normal Distribution - Introduction
10:25

Chapter 6: The Normal Distribution - Calculation
08:03

Chapter 6: The Normal Distribution - Last Spring Frost Example
08:00

Chapter 8: Estimation in Sampling - Introduction
09:50

Chapter 8: Estimation in Sampling - Central Limit Theorem
18:18

Chapter 8: Estimation in Sampling - Confidence Intervals
15:22

Chapter 8: Estimation in Sampling - Examples
15:17
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Inferential Problem Solving in Geography
9 Lectures 02:03:51
Chapter 9: Elements of Inferential Statistics - Terms and Concepts
19:20

Chapter 9: Elements of Inferential Statistics - one sample difference of means
12:31

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.

Preview 11:22

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.

Preview 14:28

Chapter 10: Difference of Proportions - calculation
12:31

Chapter 10: Matched Pairs Test
15:44

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 - Introduction
10:53

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 - Calculation
09:14

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.

Preview 17:48
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Inferential Spatial Statistics
6 Lectures 01:50:06

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.

Preview 19:55

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 - Nearest Neighbor
18:35

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 14: Point Pattern Analysis - Quadrat Analysis
19:09

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 - Join Count
18:46

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 (Introduction)
15:50

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 15: Area Pattern Analysis - Moran's I (conclusion)
17:51
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Statistical Relationships Between Variables
7 Lectures 01:54:59

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 - Introduction
10:27

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 - Pearson
15:09

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 16: Correlation - Spearman
17:13

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 - Introduction
16:47

This lecture will show you the nitty-gritty of how simple regression is calculated.

Chapter 17: Simple Linear Regression - Calculation
19:14

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 17: Simple Linear Regression - Interpretation
16:49

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!!).

Chapter 18: Multiple Regression - Introduction
19:20
About the Instructor
4.5 Average rating
477 Reviews
2,722 Students
10 Courses

Dr. Arthur J. Lembo, Jr. is an educator with a passion for GIS and almost 30 years of GIS industry experience.

Currently, Dr. Lembo is an Associate Professor in the Department of Geography and Geosciences at Salisbury University, where he is also the Technical Director of the Eastern Shore Regional GIS Cooperative. Dr. Lembo has published numerous academic papers on GIS, authored a leading textbook on Statistical Problem Solving in Geography, and conducted sponsored research for organizations like the National Science Foundation, NASA, the United States Department of Agriculture, and the Kellogg Foundation.

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