Statistical Problem Solving in Geography from Udemy

What's inside

Learning objectives

Perform statistical analysis with geographic data
Understand descriptive and inferential statistics

Correctly interpret statistical results

Perform statistical analysis with geographic data
Understand descriptive and inferential statistics
Correctly interpret statistical results

Syllabus

At the end of this section, the student will understand the role of statistics in geography, how to develop a hypothesis, and the strengths and limitations of different data and classification methods

Chapter 1: Introduction to Statistics and Geography

Chapter 1: Examples of hypotheses

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 Types

Chapter 2: Classification

Chapter 2: Classification Map Examples

At the end of this section, you will understand concepts of descriptive statistics including measures of central tendency, shape and position, and spatial considerations in descriptive statistics.

Chapter 3: Measures of Central Tendency

Chapter 3 - Measures of Dispersion

Chapter 3: Shape and Relative Position

Chapter 3: Considerations for Spatial Data and Descriptive Statistics

Chapter 4: Descriptive Spatial Statistics - Central Tendency

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

At the end of this section you will make the transition to inferential statistics, understanding concepts related to probability, distribution, sampling, and confidence interval estimation.

Chapter 5: Probability - Terms and Definitions

Chapter 5: Probability - Probability Rules

Chapter 5: Probability - Binomial Distribution

Chapter 5: Probability - Geometric Distribution

Chapter 5: Probability - Poisson

Chapter 5: Probability - Poisson Spatial

Chapter 6: The Normal Distribution - Introduction

Chapter 6: The Normal Distribution - Calculation

Chapter 6: The Normal Distribution - Last Spring Frost Example

Chapter 8: Estimation in Sampling - Introduction

Chapter 8: Estimation in Sampling - Central Limit Theorem

Chapter 8: Estimation in Sampling - Confidence Intervals

Chapter 8: Estimation in Sampling - Examples

In this section you apply your knowledge of inferential statistics to solve practical problems for multiple samples, conduct hypothesis tests, and draw conclusions from statistical analysis.

Chapter 9: Elements of Inferential Statistics - Terms and Concepts

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

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.

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 - calculation

Chapter 10: Matched Pairs Test

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.

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.

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.

This is where we focus purely on the spatial aspects of statistics. You will applying your knowledge of inferential statistics to evaluate hypotheses of spatial patterns and geographic examples.

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.

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.

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.

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.

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.

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.

In this section you will learn to use inferential statistics to evaluate relationships between different samples. Also, you will learn to use regression techniques in an understandable way.

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.

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.

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.

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.

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

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.

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!!).

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Ideal for those with a foundational understanding of geography, aiming to enhance their statistical knowledge

Instructors Arthur Lembo bring a wealth of knowledge and expertise in the application of statistics in geography

Practical focus on real-world scenarios, fostering direct application of statistical concepts in geographical contexts

Provides a comprehensive overview, spanning introductory to advanced statistical techniques

Requires students to have a basic understanding of GIS software

Aligned with undergraduate-level courses in statistical geography, offering a supplementary resource for students within this field

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Statistical Problem Solving in Geography with these activities:

Review: Discovering Statistics Using IBM SPSS Statistics

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Review a textbook that provides a comprehensive overview of statistical concepts and techniques, with a focus on using IBM SPSS Statistics software for analysis.

View Discovering Statistics Using R on Amazon

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Read through the chapters that cover the concepts and techniques relevant to the course.
Complete the practice exercises provided in the book.
Apply what you have learned to analyze a small dataset using IBM SPSS Statistics.

Form a Study Group with Classmates

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Form a study group with classmates to discuss course concepts, work on assignments together, and support each other's learning.

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Reach out to classmates and propose forming a study group.
Schedule regular meetings and agree on a meeting format.
Discuss course materials, complete assignments together, and share knowledge.

Tutorial: Working with Geospatial Data in R

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Follow a guided tutorial to learn how to work with geospatial data in R, a widely used statistical programming language with powerful capabilities for spatial analysis.

Browse courses on Geospatial Data Analysis

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Find a suitable tutorial on working with geospatial data in R.
Follow the steps outlined in the tutorial.
Complete the practice exercises provided in the tutorial.
Apply what you have learned to a small project of your own.

Five other activities

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Practice Calculations with Geographic Data

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Engage in practice drills to reinforce your understanding of statistical calculations with geographic data, covering both descriptive and inferential statistics.

Browse courses on Descriptive Statistics

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Find a set of practice problems online or in a textbook.
Solve the practice problems using the appropriate formulas and methods.
Check your answers and identify areas where you need more practice.

Create a GIS Map of a Local Area

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Create a GIS map of a local area that visualizes and analyzes spatial data, demonstrating your understanding of GIS principles and cartographic techniques.

Browse courses on GIS

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Choose a local area and identify the data you need.
Collect and prepare the data for GIS analysis.
Create a GIS map using appropriate software (e.g., ArcGIS, QGIS).
Analyze the data and identify patterns or trends.
Design and finalize the map, ensuring clarity and effective communication.

Perform Spatial Data Analysis in Python

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Start a project that will test your skills in spatial data analysis using Python, a popular programming language for this type of analysis.

Browse courses on Spatial Data Analysis

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Choose a dataset and problem to work on.
Import the dataset into Python using the appropriate libraries.
Perform exploratory data analysis to understand the data.
Apply spatial analysis techniques to the data.
Visualize the results of your analysis.

Attend a Workshop on Geospatial Modeling

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Attend a workshop that provides hands-on experience with geospatial modeling techniques, utilizing GIS software and real-world datasets.

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Find a suitable workshop on geospatial modeling.
Register for the workshop and make necessary arrangements.
Attend the workshop and actively participate in the activities.
Complete any assignments or projects assigned during the workshop.

Develop a Story Map to Showcase a Geographic Concept

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Create a story map that effectively communicates a geographic concept, combining narrative, interactive maps, and multimedia elements to engage your audience.

Browse courses on Story Mapping

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Choose a geographic concept and develop a narrative around it.
Gather and prepare relevant maps, images, and other media.
Use a story mapping platform (e.g., ArcGIS StoryMaps, Knight Lab StoryMap JS) to create your story map.
Design the layout, write the text, and incorporate the media.
Publish and share your story map with others.

Career center

Learners who complete Statistical Problem Solving in Geography will develop knowledge and skills that may be useful to these careers:

Cartographer

Cartographers design and produce maps and other visual representations of spatial data. They use their knowledge of geography, statistics, and computer-aided design (CAD) software to create maps that are both accurate and visually appealing. This course will help you develop the skills you need to be a successful cartographer. You will learn how to collect, analyze, and interpret spatial data; create maps using CAD software; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your maps are accurate and reliable.

See salaries and explore the career path for Cartographer

Geographer

Geographers study the physical features of the Earth and the human activities that shape them. They use their knowledge of geography, statistics, and GIS to analyze spatial data and identify patterns and trends. This course will help you develop the skills you need to be a successful geographer. You will learn how to collect, analyze, and interpret spatial data; create maps using GIS software; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your research is accurate and reliable.

See salaries and explore the career path for Geographer

GIS Analyst

GIS analysts use geographic information systems (GIS) software to create and analyze maps. They use their knowledge of geography, statistics, and GIS to solve problems and make decisions. This course will help you develop the skills you need to be a successful GIS analyst. You will learn how to collect, analyze, and interpret spatial data; create maps using GIS software; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your analysis is accurate and reliable.

See salaries and explore the career path for GIS Analyst

Environmental Scientist

Environmental scientists study the environment and its components. They use their knowledge of science, statistics, and policy to develop solutions to environmental problems. This course will help you develop the skills you need to be a successful environmental scientist. You will learn how to collect, analyze, and interpret environmental data; develop and implement environmental policies; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your research is accurate and reliable.

See salaries and explore the career path for Environmental Scientist

Urban Planner

Urban planners develop plans for the development and use of land in urban areas. They use their knowledge of geography, statistics, and planning to create plans that are both sustainable and equitable. This course will help you develop the skills you need to be a successful urban planner. You will learn how to collect, analyze, and interpret data; develop and implement planning policies; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your plans are based on sound evidence.

See salaries and explore the career path for Urban Planner

Transportation Planner

Transportation planners develop plans for the transportation system in a region. They use their knowledge of geography, statistics, and transportation planning to create plans that are both efficient and sustainable. This course will help you develop the skills you need to be a successful transportation planner. You will learn how to collect, analyze, and interpret data; develop and implement transportation plans; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your plans are based on sound evidence.

See salaries and explore the career path for Transportation Planner

Data Analyst

Data analysts collect, analyze, and interpret data to help businesses make better decisions. They use their knowledge of statistics, programming, and data analysis tools to identify trends and patterns in data. This course will help you develop the skills you need to be a successful data analyst. You will learn how to collect, analyze, and interpret data; develop and implement data analysis models; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your analysis is accurate and reliable.

See salaries and explore the career path for Data Analyst

Biostatistician

Biostatisticians use statistics to design and analyze studies in the biomedical sciences. They use their knowledge of statistics, biology, and medicine to develop and implement statistical methods that can be used to answer research questions. This course will help you develop the skills you need to be a successful biostatistician. You will learn how to design and analyze statistical studies; develop and implement statistical methods; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your research is accurate and reliable.

See salaries and explore the career path for Biostatistician

Statistician

Statisticians collect, analyze, and interpret data to help solve problems. They use their knowledge of statistics, mathematics, and computer science to develop and implement statistical methods that can be used to answer research questions. This course will help you develop the skills you need to be a successful statistician. You will learn how to collect, analyze, and interpret data; develop and implement statistical methods; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your research is accurate and reliable.

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Market Researcher

Market researchers collect and analyze data to understand consumer behavior. They use their knowledge of statistics, marketing, and consumer psychology to develop and implement research studies that can be used to answer marketing questions. This course will help you develop the skills you need to be a successful market researcher. You will learn how to collect, analyze, and interpret data; develop and implement research studies; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your research is accurate and reliable.

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Epidemiologist

Epidemiologists study the causes and distribution of disease in populations. They use their knowledge of statistics, epidemiology, and public health to develop and implement strategies to prevent and control disease. This course will help you develop the skills you need to be a successful epidemiologist. You will learn how to collect, analyze, and interpret data; develop and implement epidemiological studies; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your research is accurate and reliable.

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Data Scientist

Data scientists use scientific methods to extract knowledge and insights from data. They use their knowledge of statistics, computer science, and domain expertise to develop and implement data science solutions that can be used to solve business problems. This course will help you develop the skills you need to be a successful data scientist. You will learn how to collect, analyze, and interpret data; develop and implement data science models; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your data science solutions are accurate and reliable.

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Risk Analyst

Risk analysts assess the likelihood and impact of risks. They use their knowledge of statistics, finance, and risk management to develop and implement risk assessment models that can be used to make decisions about how to manage risk. This course will help you develop the skills you need to be a successful risk analyst. You will learn how to collect, analyze, and interpret data; develop and implement risk assessment models; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your risk assessment models are accurate and reliable.

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Financial Analyst

Financial analysts use financial data to make investment decisions. They use their knowledge of statistics, finance, and accounting to analyze financial statements and make recommendations about which investments to buy, sell, or hold. This course will help you develop the skills you need to be a successful financial analyst. You will learn how to collect, analyze, and interpret financial data; develop and implement financial models; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your financial analysis is accurate and reliable.

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Operations Research Analyst

Operations research analysts use mathematical and statistical techniques to solve problems in business and industry. They use their knowledge of statistics, mathematics, and operations research to develop and implement solutions that can improve efficiency, productivity, and profitability. This course will help you develop the skills you need to be a successful operations research analyst. You will learn how to collect, analyze, and interpret data; develop and implement operations research models; and communicate your findings effectively. With a strong foundation in statistics, you will be able to ensure that your operations research models are accurate and reliable.

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