An Introduction to Statistical Problem Solving in Geography Chapter 1 Nick Fillo 872018138

1. GEOG 3000 An Introduction to Statistical Problem Solving in GeographyChapter 1Nick Fillo872018138

2. Things we’ll cover in this chapter: • What is Geography? • What role does statistics play in solving geographic problems? • Discuss the steps and methodology in performing research. • Identify key terms that we’ll use regularly in statistics.

3. What is Geography: Simply put, Geography is the spatial science we use to understand: how human activity varies from place to place and through time. the distribution of features on the earth’s surface. all things when, where, why, how and who concerning problems that human society faces. Geography is old. Humans have been using concepts of geography to answer questions about their environment since the beginning of civilization. That’s a long time.

4. What is Geography: • As civilization advanced, so did our need to expand upon the basic principles of geography. • Geography began as a way to observe spatial patterns and describe “when”, “where” and “what”. • - Noticing certain trees grow faster on north-facing slopes. • Over time, Geography expanded to include ways to explain “why” and “how”certainpatterns exist in nature. • - They prefer north-facing slopes which are cooler and more humid. • Geography continued to evolve to its current form as a problem-solving tool when people began using it to predict future patterns. • - We can increase timber production if we purchase land on the north side of these hills. The trees will grow more quickly there.

5. Statistics in Geography • Statistics is the collection, classification, interrogation and presentation of numerical data. • Statistics allow us to: • Describe and summarize spatial data. • Make simplified generalizations of complex data. • Estimate the probability of an event occurring. • Sample data to make assumptions about a larger data set. • Determine if an event occurs at the same rate (frequency) from location to location. • Determine if an actual spatial pattern will match one that we predicted will happen.

6. Examples of Everyday Statistics • We are surrounded by statistics everyday: • Weather: The chance of precipitation is a statistic. • - “There’s an 80% chance of rain today” means out of 100 times this type of storm system occurs, we’d get rain out of 80 of them. • Advertising: Companies use statistic to get you to buy their product. • - “4 out of 5 dentists recommend you buy our toothpaste.” • Sports: Teams use statistics heavily to prepare for games. • - Earned-run-averages, assists-per-game, yards-per-game, etc. are all statistics used to measure a player’s or team’s skill.

7. Statistical Methodology • Rule: In order for statistics to be truly meaningful, they must be placed within the context of a quantifiable research process. • -Being quantifiable allows spatial data to be analyzed with mathematical equations and computer applications. ☺ • Definition: Qualitative analysis: the description of an object based on its physical characteristics. • - The line of people waiting to buy tickets stretched for 6 blocks. • Definition: Quantitative analysis: the description of an object based on numerical measurements. • - There were an estimated 3,000 people waiting to buy tickets. • During the 1950s and early 1960s, geographic analysis moved away from qualitative analysis and toward quantitative analysis.

8. Research Methodology Diagram • Most researchers follow some variation of this diagram as they perform research. • This diagram is meant to be thought of as a fluid series of steps, where any step can be revisited as needed.

9. Research Methodology Details 1 • Step 1: Problem identification. In order to efficiently address the problem, the research must have a background knowledge of the subject area. • - A member of the census bureau wants to know why people are moving from the Northeast and Midwest to the southern U.S. • Step 2: Develop questions. These could be based on observations and patterns you have noticed. • - It is noticed that many of those moving are in a younger age group. What is attracting them to move? • Step 3: Collect data. Can be accomplished through a variety of methods. • - “I am conducting a survey as to why you are moving….” • - Gather employment data, records, previous studies, etc.

10. Research Methodology Details 2 • Step 4: Process the data. Organize the data in such a way that patterns become more obvious. • - Movement tends to occur more frequently during the spring. • - Movement fluctuates as some industries move out of the region. • Step 5: Reach conclusions. Make an educated guess as to why something is occurring. • - There appears to be no single reason to explain the migration. It appears to be attributed to multiple reason. • Step 6: Form a hypothesis. Sometimes a hypothesis is based on a model. • - People are moving because they are following the jobs, and others are moving to be in a warmer climate.

11. Research Methodology Details 3 • Step 7: Collect and prepare sample data. Use the best method(s) available depending on what data you want. • Step 8: Test your hypothesis. Perform a statistical analysis of your sample data. • - I predict that we will have a 4.8% spike in the rate people are moving out of the region next spring. • Step 9: Hypothesis evaluation. Is your hypothesis in line with the findings of your sample data? • - If yes, your hypothesis may be sound enough to be incorporated into spatial policies or planning. • - If no, re-formulate your hypothesis based on what you learned during testing. New questions may be necessary at this point. • - Often, there will be no clear yes or no answer to this question.

12. Key Terms to Know and Remember • Data: The most basic element in statistics or numerical analysis. This is simply a piece of information. • Data Set: A collection of several data. Also contains observations and variables. • Observation: Also called individuals or cases, this is the element under study for which data is being collected. • Variable: A characteristic of an observation which can be counted or measured. Values of a variable often differ within the data set. • Data Value: An individual measurement or count for each observation within the data set. • Descriptive Statistics: A precise numerical and quantitative summary of the characteristics of a variable or data set.

13. Key Terms to Know and Remember • Inferential Statistics: A generalization of the statistical population which is based on a sample of the population. • Statistical Population: The total set of information or data under investigation in the study. • Sample: A subset of the observations of the statistical population which is clearly identified and used to represent the population as a whole.