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GEOGRAPHY 204: STATISTICAL PROBLEM

SOLVING IN GEOGRAPHY

Fall 2015

Attendance, attendance, attendance…

Engagement

Turn off phones, don’t surf web, answer questions

Complete lab assignments – IN LAB!

Read textbook prior to corresponding lecture

At least twice…most of you cannot learn statistics the night before an exam

Place important dates in a calendar or planner

Allocate time wisely…..

Lab assistant will not provide help to students 24 hours prior to lab assignment due date

HOW DO YOU PASS THIS COURSE?

Rapidly evolving…

Online/Real Time mapping

Technologically advanced…

Smartphones

Ubiquitous!

Google Earth/Maps!

Most users and even content creators lack

geographic training

GEOGRAPHY TODAY

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Importance – cancer clusters

http://video.foxnews.com/v/4555087/cancer-cluster-in-maryland

Induced seismicity

Fluid injection

Water quality – Kanawha River

MCHM, or 4-methylcyclohexane methanol

Coal cleansing chemical

300,000 residents impacted

STATISTICS IN GEOGRAPHY

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Distinguishes scientists from activists!

quantitative evaluations often more influential to policy makers

Tools for your analytic toolbox…

Employment opportunities after graduation

Scientist or technician?

Develop skills to ask and answer spatial questions

Good use of statistics can help make better decisions

STATISTICAL ANALYSIS

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DATA SETS WE WILL EXAMINE…

0

5

10

15

20

25

30

1990 1995 2000 2005 2010

OBESITY

OBESITY

We are getting fatter as a nation!

DATA SETS WE WILL EXAMINE…

• What factors impact life expectancy?

• What policy initiatives can be implemented to increase life

expectancy?

DATA SETS WE WILL EXAMINE…

• Is there a spatial pattern for last spring frost dates?

• What potential factors might explain the variation in last spring

frost dates?

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Geography – Integrative Spatial Science

Explains and predicts spatial distribution and

variation of human activity and physical features

Spatial patterns

Human geography – industrial cities

Physical geography – natural resources

Interactions between human-physical phenomena

Spatial perspective – patterns and processes

Ecological perspective – relationship between living

and non-living elements

CHAPTER 1: INTRODUCTION: THE CONTEXT OF

STATISTICAL TECHNIQUES

Where? Internet, Google Earth, GIS – spatial data everywhere!

Why? Differences between locations

Differences persist or change

Relationships between multiple variables

What can we do about it? Inform policy

“Geography involves the study of major problems facing humankind such as environmental degradation, unequal distribution of resources and international conflicts. It prepares one to be a good citizen and educated human being” Risa Palm

TRADITIONAL QUESTIONS OF GEOGRAPHY

Statistics – collection, classification, presentation and analysis of numerical data

Widely Applied

Sports – batting average, free throw percentage

Political/Opinion Polls – exit polling, infer population voting pattern from sample

Market Analysis – consumer behavior, advertising, television ratings/demographics

Financial Decisions – budgeting, investment

Weather – likelihood of rain/snow, forecast models (statistical probability of the occurrence of an event)

ROLE OF STATISTICS IN GEOGRAPHY

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Describe and summarize spatial data

Generalizations about complex spatial patterns

Estimate likelihood or probability of outcome of an event at a location

Use sample (limited geographic data) to make inferences about population (larger geographic data)

Determine if the magnitude or frequency of phenomena differs from one location to another

Confirm that actual spatial pattern matches some expected pattern

COMMON USES OF STATISTICS

Medical geographer – patterns/clusters

Law enforcement – crimes

Economic geographer

distance decay

home prices

EXAMPLES

Agronomist – crop yields on experimental plots

Compare soil types, fertilizer input, etc.

EXAMPLES

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Planners - New York Real Estate Price Change

EXAMPLES

Identify worthwhile geographic problem

Based on background knowledge/experience

Hypothesis – unproven or unsubstantiated general statement concerning the problem under consideration

Subject to refinement…

Hypothesis formulation may begin with descriptive statements

Collect location-based data, summarize with maps, graphs, descriptive statistics

What do the data tell you?

GEOGRAPHIC RESEARCH PROCESS

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Human Development Index

Figure 1.1

Very important!

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Descriptive Statistics – provides summaries/numerical descriptions of data

Summarize characteristics of variable in data set

Center, dispersion

Lose data details but presents concise representation

May inform hypothesis construction

May use for model development

Model – simplified replication of the real world

May generate inferential hypotheses

Infer population characteristics from sample characteristics

GEOGRAPHIC RESEARCH

Create and test hypotheses about a statistical population based on information obtained from a sample of that population

Statistical population – total set of information or data under investigation

Microbreweries in United States

Sample – clearly identifiable subset of observations in statistical population

Microbreweries in Maryland and Delaware

Potential hypotheses…

INFERENTIAL STATISITCS

Use research findings to inform actual spatial

policies and plans

Examples

adjust pricing to increase competiveness in the market

increase advertising budget to increase percentage of

microbrew beer consumption in state

Refine results into spatial model to enable

prediction under various scenarios

Examples….

HYPOTHESIS TEST RESULTS

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Hypothesis incorrect – still

provides information and can

be used to re-direct research

question (modify hypothesis,

collect additional data, etc.)

Hypothesis repeatedly verified

as correct under a variety of

circumstances – Law

Law – scientific generalization

thought to be universal and

invariable

Laws concerning related

behaviors combined – Theory

Theory – a repeated,

confirmed explanation of some

aspect of the natural world

BASIC ELEMENTS – DATA/NUMERICAL INFO

Data set – group of data

Includes – observations,

variables, data values

Observations –elements

under study

Spatial examples – cities,

states, etc.

Non-spatial examples –

households, individuals, etc.

Variable – characteristic of

observation that can be

measured, classified or

coded

Data value – measurement,

code or count

Data matrix or array – multiple data values for a single variable

Growing Concern – US and Developing World

Less healthy food, less physical activity

Health risks

Coronary heart disease, certain cancers, type-2 diabetes

Health care costs increase

Lower worker productivity

CDC estimate – medical costs of $147 billion each year

Policy recommendations

“Happy” meal ban, “fat tax”, post nutritional information, physical education, healthy food choices

EXAMPLES: OBESITY

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Body Mass Index = [Weight/(height)2] X 703 ex. [150/(65)2] x 703 = 24.96

Obese: BMI ≥30.0 Overweight: BMI 25.0 to 29.9 Neither: BMI≤ 24.9

Geographically, is there a pattern (i.e. where)?

OBESITY

What descriptive statements (why) can we develop?

State or regional differences?

Background knowledge

What do we know about the states/regions with higher rates? Lower rates?

Population level data available– “conclusive”

Disaggregate to smaller units and sample

counties/census tracts

Inferential hypotheses can be developed

Reject or not reject, estimate probability or likelihood conclusion is correct

Examples?

OBESITY

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Select a random sample of counties from the

four major census regions (Northeast, South,

Midwest, and West

Record obesity rate for multiple time periods

“There are significant differences in obesity rates

between the four census regions”

OBESITY – INFERENTIAL HYPOTHESIS

Select random sample of individuals from

nation

Physically active or inactive? Obese or not?

“The number of physically active people that

are obese is significantly lower than the

number of physically active people that are

expected to be obese if no relationship exists

between obesity and physical activity”

OBESITY – INFERENTIAL HYPOTHESIS

Policy recommendations

More physical activity in public school

Reduced health insurance rates

School lunches

Geographically

Focus policy on specific locations...

OBESITY – WHAT TO DO?

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Common measure of country’s overall health

Cumulative result of multiple, interrelated factors

economic, social, political, environmental

Shorter life expectancies – war, famine, disease, health care

access

Longer life expectancies – exercise, diet, health care system

Where? Why? What to do?

Map it or plot it...

EXAMPLE: LIFE EXPECTANCY

LIFE EXPECTANCY

Descriptive statements....

LIFE EXPECTANCY

Three stages

apparent…

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Inferential hypotheses Need sample data from countries – spatial or

individual...can be difficult!

Two representative samples of individuals from MDCs (more developed countries) and LDCs (less developed countries)

“The life expectancy of those individuals sampled from MDCs is longer than the life expectancy of those sampled from LDCs”

Conclusion accepted: relationship true for MDCs and LDCs populations

LIFE EXPECTANCY

Select random sample of countries with high

per capita health expenditures (e.g., > $1500)

and another random sample of countries with

low per capita health expenditures (e.g. <$300)

“Countries with a high total annual expenditure

on health per capita have longer life

expectancies than countries with a low total

annual expenditure on health per capita”

LIFE EXPECTANCY

Growing season length important for crop

selection and management

Late freezes adversely impact crops

Dr. Parnell collected last frost data from a

sample of weather stations in SE U.S.

EXAMPLE: TIMING OF LAST SPRING FROST

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LAST SPRING FROST

76 stations.....Pattern?

Inferential hypotheses

“Weather stations with more northerly latitudes have average last spring frost dates later in the spring than weather stations with less northerly latitudes”

“Weather stations with higher elevations above sea level have average last spring frost dates later in the spring than weather stations at lower elevations”

Conclusions apply to the entire region (i.e., population)!

LAST SPRING FROST

Interested in state-level population growth in

US over past few decades

EXAMPLE: POPULATION CHANGE

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Patterns (where)?

Descriptive statements

(why)?

Climate?

Economies?

Immigration?

What to do…

Same patterns? Within state variation?

What are spatial processes are at work?

Do spatial processes vary? Etc…