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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…