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### What is the most common mistake in GIS analysis?

- Description versus Analysis
- The concepts of Process, Pattern and Analysis
- Issues and challenges in spatial data analysis
- Measuring space

Briggs Henan University 2012

Description and Analysis

Description

- Most GIS systems are used by governments and private companies to describe the real world
- this helps the organization “do its job”
- For example, manage sewer and water networks
- Manage land resources
- Most GIS systems are primarily designed for this purpose
- They are used to develop spatial databases to describe the real world and help manage it.

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Description and Analysis

Is the locations of the software industry different from the telecommucations industry?

Analysis

- Tries to understand the processes which cause or create the patterns in the real world
- Understanding processes:
- Helps the organization do its job better
- Make better decisions, for example
- Helps us understand the phenomena itself
- This is the role of science

Here, we are using “centrographic statistics” to help answer this question

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Description: Water and Sewer system

Analysis: Do the locations of the software and telecommucations industries differ?

We will talk about analysis.

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Process, Pattern and Analysis

- Processesoperating in space create patterns
- Spatial Analysis is aimed at:
- Identifying and describing the pattern
- Identifying and understanding the process

Create

Patterns

Processes

(or cause)

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- Identifying and describing the pattern

The pattern is clearly clustered

(points are in “groups”)

- Identifying and understanding the process

Access to transportation.

Agglomeration economies* from sharing ideas, access to skilled labor, access to business services.

We will focus on spatial analysis

*cost savings from many firms locating in the same area

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Process, Pattern and Analysis

- Often, we cannot observe (or “see’) the process, so we have to infer (“guess at” ?) the process by observing the pattern

No

Infer

Yes

Create

Patterns

Processes

(or “cause”)

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Spatial Analysis:successive levels of sophistication

Four levels of Spatial Analysis:

--Each is more advanced (more difficult!)

- Spatial data description:
- Exploratory Spatial Data Analysis (ESDA)
- Spatial statistical analysis and hypothesis testing
- Spatial modeling and prediction

We will look at all 4 levels in this lecture series

More difficult,

but more useful!

(more powerful)

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Spatial Analysis:successive levels of sophistication

- Spatial data description:
- Focus is on describing the world,

and representing it in a digital

format

--computer map

--computer database

- Uses classic GIS capabilities

--buffering, map layer overlay

--spatial queries & measurement

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Spatial Analysis:successive levels of sophistication

2. Exploratory Spatial Data Analysis (ESDA):

- searching for patterns and possible explanations
- GeoVisualization through data graphing and mapping

--Density Kernel Estimation

--Overlay transportation network

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Spatial Analysis:successive levels of sophistication

2. Exploratory Spatial Data Analysis (ESDA):

- searching for patterns and possible explanations
- GeoVisualization through calculation and display of Centrographic statistics

--Calculation of Centrographic

Statistics

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2.5%

-1.96

1.96

0

Spatial Analysis:successive levels of sophistication3. Spatial statistical analysis and hypothesis testing

- Are data “to be expected” or are they

“unexpected” relative to some statistical model,

usually of a random process (pure chance)

We will look at

statistical hypothesis testing for:

--point patterns

--also for polygon data

We can test if the spatial pattern for software & telecommmunications companies in Dallas is

clustered (a pattern) or “random” (no pattern)

Briggs Henan University 2012

Spatial Analysis:successive levels of sophistication

4. Spatial modeling: prediction

- Construct models (of processes) to

predict spatial outcomes (patterns)

Notice how the density of points (number per square km) decreases as we move away from the highway.

We can construct regression

models to predict location patterns.

- Density of points = f (distance from highway)
- However, for spatial data, we need special:
- Spatial regression models

Density of points

Distance from highway

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The first example of Spatial Analysis

- John Snow’s maps of cholera in 1850s London

Was it ESDA or hypothesis testing?

- Did he discover the association between water and cholera after drawing the map: ESDA
- Did he draw the map in order to prove the association: using a map for hypothesis testing

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Maps are good—but more is needed!

A. Is this clustered?

B. Is this clustered?

We must test rigorously using spatial analysis methods.

Not just look and guess

Source: R & Y, p. 5

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Why is this important?

?

We must measure and test

--not just look and guess!

Is it clustered?

Because that is science!

Because that is how earth management decisions must be made!

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Why we need analysis--and not just visual examinationSee handout!!!

You need this course!

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Clustered or random?

B

Clustered or random?

(clustered = points are in “groups”)

= pattern exists

(random = points can be anywhere)

= no pattern

Source: Rogerson and Yamada, 2009

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in Spatial Analysis

Summarize these now.

Talk in greater detail about them throughout this lecture series.

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Critical Issues in Spatial Analysis

- Spatial autocorrelation
- Data from locations near to each other are usually more similar than data from locations far away from each other
- Modifiable areal unit problem (MAUP-zone )
- Results may depend on the specific geographic unit used in the study
- Province or county; county or city
- Scale affects representation and results
- Cities may be represented as points or polygons
- Results depend on the scale at which the analysis is conducted: province or county
- MAUP—scale effect
- Ecological fallacy
- Results obtained from aggregated data (e.g. provinces) cannot be assumed to apply to individual people
- MAUP—individual effect
- Non-uniformity of Space
- Phenomena are not distributed evenly in space
- Be careful how you interpret results!
- Edge issues
- Edges of the map, beyond which there is no data, can significantly affect results

Briggs Henan University 2012

Critical Issues in Spatial Analysis

- Modifiable areal unit problem (MAUP)
- Results may depend on the specific geographic unit used in the study
- Province or county; county or city
- MAUP—zone effect
- Scale affects representation and results
- Results depend on the scale at which the analysis is conducted
- MAUP—scale effect
- Ecological fallacy
- Results obtained from aggregated data (e.g. provinces) cannot be assumed to apply to individual people
- MAUP—individual effect
- Non-uniformity of Space
- Phenomena are not distributed evenly in space
- Be careful how you interpret results!
- Edge issues
- Edges of the map, beyond which there is no data, can significantly affect results
- Spatial autocorrelation –the biggest of all!
- Data from locations near to each other are usually more similar than data from locations far away from each other

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Modifiable areal unit problem (MAUP): 3 in 1!!!

- Results may depend on the specific geographic unit used in the study
- Dangerous to assume results for one set of units will also apply for another
- Zonal effect: Similar size and number of units, but different boundaries
- Zip codes versus census tracts
- Postal zones versus city neighborhoods
- Scale effect: increases size and decreases number of units
- Counties versus provinces
- Individual effect: ecological fallacy
- results from geograhic units
- may not apply to individual people

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Modifiable areal unit problem (MAUP): zonal

- Census Tracts versus Zip codes
- Problem not as big—usually—as for scale differences

Census Tract

(used by US Census Bureau for data)

Zipcode Areas

(used by US Post Office)

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Modifiable areal unit problem (MAUP): scale

- Counties versus Provinces codes
- Usually a bigger problem than for zonal

Will results be the same?

Do conclusions still apply?

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- combining smaller units into bigger units
- affects results!
- Note how:
- variance (s2) decreases
- Correlation coefficient (rXY) increases (‘cos of less variability)

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- Results obtained at one scale do not necessarily apply at other scales
- A pattern may be clustered at one scale but dispersed at another scale

Population clustered into cities

City populations are dispersed

Scale is always very important in spatial analysis!

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Large scale >objects are large, small area covered

Scale: Always Important

- ratio of distance on a map, to the equivalent distance on the earth's surface.
- Scale must be shown on every map
- Use scale bar because that is correct when map is enlarged or reduced
- Affects how objects are represented on a map and how data is stored in a data base
- Important for research design and data collection
- Cities may be points or polygons

- Small scale >objects are small, large area covered

Dallas

0

1

2

10 km

polygon

point

Km

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- Results from aggregated data (e.g. provinces) cannot be applied to individual people
- A special case of the MAUP problem
- Encountered in spatial and non-spatial analysis
- Usually because a variable was left out (omitted variable)

crime rate

income

Cannot assume low income people commit crimes.

Perhaps low income provinces do not have money to pay for police.

--”Yuans on policing” is omitted variable

If low income provinces have high crime rate.

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Non-uniformity of Space: things are not evenly distributed in space

Bank robbery

Banks

- Bank robberies are clustered
- But only because banks are clustered

Bank Robberies

Bonnie and Clyde were two very famous bank robbers in Texas in the 1930s

They were asked “Why do you rob banks?”

They replied “Because that is where the money is!

What a stupid question!”

(the expected answer was, perhaps, “because we needed money for food”)

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Non-uniformity of Space and Choropleth Maps

Henan does not have high illiteracy!

Henan has high illiteracy!

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Non-uniformity of Space and Choropleth Maps

Always normalize data if drawing a choropleth map

- By total population
- By geographic area
- Do not map “counts” unless population and/or geographic area are the same size for all observation units
- Failing to “normalize” is a very common mistakes made by non-professional GIS people
- You are professionals
- Do not make that mistake!

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- Every study region has a boundary (unless you study the entire world!)
- You do not have data for outside your study region
- However, the outside data can affect the inside data if there is spatial autocorrelation
- Consequently, edges of the map, beyond which there is no data, can significantly effect results

Solutions:

Core study region

periphery or guard area

Use the toroid concept

--bends the left edge to meet the right and the top to meet the bottom

--uses all the data

--assumes that there is no systematic spatial trend in data

Use Core/Periphery

--analyze only the core

--use edge only for “neighborhood”

calculations

--reduces amount of data available

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Spatial Autocorrelation

- Spatial organization is usually important
- The results from a traditional regression analysis ignore how the observation units are organized spatially!
- Datafrom location near to each other are usually more similar than data from locations far away
- Must be considered in your analysis
- Also causes serious problems with traditional statistical hypotheses testing
- Spatial statistical models are essential

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Much more basic than any discussed above.

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Single most common error in GIS Analysis

--intending a one to one join of attribute data to spatial table

--getting a one to many join of attribute data to spatial table

51 states

Hawaii

Spatial

After joining attribute to spatial data

54 observations

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Errors with the attribute data will occur if Hong Kong or Guangdong are not correctly drawn in the shapefile (spatial data)

If there are islands, the province must drawn as a multi-part feature in the shapefile (the spatial data)

--then there is only one row in the attribute table

If each island is drawn as a separate feature, there will be multiple rows in the attribute table

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Spatial is special: 3 primary concepts

- Distance
- Adjacency

or neighborhood

- Interaction

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Fundamental Spatial Concepts

- Distance
- The magnitude of spatial separation
- Euclidean (straight line) distance often only an approximation
- Adjacency or neighborhood
- Nominal or binary (0,1) equivalent of distance
- Levels of adjacency exist: 1st, 2nd, 3rd nearest neighbor, etc..
- Interaction
- The strength of the relationship between entities
- An inverse function of distance

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Distance is not simple!

- Cartesian distance via Pythagorus
- Use for projecteddata, at local scale
- Spherical distance via spherical coordinates
- Cos d = (sin a sin b) + (cos a cos b cos P)
- where: d = arc distance
- a = Latitude of A
- b = Latitude of B
- P = degrees of long. A to B
- Use for unprojecteddata, or at world scale
- possible distance metrics:
- Euclidean straight line/airline
- city block/manhattan metric
- distance through network
- time/friction through network

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Spatial neighbors based on adjacency

Hexagons

Square raster

Irregular

Rook:

Sharing a boundary

- Queen:
- Sharing a boundary or a point

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Interaction

Based on a Hierarchy

Based on the Gravity Model

How do you fly from Zhengzhou to Wuhan?

Gravity Model: Interaction between i and j is a function of:

Pi --the population (size) at i

Pj --the population (size) at j

dij --the distance from i to j

Shanghai

Zhengzhou

Wuhan

Central Place Hierarchy

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Today, we discussed spatial analysisand some of its problems and challenges However, to do spatial analysis you must have spatial data

Next time:

Spatial data and why it is special!

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Texts

O’Sullivan, David and David Unwin, 2010. Geographic Information Analysis. Hoboken, NJ: John Wiley, 2nd ed.

Other Useful Books:

Mitchell, Andy 2005. ESRI Guide to GIS Analysis Volume 2: Spatial Measurement & Statistics. Redlands, CA: ESRI Press.

Allen, David W 2009. GIS Tutorial II: Spatial Analysis Workbook. Redlands, CA: ESRI Press.

Wong, David W.S. and Jay Lee 2005. Statistical Analysis of Geographic Information. Hoboken, NJ: John Wiley, 2nd ed.

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