GES673 SP2014 Intro Lecture

Geoprocessing & Spatial Analysis
GES673
at Shady Grove
!

Richard Heimann

Richard Heimann © 2013

Course Description: GES 673
Course Description: 
 

The increased access to spatial data and overall improved application of spatial analytical methods
present certain challenges to social scientific research. This graduate course is designed to focus
on substantive social science research topics and methodologies, while exposing rewards and
potential risks involved in the application of geographic information systems (GIS), spatial analysis,
and spatial statistics in their own research.

The course will highlight connections between spatial concepts and data availability. Both
traditional spatial science data will be used as well as new emerging social media data, which
better reflect some of the more recently developments in Big Data, a topic that will receive cursory
treatment - most notably the social critical exploration of such data. Substantive focus will include
readings, discussions and practical steps to spatial data analysis and the spatially explicit theory
leaning toward acknowledgment of a spatial turn in Big Data.

!

Throughout the course, lectures and discussions will be complemented with lab sessions
introducing spatial analysis methodology using spatial analysis software - namely GeoDa, ArcGIS
and a minor introduction to R. These lab sessions will introduce many methodological and
technical issues relevant to data analysis generally and spatial analysis specifically. Assignments
for the courses include up to four writing assignments, up to six lab assignments, and a final
project which will be presented as a short 15-minute presentation as well as a term paper.


Course Objectives: GES 673
1. Learn about solving problems and answering questions using GIS.

2. Learn a sound methodological approach to spatial data analysis
and a blended approach that oﬀers ﬂexibility.

3. Examine a useful sample of methods and literature of geographic
information science.

4. Use GIS software to learn some of the analytical methods available
- ArcGIS Desktop & GeoDa… and R.

5. Gain experience working with traditional (e.g. Census) and
nontraditional social science data (i.e. Flickr, Twitter).


Course Texts: GES 673
1. FREE Geospatial Analysis, 3rd edition. By: Michael J. de Smith, Michael Goodchild, and Paul A.
Longley. The text is available as an Adobe readable ﬁle for download (uses special secure PDF reader),
a version for the Kindle, on-line via a website, and as a printed book. See http://
www.spatialanalysisonline.com/ for further information.

2. Required. Making Spatial Decisions Using GIS: A Workbook. 2nd edition. By: Kathryn Keranen and
Robert Kolvoord. Should be available in the Shady Grove Bookstore or ESRI Press or Amazon: http://
www.amazon.com/Making-Spatial-Decisions-Using-GIS/dp/1589482808

3. Required. GeoDa User Guide 0.9.3. (UG) The documentation will be somewhat unsyncronized with
the software but not so much so that you will be prevented from completing labs. https://
geodacenter.asu.edu/software/documentation

4. Required. Exploring Spatial Data with GeoDa: A Workbook (UGW) http://www.csiss.org/
clearinghouse/GeoDa/geodaworkbook.pdf

5. Other readings will be required and further suggested. They will be noted in the syllabus and
either provided or will be cited for your discovery.

!

Course Rubrics: GES 673
Blackboard: 
 

Copies of lecture notes and assignments will be available on the class’ Blackboard site which will
also be used for questions, discussions, and class announcements. Please check the Blackboard
site regularly

Grading Criteria:

Points:

Midterm

30

Lab Assignments (6 x 10)

50

Reading Labs (4 x 10)

40

Paper (60)

60

Presentation (20)

20

Total:

200

*Incomplete grades are rarely given and only under very unusual circumstances. In other words, incomplete assignments will be evaluated as a failure and your final grade will reflect that failure and
final grades are final.


Course Format: GES 673
Hybrid approach with face-to-face class meetings, independent learning, on-line discussions and
collaboration using the University’s Blackboard system, and projects. At least 10 face to face
meetings will take place. 
Midterm, NO Final.

Paper: This six to eight page (double spaced) empirical geographic analysis paper will use
geographic data of either a social nature - though physical geographic features or themes will be
accepted in an explanatory nature.

Presentation: Each student will make an 12 to 15 minute presentation reﬂecting learned
competency. The presentation will be a summarization of your paper and should include the thrust
of your research pursuit and include maps, graphs, charts and other visualizations.


Course Labs: GES 673
Lab assignments are found in the Keranen and Kolvoord text and are as follows:

Lab 1 (Week 3) – Demographic Decisions, complete project #1, #2, pages 30-58. 
Lab 2 (Week 4) – Urban Planning Decisions, complete project #1, #2 pages 126-142.

Lab 3 (Week 5) – Law Enforcement Decisions, complete project #1, #2 pages 62-84.

Lab 4 (Week 11) - Dealing with Big Data - Twitter (Handout) & Flesch Kincaid Index

Lab 5 (Week 12) - Dealing with Big Data - Twitter (Handout) & Sentiment Analysis

Lab 6 (Week 14) - Regression & Spatial Autocorrelation Lab - (Handout)


Course Outline: GES 673
Methods

Theory
-First Law of Geography
-Spatial Heterogeneity
-Spatially Explicit Theory

-Visual Data Analysis
-Spatial Analysis
-ESDA
-Spatial Analysis
-Geographic Knowledge Discovery
-Spatial Econometrics
-Spatial Modeling

Data

Big Data, Nontraditional Social Data (Social Media), Traditional Social Data (e.g.
Census) Small Data vs. Big Data, Inference and Inferential Pitfalls (Ecological
Fallacy, Atomistic Fallacy), Pattern Paradoxes (e.g. MAUP), etc.

GES 673: GeoDa, Python & R
Not a GIS, but…

• Complements all major GIS packages.

• Windows based, so familiar interface.

• Relies on same programming/math as the R package spdep
and extends into Python using PySAL.

• Incorporates more sophisticated statistical routines into spatial
analysis than a GIS (e.g. ArcGIS Desktop).

• GeoDa: Developed by Dr. Luc Anselin, Arizona State U &
FREE!

• R is open sourced, has a large following and FREE!

• Python is an OS interpreted, object-oriented, high-level
programming language and FREE!

GES 673: Common Problems in GIS

http://www.amazon.com/GIS-20-Essential-Skills/dp/1589482565


Free and Open Source: you can think of it as “free” as in “free
speech,” and “free” as in “free beer.”
!

Open GeoDa is a cross-platform,
open source version.

!
PySAL is the underlying open source
library with extended functionality.

!
R is open source domain speciﬁc
statistical language.


GeoDa with more than 89,026 downloads (May 2013)



Software

# of Blogs

R

452

SAS

40

Stata

8

Others

0-3



A program run on 3/19/2013 counted 6,275 R packages at all major repositories, 4,315 of which were at CRAN.

GES 673: @ UMBC - The Three ’s'
Systems:
Advanced Seminar is GIS GES670
Professional Seminar in Geospatial Technologies GES659
*Geoprocessing and Spatial Analysis GES673
*Spatial Social Science GES679

Science:
GIS Modeling Techniques GES773
Spatial Social Science GES679
*Spatial Statistics GES774
Advanced Visualization and Presentation

Studies:
GIS Modeling Techniques GES773
*Spatial Social Science GES679
*Combine hands-on technical training with an understanding of the underlying science, and an emphasis on multidisciplinary applications

GES 673: Live and Work…


GES 673: Introductions
Name and where you live:

!
Background:

!
Experience w/ Spatial Analysis & Statistics:

!
Expectations…

!
Recently watched movie or book read…


GES 673: ME!
Name: Richard Heimann, Washington DC

!
Background: Geography, GIS, Statistics, Data Science & Big Data

!

EMC Certiﬁed Data Scientist, Lead Data Scientist at Data Tactics
Corporation, Instructor of GES673 & (Formerly) GES 659, Instructor of
Human Terrain Analysis at George Mason University, most recently
supported DARPA, DHS, Human Terrain Systems and the Pentagon.
Author of Social Media Mining in R (coming Q1 2014) and Selection
Committee Member AAAS Big Data & Analytics Fellowship Program.

!
Experience w/ Spatial Analysis: Extensive!

!
Recently watched movie or book read… Troll 2


GES 673: Today’s talk
What is Geography? Geographic Literacy.
!

What is GIS? … and the GIS Data Model.
!

What is Spatial Analysis and what are the levels and
approaches?
!

The Spatial Turn in Big Data and mining for knowledge
discovery.
!

Just an introduction...but we will be gaining momentum.

GES 673: What is Geography?
•

Geography is the study of the earth’s surface as the space within
which human population live - their interaction with the environment
and each other.

•

Space is the unifying theme for geographers.

•

Geography is the science of space and place.

•

Geographers are interested in …

•
•

Why they are located where they are,

•

How places diﬀer from one another,

•

•

Where things are located on the earth’s surface,

How people interact with the environment.

Geographers were among the ﬁrst scientists to sound the alarm that
human-induced changes to the environment are beginning to
threaten the balance of life, but some of the notable contributions to
geography have been on the part of non geographers.

GES 673: Geographic Literacy
Despite having a highly education society, Americans are arguably the
world’s most geographically ignorant people.

!
By comparison, children throughout much of the world are exposed to
geographic training in both primary and secondary schools.

!
Most Americans learn what little geography they know in elementary or
middle school.

!
In the United States, the last time a student hears the word geography is
usually in the third grade.

!
Concern over geographical illiteracy led President Reagan to declare
November 15-21, 1987 as the ﬁrst Geography Awareness Week (a joint
resolution of the One Hundredth Congress)

The National Geographic Society released the Roper Public
Affairs 2006 Geographic Literacy Study in May, 2006

!
510 interviews were conducted among a sample of 18- to 24-year old adults in the continental
United States between December 17, 2006 and January 20, 2006)
The sample has a margin or error of +/- 4.4 % at the 95% confidence level

!
Survey results …
Over 6 in ten (63%) of those surveyed could not locate Iraq on a map of the Middle East

Nearly nine in ten (88%) could not identify Afghanistan on a map of Asia

Seven in ten (70%) could not find North Korea on a map, and 63% did not know its
border with South Korea is the most heavily fortified in the world

Sizable percentages did not know that Sudan and Rwanda are in located in Africa (54%
and 40%, respectively)

Three-quarters could not find Indonesia on a world map and were unaware that a
majority of Indonesia’s population is Muslin, making it the largest Muslim country in
the world.

!
A third or more could not find Louisiana or Mississippi on a map of the United States.

!
Only 18% could correctly answer a multiple-choice question about the most widely
spoken native language in the world. (5 Part Questionnaire)

!
Although half said map reading skills are “absolutely necessary” in today’s world,
many Americans lack basic practical skills necessary for safety and employment in
today’s world.

!
One-third (34%) would go in the wrong direction in the event of an evacuation

One third (32%) would miss a conference call scheduled with colleagues in another
Recommended Link
time zone.
2006 National Geographic – Roper Survey of Geographic Literacy
http://www.nationalgeographic.com/roper2006/findings.html


This college-level course introduces
students to the systematic study of
patterns and processes that have
shaped human understanding, use, and
alteration of Earth's surface. Students
employ spatial concepts and landscape
analyses to analyze human social
organization and its environmental
consequences. They also learn about the
methods and tools geographers use in
their science and practice.


Score

Percent

5

11.6%

4

16.7%

3

21.9%

2

16.6%

1

33.2%

In the 2009
administration, 50,730
students took the exam
and the mean score was
a 2.57.


http://www.benjaminbarber.com/bio.html


GES 673: Criticisms of Geography
Geography had a number of problems, including:
1. It was overly descriptive:

Geography followed a set format for the inventory of physical and cultural
features;

2. It was almost purely educational:

Regions don't really exist;

3. It failed to explain geographic patterns:

Geography was descriptive and did not explain why patterns were the
way they were;

Where attempts at explanation did exist, they favored historical
approaches.

4. The biggest problem of geography was the fact that it was unscientiﬁc:

…the Nomothetic & Idiographic debate in geography begins!

…all in a time after WWII, which science and mathematics enjoyed unrivaled
importance

GES 673: What is GIS?
The common ground between information processing and the many
ﬁelds using spatial analysis techniques. (Tomlinson, 1972)

Tomlinson: Very General - ‘common ground’
A powerful set of tools for collecting, storing, retrieving, transforming,
and displaying spatial data from the real world. (Burroughs, 1986)

Burroughs: ‘tool box’ but how items are linked together.
A computerized database management system for the capture,
storage, retrieval, analysis and display of spatial (locationally deﬁned)
data. (NCGIA, 1987)

NCGIA: DBMS for spatial data but adds analyze and display.
A decision support system involving the integration of spatially
referenced data in a problem solving environment. (Cowen, 1988)

Cowen: Adds integration and decision support to solve problems.

GES 673: What is GIS?
A map with a database behind it; a virtual representation of
the real world and its infrastructure.

!


GIS Data Model


GES 673: GIS Data Model


GES 673: GIS Model: Spatial & Attributes
Spatial data (where)
Specifies location; stored in a shapefile (.shp),
geodatabase or similar geographic file.

!
Attribute (descriptive) data (what, how much, when)
Specifies characteristics at that location, natural or
human-created stored in a data base table.

!
GIS systems traditionally maintain spatial and attribute
data separately, then “join” them for display or analysis.

!


GES 673: GIS Data Model - Lattices

Irregular Lattice

Regular Lattice

Irregular Lattice

GES 673: GIS - Raster & Vector
Raster Model
Area is covered by grid with (usually) equal-sized, square cells; Regular Lattices.

Attributes are recorded by assigning each cell a single value based on the majority
feature (attribute) in the cell, such as land use type.

Image data is a special case of raster data in which the attribute is a reﬂectance
value from the geomagnetic spectrum

Cells in image data often called pixels (picture elements)

!
Vector Model
The fundamental concept of vector GIS is that all geographic features in the real
work can be represented either as:

Points or dots (nodes): Cities, human sensors (like Tweets or Flickr), individual obs
(e.g. crime incident).

Lines (arcs): movement, connectedness, networks

Areas (polygons): Countries, States, Census Tracts, Cities, Irregular Lattices Multivariate in nature.


Spatial Analysis


GES 673: What is Analysis?
Turns raw data into useful information by adding greater
informative content and value.

!

Reveals patterns, trends, and anomalies that might
otherwise be missed.

!

Provides a check on human intuition by helping in
situations where the eye might deceive.

!

Thousands of techniques exist…

GES 673: Why Quantitative Analysis?
Academic Publications Scale
Data Scales
N

Web Scales
Social Media Scales
t

If this guy doesn’t scale - none of us do.

t

GES 673: Why Quantitative Analysis?
Why is data analysis so important?

!

“…the alternative to good statistics is not “no
statistics,” it’s bad statistics. People who argue
against statistical reasoning often end up backing up
their arguments with whatever numbers they have at
their command, over- or under-adjusting in their
eagerness to avoid anything systematic”
!

Bill James

GES 673: Why Analysis?
"companies that have massive amounts of data
without massive amounts of clue are going to be
displaced by startups that have less data but more
clue" (Tim O’Reilly)


GES 673: Why Analysis? … Order from Chaos


Analytics in Perspective: An Inquiry into Modes of Inquiry

http://datatactics.blogspot.com/2013/07/analytics-in-perspective-inquiry-into.html

!

“Analytics in Perspective” reﬂects how people arrive at
decisions.

!
GOOD: Induction, Abduction, Circumscription, Counterfactuals.

!
BAD: Deduction, Speculation, Justiﬁcation, Groupthink

!
!


GES 673: What is Spatial Analysis?
Lack of Locational Invariance (Goodchild et al)
Fundamental property of spatial analysis

Results change when location changes


ALABAMA
ALASKA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
DISTRICT OF COLUMBIA
FLORIDA
GEORGIA
HAWAII
IDAHO
ILLINOIS
INDIANA
IOWA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEW HAMPSHIRE
NEW JERSEY
NEW MEXICO
NEW YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
WASHINGTON
WEST VIRGINIA
WISCONSIN
WYOMING

AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY

From Data to Information:

...beyond mapping;
transformations, manipulations and application of analytical methods
to spatial (geographic) data.

!
Lack of locational invariance (Goodchild et al):

Fundamental property of spatial analysis;
Analyses where the outcome changes when the locations of the
objects under study change;
Median center vs. Median, Standard Deviational Ellipses vs. Standard
Deviations, Autocorrelation vs. Spatial Autocorrelation.

!
Where matters:

In an absolute sense (coordinates)
In a relative sense (spatial arrangement, distance)

Application of statistical methods to the solution of
geographical research questions (Gattrell)

!
Relatively new area:

Two perspectives (Anselin):

Data-driven: exploratory, descriptive, geo-visualisation;

Model-driven: spatial econometrics, spatial prediction, spatial
statistics, hypothesis testing and model fitting.

!
Limited functionality available in existing statistical softwares
like SAS, SPSS.


Methodologies


GES 673: Methodologies
Mitchell (2005)

Draper et al (2005)


GES 673: Methodologies - PPDAC
Mackay & Oldford (2002)


GES 673: Methodologies
Prepare and Explore
Define & Design
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Spatial Analysis Components


!

GES 673: Intro to Spatial Analysis

Topics
• Description versus Analysis

•CSR

•Process, Pattern and Analysis

• Issues and challenges in spatial data
analysis


GES 673: Description vs. Analysis
Do regions of the US having lower linguistic
sophistication than others?
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,

Understand the phenomena itself,

This is the role of science.

Here, we are using Twitter and box plots with
Jitter to help answer this question!

library(ggplot2)
ggplot(Twitter, aes(x=regiontxt, y=flecMC, ylab="Flesch Kincaid Index", xlab="Region", data=Twitter))

geom_point(colour="lightblue", alpha=0.1, position="jitter") +

geom_boxplot(outlier.size=1, alpha=0.1)
boxplot(flecMC~regiontxt, ylab="flecMC", xlab="regiontxt", data=Twitter)


GES 673: Dr. Snow (1854)


GES 673: Dr. Snow (1854)
Classic example of using location to
draw inferences:

!
Contagion was the hypothesis Snow
was trying to refute - contagion
would produce a concentric
sequence, whereas drinking water a
clustered sequence around wells.

!
Today, a GIS could be used to show
a sequence of maps as the outbreak
developed.

GES 673: Spatial Analysis - 4 Levels
Four levels of Spatial Analysis:

--Each is more advanced (more diﬃcult!)
!

Description (the primitives)

Exploratory /Spatial/ Data Analysis (E/S/DA)
Spatial statistical analysis and hypothesis testing

Spatial modeling and prediction

1.
2.
3.
4.
!

We will look at all 4 levels in this class!!!


GES 673: Level 1
1. Spatial data description (primitive):

Focus is on describing the world,

and representing it in a digital

format (e.g. digital maps)

!
Uses classic GIS capabilities:

Buffering, Proximity functions,
map layer overlay, spatial queries,
measurement, local operations, neighborhood functions,
global functions, clip/union/intersect, classiﬁcation.


GES 673: Level 2
2. Exploratory /Spatial/ Data Analysis:

!
Discovering and recognizing patterns and possible explanations
through geovisualization using spatially descriptive statistics
(moments). e.g. maps, boxplots, charts, histograms

mean x,y
mean x
Map showing changes to the mean center of population for the United States, 1790–2010 (U.S. Census Bureau)[1]


mean y

GES 673: Level 3
3. Spatial statistical analysis and hypothesis testing:

Set up Null Hypotheses H0

Set up Alternate Hypotheses H1

Test Hypotheses.


GES 673: CSR
Deviations from spatial randomness
suggests underlying social
processes.

!

“Every observable eﬀect has a
physical cause

!

Randomized Variable –
500 meter cell

Perhaps the most profound insightcausality is a rejection of the
randomness.

Total TTL Count –
500 meter cell

“Every observable effect has a physical cause” (Thales) Perhaps the most profound insight-causality is a rejection of the randomness.

GES 673: Level 4
4. Spatial modeling: prediction

Construct models (of processes) to predict spatial outcomes
(patterns).

!Coefficient: % Poverty

Coefficient: % FB

Coefficient: % Elderly


Coefficient: % Black

GES 673: Analysis; Process vs Patterns
!

Processes operating in space produce
patterns

!

Spatial Analysis is aimed at:

1., 2. Identifying and describing the pattern

3., 4. Identifying and understanding the process


GES 673: Level 1 - 4
• 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

New Aged Experimentation


GES 673: Experimentation
George Box
“”The only way to understand complex
systems is to shock those systems and
observe the way they react””
!

New motivation for experimentation especially
in quasi-experimental methods.
(...more later)


GES 673: Experimentation


GES 673: Review Part 1
Geography GIS and the GIS Data Model

!

CSR, Form & Process.

 
What & Why /Spatial/ Data Analysis

!

Methodologies

!

Levels of sophistication

GES 673: Making things harder!
Inward and outward asymptotics i.e. increasing spatial
extent, increasing temporal lags, ﬁner spatial
resolution, ﬁner temporal resolution.

Increased number of cross sections.

…visual correlations and visual detection of change
over space and time do not exist.

Apophenia is real!

Spatial Analysis and Geographic Pattern Recognition
will reduce patternicity (Sherman, 2008).

Big Data

/Nontraditional Social Science
Data/


GES 673: DATA IS THE NEW OIL!


GES 673: Going Viral
It took radio broadcasters 38 years to reach an audience of 50M
people and television 13 years, but the internet did it in just 4.

There were 50 webpages in 1993 and while the full impact of the
internet is difficult to measure we can estimate about 2.1B webpages
today.

We create 2.5 quintillion bytes (2.5 * 1018) of data, or nearly 2.3 million
TBs of data every day.

Twitter has more than 200M monthly active users.


GES 673: Nontraditional Social Data
The preponderance of data created today is free text, not structured
numerical data.

Text is not only big, but is growing at an increasing rate. Twitter was
launched March 21, 2006 and it took 3 years, 2 months and 1 day to
reach 1 billion tweets. Twitter users now send one billion every 2.5
days.

People are highly opinionated. We hold opinions about everything
from international politics to pizza delivery.

Several different datasets will be made available to you but Twitter will
be promoted above all - mainly FK Index & Text Scaling (explained
later).


GES 673: Nontraditional Social Data
We will learn where this data is and how we can get to it…

We will consider the inferential and measurement challenges - as well
as gotchas.

We will compare traditional and nontraditional sources as well as
structure vs. unstructured data.

We will cover, at a high level some approaches to analyzing these data
- namely text analysis that will provide us intuition for subsequent
geographical analyses.

We will have fun!!


GES 673: Review
Lacking Locational Invariance (Goodchild et al):

Fundamental property of spatial analysis
Results change when location changes.

!
Two Data Models:

Raster Model & Vector Model; Regular and Irregular Lattices

!
Components of Spatial Analysis:

Visualization
Showing Interesting Patterns.

Exploratory Spatial Data Analysis
Finding Interesting Patterns.

Spatial Modeling, Regression
Explaining Interesting Patterns.


GES 673: Review
!

Description versus Analysis:

Process, Pattern and Analysis
Qualitative vs. Quantitative

!

Four levels of Spatial Analysis:

Spatial Data Description
Exploratory Spatial Data Analysis - ESDA
Spatial statistical analysis and hypothesis testing
Spatial modeling and prediction

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Why is Spatial Data Special; Potentials and Pitfalls:

Spatial Autocorrelation, MAUP (scale & zone), Scale effects, Ecological Fallacy, Nonuniformity of space, Edge Effects.

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Big Data:

Big Data e.g. Twitter
Geographic Knowledge Discovery

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GES 673: Contact me
Richard Heimann
Ofﬁce: UMBC Common Faculty Area 3rd Floor
Phone: 571-403-0119 (C)
Ofﬁce hours:
Tues. 6:30-7:00 (Virtual); or by appointment (send e-mail)
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I (try) promptly respond to emails. Phone calls are another
matter.
Email: rheimann@umbc.edu or heimann.richard@gmail.com
Email, Text, Tweet (@rheimann), Chat (UMBC Chat).


GES673 SP2014 Intro Lecture

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GES673 SP2014 Intro Lecture