GES673 SP2014 Intro Lecture
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GES673 SP2014 Intro Lecture Presentation Transcript

  • 1. Geoprocessing & Spatial Analysis GES673 at Shady Grove ! Richard Heimann Richard Heimann © 2013
  • 2. 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. Richard Heimann © 2013
  • 3. 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). Richard Heimann © 2013
  • 4. 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. ! Richard Heimann © 2013
  • 5. 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. Richard Heimann © 2013
  • 6. 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. Richard Heimann © 2013
  • 7. 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) Richard Heimann © 2013
  • 8. 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. Richard Heimann © 2013
  • 9. 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! Richard Heimann © 2013
  • 10. GES 673: Common Problems in GIS http://www.amazon.com/GIS-20-Essential-Skills/dp/1589482565 Richard Heimann © 2013
  • 11. GES 673: GeoDa, Python & R 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. Richard Heimann © 2013
  • 12. GES 673: GeoDa, Python & R GeoDa with more than 89,026 downloads (May 2013) Richard Heimann © 2013
  • 13. GES 673: GeoDa, Python & R Software # of Blogs R 452 SAS 40 Stata 8 Others 0-3 Richard Heimann © 2013
  • 14. GES 673: GeoDa, Python & R A program run on 3/19/2013 counted 6,275 R packages at all major repositories, 4,315 of which were at CRAN. Richard Heimann © 2013
  • 15. 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: *Geoprocessing and Spatial Analysis GES673 GIS Modeling Techniques GES773 Spatial Social Science GES679 *Spatial Statistics GES774 Advanced Visualization and Presentation Studies: *Geoprocessing and Spatial Analysis GES673 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 Richard Heimann © 2013
  • 16. GES 673: Live and Work… Richard Heimann © 2013
  • 17. GES 673: Introductions Name and where you live: ! Background: ! Experience w/ Spatial Analysis & Statistics: ! Expectations… ! Recently watched movie or book read… Richard Heimann © 2013
  • 18. 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 Richard Heimann © 2013
  • 19. 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. Richard Heimann © 2013
  • 20. 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. Richard Heimann © 2013
  • 21. 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) Richard Heimann © 2013
  • 22. GES 673: Geographic Literacy 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) Richard Heimann © 2013
  • 23. GES 673: Geographic Literacy 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 Richard Heimann © 2013
  • 24. GES 673: Geographic Literacy 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. Richard Heimann © 2013 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. 
  • 25. GES 673: Geographic Literacy http://www.benjaminbarber.com/bio.html Richard Heimann © 2013
  • 26. GES 673: Geographic Literacy Richard Heimann © 2013
  • 27. 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 Richard Heimann © 2013
  • 28. GIS Richard Heimann © 2013
  • 29. 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. Richard Heimann © 2013
  • 30. GES 673: What is GIS? A map with a database behind it; a virtual representation of the real world and its infrastructure. ! Richard Heimann © 2013
  • 31. GIS Data Model Richard Heimann © 2013
  • 32. GES 673: GIS Data Model Richard Heimann © 2013
  • 33. GES 673: GIS Data Model Richard Heimann © 2013
  • 34. 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. ! Richard Heimann © 2013
  • 35. GES 673: GIS Data Model - Lattices Irregular Lattice Regular Lattice Irregular Lattice Richard Heimann © 2013
  • 36. 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. Richard Heimann © 2013
  • 37. Spatial Analysis Richard Heimann © 2013
  • 38. 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… Richard Heimann © 2013
  • 39. 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. Richard Heimann © 2013 t
  • 40. 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 Richard Heimann © 2013
  • 41. 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) Richard Heimann © 2013
  • 42. GES 673: Why Analysis? … Order from Chaos Richard Heimann © 2013
  • 43. GES 673: Why Analysis? … Order from Chaos Richard Heimann © 2013
  • 44. GES 673: Why Analysis? Analytics in Perspective: An Inquiry into Modes of Inquiry http://datatactics.blogspot.com/2013/07/analytics-in-perspective-inquiry-into.html Richard Heimann © 2013
  • 45. ! GES 673: Why Analysis? “Analytics in Perspective” reflects how people arrive at decisions. ! GOOD: Induction, Abduction, Circumscription, Counterfactuals. ! BAD: Deduction, Speculation, Justification, Groupthink ! ! Richard Heimann © 2013
  • 46. Spatial Analysis Richard Heimann © 2013
  • 47. GES 673: What is Spatial Analysis? Lack of Locational Invariance (Goodchild et al) Fundamental property of spatial analysis Results change when location changes Richard Heimann © 2013 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
  • 48. GES 673: What is Spatial Analysis? 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) Richard Heimann © 2013
  • 49. GES 673: What is Spatial Analysis? 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. Richard Heimann © 2013
  • 50. Methodologies Richard Heimann © 2013
  • 51. GES 673: Methodologies Mitchell (2005) Draper et al (2005) Richard Heimann © 2013
  • 52. GES 673: Methodologies - PPDAC Mackay & Oldford (2002) Richard Heimann © 2013
  • 53. GES 673: Methodologies Prepare and Explore Define & Design e em ur n. ch ar e es tr 1 e rit .W ou 2. efi D qu ne les n. d an t de m b ria ig es D e os ho C 3. m er tio es e in va 4. e Cl an . les iab a at / e Cr ia ar iv Un 5. e at ew r Va N te D A SD E s ea Test and Refine ) . nt (E tiv ul M ns io pt un od ics |C as he s no iag D 7. Richard Heimann © 2013 R 8. e e fin e od et pr r M e nt I 9. s Re . 10 s. t ul l t tia i In el e tiv m su ck lM r. tis a d an R 6. e t ria a St s tic / DA en es Pr lts su re t in an i tu in m a e nn
  • 54. Richard Heimann © 2013
  • 55. Spatial Analysis Components Richard Heimann © 2013
  • 56. ! GES 673: Intro to Spatial Analysis Topics • Description versus Analysis •CSR •Process, Pattern and Analysis • Issues and challenges in spatial data analysis Richard Heimann © 2013
  • 57. 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) Richard Heimann © 2013
  • 58. GES 673: Dr. Snow (1854) Richard Heimann © 2013
  • 59. 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. Richard Heimann © 2013
  • 60. 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!!! Richard Heimann © 2013
  • 61. 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. Richard Heimann © 2013
  • 62. 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] Richard Heimann © 2013 mean y
  • 63. GES 673: Level 3 3. Spatial statistical analysis and hypothesis testing: Set up Null Hypotheses H0 Set up Alternate Hypotheses H1 Test Hypotheses. Richard Heimann © 2013
  • 64. 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. Richard Heimann © 2013
  • 65. GES 673: Level 4 4. Spatial modeling: prediction Construct models (of processes) to predict spatial outcomes (patterns). !Coefficient: % Poverty Coefficient: % FB Coefficient: % Elderly Richard Heimann © 2013 Coefficient: % Black
  • 66. 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 Richard Heimann © 2013
  • 67. 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 Richard Heimann © 2013
  • 68. New Aged Experimentation Richard Heimann © 2013
  • 69. 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) Richard Heimann © 2013
  • 70. GES 673: Experimentation Richard Heimann © 2013
  • 71. GES 673: Review Part 1 Geography GIS and the GIS Data Model ! CSR, Form & Process. 
 What & Why /Spatial/ Data Analysis ! Methodologies ! Levels of sophistication Richard Heimann © 2013
  • 72. 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). Richard Heimann © 2013
  • 73. Big Data /Nontraditional Social Science Data/ Richard Heimann © 2013
  • 74. GES 673: DATA IS THE NEW OIL! Richard Heimann © 2013
  • 75. 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. Richard Heimann © 2013
  • 76. 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). Richard Heimann © 2013
  • 77. 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!! Richard Heimann © 2013
  • 78. 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. Richard Heimann © 2013
  • 79. 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 ! Why is Spatial Data Special; Potentials and Pitfalls: Spatial Autocorrelation, MAUP (scale & zone), Scale effects, Ecological Fallacy, Nonuniformity of space, Edge Effects. ! Big Data: Big Data e.g. Twitter Geographic Knowledge Discovery ! ! Richard Heimann © 2013
  • 80. 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) ! 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). Richard Heimann © 2013