Spatial Analysis and Modeling (GIST 4302/5302) Guofeng Cao Department of Geosciences Texas Tech University

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1 Spatial Analysis and Modeling (GIST 4302/5302) Guofeng Cao Department of Geosciences Texas Tech University

2 TTU Graduate Certificate Geographic Information Science and Technology (GIST) 3 Core Courses and 2 Electives GIST Geographic Information Systems (3) GIST 5302.Spatial Analysis and Modeling (3) GIST Advanced Geographic Information Systems (3) GIST Cartographic Design (3) GIST GPS Field Mapping (3) GIST Internet Mapping (3) GEOG Remote Sensing of the Environment (3) GEOL Digital Imagery in the Geosciences (3) GEOL Spatial Data Analysis and Modeling in Geosciences (3) NRM Aerial Terrain Analysis (4)

3 Geographic Information Science and Technology (GIST) 2 Core Courses and 4 Approved Electives GIST Geographic Information Systems (3) (Required for Geography Major) GIST Spatial Analysis and Modeling (3) GIST Advanced Geographic Information Systems (3) GIST Cartographic Design (3) GIST GPS Field Mapping (3) GIST Internet Mapping (3) GEOG Remote Sensing of the Environment (3) GEOL Digital Imagery in the Geosciences (3) GEOL Spatial Data Analysis and Modeling in Geosciences (3) NRM Aerial Terrain Analysis (4) Undergraduate Minor in GIST

4 Course Description This course will introduce concepts and commonly used methods in quantitative analysis of (geographic) spatial data Contents include: Representation and characteristics of spatial data (fundamentals of spatial databases) Concepts in spatial analysis and spatial statistics Specific spatial analytical and spatial statistical methods

5 Course Objectives After completing this course, the students are expected to learn how to: formulate real-world problems in the context of geographic information systems and spatial analysis apply appropriate spatial analytical methods to solve the problems utilize mainstream software tools (commercial or open-source) to solve spatial problems evaluate and assess the results of alternative methods communicate results of spatial analysis in the forms of writing and presentation

6 Course Format Lectures Instructor: Guofeng Cao Science building Room 234 T, Th: 2:00-2:50pm Office hours: T, Th: 1:00pm-2:00pm at Holden Hall 211 Lab sessions: TA: Samaneh Sammy Tabrizi GIS Lab: Hoden Hall 204 Office hours: M 5:00pm-6:00pm, and T 11:00am-noon at Holden Hall 209

7 Grading Two written exams: 30% (15% each) Eight lab assignments: 40% (5% each) Final project: 30% including proposal (5%), class presentation (10%) and project report (15%) Class and lab attendance is mandatory

8 Lab Assignments Multiple software will be utilized: ArcGIS CrimeStat GeoDa R or Matlab (optional)

9 Final Project The project could be used as a setting for your thesis and dissertation topics, other course topics or research interests Start to think of the project ideas early and communicate with the instructor and TA for comments

10 Textbook O'Sullivan, David and David J. Unwin, Geographic Information Analysis (Required) Optional: de Smith, Michael J., Paul A. Longley and Michael F. Goodchild (2013), Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools, 4th Edition. Available in both print and web (free!) version at Allen, David W. (2011), GIS Tutorial 2, Spatial Analysis Workbook for ArcGIS 10, Esri Press. Mitchell, A. (2009), The ESRI Guide to GIS Analysis, vol. 2: spatial measurements and statistics, ESRI Press

11 Other Logistics You are required to have a valid TTU address for setting up your Esri Global Account. USB Flash Drive: To save your homework, lab assignments and projects, you will need a USB flash drive. Given that GIS data can take up a lot of space, a minimum 2 GB flash drive is recommended. Withdrawing: You are responsible for dropping the class.

12 Survey Name, major, year GIS experiences (courses taken, projects participated, etc.) What do you expect to learn from this class? Programming experiences (programming languages, databases, and etc). There would be file if you don t have

13 Introduction

14 Scope of Spatial Analysis Data do not equal information Analysis of spatial data (geospatial data in particular) Spatial data manipulation (in GIS) Spatial query, measurements, transformation, network analysis, location analysis (spatial optimization) Spatial data analysis Exploratory spatial analysis Visual analytics Data-driven, let data speak themselves Spatial statistics An extension of traditional statistics into a spatial settings to determine whether or not data are typical or unexpected Geostatistics: Quantify the spatial relationships between observations of different locations for estimation of unknown locations Spatial modeling Involve constructing models to predict spatial outcomes

15 Topics Spatial data representation and manipulation Buffer, spatial query, overlay analysis (lab 2-3) Surface analysis and map algebra (lab 6) Point pattern analysis (lab 4) Spatial interpolation Deterministic interpolation (lab 6) Kriging (lab 7) Spatial statistics Spatial autocorrelation (lab 5) Spatial regression (lab 8) Geographically weighted regression (lab 9 ) Spatial uncertainty (if time permits) Multivariate spatial data analysis (if time permits)

16 Characteristics of (Geographic) Spatial Data Spatial (and temporal) Context: Everything is related to everything else, but near things are more related than distant things Waldo Tobler s First Law (TFL) of geography nearby things are more similar than distant things phenomena vary slowly over the Earth's surface Compare time series

17 Characteristics of (Geographic) Spatial Data Implications of Tobler s First Law: We can do samplings and fill the gap using estimation procedures (e.g. weather stations) Spatial patterns Image a world without TFL: White noise No polygons (how to draw a polygon on a white noise map?)

18 Characteristics of (Geographic) Spatial Data Spatial Heterogeneity Earth s surface is non-stationary Laws of physical sciences remain constant, virtually everything else changes Elevation, Climate, temperatures Social conditions Global model might be inconsistent with regional models: Spatial Simpson s Paradox

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20 Characteristics of (Geographic) Spatial Data Fractal Behavior What happens as scale of map changes? Coast of Maine Implications: Volume of geographic features tends to be underestimated Lengths of lines Surface areas

21 Lab of this week Review of map projection: Mercator puzzle: ag.html