Overview of Statistical Analysis of Spatial Data

Size: px
Start display at page:

Download "Overview of Statistical Analysis of Spatial Data"

Transcription

1 Overview of Statistical Analysis of Spatial Data Geog 2C Introduction to Spatial Data Analysis Phaedon C. Kyriakidis phaedon Department of Geography University of California Santa Barbara Santa Barbara, CA Spring Quarter 9 Outline Preliminaries Types of Spatial Data Why Spatial Statistics? Points to Remember Ph. Kyriakidis (UCSB) Geog 2C Spring 9 2 /

2 Introduction & Objectives Preliminaries Spatial data Geo-referenced attribute measurements; each measurement is associated with a location (point) or an entity (region or object) in geographical (or other) space attribute measurement scale can be continuous or discrete, e.g., chemical concentration, soil types, disease occurrences sample locations can have a regular or irregular spatial arrangement, i.e., data locations on a raster (regular lattice) or scattered in space; domain informed by a measurement is called the sample unit or support, e.g., points, pixels, polygons spatial data often have an additional temporal component; dynamic attribute evolution in space and time, spatiotemporal support Objectives of this handout to provide a brief overview of types of spatial data to highlight the role of spatial statistics in analyzing data of each type Ph. Kyriakidis (UCSB) Geog 2C Spring 9 3 / Preliminaries Stages in Spatial Data Analysis Exploratory analysis explore spatial data using cartographic (or other visual) representations statistical analysis for detecting possible sub-populations, outliers, trends, relationships with neighboring values or other spatial variables Modeling or confirmatory analysis establish parametric or non-parametric model(s) characterizing attribute spatial distribution estimate model parameters from data; evaluate their statistical significance; predict attribute values at other locations and/or future time instants Notes any processing of spatial data, e.g., filtering or interpolation, affects any inference made from them boundaries between above stages not always clear-cut Ph. Kyriakidis (UCSB) Geog 2C Spring 9 4 /

3 Types of Spatial Data Attributes Varying Continuously in Space Characteristics also known (unfortunately) as geostatistical data, e.g., temperature, rainfall, elevation, population density measurements of nominal scale, e.g., land cover types, or interval/ratio scale, e.g., sea floor depth often, sparse samples are available only at fixed set of locations Bay Area rain gauge precipitation mm/day NDJ average Ph. Kyriakidis (UCSB) Geog 2C Spring 9 5 / Area or Lattice Data Characteristics Types of Spatial Data attributes take values only at fixed set of areas or zones, e.g., administrative districts, pixels of satellite images typically, all possible locations have been sampled; no attribute values between sampling units (unless there are missing values) From 1979 to 1984 SIDS Cases in North Carolina Distinction between spatially continuous and area (lattice) data not always clear-cut, particularly when the latter are derived via aggregation from the former Ph. Kyriakidis (UCSB) Geog 2C Spring 9 6 /

4 Types of Spatial Data Point Pattern Data Characteristics series of point locations with recorded events, e.g., locations of trees, disease or crime incidents point locations correspond to all possible events (mapped point pattern), or to a subset (sampled point pattern) attribute values also possible at same locations, e.g., tree diameter, magnitude of earthquakes (marked point pattern) Lansing Woods tree locations Bay Area earthquake magnitudes.8.6 maple hickory Ph. Kyriakidis (UCSB) Geog 2C Spring 9 7 / Types of Spatial Data Spatial Interaction or Network Data Characteristics attributes relate to pairs of points or areas: flows from origins to destinations, e.g., patients flow from residences to hospitals less tangible flows, e.g., information, could be defined Analysis objectives modeling of flow patterns = finding relationships between observed flows and explanatory variables, e.g., number of trips from origins to destinations as function of income classical analysis methods focus on patterns of aggregate interaction, rather than individuals themselves; more recent focus is placed on understanding individual preferences and choice modeling spatial location/allocation problems, and more generally spatial optimization problems, typically involve network data Methods for analyzing spatial interaction data are not covered in this course Ph. Kyriakidis (UCSB) Geog 2C Spring 9 8 /

5 Why Spatial Statistics? Univariate Statistics and Spatial Pattern? Two 1D attribute profiles with the same histogram: 3 1D population 3 1D population value value x x Shortcomings of univariate statistics Univariate statistics, e.g., average, variance, histogram, do not suffice to describe spatial pattern; the spatial arrangement of attribute values matters, too Spatial auto-correlation an aspect of spatial pattern Attribute values measured at nearby supports tend to be more similar than those measured at distant supports; Tobler s 1st law(?) of Geography Ph. Kyriakidis (UCSB) Geog 2C Spring 9 9 / Why Spatial Statistics? Role of Spatial Statistics in Spatial Data Analysis Spatially continuous data model attribute spatial variation over study area from sampled point values predict attribute values at non-sampled locations (accounting for covariates) Area (lattice) data detect and model spatial patterns or trends in area values; no prediction at non-sampled locations, unless smoothing of existing values or imputation of missing values is required use covariates or relationships with adjacent attribute values for inference, e.g., disease rates in light of socioeconomic variables Point patterns detect clustering or regularity, as opposed to complete randomness, of event locations in space and/or time if clustering is detected, investigate possible relations between clusters and nearby sources or pertinent covariates Ph. Kyriakidis (UCSB) Geog 2C Spring 9 /

6 Why Spatial Statistics? Spatial Versus Non-Spatial Statistics Classical statistics samples assumed realizations of independent and identically distributed random variables (iid) most hypothesis testing procedures call for samples from iid random variables problems with inference and hypothesis testing in a spatial setting Spatial statistics multivariate statistics in a spatial/temporal context: each observation is viewed as a realization from a different random variable, but such random variables are auto-correlated in space and/or time each sample is not an independent piece of information, because precisely it is redundant with other samples (due to the corresponding random variables being auto-correlated) auto- and cross-correlation (in space and/or time) is explicitly accounted for to establish confidence intervals for hypothesis testing One can always choose to analyze spatial data with non-spatial statistics; problems arise when confidence intervals need to be reported... Ph. Kyriakidis (UCSB) Geog 2C Spring 9 11 / Why Spatial Statistics? Software for Statistical Analysis of Spatial Data GIS-based ESRI s Spatial Analyst, Geostatistical Analyst... opt for close or loose coupling with specialized external packages when specific functionalities are missing from a GIS Statistical packages extremely versatile in modeling; recent improvements in visualization R and SpaceStat/GeoDa most popular in Geography Image processing packages mature technology, lots of new developments IDL and Matlab most popular in Remote Sensing and Electrical Engineering Access to source code written in a straight-forward programming language is critical for research development in an academic environment... Ph. Kyriakidis (UCSB) Geog 2C Spring 9 12 /

7 Some Issues Specific to Spatial Data Analysis A first look differences from times series analysis: 1. irregular sampling 2. lack of clear indexing; no notion of past-present-future 3. auto- and cross-correlation in multiple directions multi-source data associated with different spatial/temporal resolutions data often reported as aggregates over arbitrarily defined zones/areas; statistics of aggregates are not the same as those of individuals: 1. Modifiable Area Unit Problem (MAUP) 2. Ecological Fallacy or Inference Problem (EIP) edge/boundary effects: samples near the edges of a study region have fewer neighbors than samples in the interior; near-edge samples might bear the effects of different spatial processes spatial process models typically distinguish between first- and second-order effects, i.e., between environmental controls and interactions (distinction between the two not always clear-cut) Ph. Kyriakidis (UCSB) Geog 2C Spring 9 13 / Modifiable Area-Unit Problem: Aggregation Effect Two spatial variables and their univariate/bivariate statistics Spatial Variable # Spatial Variable # ρ 12 = m = s = m = s = Aggregation Scheme # ρ 12 = m = s = m = s = Statistics and relationships between spatial attributes depend on aggregation extent Ph. Kyriakidis (UCSB) Geog 2C Spring 9 14 /

8 Modifiable Area-Unit Problem: Zonation Effect Upscaling spatial variables using two different aggregation schemes Aggregation Scheme # ρ 12 = m = s = m = s = Aggregation Scheme # ρ 12 = m = s = m = s = For a given aggregation extent, statistics and relationships between spatial attributes depend on which individual values are aggregated and how Ph. Kyriakidis (UCSB) Geog 2C Spring 9 15 / Ecological Inference Problem I Downscaling spatial variables Observed variables ρ 12 = m = s = m = s = Spatial Variable # Spatial Variable # ρ 12 = m = s = m = s = Statistics and relationships between spatial variables at a finer spatial resolution are different than those derived at the original coarse resolution Ph. Kyriakidis (UCSB) Geog 2C Spring 9 16 /

9 Ecological Inference Problem II Under-determined inverse problem Observed variables ρ 12 = m = s = m = s = Spatial Variable #1 Spatial Variable # ρ 12 = m = s =.17 m = s = Multiple combinations of fine spatial resolution attribute values can lead to the same aggregate values at a coarser resolution (equi-finality) Ph. Kyriakidis (UCSB) Geog 2C Spring 9 17 / First- Versus Second-Order Effects 3 1D population 2 1 value x First-order effects Spatial pattern explained by environmental (or extrinsic) factors, e.g., attribute value y(x) is high at location x due to another attribute value y (x) at the same location x, or another attribute value y (x ) at a nearby location x Second-order effects Spatial pattern explained by interaction (or intrinsic) factors, e.g., attribute value y(x) is low at location x due to another (same-attribute) value y(x ) at a nearby location x, provided both locations x and x lie in the same environment Ph. Kyriakidis (UCSB) Geog 2C Spring 9 18 /

10 Points to Remember Recap I Spatial data set of geo-referenced measurements with attribute values and coordinates (topology & context also important) data types: 1. spatial point patterns events 2. data continuously varying in space fields 3. area or lattice data objects 4. spatial interaction data flows Spatial data analysis objectives exploratory analysis: looking for patterns/relationships confirmatory analysis: establishing spatial process models from spatial patterns + model parameter estimation Ph. Kyriakidis (UCSB) Geog 2C Spring 9 19 / Recap II Spatial statistics Points to Remember statistical framework for analysis and modeling of spatial data: accounts for spatial auto-correlation and scale effects; allows assessing uncertainty in spatial analysis results multivariate statistics tailored to the analysis of spatial data Issues to be aware of any spatial analysis result is tied to a particular observation scale, i.e., to the particular sample support(s); the Modifiable Area Unit Problem (MAUP) and the Ecological Inference Problem (EIP) are consequences of this spatial process models typically distinguish between: first-order effects or environmental controls second-order effects or interactions (spatial auto-correlation) this dichotomy does not apply to actual data, only to data generating models... Ph. Kyriakidis (UCSB) Geog 2C Spring 9 /

GIST 4302/5302: Spatial Analysis and Modeling

GIST 4302/5302: Spatial Analysis and Modeling GIST 4302/5302: Spatial Analysis and Modeling Basics of Statistics Guofeng Cao www.myweb.ttu.edu/gucao Department of Geosciences Texas Tech University guofeng.cao@ttu.edu Spring 2015 Outline of This Week

More information

Michael Harrigan Office hours: Fridays 2:00-4:00pm Holden Hall

Michael Harrigan Office hours: Fridays 2:00-4:00pm Holden Hall Announcement New Teaching Assistant Michael Harrigan Office hours: Fridays 2:00-4:00pm Holden Hall 209 Email: michael.harrigan@ttu.edu Guofeng Cao, Texas Tech GIST4302/5302, Lecture 2: Review of Map Projection

More information

GIST 4302/5302: Spatial Analysis and Modeling

GIST 4302/5302: Spatial Analysis and Modeling GIST 4302/5302: Spatial Analysis and Modeling Lecture 2: Review of Map Projections and Intro to Spatial Analysis Guofeng Cao http://thestarlab.github.io Department of Geosciences Texas Tech University

More information

GIST 4302/5302: Spatial Analysis and Modeling Lecture 2: Review of Map Projections and Intro to Spatial Analysis

GIST 4302/5302: Spatial Analysis and Modeling Lecture 2: Review of Map Projections and Intro to Spatial Analysis GIST 4302/5302: Spatial Analysis and Modeling Lecture 2: Review of Map Projections and Intro to Spatial Analysis Guofeng Cao http://www.spatial.ttu.edu Department of Geosciences Texas Tech University guofeng.cao@ttu.edu

More information

Interaction Analysis of Spatial Point Patterns

Interaction Analysis of Spatial Point Patterns Interaction Analysis of Spatial Point Patterns Geog 2C Introduction to Spatial Data Analysis Phaedon C Kyriakidis wwwgeogucsbedu/ phaedon Department of Geography University of California Santa Barbara

More information

Types of spatial data. The Nature of Geographic Data. Types of spatial data. Spatial Autocorrelation. Continuous spatial data: geostatistics

Types of spatial data. The Nature of Geographic Data. Types of spatial data. Spatial Autocorrelation. Continuous spatial data: geostatistics The Nature of Geographic Data Types of spatial data Continuous spatial data: geostatistics Samples may be taken at intervals, but the spatial process is continuous e.g. soil quality Discrete data Irregular:

More information

Introduction. Spatial Processes & Spatial Patterns

Introduction. Spatial Processes & Spatial Patterns Introduction Spatial data: set of geo-referenced attribute measurements: each measurement is associated with a location (point) or an entity (area/region/object) in geographical (or other) space; the domain

More information

Nature of Spatial Data. Outline. Spatial Is Special

Nature of Spatial Data. Outline. Spatial Is Special Nature of Spatial Data Outline Spatial is special Bad news: the pitfalls of spatial data Good news: the potentials of spatial data Spatial Is Special Are spatial data special? Why spatial data require

More information

POPULAR CARTOGRAPHIC AREAL INTERPOLATION METHODS VIEWED FROM A GEOSTATISTICAL PERSPECTIVE

POPULAR CARTOGRAPHIC AREAL INTERPOLATION METHODS VIEWED FROM A GEOSTATISTICAL PERSPECTIVE CO-282 POPULAR CARTOGRAPHIC AREAL INTERPOLATION METHODS VIEWED FROM A GEOSTATISTICAL PERSPECTIVE KYRIAKIDIS P. University of California Santa Barbara, MYTILENE, GREECE ABSTRACT Cartographic areal interpolation

More information

Lecture 8. Spatial Estimation

Lecture 8. Spatial Estimation Lecture 8 Spatial Estimation Lecture Outline Spatial Estimation Spatial Interpolation Spatial Prediction Sampling Spatial Interpolation Methods Spatial Prediction Methods Interpolating Raster Surfaces

More information

Spatial Analysis 1. Introduction

Spatial Analysis 1. Introduction Spatial Analysis 1 Introduction Geo-referenced Data (not any data) x, y coordinates (e.g., lat., long.) ------------------------------------------------------ - Table of Data: Obs. # x y Variables -------------------------------------

More information

Lecture 5 Geostatistics

Lecture 5 Geostatistics Lecture 5 Geostatistics Lecture Outline Spatial Estimation Spatial Interpolation Spatial Prediction Sampling Spatial Interpolation Methods Spatial Prediction Methods Interpolating Raster Surfaces with

More information

A spatial literacy initiative for undergraduate education at UCSB

A spatial literacy initiative for undergraduate education at UCSB A spatial literacy initiative for undergraduate education at UCSB Mike Goodchild & Don Janelle Department of Geography / spatial@ucsb University of California, Santa Barbara ThinkSpatial Brown bag forum

More information

ENGRG Introduction to GIS

ENGRG Introduction to GIS ENGRG 59910 Introduction to GIS Michael Piasecki October 13, 2017 Lecture 06: Spatial Analysis Outline Today Concepts What is spatial interpolation Why is necessary Sample of interpolation (size and pattern)

More information

Intensity Analysis of Spatial Point Patterns Geog 210C Introduction to Spatial Data Analysis

Intensity Analysis of Spatial Point Patterns Geog 210C Introduction to Spatial Data Analysis Intensity Analysis of Spatial Point Patterns Geog 210C Introduction to Spatial Data Analysis Chris Funk Lecture 4 Spatial Point Patterns Definition Set of point locations with recorded events" within study

More information

Spatial Analysis I. Spatial data analysis Spatial analysis and inference

Spatial Analysis I. Spatial data analysis Spatial analysis and inference Spatial Analysis I Spatial data analysis Spatial analysis and inference Roadmap Outline: What is spatial analysis? Spatial Joins Step 1: Analysis of attributes Step 2: Preparing for analyses: working with

More information

Luc Anselin Spatial Analysis Laboratory Dept. Agricultural and Consumer Economics University of Illinois, Urbana-Champaign

Luc Anselin Spatial Analysis Laboratory Dept. Agricultural and Consumer Economics University of Illinois, Urbana-Champaign GIS and Spatial Analysis Luc Anselin Spatial Analysis Laboratory Dept. Agricultural and Consumer Economics University of Illinois, Urbana-Champaign http://sal.agecon.uiuc.edu Outline GIS and Spatial Analysis

More information

Intensity Analysis of Spatial Point Patterns Geog 210C Introduction to Spatial Data Analysis

Intensity Analysis of Spatial Point Patterns Geog 210C Introduction to Spatial Data Analysis Intensity Analysis of Spatial Point Patterns Geog 210C Introduction to Spatial Data Analysis Chris Funk Lecture 5 Topic Overview 1) Introduction/Unvariate Statistics 2) Bootstrapping/Monte Carlo Simulation/Kernel

More information

Overview of Spatial analysis in ecology

Overview of Spatial analysis in ecology Spatial Point Patterns & Complete Spatial Randomness - II Geog 0C Introduction to Spatial Data Analysis Chris Funk Lecture 8 Overview of Spatial analysis in ecology st step in understanding ecological

More information

Geometric Algorithms in GIS

Geometric Algorithms in GIS Geometric Algorithms in GIS GIS Software Dr. M. Gavrilova GIS System What is a GIS system? A system containing spatially referenced data that can be analyzed and converted to new information for a specific

More information

Geog183: Cartographic Design and Geovisualization Spring Quarter 2018 Lecture 11: Dasymetric and isarithmic mapping

Geog183: Cartographic Design and Geovisualization Spring Quarter 2018 Lecture 11: Dasymetric and isarithmic mapping Geog183: Cartographic Design and Geovisualization Spring Quarter 2018 Lecture 11: Dasymetric and isarithmic mapping Discrete vs. continuous revisited Choropleth suited to discrete areal, but suffers from

More information

Improving Spatial Data Interoperability

Improving Spatial Data Interoperability Improving Spatial Data Interoperability A Framework for Geostatistical Support-To To-Support Interpolation Michael F. Goodchild, Phaedon C. Kyriakidis, Philipp Schneider, Matt Rice, Qingfeng Guan, Jordan

More information

Introduction to Spatial Analysis. Spatial Analysis. Session organization. Learning objectives. Module organization. GIS and spatial analysis

Introduction to Spatial Analysis. Spatial Analysis. Session organization. Learning objectives. Module organization. GIS and spatial analysis Introduction to Spatial Analysis I. Conceptualizing space Session organization Module : Conceptualizing space Module : Spatial analysis of lattice data Module : Spatial analysis of point patterns Module

More information

ARIC Manuscript Proposal # PC Reviewed: _9/_25_/06 Status: A Priority: _2 SC Reviewed: _9/_25_/06 Status: A Priority: _2

ARIC Manuscript Proposal # PC Reviewed: _9/_25_/06 Status: A Priority: _2 SC Reviewed: _9/_25_/06 Status: A Priority: _2 ARIC Manuscript Proposal # 1186 PC Reviewed: _9/_25_/06 Status: A Priority: _2 SC Reviewed: _9/_25_/06 Status: A Priority: _2 1.a. Full Title: Comparing Methods of Incorporating Spatial Correlation in

More information

The Nature of Geographic Data

The Nature of Geographic Data 4 The Nature of Geographic Data OVERVIEW Elaborates on the spatial is special theme Focuses on how phenomena vary across space and the general nature of geographic variation Describes the main principles

More information

Spatial analysis. Spatial descriptive analysis. Spatial inferential analysis:

Spatial analysis. Spatial descriptive analysis. Spatial inferential analysis: Spatial analysis Spatial descriptive analysis Point pattern analysis (minimum bounding box, mean center, weighted mean center, standard distance, nearest neighbor analysis) Spatial clustering analysis

More information

KAAF- GE_Notes GIS APPLICATIONS LECTURE 3

KAAF- GE_Notes GIS APPLICATIONS LECTURE 3 GIS APPLICATIONS LECTURE 3 SPATIAL AUTOCORRELATION. First law of geography: everything is related to everything else, but near things are more related than distant things Waldo Tobler Check who is sitting

More information

Concepts and Applications of Kriging. Eric Krause

Concepts and Applications of Kriging. Eric Krause Concepts and Applications of Kriging Eric Krause Sessions of note Tuesday ArcGIS Geostatistical Analyst - An Introduction 8:30-9:45 Room 14 A Concepts and Applications of Kriging 10:15-11:30 Room 15 A

More information

Lecture 3: Exploratory Spatial Data Analysis (ESDA) Prof. Eduardo A. Haddad

Lecture 3: Exploratory Spatial Data Analysis (ESDA) Prof. Eduardo A. Haddad Lecture 3: Exploratory Spatial Data Analysis (ESDA) Prof. Eduardo A. Haddad Key message Spatial dependence First Law of Geography (Waldo Tobler): Everything is related to everything else, but near things

More information

Spatial Process VS. Non-spatial Process. Landscape Process

Spatial Process VS. Non-spatial Process. Landscape Process Spatial Process VS. Non-spatial Process A process is non-spatial if it is NOT a function of spatial pattern = A process is spatial if it is a function of spatial pattern Landscape Process If there is no

More information

Statistical Perspectives on Geographic Information Science. Michael F. Goodchild University of California Santa Barbara

Statistical Perspectives on Geographic Information Science. Michael F. Goodchild University of California Santa Barbara Statistical Perspectives on Geographic Information Science Michael F. Goodchild University of California Santa Barbara Statistical geometry Geometric phenomena subject to chance spatial phenomena emphasis

More information

Concepts and Applications of Kriging. Eric Krause Konstantin Krivoruchko

Concepts and Applications of Kriging. Eric Krause Konstantin Krivoruchko Concepts and Applications of Kriging Eric Krause Konstantin Krivoruchko Outline Introduction to interpolation Exploratory spatial data analysis (ESDA) Using the Geostatistical Wizard Validating interpolation

More information

Introduction to Spatial Data and Models

Introduction to Spatial Data and Models Introduction to Spatial Data and Models Sudipto Banerjee 1 and Andrew O. Finley 2 1 Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, U.S.A. 2 Department of Forestry

More information

Geographic Information Systems (GIS) in Environmental Studies ENVS Winter 2003 Session III

Geographic Information Systems (GIS) in Environmental Studies ENVS Winter 2003 Session III Geographic Information Systems (GIS) in Environmental Studies ENVS 6189 3.0 Winter 2003 Session III John Sorrell York University sorrell@yorku.ca Session Purpose: To discuss the various concepts of space,

More information

Bayesian Hierarchical Models

Bayesian Hierarchical Models Bayesian Hierarchical Models Gavin Shaddick, Millie Green, Matthew Thomas University of Bath 6 th - 9 th December 2016 1/ 34 APPLICATIONS OF BAYESIAN HIERARCHICAL MODELS 2/ 34 OUTLINE Spatial epidemiology

More information

Lecture 3: Exploratory Spatial Data Analysis (ESDA) Prof. Eduardo A. Haddad

Lecture 3: Exploratory Spatial Data Analysis (ESDA) Prof. Eduardo A. Haddad Lecture 3: Exploratory Spatial Data Analysis (ESDA) Prof. Eduardo A. Haddad Key message Spatial dependence First Law of Geography (Waldo Tobler): Everything is related to everything else, but near things

More information

Cell-based Model For GIS Generalization

Cell-based Model For GIS Generalization Cell-based Model For GIS Generalization Bo Li, Graeme G. Wilkinson & Souheil Khaddaj School of Computing & Information Systems Kingston University Penrhyn Road, Kingston upon Thames Surrey, KT1 2EE UK

More information

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

Spatial Analysis and Modeling (GIST 4302/5302) Guofeng Cao Department of Geosciences Texas Tech University Spatial Analysis and Modeling (GIST 4302/5302) Guofeng Cao Department of Geosciences Texas Tech University TTU Graduate Certificate Geographic Information Science and Technology (GIST) 3 Core Courses and

More information

Exploratory Spatial Data Analysis (ESDA)

Exploratory Spatial Data Analysis (ESDA) Exploratory Spatial Data Analysis (ESDA) VANGHR s method of ESDA follows a typical geospatial framework of selecting variables, exploring spatial patterns, and regression analysis. The primary software

More information

Introduction to Spatial Data and Models

Introduction to Spatial Data and Models Introduction to Spatial Data and Models Sudipto Banerjee 1 and Andrew O. Finley 2 1 Department of Forestry & Department of Geography, Michigan State University, Lansing Michigan, U.S.A. 2 Biostatistics,

More information

Software. People. Data. Network. What is GIS? Procedures. Hardware. Chapter 1

Software. People. Data. Network. What is GIS? Procedures. Hardware. Chapter 1 People Software Data Network Procedures Hardware What is GIS? Chapter 1 Why use GIS? Mapping Measuring Monitoring Modeling Managing Five Ms of Applied GIS Chapter 2 Geography matters Quantitative analyses

More information

Introduction to Geostatistics

Introduction to Geostatistics Introduction to Geostatistics Abhi Datta 1, Sudipto Banerjee 2 and Andrew O. Finley 3 July 31, 2017 1 Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore,

More information

Outline. Geographic Information Analysis & Spatial Data. Spatial Analysis is a Key Term. Lecture #1

Outline. Geographic Information Analysis & Spatial Data. Spatial Analysis is a Key Term. Lecture #1 Geographic Information Analysis & Spatial Data Lecture #1 Outline Introduction Spatial Data Types: Objects vs. Fields Scale of Attribute Measures GIS and Spatial Analysis Spatial Analysis is a Key Term

More information

An Introduction to Geographic Information System

An Introduction to Geographic Information System An Introduction to Geographic Information System PROF. Dr. Yuji MURAYAMA Khun Kyaw Aung Hein 1 July 21,2010 GIS: A Formal Definition A system for capturing, storing, checking, Integrating, manipulating,

More information

Geostatistics and Spatial Scales

Geostatistics and Spatial Scales Geostatistics and Spatial Scales Semivariance & semi-variograms Scale dependence & independence Ranges of spatial scales Variable dependent Fractal dimension GIS implications Spatial Modeling Spatial Analysis

More information

GIST 4302/5302: Spatial Analysis and Modeling

GIST 4302/5302: Spatial Analysis and Modeling GIST 4302/5302: Spatial Analysis and Modeling Review Guofeng Cao www.gis.ttu.edu/starlab Department of Geosciences Texas Tech University guofeng.cao@ttu.edu Spring 2016 Course Outlines Spatial Point Pattern

More information

SPATIAL-TEMPORAL TECHNIQUES FOR PREDICTION AND COMPRESSION OF SOIL FERTILITY DATA

SPATIAL-TEMPORAL TECHNIQUES FOR PREDICTION AND COMPRESSION OF SOIL FERTILITY DATA SPATIAL-TEMPORAL TECHNIQUES FOR PREDICTION AND COMPRESSION OF SOIL FERTILITY DATA D. Pokrajac Center for Information Science and Technology Temple University Philadelphia, Pennsylvania A. Lazarevic Computer

More information

COMPARISON OF DIGITAL ELEVATION MODELLING METHODS FOR URBAN ENVIRONMENT

COMPARISON OF DIGITAL ELEVATION MODELLING METHODS FOR URBAN ENVIRONMENT COMPARISON OF DIGITAL ELEVATION MODELLING METHODS FOR URBAN ENVIRONMENT Cahyono Susetyo Department of Urban and Regional Planning, Institut Teknologi Sepuluh Nopember, Indonesia Gedung PWK, Kampus ITS,

More information

Spatial Analysis II. Spatial data analysis Spatial analysis and inference

Spatial Analysis II. Spatial data analysis Spatial analysis and inference Spatial Analysis II Spatial data analysis Spatial analysis and inference Roadmap Spatial Analysis I Outline: What is spatial analysis? Spatial Joins Step 1: Analysis of attributes Step 2: Preparing for

More information

Spatial Regression. 1. Introduction and Review. Luc Anselin. Copyright 2017 by Luc Anselin, All Rights Reserved

Spatial Regression. 1. Introduction and Review. Luc Anselin.  Copyright 2017 by Luc Anselin, All Rights Reserved Spatial Regression 1. Introduction and Review Luc Anselin http://spatial.uchicago.edu matrix algebra basics spatial econometrics - definitions pitfalls of spatial analysis spatial autocorrelation spatial

More information

Concepts and Applications of Kriging

Concepts and Applications of Kriging 2013 Esri International User Conference July 8 12, 2013 San Diego, California Technical Workshop Concepts and Applications of Kriging Eric Krause Konstantin Krivoruchko Outline Intro to interpolation Exploratory

More information

Introduction. Semivariogram Cloud

Introduction. Semivariogram Cloud Introduction Data: set of n attribute measurements {z(s i ), i = 1,, n}, available at n sample locations {s i, i = 1,, n} Objectives: Slide 1 quantify spatial auto-correlation, or attribute dissimilarity

More information

Concepts and Applications of Kriging

Concepts and Applications of Kriging Esri International User Conference San Diego, California Technical Workshops July 24, 2012 Concepts and Applications of Kriging Konstantin Krivoruchko Eric Krause Outline Intro to interpolation Exploratory

More information

GIS and Spatial Statistics: One World View or Two? Michael F. Goodchild University of California Santa Barbara

GIS and Spatial Statistics: One World View or Two? Michael F. Goodchild University of California Santa Barbara GIS and Spatial Statistics: One World View or Two? Michael F. Goodchild University of California Santa Barbara Location as attribute The data table Census summary table What value is location as an explanatory

More information

Interpolating Raster Surfaces

Interpolating Raster Surfaces Interpolating Raster Surfaces You can use interpolation to model the surface of a feature or a phenomenon all you need are sample points, an interpolation method, and an understanding of the feature or

More information

Models to carry out inference vs. Models to mimic (spatio-temporal) systems 5/5/15

Models to carry out inference vs. Models to mimic (spatio-temporal) systems 5/5/15 Models to carry out inference vs. Models to mimic (spatio-temporal) systems 5/5/15 Ring-Shaped Hotspot Detection: A Summary of Results, IEEE ICDM 2014 (w/ E. Eftelioglu et al.) Where is a crime source?

More information

The Case for Space in the Social Sciences

The Case for Space in the Social Sciences The Case for Space in the Social Sciences Don Janelle Center for Spatially Integrated Social Science University of California, Santa Barbara Roundtable on Geographical Voices and Geographical Analysis

More information

Popular Mechanics, 1954

Popular Mechanics, 1954 Introduction to GIS Popular Mechanics, 1954 1986 $2,599 1 MB of RAM 2017, $750, 128 GB memory, 2 GB of RAM Computing power has increased exponentially over the past 30 years, Allowing the existence of

More information

Fundamental Spatial Concepts. Michael F. Goodchild University of California Santa Barbara

Fundamental Spatial Concepts. Michael F. Goodchild University of California Santa Barbara Fundamental Spatial Concepts Michael F. Goodchild University of California Santa Barbara A spatial turn in science Adding space to theory the New Economic Geography space impeding flows of information,

More information

Mapping and Analysis for Spatial Social Science

Mapping and Analysis for Spatial Social Science Mapping and Analysis for Spatial Social Science Luc Anselin Spatial Analysis Laboratory Dept. Agricultural and Consumer Economics University of Illinois, Urbana-Champaign http://sal.agecon.uiuc.edu Outline

More information

Spatial Data Mining. Regression and Classification Techniques

Spatial Data Mining. Regression and Classification Techniques Spatial Data Mining Regression and Classification Techniques 1 Spatial Regression and Classisfication Discrete class labels (left) vs. continues quantities (right) measured at locations (2D for geographic

More information

Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages:

Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages: Glossary The ISI glossary of statistical terms provides definitions in a number of different languages: http://isi.cbs.nl/glossary/index.htm Adjusted r 2 Adjusted R squared measures the proportion of the

More information

Spatial Analyst. By Sumita Rai

Spatial Analyst. By Sumita Rai ArcGIS Extentions Spatial Analyst By Sumita Rai Overview What does GIS do? How does GIS work data models Extension to GIS Spatial Analyst Spatial Analyst Tasks & Tools Surface Analysis Surface Creation

More information

Class 9. Query, Measurement & Transformation; Spatial Buffers; Descriptive Summary, Design & Inference

Class 9. Query, Measurement & Transformation; Spatial Buffers; Descriptive Summary, Design & Inference Class 9 Query, Measurement & Transformation; Spatial Buffers; Descriptive Summary, Design & Inference Spatial Analysis Turns raw data into useful information by adding greater informative content and value

More information

Bivariate Distributions. Discrete Bivariate Distribution Example

Bivariate Distributions. Discrete Bivariate Distribution Example Spring 7 Geog C: Phaedon C. Kyriakidis Bivariate Distributions Definition: class of multivariate probability distributions describing joint variation of outcomes of two random variables (discrete or continuous),

More information

Outline. Introduction to SpaceStat and ESTDA. ESTDA & SpaceStat. Learning Objectives. Space-Time Intelligence System. Space-Time Intelligence System

Outline. Introduction to SpaceStat and ESTDA. ESTDA & SpaceStat. Learning Objectives. Space-Time Intelligence System. Space-Time Intelligence System Outline I Data Preparation Introduction to SpaceStat and ESTDA II Introduction to ESTDA and SpaceStat III Introduction to time-dynamic regression ESTDA ESTDA & SpaceStat Learning Objectives Activities

More information

Outline ESDA. Exploratory Spatial Data Analysis ESDA. Luc Anselin

Outline ESDA. Exploratory Spatial Data Analysis ESDA. Luc Anselin Exploratory Spatial Data Analysis ESDA Luc Anselin University of Illinois, Urbana-Champaign http://www.spacestat.com Outline ESDA Exploring Spatial Patterns Global Spatial Autocorrelation Local Spatial

More information

way and atmospheric models

way and atmospheric models Scale-consistent consistent two-way way coupling of land-surface and atmospheric models COSMO-User-Seminar 9-11 March 2009 Annika Schomburg, Victor Venema, Felix Ament, Clemens Simmer TR / SFB 32 Objective

More information

IV Course Spring 14. Graduate Course. May 4th, Big Spatiotemporal Data Analytics & Visualization

IV Course Spring 14. Graduate Course. May 4th, Big Spatiotemporal Data Analytics & Visualization Spatiotemporal Data Visualization IV Course Spring 14 Graduate Course of UCAS May 4th, 2014 Outline What is spatiotemporal data? How to analyze spatiotemporal data? How to visualize spatiotemporal data?

More information

Outline. 15. Descriptive Summary, Design, and Inference. Descriptive summaries. Data mining. The centroid

Outline. 15. Descriptive Summary, Design, and Inference. Descriptive summaries. Data mining. The centroid Outline 15. Descriptive Summary, Design, and Inference Geographic Information Systems and Science SECOND EDITION Paul A. Longley, Michael F. Goodchild, David J. Maguire, David W. Rhind 2005 John Wiley

More information

Spatial analysis. 0 move the objects and the results change

Spatial analysis. 0 move the objects and the results change 0 Outline: Roadmap 0 What is spatial analysis? 0 Transformations 0 Introduction to spatial interpolation 0 Classification of spatial interpolation methods 0 Interpolation methods 0 Areal interpolation

More information

7 Geostatistics. Figure 7.1 Focus of geostatistics

7 Geostatistics. Figure 7.1 Focus of geostatistics 7 Geostatistics 7.1 Introduction Geostatistics is the part of statistics that is concerned with geo-referenced data, i.e. data that are linked to spatial coordinates. To describe the spatial variation

More information

ENV208/ENV508 Applied GIS. Week 1: What is GIS?

ENV208/ENV508 Applied GIS. Week 1: What is GIS? ENV208/ENV508 Applied GIS Week 1: What is GIS? 1 WHAT IS GIS? A GIS integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information.

More information

GIS and the Built Environment

GIS and the Built Environment GIS and the Built Environment GIS Workshop Active Living Research Conference February 9, 2010 Anne Vernez Moudon, Dr. es Sc. University of Washington Chanam Lee, Ph.D., Texas A&M University OBJECTIVES

More information

Hierarchical Modeling and Analysis for Spatial Data

Hierarchical Modeling and Analysis for Spatial Data Hierarchical Modeling and Analysis for Spatial Data Bradley P. Carlin, Sudipto Banerjee, and Alan E. Gelfand brad@biostat.umn.edu, sudiptob@biostat.umn.edu, and alan@stat.duke.edu University of Minnesota

More information

Geog 469 GIS Workshop. Data Analysis

Geog 469 GIS Workshop. Data Analysis Geog 469 GIS Workshop Data Analysis Outline 1. What kinds of need-to-know questions can be addressed using GIS data analysis? 2. What is a typology of GIS operations? 3. What kinds of operations are useful

More information

Soil Moisture Modeling using Geostatistical Techniques at the O Neal Ecological Reserve, Idaho

Soil Moisture Modeling using Geostatistical Techniques at the O Neal Ecological Reserve, Idaho Final Report: Forecasting Rangeland Condition with GIS in Southeastern Idaho Soil Moisture Modeling using Geostatistical Techniques at the O Neal Ecological Reserve, Idaho Jacob T. Tibbitts, Idaho State

More information

What is GIS? ESRI Canada. August 2011

What is GIS? ESRI Canada. August 2011 What is GIS? ESRI Canada August 2011 Geography Matters! Environmental Park Management Agriculture Public Utilities Health Care Emergency 911 Real Estate Marketing Environmental What are the effects of

More information

Bayesian SAE using Complex Survey Data Lecture 4A: Hierarchical Spatial Bayes Modeling

Bayesian SAE using Complex Survey Data Lecture 4A: Hierarchical Spatial Bayes Modeling Bayesian SAE using Complex Survey Data Lecture 4A: Hierarchical Spatial Bayes Modeling Jon Wakefield Departments of Statistics and Biostatistics University of Washington 1 / 37 Lecture Content Motivation

More information

Temporal vs. Spatial Data

Temporal vs. Spatial Data Temporal vs. Spatial Data Temporal 1 dimensional Units: day, week, month Lag: t, t-1, t-2 Durbin-Watson Spatial 2-3 dimensional Units: county, mile, region Lag: near neighbor, networks (?) Moran s I Differencing

More information

Lecture 1: Geospatial Data Models

Lecture 1: Geospatial Data Models Lecture 1: GEOG413/613 Dr. Anthony Jjumba Introduction Course Outline Journal Article Review Projects (and short presentations) Final Exam (April 3) Participation in class discussions Geog413/Geog613 A

More information

Introduction to Spatial Statistics and Modeling for Regional Analysis

Introduction to Spatial Statistics and Modeling for Regional Analysis Introduction to Spatial Statistics and Modeling for Regional Analysis Dr. Xinyue Ye, Assistant Professor Center for Regional Development (Department of Commerce EDA University Center) & School of Earth,

More information

Why Is It There? Attribute Data Describe with statistics Analyze with hypothesis testing Spatial Data Describe with maps Analyze with spatial analysis

Why Is It There? Attribute Data Describe with statistics Analyze with hypothesis testing Spatial Data Describe with maps Analyze with spatial analysis 6 Why Is It There? Why Is It There? Getting Started with Geographic Information Systems Chapter 6 6.1 Describing Attributes 6.2 Statistical Analysis 6.3 Spatial Description 6.4 Spatial Analysis 6.5 Searching

More information

SRJC Applied Technology 54A Introduction to GIS

SRJC Applied Technology 54A Introduction to GIS SRJC Applied Technology 54A Introduction to GIS Overview Lecture of Geographic Information Systems Fall 2004 Santa Rosa Junior College Presented By: Tim Pudoff, GIS Coordinator, County of Sonoma, Information

More information

What are the five components of a GIS? A typically GIS consists of five elements: - Hardware, Software, Data, People and Procedures (Work Flows)

What are the five components of a GIS? A typically GIS consists of five elements: - Hardware, Software, Data, People and Procedures (Work Flows) LECTURE 1 - INTRODUCTION TO GIS Section I - GIS versus GPS What is a geographic information system (GIS)? GIS can be defined as a computerized application that combines an interactive map with a database

More information

What is GIS? Introduction to data. Introduction to data modeling

What is GIS? Introduction to data. Introduction to data modeling What is GIS? Introduction to data Introduction to data modeling 2 A GIS is similar, layering mapped information in a computer to help us view our world as a system A Geographic Information System is a

More information

2.6 Two-dimensional continuous interpolation 3: Kriging - introduction to geostatistics. References - geostatistics. References geostatistics (cntd.

2.6 Two-dimensional continuous interpolation 3: Kriging - introduction to geostatistics. References - geostatistics. References geostatistics (cntd. .6 Two-dimensional continuous interpolation 3: Kriging - introduction to geostatistics Spline interpolation was originally developed or image processing. In GIS, it is mainly used in visualization o spatial

More information

Performance Analysis of Some Machine Learning Algorithms for Regression Under Varying Spatial Autocorrelation

Performance Analysis of Some Machine Learning Algorithms for Regression Under Varying Spatial Autocorrelation Performance Analysis of Some Machine Learning Algorithms for Regression Under Varying Spatial Autocorrelation Sebastian F. Santibanez Urban4M - Humboldt University of Berlin / Department of Geography 135

More information

Spatial Downscaling of TRMM Precipitation Using DEM. and NDVI in the Yarlung Zangbo River Basin

Spatial Downscaling of TRMM Precipitation Using DEM. and NDVI in the Yarlung Zangbo River Basin Spatial Downscaling of TRMM Precipitation Using DEM and NDVI in the Yarlung Zangbo River Basin Yang Lu 1,2, Mingyong Cai 1,2, Qiuwen Zhou 1,2, Shengtian Yang 1,2 1 State Key Laboratory of Remote Sensing

More information

Applied Cartography and Introduction to GIS GEOG 2017 EL. Lecture-2 Chapters 3 and 4

Applied Cartography and Introduction to GIS GEOG 2017 EL. Lecture-2 Chapters 3 and 4 Applied Cartography and Introduction to GIS GEOG 2017 EL Lecture-2 Chapters 3 and 4 Vector Data Modeling To prepare spatial data for computer processing: Use x,y coordinates to represent spatial features

More information

Raster Spatial Analysis Specific Theory

Raster Spatial Analysis Specific Theory RSATheory.doc 1 Raster Spatial Analysis Specific Theory... 1 Spatial resampling... 1 Mosaic... 3 Reclassification... 4 Slicing... 4 Zonal Operations... 5 References... 5 Raster Spatial Analysis Specific

More information

Introduction to GIS. Dr. M.S. Ganesh Prasad

Introduction to GIS. Dr. M.S. Ganesh Prasad Introduction to GIS Dr. M.S. Ganesh Prasad Department of Civil Engineering The National Institute of Engineering, MYSORE ganeshprasad.nie@gmail.com 9449153758 Geographic Information System (GIS) Information

More information

Introduction to GIS I

Introduction to GIS I Introduction to GIS Introduction How to answer geographical questions such as follows: What is the population of a particular city? What are the characteristics of the soils in a particular land parcel?

More information

Institutional Opportunities and Constraints. Michael F. Goodchild

Institutional Opportunities and Constraints. Michael F. Goodchild Institutional Opportunities and Constraints Michael F. Goodchild A conceptual framework Nomothetic science knowledge that is true everywhere in space and time Idiographic science the study of the unique

More information

Spatial and Environmental Statistics

Spatial and Environmental Statistics Spatial and Environmental Statistics Dale Zimmerman Department of Statistics and Actuarial Science University of Iowa January 17, 2019 Dale Zimmerman (UIOWA) Spatial and Environmental Statistics January

More information

Section C: Management of the Built Environment GIS As A Tool: Technical Aspects of Basic GIS

Section C: Management of the Built Environment GIS As A Tool: Technical Aspects of Basic GIS Section C: Management of the Built Environment GIS As A Tool: Technical Aspects of Basic GIS This lecture covers five topics: 1.Scale, 2.Framework data, 3.Generalisation, 4.Aggregation, 5.Modifiable unit

More information

Spatial Units, Scaling and Aggregation (Level 1) October 2017

Spatial Units, Scaling and Aggregation (Level 1) October 2017 Spatial Units, Scaling and Aggregation (Level 1) October 2017 Overview: Spatial Units 1. Learning objectives 2. Review of Level 0 (5m) 3. Level 1 (Compilers): Presentation & group exercise Spatial units

More information

ENGRG Introduction to GIS

ENGRG Introduction to GIS ENGRG 59910 Introduction to GIS Michael Piasecki March 17, 2014 Lecture 08: Terrain Analysis Outline: Terrain Analysis Earth Surface Representation Contour TIN Mass Points Digital Elevation Models Slope

More information

GIST 4302/5302: Spatial Analysis and Modeling Point Pattern Analysis

GIST 4302/5302: Spatial Analysis and Modeling Point Pattern Analysis GIST 4302/5302: Spatial Analysis and Modeling Point Pattern Analysis Guofeng Cao www.spatial.ttu.edu Department of Geosciences Texas Tech University guofeng.cao@ttu.edu Fall 2018 Spatial Point Patterns

More information

Combining Incompatible Spatial Data

Combining Incompatible Spatial Data Combining Incompatible Spatial Data Carol A. Gotway Crawford Office of Workforce and Career Development Centers for Disease Control and Prevention Invited for Quantitative Methods in Defense and National

More information