IS415 Geospatial Analytics for Business Intelligence Lesson 10: Geospatial Data Analysis- Point Patterns Analysis.

Slides:

Advertisements

Similar presentations

Simulation - An Introduction Simulation:- The technique of imitating the behaviour of some situation or system (economic, military, mechanical, etc.) by.

Advertisements

Hotspot/cluster detection methods(1) Spatial Scan Statistics: Hypothesis testing – Input: data – Using continuous Poisson model Null hypothesis H0: points.

Multi-Scale Analysis of Crime and Incident Patterns in Camden Dawn Williams Department of Civil, Environmental & Geomatic.

STATISTICAL INFERENCE PART V CONFIDENCE INTERVALS 1.

Spatial Autocorrelation using GIS

LECTURE 3 Introduction to Linear Regression and Correlation Analysis

Empirical/Asymptotic P-values for Monte Carlo-Based Hypothesis Testing: an Application to Cluster Detection Using the Scan Statistic Allyson Abrams, Martin.

Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.

Improving the versatility of D.C.F. models by simple computer applications.

A SCALE-SENSITIVE TEST OF ATTRACTION AND REPULSION BETWEEN SPATIAL POINT PATTERNS Tony E. Smith University of Pennsylvania Diggle-Cox Test Lotwick-Hartwick.

Applied Geostatistics Geostatistical techniques are designed to evaluate the spatial structure of a variable, or the relationship between a value measured.

Pricing an Option Monte Carlo Simulation. We will explore a technique, called Monte Carlo simulation, to numerically derive the price of an option or.

Results 2 (cont’d) c) Long term observational data on the duration of effective response Observational data on n=50 has EVSI = £867 d) Collect data on.

G. Cowan Lectures on Statistical Data Analysis 1 Statistical Data Analysis: Lecture 8 1Probability, Bayes’ theorem, random variables, pdfs 2Functions of.

Why Geography is important.

Lecture II-2: Probability Review

IS415 Geospatial Analytics for Business Intelligence

Geographic Information Science

Exploratory Research & Secondary Data

University of Wisconsin-Milwaukee Geographic Information Science Geography 625 Intermediate Geographic Information Science Instructor: Changshan Wu Department.

Title: Spatial Data Mining in Geo-Business. Overview  Twisting the Perspective of Map Surfaces — describes the character of spatial distributions through.

Lecture 7: Simulations.

Safer College Campuses and Communities Through the Use of Geospatial Information Technology George Roedl and Gregory Elmes West Virginia University.

Overview G. Jogesh Babu. Probability theory Probability is all about flip of a coin Conditional probability & Bayes theorem (Bayesian analysis) Expectation,

SPONSOR JAMES C. BENNEYAN DEVELOPMENT OF A PRESCRIPTION DRUG SURVEILLANCE SYSTEM TEAM MEMBERS Jeffrey Mason Dan Mitus Jenna Eickhoff Benjamin Harris.

Food Store Location Analysis Albuquerque New Mexico, 2010 Prepared for: Geography 586L - Spring Semester, 2014 Larry Spear M.A., GISP Sr. Research Scientist.

Spatial Statistics Applied to point data.

Mote Carlo Method for Uncertainty The objective is to introduce a simple (almost trivial) example so that you can Perform.

Marketing and the Marketing Concept 1.1

material assembled from the web pages at

Agronomic Spatial Variability and Resolution What is it? How do we describe it? What does it imply for precision management?

Extending Spatial Hot Spot Detection Techniques to Temporal Dimensions Sungsoon Hwang Department of Geography State University of New York at Buffalo DMGIS.

Evaluating the Effectiveness of the Organization

Valuation of Asian Option Qi An Jingjing Guo. CONTENT Asian option Pricing Monte Carlo simulation Conclusion.

Managerial Economics Demand Estimation & Forecasting.

Simulation is the process of studying the behavior of a real system by using a model that replicates the behavior of the system under different scenarios.

1 Copyright © 2005 ACNielsen a VNU business China Retail Market Development.

What's New in eCognition Essentials 1.1 Christian Weise.

Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 13-1 Introduction to Regression Analysis Regression analysis is used.

CHAPTER 11 VECTOR DATA ANALYSIS 11.1 Buffering

Three Frameworks for Statistical Analysis. Sample Design Forest, N=6 Field, N=4 Count ant nests per quadrat.

Data Mining BY JEMINI ISLAM. Data Mining Outline: What is data mining? Why use data mining? How does data mining work The process of data mining Tools.

Spatial Statistics in Ecology: Point Pattern Analysis Lecture Two.

Point Pattern Analysis Point Patterns fall between the two extremes, highly clustered and highly dispersed. Most tests of point patterns compare the observed.

Point Pattern Analysis. Methods for analyzing completely censused population data F Entire extent of study area or F Each unit of an array of contiguous.

Methods for point patterns. Methods consider first-order effects (e.g., changes in mean values [intensity] over space) or second-order effects (e.g.,

Point Pattern Analysis

1 1 Slide Simulation Professor Ahmadi. 2 2 Slide Simulation Chapter Outline n Computer Simulation n Simulation Modeling n Random Variables and Pseudo-Random.

Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 7-1 Chapter 7 Sampling and Sampling Distributions Basic Business Statistics 11 th Edition.

Probability and Distributions. Deterministic vs. Random Processes In deterministic processes, the outcome can be predicted exactly in advance Eg. Force.

Chapter 5 Sampling Distributions. The Concept of Sampling Distributions Parameter – numerical descriptive measure of a population. It is usually unknown.

1 Introduction to Statistics − Day 4 Glen Cowan Lecture 1 Probability Random variables, probability densities, etc. Lecture 2 Brief catalogue of probability.

CHAPTER 9 Inference: Estimation The essential nature of inferential statistics, as verses descriptive statistics is one of knowledge. In descriptive statistics,

Extra Vocabulary-Thinking Geographically. Reference Maps vs. Thematic Maps Reference Maps A highly generalized map type designed to show general spatial.

Basic Business Statistics

26134 Business Statistics Week 4 Tutorial Simple Linear Regression Key concepts in this tutorial are listed below 1. Detecting.

Chapter Seventeen Copyright © 2004 John Wiley & Sons, Inc. Multivariate Data Analysis.

METU, GGIT 538 CHAPTER V MODELING OF POINT PATTERNS.

Problem 1: Service System Capacity CustomersServed Customers Queue Server Problem: Can a server taking an average of x time units meet the demand? Solution.

Chapter 16 Introduction to Quality ©. Some Benefits of Utilizing Statistical Quality Methods Increased Productivity Increased Sales Increased Profits.

Spatial statistics Lecture 3 2/4/2008. What are spatial statistics Not like traditional, a-spatial or non-spatial statistics But specific methods that.

26134 Business Statistics Week 4 Tutorial Simple Linear Regression Key concepts in this tutorial are listed below 1. Detecting.

Introduction to Spatial Statistical Analysis

Basic simulation methodology

Point-pattern analysis of Nashville, TN robberies: It’s all about that kernel Ingrid Luffman and Andrew Joyner, Department of Geosciences, East Tennessee.

Summary of Prev. Lecture

Inference about the Slope and Intercept

Spatial Point Pattern Analysis

Inference about the Slope and Intercept

DESIGN OF EXPERIMENTS by R. C. Baker

Presentation transcript:

IS415 Geospatial Analytics for Business Intelligence Lesson 10: Geospatial Data Analysis- Point Patterns Analysis

2 What will you learn from this lesson The differences between GIS analysis and geospatial data analysis Challenges face in analysing geospatial data The basic concepts of point patterns and point patterns analysis techniques

Core Competencies Capable to apply appropriate spatial point analysis techniques to gain insights Capable to provide accurate interpretation of spatial point analysis results 3

4 Background of the study Shanghai retail (tobacco) audit study –Account classification –Total market volume –Volume across key price points and channels –Individual brand profiles

5 The study area CHANGNING DISTRICT LUWAN DISTRICT ZHABEI DISTRICT XUHUI DISTRICT PUDONG NEW AREA HUANGPU DISTRICT YANGPU AREA PUTUO DISTRICT

6 Sales by channel Mom & Pop outlets account for 58% of total sales volume. The average sales per Mom & Pop is much lower than Supermarkets, Hypermarkets and Convenience stores. 16% of the market is made up of brands priced less than 3RMB. 11% of this volume is generated by Mom & Pops. Supermarkets are the second most important channel with 15% of total sales being generated through this channel. Convenience stores generate 13% of total sales while tobacconists are a the fourth most important channel

7 Questions: Where are the locations of the different channel stores? Are these channel stores tend to cluster together or they are evenly distributed? Where are the locations of the top 10% channel stores? Are the locations of the top 10% channel stores even distributed spatially? Is there any association between the distribution of the top 10% channel stores and the distribution of the offices

8 The evil of pin mapping!

9 Thematic map

10 GIS crime!

11 Spatial point pattern analysis methods Kernel density estimation Ripley’s K function L function D function K.hat 12 function

12 Kernel density estimation (Silverman 1986) A method to compute the intensity of a point distribution The general formula: Graphically:

13 Kernel density estimation: simple computation

14 The kernel functions Normal distribution, quartic, triangular

15 KDE: Stores surveyed

16 Thematic map: Stores surveyed

17 KDE: 24hr Convenience store

18 KDE: SME supermarket

19 KDE: Mama/Papa store

20 KDE: Grocery shop

21 KDE: Top 10%

22 The Ripley’s K function (Ripley, 1981) A method to estimate the second-order properties of a point process

23 The L function (Besag 1977) In practice, K function will be normalised to obtained a benchmark of zero L(r)>0 indicates that the observed distribution is geographically concentrated L(r)<0 implies dispersion L(r)=0 indicates complete spatial randomness (CRS)

24 Monte Carlo simulation test of CSR Perform m independent simulation of n events (i.e. 999) in the study region. For each simulated point pattern, estimate K(d) and use the maximum and minimum of these functions for the simulated patterns to define an upper and lower simulation envelope. If the estimated K(d) lies above the upper envelope or below the lower envelope, the estimated K(d) is statistically significant

25 L.hat: 24hr Convenience store

26 L.hat: SME supermarket

27 L.hat: Mama/Papa store

28 L.hat: Grocery shop

29 L.hat: Grocery shop

30 Question: Is the observed pattern of one set of event just a random subset of the overall pattern of a set of combined point patterns?

31 D function (Diggle & Chetwynd 1991) Assuming heterogeneity of the distribution The significant of D(r) can be testing by performing Monte Carlo simulation

32 D function: 24hr convenience store

33 D function: SME supermarket

34 D function: Mama/Papa Store

35 D function: Grocery shop

36 Point map: Top 10% vs all stores

37 D function: Top10%

38 Point map: Top 10% store with sales GT 9RM vs all store

39 D function: Top 10% store with sales GT 9RM

40 Bivariate point process Is the spatial distribution of top10% store independent of the distribution of office locations?

41 Bivariate K function The general formula: The significance of the estimated K.hat 12 can be testing using Monte Carlo simulation

42 K.hat 12 : Top10 vs office

43 SPA (Spatial Point Pattern Analysis) A collection of spatial point pattern analysis functions available within a GIS environment Tight (shared) coupling Data GIS R R Library COM Server

44 Sample code: splancs – Kernel density

45 Sample interface: splancs - Kernel Density Kernel density (kernel2d) K-function (Khat, KenvCsr, KenvLael and KenvTor) L-function (Lhat, LenvCsr, LenvTor) D-function K.hat 12

Useful Spatial Point Data Analysis Tools Spatstat: An R library for spatial statistics ( CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident Locations ( SaTScan™ : a free software that analyzes spatial, temporal and space-time data using the spatial, temporal, or space-time scan statistics. ( 46