Introduction of geospatial data visualization and geographically weighted reg
|
|
|
- Henry Johns
- 10 years ago
- Views:
Transcription
1 Introduction of geospatial data visualization and geographically weighted regression (GWR) Vanderbilt University August 16, 2012
2 Study Background Study Background Data Overview Algorithm (1) Information of breast cancer or population characteristic such as mortality rate, median household income, and ethnicity...etc around Nashville area or US.
3 Study Background Study Background Data Overview Algorithm (1) Information of breast cancer or population characteristic such as mortality rate, median household income, and ethnicity...etc around Nashville area or US. (2) Data are summarized in different levels. State County Zip Code
4 Study Background Study Background Data Overview Algorithm (1) Information of breast cancer or population characteristic such as mortality rate, median household income, and ethnicity...etc around Nashville area or US. (2) Data are summarized in different levels. State County Zip Code (3) The goal is generating several geographic maps with color representing information of interests.
5 Tennessee Study Background Data Overview Algorithm Zip Code Zip City County Female Mortality Rate per 100K Antioch Davidson Goodlettsville Davidson Hermitage Davidson Joelton Davidson Madison Davidson Old Hickory Davidson Whites Creek Davidson Nashville Davidson
6 Tasks Study Background Data Overview Algorithm Generate a color scale How to represent, for example mortality rate, in a map with color?
7 Study Background Data Overview Algorithm Tasks Generate a color scale How to represent, for example mortality rate, in a map with color? Draw a map Where to get information or R functions to plot the map?
8 Study Background Data Overview Algorithm Tasks Generate a color scale How to represent, for example mortality rate, in a map with color? Draw a map Where to get information or R functions to plot the map? Fill in the color Who is who?
9 Get Colors The idea is get a single color scale showing severity of mortality from light (less server) to dark (more server).
10 Get Colors The idea is get a single color scale showing severity of mortality from light (less server) to dark (more server). colorramppalette {grdevices} Return as a function
11 Get Colors The idea is get a single color scale showing severity of mortality from light (less server) to dark (more server). colorramppalette {grdevices} Return as a function Usage : color.scales <- colorramppalette(c( orange, red )) col.lists <- color.scales(8)
12 Get Colors The idea is get a single color scale showing severity of mortality from light (less server) to dark (more server). colorramppalette {grdevices} Return as a function Usage : color.scales <- colorramppalette(c( orange, red )) col.lists <- color.scales(8) col.lists #FFA500 #FF 8D00 #FF 7500 #FF 5E00 #FF 4600 #FF 2F 00 #FF 1700 #FF 0000
13 Get Colors The idea is get a single color scale showing severity of mortality from light (less server) to dark (more server). colorramppalette {grdevices} Return as a function Usage : color.scales <- colorramppalette(c( orange, red )) col.lists <- color.scales(8) col.lists #FFA500 #FF 8D00 #FF 7500 #FF 5E00 #FF 4600 #FF 2F 00 #FF 1700 #FF 0000 It s called hexadecimal color codes. There are also RGB color function in R available. rgb(228,26,28,max=255)
14 8 Levels Between Orange and Red
15 More Colors?!?! Can we get more than two colors and have the scales between all of them?
16 More Colors?!?! Can we get more than two colors and have the scales between all of them? Usage : color.scales <- colorramppalette(c( orange, red, green )) col.lists <- color.scales(21)
17 Zip Code Map Polygon Maps package doesn t have either zip code s boundary or maps of Alaska & Hawaii, so I can t use map function to get those plotted.
18 Zip Code Map Polygon Maps package doesn t have either zip code s boundary or maps of Alaska & Hawaii, so I can t use map function to get those plotted. More tasks need to be done.
19 Polygon Zip Code Map Maps package doesn t have either zip code s boundary or maps of Alaska & Hawaii, so I can t use map function to get those plotted. More tasks need to be done. Zip code boundary data (or Alaska & Hawaii boundary data)
20 Zip Code Map Polygon Maps package doesn t have either zip code s boundary or maps of Alaska & Hawaii, so I can t use map function to get those plotted. More tasks need to be done. Zip code boundary data (or Alaska & Hawaii boundary data) R function to draw the frame of each zip code area.
21 Zip Code Map Polygon Maps package doesn t have either zip code s boundary or maps of Alaska & Hawaii, so I can t use map function to get those plotted. More tasks need to be done. Zip code boundary data (or Alaska & Hawaii boundary data) R function to draw the frame of each zip code area. Same or different function to fill in the color.
22 Zip Code Boundaries Polygon Boundary Data.
23 Polygon Zip Code Boundaries Boundary Data. US Census
24 Polygon Zip Code Boundaries Boundary Data. US Census It s back!! called TIGER/Line Shapefiles
25 Polygon Zip Code Boundaries Boundary Data. US Census It s back!! called TIGER/Line Shapefiles
26 Polygon Zip Code Boundaries Boundary Data. US Census It s back!! called TIGER/Line Shapefiles
27 Maptools Package Polygon Need a way to read.shp file into R.
28 Polygon Maptools Package Need a way to read.shp file into R. readshapespatial{maptools}
29 Polygon Maptools Package Need a way to read.shp file into R. readshapespatial{maptools} TN.zip.map <- readshapespatial( zt47 d00.shp )
30 Polygon Maptools Package Need a way to read.shp file into R. readshapespatial{maptools} TN.zip.map <- readshapespatial( zt47 d00.shp ) str(tn.zip.map) Formal class SpatialPolygonsDataFrame [package sp"] with 5 slots..@ data : data.frame : 964 obs. of 8 variables:
31 Polygon Maptools Package Need a way to read.shp file into R. readshapespatial{maptools} TN.zip.map <- readshapespatial( zt47 d00.shp ) str(tn.zip.map) Formal class SpatialPolygonsDataFrame [package sp"] with 5 slots..@ data : data.frame : 964 obs. of 8 variables: and another 1277 more lines!!!
32 Stracture of.shp File Polygon Formal class SpatialPolygonsDataFrame [package sp"] with 5 slots..@ data : data.frame : 964 obs. of 8 variables:....$ AREA : num [1:964] 2.92e e e e e $ PERIMETER : num [1:964] $ ZT47 D00 : int [1:964] $ ZT47 D00 I: int [1:964] $ ZCTA : Factor w/ 634 levels 37010", 37012",..: $ NAME : Factor w/ 634 levels 37010", 37012",..: $ LSAD : Factor w/ 1 level Z5": $ LSAD TRANS: Factor w/ 1 level 5-Digit ZCTA": attr(*, data types")= chr [1:8] F" F" N" N".....@ polygons :List of $ :Formal class Polygons [package sp"] with 5 slots @ Polygons :List of $ :Formal class Polygon [package sp"] with 5 slots @ labpt : num [1:2] @ area : num @ hole : logi FALSE @ ringdir: int @ coords : num [1:169, 1:2] @ plotorder: int @ labpt : num [1:2] @ ID : chr "0" @ area : num
33 Decompose the Structure Polygon Take Hawaii as an example.
34 Polygon Decompose the Structure Take Hawaii as an example. Think it as a box with lots boxes inside.
35 Polygon Decompose the Structure Take Hawaii as an example. Think it as a box with lots boxes inside. One huge box : object itself.
36 Polygon Decompose the Structure Take Hawaii as an example. Think it as a box with lots boxes inside. One huge box : object itself. 5 middle size box :
37 Polygon Decompose the Structure Take Hawaii as an example. Think it as a box with lots boxes inside. One huge box : object itself. 5 middle size box : General information.
38 Decompose the Structure Polygon Take Hawaii as an example. Think it as a box with lots boxes inside. One huge box : object itself. 5 middle size box : General information. Main parts with numbers of small boxes inside.
39 Decompose the Structure Polygon Take Hawaii as an example. Think it as a box with lots boxes inside. One huge box : object itself. 5 middle size box : General information. Main parts with numbers of small boxes inside. Coordinates.
40 Decompose the Structure Polygon Take Hawaii as an example. Think it as a box with lots boxes inside. One huge box : object itself. 5 middle size box : General information. Main parts with numbers of small boxes inside. Coordinates. Center Coordinate.
41 Decompose the Structure Polygon Take Hawaii as an example. Think it as a box with lots boxes inside. One huge box : object itself. 5 middle size box : General information. Main parts with numbers of small boxes inside. Coordinates. Center Coordinate. Area, ID,...etc.
42 Decompose the Structure Polygon Take Hawaii as an example. Think it as a box with lots boxes inside. One huge box : object itself. 5 middle size box : General information. Main parts with numbers of small boxes inside. Coordinates. Center Coordinate. Area, ID,...etc. No zip code.
43 Decompose the Structure Polygon Take Hawaii as an example. Think it as a box with lots boxes inside. One huge box : object itself. 5 middle size box : General information. Main parts with numbers of small boxes inside. Coordinates. Center Coordinate. Area, ID,...etc. No zip code. The other 3 median boxes which is not so important.
44 Decompose the Structure Polygon Take Hawaii as an example. Think it as a box with lots boxes inside. One huge box : object itself. 5 middle size box : General information. Main parts with numbers of small boxes inside. Coordinates. Center Coordinate. Area, ID,...etc. No zip code. The other 3 median boxes which is not so important. Assumption : the zip code is saved with the order.
45 Polygon Plot the Shape & Colored Only by Coordinates Need a function to plot the shape by only giving a set of coordinates.
46 Polygon Plot the Shape & Colored Only by Coordinates Need a function to plot the shape by only giving a set of coordinates. If can handle the color as welll will be better.
47 Polygon Plot the Shape & Colored Only by Coordinates Need a function to plot the shape by only giving a set of coordinates. If can handle the color as welll will be better. polygon{graphics}
48 Polygon Plot the Shape & Colored Only by Coordinates Need a function to plot the shape by only giving a set of coordinates. If can handle the color as welll will be better. polygon{graphics} Useage : polygon(x, y = NULL, density = NULL, angle = 45, border = NULL, col = NA, lty = par( lty ),...)
49 Representing Information with Colors Zip Code Female Mortality Rate 20% Quantile per 100K
50 Representing Information with Colors Zip Code Female Mortality Rate 20% Quantile Corresponding per 100K Color #9999FF #4C 4CFF #6666FF #7F 7FFF #3333FF #3333FF #4C 4CFF
51 Two Parts of Data Zip code with corresponding color.
52 Two Parts of Data Zip code with corresponding color. Boundary coordinates for each zip code.
53 Two Parts of Data Zip code with corresponding color. Boundary coordinates for each zip code. Now we are ready to plot!!!!
54 GWR spgwr gwr.sel gwr PI s question : Does breast cancer mortality related to education, household income...etc.
55 spgwr gwr.sel gwr GWR PI s question : Does breast cancer mortality related to education, household income...etc. My first idea : Spearman s correlation.
56 GWR spgwr gwr.sel gwr PI s question : Does breast cancer mortality related to education, household income...etc. My first idea : Spearman s correlation. The problem are : What if we have more or fewer data points? Simple correlation can t deal with multiple covariates at the same time.
57 GWR spgwr gwr.sel gwr PI s question : Does breast cancer mortality related to education, household income...etc. My first idea : Spearman s correlation. The problem are : What if we have more or fewer data points? Simple correlation can t deal with multiple covariates at the same time. Brunsdon (1996), : A Method for Exploring Spatial Nonstationarity, Geographical analysis, Vol. 28, No. 4, P.281.
58 GWR spgwr gwr.sel gwr Wherever a house is located, for example, the marginal price increase associated with an additional bedroom is fixed.
59 spgwr gwr.sel gwr GWR Wherever a house is located, for example, the marginal price increase associated with an additional bedroom is fixed. It might be more reasonable to assume that rates of change are determined by local culture or local knowledge, rather than a global utility assumed for each commodity.
60 spgwr gwr.sel gwr GWR Wherever a house is located, for example, the marginal price increase associated with an additional bedroom is fixed. It might be more reasonable to assume that rates of change are determined by local culture or local knowledge, rather than a global utility assumed for each commodity. Variations in relationships over space, such as those described above, are referred to as spatial nonstationarity.
61 spgwr gwr.sel gwr GWR Wherever a house is located, for example, the marginal price increase associated with an additional bedroom is fixed. It might be more reasonable to assume that rates of change are determined by local culture or local knowledge, rather than a global utility assumed for each commodity. Variations in relationships over space, such as those described above, are referred to as spatial nonstationarity. Simple linear model is not not appropriate, but widely used in early 90s for the data with geographic information.
62 Simpson s Paradox spgwr gwr.sel gwr
63 GWR spgwr gwr.sel gwr y = α + βx + ɛ
64 spgwr gwr.sel gwr GWR y = α + βx + ɛ y i = α i + β i X + ɛ i Where i indicates that there is a set of coefficients estimated for every observation in the data set.
65 spgwr gwr.sel gwr GWR y = α + βx + ɛ y i = α i + β i X + ɛ i Where i indicates that there is a set of coefficients estimated for every observation in the data set. Key difference : We estimate a set of regression coefficients for each observation (i).
66 spgwr gwr.sel gwr GWR y = α + βx + ɛ y i = α i + β i X + ɛ i Where i indicates that there is a set of coefficients estimated for every observation in the data set. Key difference : We estimate a set of regression coefficients for each observation (i). To do so we weight near observations more heavily than more distant ones.
67 spgwr gwr.sel gwr GWR y = α + βx + ɛ y i = α i + β i X + ɛ i Where i indicates that there is a set of coefficients estimated for every observation in the data set. Key difference : We estimate a set of regression coefficients for each observation (i). To do so we weight near observations more heavily than more distant ones. First law of geography: everything is related with everything else, but closer things are more related.
68 GWR spgwr gwr.sel gwr Decide how you are going to weight your nearby locations. Fixed bandwidth Variable bandwidth User-defined bandwidth
69 GWR spgwr gwr.sel gwr Decide how you are going to weight your nearby locations. Fixed bandwidth Variable bandwidth User-defined bandwidth Bandwidth specifies shape of weights curve.
70 GWR spgwr gwr.sel gwr Decide how you are going to weight your nearby locations. Fixed bandwidth Variable bandwidth User-defined bandwidth Bandwidth specifies shape of weights curve. Whether we will define our bandwidth based on distance (fixed) or number of neighbors (adaptive).
71 Fixed Bandwidth spgwr gwr.sel gwr
72 Adaptive Bandwidth spgwr gwr.sel gwr
73 GWR spgwr gwr.sel gwr R package : spgwr (dependent packages: sp, maptools)
74 spgwr gwr.sel gwr GWR R package : spgwr (dependent packages: sp, maptools) data(columbus)
75 GWR spgwr gwr.sel gwr R package : spgwr (dependent packages: sp, maptools) data(columbus) 5 variables crime - recorded crime per inhabitant income - average income values housing - average housing costs x - latitude y - longitude
76 GWR spgwr gwr.sel gwr R package : spgwr (dependent packages: sp, maptools) data(columbus) 5 variables crime - recorded crime per inhabitant income - average income values housing - average housing costs x - latitude y - longitude Relationship between crime and income & housing.
77 GWR spgwr gwr.sel gwr Need to find a bandwidth for a given geographically weighted regression by optimizing a selected function.
78 GWR spgwr gwr.sel gwr Need to find a bandwidth for a given geographically weighted regression by optimizing a selected function. gwr.sel(formula, data, coords, adapt=false,...)
79 spgwr gwr.sel gwr GWR Need to find a bandwidth for a given geographically weighted regression by optimizing a selected function. gwr.sel(formula, data, coords, adapt=false,...) formula, data, coords.
80 GWR spgwr gwr.sel gwr Need to find a bandwidth for a given geographically weighted regression by optimizing a selected function. gwr.sel(formula, data, coords, adapt=false,...) formula, data, coords. adapt - either TRUE: find the proportion between 0 and 1 of observations to include in weighting scheme (k-nearest neighbors), or FALSE find global bandwidth.
81 GWR spgwr gwr.sel gwr Need to find a bandwidth for a given geographically weighted regression by optimizing a selected function. gwr.sel(formula, data, coords, adapt=false,...) formula, data, coords. adapt - either TRUE: find the proportion between 0 and 1 of observations to include in weighting scheme (k-nearest neighbors), or FALSE find global bandwidth. Returns the cross-validation bandwidth.
82 GWR spgwr gwr.sel gwr Fit GWR model.
83 GWR spgwr gwr.sel gwr Fit GWR model. gwr(formula, data, coords, bandwidth,...)
84 GWR spgwr gwr.sel gwr Fit GWR model. gwr(formula, data, coords, bandwidth,...) formula, data, coords.
85 GWR spgwr gwr.sel gwr Fit GWR model. gwr(formula, data, coords, bandwidth,...) formula, data, coords. bandwidth - bandwidth used in the weighting function.
86 GWR spgwr gwr.sel gwr Fit GWR model. gwr(formula, data, coords, bandwidth,...) formula, data, coords. bandwidth - bandwidth used in the weighting function. An example.
87 QR & GWQR Quantile regression is also a method that could be applied to spatial data.
88 QR & GWQR Quantile regression is also a method that could be applied to spatial data. Another regression method called Geographically Weighted Quantile Regression (GWQR), as well as the corresponding R package are under development.
89 QR & GWQR Quantile regression is also a method that could be applied to spatial data. Another regression method called Geographically Weighted Quantile Regression (GWQR), as well as the corresponding R package are under development. By integrating the features of QR into GWR, the GWQR permits researchers to investigate the spatial non-stationary across both space and the whole distribution of a dependent variable.
90 We can find lots of boundaries on line. If it s a.shp file, we can easily read it into R and use it now.
91 We can find lots of boundaries on line. If it s a.shp file, we can easily read it into R and use it now. However, the biggest assumption (but it should always followed) is, those blocks are following certain order.
92 We can find lots of boundaries on line. If it s a.shp file, we can easily read it into R and use it now. However, the biggest assumption (but it should always followed) is, those blocks are following certain order. There are also lots of color platter available on line.
93 We can find lots of boundaries on line. If it s a.shp file, we can easily read it into R and use it now. However, the biggest assumption (but it should always followed) is, those blocks are following certain order. There are also lots of color platter available on line. GWR, QR, or GWQR should be applied for spatial data.
94 We can find lots of boundaries on line. If it s a.shp file, we can easily read it into R and use it now. However, the biggest assumption (but it should always followed) is, those blocks are following certain order. There are also lots of color platter available on line. GWR, QR, or GWQR should be applied for spatial data. : The Analysis of Spatially Varying Relationships. WILEY (2002). $120!!!
95 We can find lots of boundaries on line. If it s a.shp file, we can easily read it into R and use it now. However, the biggest assumption (but it should always followed) is, those blocks are following certain order. There are also lots of color platter available on line. GWR, QR, or GWQR should be applied for spatial data. : The Analysis of Spatially Varying Relationships. WILEY (2002). $120!!! Thank you!! & Questions??
Geographically Weighted Regression
Geographically Weighted Regression CSDE Statistics Workshop Christopher S. Fowler PhD. February 1 st 2011 Significant portions of this workshop were culled from presentations prepared by Fotheringham,
EXPLORING SPATIAL PATTERNS IN YOUR DATA
EXPLORING SPATIAL PATTERNS IN YOUR DATA OBJECTIVES Learn how to examine your data using the Geostatistical Analysis tools in ArcMap. Learn how to use descriptive statistics in ArcMap and Geoda to analyze
Package GWmodel. February 19, 2015
Version 1.2-5 Date 2015-02-01 Title Geographically-Weighted Models Package GWmodel February 19, 2015 Depends R (>= 3.0.0),maptools (>= 0.5-2), robustbase,sp Suggests mvoutlier, RColorBrewer, gstat In GWmodel,
Location matters. 3 techniques to incorporate geo-spatial effects in one's predictive model
Location matters. 3 techniques to incorporate geo-spatial effects in one's predictive model Xavier Conort [email protected] Motivation Location matters! Observed value at one location is
Data Visualization Techniques and Practices Introduction to GIS Technology
Data Visualization Techniques and Practices Introduction to GIS Technology Michael Greene Advanced Analytics & Modeling, Deloitte Consulting LLP March 16 th, 2010 Antitrust Notice The Casualty Actuarial
Geostatistics Exploratory Analysis
Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Master of Science in Geospatial Technologies Geostatistics Exploratory Analysis Carlos Alberto Felgueiras [email protected]
ZIP PLUS 4 Centroid/Census to Block Group Geographic Conversion File AND EASI ZIP4 Updated Demographic Files
ZIP PLUS 4 Centroid/Census to Block Group Geographic Conversion File AND EASI ZIP4 Updated Demographic Files Introduction Easy Analytic Software, Inc. (EASI) is a New York-based independent developer and
Introduction to Exploratory Data Analysis
Introduction to Exploratory Data Analysis A SpaceStat Software Tutorial Copyright 2013, BioMedware, Inc. (www.biomedware.com). All rights reserved. SpaceStat and BioMedware are trademarks of BioMedware,
Cookbook 23 September 2013 GIS Analysis Part 1 - A GIS is NOT a Map!
Cookbook 23 September 2013 GIS Analysis Part 1 - A GIS is NOT a Map! Overview 1. A GIS is NOT a Map! 2. How does a GIS handle its data? Data Formats! GARP 0344 (Fall 2013) Page 1 Dr. Carsten Braun 1) A
Thematic Map Types. Information Visualization MOOC. Unit 3 Where : Geospatial Data. Overview and Terminology
Thematic Map Types Classification according to content: Physio geographical maps: geological, geophysical, meteorological, soils, vegetation Socio economic maps: historical, political, population, economy,
Spatial Analysis with GeoDa Spatial Autocorrelation
Spatial Analysis with GeoDa Spatial Autocorrelation 1. Background GeoDa is a trademark of Luc Anselin. GeoDa is a collection of software tools designed for exploratory spatial data analysis (ESDA) based
Descriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
How to Download Census Data from American Factfinder and Display it in ArcMap
How to Download Census Data from American Factfinder and Display it in ArcMap Factfinder provides census and ACS (American Community Survey) data that can be downloaded in a tabular format and joined with
Obesity in America: A Growing Trend
Obesity in America: A Growing Trend David Todd P e n n s y l v a n i a S t a t e U n i v e r s i t y Utilizing Geographic Information Systems (GIS) to explore obesity in America, this study aims to determine
Spatial Data Analysis Using GeoDa. Workshop Goals
Spatial Data Analysis Using GeoDa 9 Jan 2014 Frank Witmer Computing and Research Services Institute of Behavioral Science Workshop Goals Enable participants to find and retrieve geographic data pertinent
Simple Predictive Analytics Curtis Seare
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.
Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C
PREDICTIVE ANALYTICS vs HOT SPOTTING
PREDICTIVE ANALYTICS vs HOT SPOTTING A STUDY OF CRIME PREVENTION ACCURACY AND EFFICIENCY 2014 EXECUTIVE SUMMARY For the last 20 years, Hot Spots have become law enforcement s predominant tool for crime
GIS III: GIS Analysis Module 1a: Network Analysis Tools
*** Files needed for exercise: MI_ACS09_cty.shp; USBusiness09_MI.dbf; MI_ACS09_trt.shp; and streets.sdc (provided by Street Map USA) Goals: To learn how to use the Network Analyst tools to perform network-based
Quick and Easy Web Maps with Google Fusion Tables. SCO Technical Paper
Quick and Easy Web Maps with Google Fusion Tables SCO Technical Paper Version History Version Date Notes Author/Contact 1.0 July, 2011 Initial document created. Howard Veregin 1.1 Dec., 2011 Updated to
IST 557 Final Project
George Slota DataMaster 5000 IST 557 Final Project Abstract As part of a competition hosted by the website Kaggle, a statistical model was developed for prediction of United States Census 2010 mailing
INTRODUCTION TO SURVEY DATA ANALYSIS THROUGH STATISTICAL PACKAGES
INTRODUCTION TO SURVEY DATA ANALYSIS THROUGH STATISTICAL PACKAGES Hukum Chandra Indian Agricultural Statistics Research Institute, New Delhi-110012 1. INTRODUCTION A sample survey is a process for collecting
GIS III: GIS Analysis Module 2a: Introduction to Network Analyst
*** Files needed for exercise: nc_cty.shp; target_stores_infousa.dbf; streets.sdc (provided by street map usa); NC_tracts_2000sf1.shp Goals: To learn how to use the Network analyst tools to perform network
GTFS: GENERAL TRANSIT FEED SPECIFICATION
GTFS: GENERAL TRANSIT FEED SPECIFICATION What is GTFS? A standard in representing schedule and route information Public Transportation Schedules GIS Data Route, trip, and stop information in one zipfile
What s New in IBM SPSS Statistics 20
Myrto Setzi Associate Sales Engineer What s New in IBM SPSS Statistics 20 Editable Text Editable Text Editable Text Business Analytics software Agenda Themes of the release Demonstration Questions 2 Themes
mapstats: An R Package for Geographic Visualization of Survey Data
mapstats: An R Package for Geographic Visualization of Survey Data Samuel Ackerman Department of Statistics, Temple University [email protected] June 14, 2015 amuel Ackerman (Temple University Department
Finding GIS Data and Preparing it for Use
Finding_Data_Tutorial.Doc Page 1 of 19 Getting Ready for the Tutorial Sign Up for the GIS-L Listserv Finding GIS Data and Preparing it for Use The Yale University GIS-L Listserv is an internal University
Statistics 151 Practice Midterm 1 Mike Kowalski
Statistics 151 Practice Midterm 1 Mike Kowalski Statistics 151 Practice Midterm 1 Multiple Choice (50 minutes) Instructions: 1. This is a closed book exam. 2. You may use the STAT 151 formula sheets and
CHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression
Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the
Visualizing Data: Scalable Interactivity
Visualizing Data: Scalable Interactivity The best data visualizations illustrate hidden information and structure contained in a data set. As access to large data sets has grown, so has the need for interactive
Benchmarking Residential Energy Use
Benchmarking Residential Energy Use Michael MacDonald, Oak Ridge National Laboratory Sherry Livengood, Oak Ridge National Laboratory ABSTRACT Interest in rating the real-life energy performance of buildings
Using kernel methods to visualise crime data
Submission for the 2013 IAOS Prize for Young Statisticians Using kernel methods to visualise crime data Dr. Kieran Martin and Dr. Martin Ralphs [email protected] [email protected] Office
Risk Management : Using SAS to Model Portfolio Drawdown, Recovery, and Value at Risk Haftan Eckholdt, DayTrends, Brooklyn, New York
Paper 199-29 Risk Management : Using SAS to Model Portfolio Drawdown, Recovery, and Value at Risk Haftan Eckholdt, DayTrends, Brooklyn, New York ABSTRACT Portfolio risk management is an art and a science
Basic Statistics and Data Analysis for Health Researchers from Foreign Countries
Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma [email protected] The Research Unit for General Practice in Copenhagen Dias 1 Content Quantifying association
Classes and Methods for Spatial Data: the sp Package
Classes and Methods for Spatial Data: the sp Package Edzer Pebesma Roger S. Bivand Feb 2005 Contents 1 Introduction 2 2 Spatial data classes 2 3 Manipulating spatial objects 3 3.1 Standard methods..........................
Tutorial 3: Working with Tables Joining Multiple Databases in ArcGIS
Tutorial 3: Working with Tables Joining Multiple Databases in ArcGIS This tutorial will introduce you to the following concepts: Identifying Attribute Data Sources Converting Tabular Data into GIS Databases
Visualization of Climate Data in R. By: Mehrdad Nezamdoost [email protected] April 2013
Visualization of Climate Data in R By: Mehrdad Nezamdoost [email protected] April 2013 Content Why visualization is important? How climate data store? Why R? Packages which used in R Some Result
Introduction to nonparametric regression: Least squares vs. Nearest neighbors
Introduction to nonparametric regression: Least squares vs. Nearest neighbors Patrick Breheny October 30 Patrick Breheny STA 621: Nonparametric Statistics 1/16 Introduction For the remainder of the course,
Week 5: Multiple Linear Regression
BUS41100 Applied Regression Analysis Week 5: Multiple Linear Regression Parameter estimation and inference, forecasting, diagnostics, dummy variables Robert B. Gramacy The University of Chicago Booth School
ArcGIS online Introduction... 2. Module 1: How to create a basic map on ArcGIS online... 3. Creating a public account with ArcGIS online...
Table of Contents ArcGIS online Introduction... 2 Module 1: How to create a basic map on ArcGIS online... 3 Creating a public account with ArcGIS online... 3 Opening a Map, Adding a Basemap and then Saving
Exploratory Data Analysis
Exploratory Data Analysis Paul Cohen ISTA 370 Spring, 2012 Paul Cohen ISTA 370 () Exploratory Data Analysis Spring, 2012 1 / 46 Outline Data, revisited The purpose of exploratory data analysis Learning
PREDICTIVE ANALYTICS VS. HOTSPOTTING
PREDICTIVE ANALYTICS VS. HOTSPOTTING A STUDY OF CRIME PREVENTION ACCURACY AND EFFICIENCY EXECUTIVE SUMMARY For the last 20 years, Hot Spots have become law enforcement s predominant tool for crime analysis.
Diagrams and Graphs of Statistical Data
Diagrams and Graphs of Statistical Data One of the most effective and interesting alternative way in which a statistical data may be presented is through diagrams and graphs. There are several ways in
STATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
Using Geostatistical Tools for Mapping Traffic- Related Air Pollution in Urban Areas
International Environmental Modelling and Software Society (iemss) 7th Intl. Congress on Env. Modelling and Software, San Diego, CA, USA, Daniel P. Ames, Nigel W.T. Quinn and Andrea E. Rizzoli (Eds.) http://www.iemss.org/society/index.php/iemss-2014-proceedings
Racial and Ethnic Diversity in Anaheim
Racial and Ethnic Diversity in Anaheim Anaheim s racial and ethnic demographics have changed dramatically in the last thirty years. Asian/Pacific Islander 4.1% Other 1.2% Hispanic/ Latino 1.1% Black/African
Solutions to Homework 6 Statistics 302 Professor Larget
s to Homework 6 Statistics 302 Professor Larget Textbook Exercises 5.29 (Graded for Completeness) What Proportion Have College Degrees? According to the US Census Bureau, about 27.5% of US adults over
COMMON CORE STATE STANDARDS FOR
COMMON CORE STATE STANDARDS FOR Mathematics (CCSSM) High School Statistics and Probability Mathematics High School Statistics and Probability Decisions or predictions are often based on data numbers in
An Interactive Tool for Residual Diagnostics for Fitting Spatial Dependencies (with Implementation in R)
DSC 2003 Working Papers (Draft Versions) http://www.ci.tuwien.ac.at/conferences/dsc-2003/ An Interactive Tool for Residual Diagnostics for Fitting Spatial Dependencies (with Implementation in R) Ernst
Geographically Weighted Regression
Geographically Weighted Regression A Tutorial on using GWR in ArcGIS 9.3 Martin Charlton A Stewart Fotheringham National Centre for Geocomputation National University of Ireland Maynooth Maynooth, County
Package tagcloud. R topics documented: July 3, 2015
Package tagcloud July 3, 2015 Type Package Title Tag Clouds Version 0.6 Date 2015-07-02 Author January Weiner Maintainer January Weiner Description Generating Tag and Word Clouds.
Regression III: Advanced Methods
Lecture 4: Transformations Regression III: Advanced Methods William G. Jacoby Michigan State University Goals of the lecture The Ladder of Roots and Powers Changing the shape of distributions Transforming
Making a Choropleth map with Google Fusion Tables
Making a Choropleth map with Google Fusion Tables Choropleth map is a thematic map based on predefined aerial units. Its areas are coloured or shaded to show the measurement of the statistical variables
R Graphics Cookbook. Chang O'REILLY. Winston. Tokyo. Beijing Cambridge. Farnham Koln Sebastopol
R Graphics Cookbook Winston Chang Beijing Cambridge Farnham Koln Sebastopol O'REILLY Tokyo Table of Contents Preface ix 1. R Basics 1 1.1. Installing a Package 1 1.2. Loading a Package 2 1.3. Loading a
CSU, Fresno - Institutional Research, Assessment and Planning - Dmitri Rogulkin
My presentation is about data visualization. How to use visual graphs and charts in order to explore data, discover meaning and report findings. The goal is to show that visual displays can be very effective
Normality Testing in Excel
Normality Testing in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. [email protected]
Using Excel for Statistical Analysis
Using Excel for Statistical Analysis You don t have to have a fancy pants statistics package to do many statistical functions. Excel can perform several statistical tests and analyses. First, make sure
Minimizing Computing Core Costs in Cloud Infrastructures that Host Location-Based Advertising Services
, 23-25 October, 2013, San Francisco, USA Minimizing Computing Core Costs in Cloud Infrastructures that Host Location-Based Advertising Services Vikram K. Ramanna, Christopher P. Paolini, Mahasweta Sarkar,
# For usage of the functions, it is necessary to install the "survival" and the "penalized" package.
###################################################################### ### R-script for the manuscript ### ### ### ### Survival models with preclustered ### ### gene groups as covariates ### ### ### ###
Hydrogeological Data Visualization
Conference of Junior Researchers in Civil Engineering 209 Hydrogeological Data Visualization Boglárka Sárközi BME Department of Photogrammetry and Geoinformatics, e-mail: [email protected] Abstract
Current Standard: Mathematical Concepts and Applications Shape, Space, and Measurement- Primary
Shape, Space, and Measurement- Primary A student shall apply concepts of shape, space, and measurement to solve problems involving two- and three-dimensional shapes by demonstrating an understanding of:
Statistical Models in R
Statistical Models in R Some Examples Steven Buechler Department of Mathematics 276B Hurley Hall; 1-6233 Fall, 2007 Outline Statistical Models Linear Models in R Regression Regression analysis is the appropriate
Branding Guidelines POWERHANDZ. Company: 1.0 Introduction 2.0 The Logo Design 2.1 The Logo Usage 3.0 Color Scheme 4.0 Typography 5.
Branding Guidelines Company: Contents: Date: POWERHANDZ 1.0 Introduction 2.0 The Logo Design 2.1 The Logo Usage 3.0 Color Scheme 4.0 Typography 5.0 Contact Details June 2014 1.0 Introduction Overview The
Cancer Mapper: Michigan s Interactive Mapping Tool
Cancer Mapper: Michigan s Interactive Mapping Tool Mike Carr, M.A. Michigan WISEWOMAN, BCCCP 8/28/2014 These slides are the property of the presenter. Do not reproduce without express written consent.
MIXED MODEL ANALYSIS USING R
Research Methods Group MIXED MODEL ANALYSIS USING R Using Case Study 4 from the BIOMETRICS & RESEARCH METHODS TEACHING RESOURCE BY Stephen Mbunzi & Sonal Nagda www.ilri.org/rmg www.worldagroforestrycentre.org/rmg
SUMAN DUVVURU STAT 567 PROJECT REPORT
SUMAN DUVVURU STAT 567 PROJECT REPORT SURVIVAL ANALYSIS OF HEROIN ADDICTS Background and introduction: Current illicit drug use among teens is continuing to increase in many countries around the world.
Package neuralnet. February 20, 2015
Type Package Title Training of neural networks Version 1.32 Date 2012-09-19 Package neuralnet February 20, 2015 Author Stefan Fritsch, Frauke Guenther , following earlier work
Copyright 2010-2012 PEOPLECERT Int. Ltd and IASSC
PEOPLECERT - Personnel Certification Body 3 Korai st., 105 64 Athens, Greece, Tel.: +30 210 372 9100, Fax: +30 210 372 9101, e-mail: [email protected], www.peoplecert.org Copyright 2010-2012 PEOPLECERT
Tutorial 3: Graphics and Exploratory Data Analysis in R Jason Pienaar and Tom Miller
Tutorial 3: Graphics and Exploratory Data Analysis in R Jason Pienaar and Tom Miller Getting to know the data An important first step before performing any kind of statistical analysis is to familiarize
What is GIS? Geographic Information Systems. Introduction to ArcGIS. GIS Maps Contain Layers. What Can You Do With GIS? Layers Can Contain Features
What is GIS? Geographic Information Systems Introduction to ArcGIS A database system in which the organizing principle is explicitly SPATIAL For CPSC 178 Visualization: Data, Pixels, and Ideas. What Can
Housing Assignment and the Limitality of Houses
Housing assignment with restrictions: theory and and evidence from Stanford campus Tim Landvoigt UT Austin Monika Piazzesi Stanford & NBER December 2013 Martin Schneider Stanford & NBER Emailaddresses:[email protected],
Market Analysis Best Practices: Using Submarkets for Search & Analysis. Digital Map Products Spatial Technology Made Easy www.digmap.
Market Analysis Best Practices: Using Submarkets for Search & Analysis Digital Map Products Market Analysis Best Practices: Using Submarkets for Search & Analysis As builders and developers try to compete
Biostatistics: Types of Data Analysis
Biostatistics: Types of Data Analysis Theresa A Scott, MS Vanderbilt University Department of Biostatistics [email protected] http://biostat.mc.vanderbilt.edu/theresascott Theresa A Scott, MS
Using self-organizing maps for visualization and interpretation of cytometry data
1 Using self-organizing maps for visualization and interpretation of cytometry data Sofie Van Gassen, Britt Callebaut and Yvan Saeys Ghent University September, 2014 Abstract The FlowSOM package provides
GAP CLOSING. 2D Measurement. Intermediate / Senior Student Book
GAP CLOSING 2D Measurement Intermediate / Senior Student Book 2-D Measurement Diagnostic...3 Areas of Parallelograms, Triangles, and Trapezoids...6 Areas of Composite Shapes...14 Circumferences and Areas
3. Data Analysis, Statistics, and Probability
3. Data Analysis, Statistics, and Probability Data and probability sense provides students with tools to understand information and uncertainty. Students ask questions and gather and use data to answer
VOLATILITY AND DEVIATION OF DISTRIBUTED SOLAR
VOLATILITY AND DEVIATION OF DISTRIBUTED SOLAR Andrew Goldstein Yale University 68 High Street New Haven, CT 06511 [email protected] Alexander Thornton Shawn Kerrigan Locus Energy 657 Mission St.
Study Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
Robert Collins CSE598G. More on Mean-shift. R.Collins, CSE, PSU CSE598G Spring 2006
More on Mean-shift R.Collins, CSE, PSU Spring 2006 Recall: Kernel Density Estimation Given a set of data samples x i ; i=1...n Convolve with a kernel function H to generate a smooth function f(x) Equivalent
Representing Geography
3 Representing Geography OVERVIEW This chapter introduces the concept of representation, or the construction of a digital model of some aspect of the Earth s surface. The geographic world is extremely
Statistical Models in R
Statistical Models in R Some Examples Steven Buechler Department of Mathematics 276B Hurley Hall; 1-6233 Fall, 2007 Outline Statistical Models Structure of models in R Model Assessment (Part IA) Anova
Forecasting Geographic Data Michael Leonard and Renee Samy, SAS Institute Inc. Cary, NC, USA
Forecasting Geographic Data Michael Leonard and Renee Samy, SAS Institute Inc. Cary, NC, USA Abstract Virtually all businesses collect and use data that are associated with geographic locations, whether
> plot(exp.btgpllm, main = "treed GP LLM,", proj = c(1)) > plot(exp.btgpllm, main = "treed GP LLM,", proj = c(2)) quantile diff (error)
> plot(exp.btgpllm, main = "treed GP LLM,", proj = c(1)) > plot(exp.btgpllm, main = "treed GP LLM,", proj = c(2)) 0.4 0.2 0.0 0.2 0.4 treed GP LLM, mean treed GP LLM, 0.00 0.05 0.10 0.15 0.20 x1 x1 0.4
Quantile Regression under misspecification, with an application to the U.S. wage structure
Quantile Regression under misspecification, with an application to the U.S. wage structure Angrist, Chernozhukov and Fernandez-Val Reading Group Econometrics November 2, 2010 Intro: initial problem The
A Cardinal That Does Not Look That Red: Analysis of a Political Polarization Trend in the St. Louis Area
14 Missouri Policy Journal Number 3 (Summer 2015) A Cardinal That Does Not Look That Red: Analysis of a Political Polarization Trend in the St. Louis Area Clémence Nogret-Pradier Lindenwood University
1.5.3 Project 3: Traffic Monitoring
1.5.3 Project 3: Traffic Monitoring This project aims to provide helpful information about traffic in a given geographic area based on the history of traffic patterns, current weather, and time of the
A Color Placement Support System for Visualization Designs Based on Subjective Color Balance
A Color Placement Support System for Visualization Designs Based on Subjective Color Balance Eric Cooper and Katsuari Kamei College of Information Science and Engineering Ritsumeikan University Abstract:
An Introduction to Point Pattern Analysis using CrimeStat
Introduction An Introduction to Point Pattern Analysis using CrimeStat Luc Anselin Spatial Analysis Laboratory Department of Agricultural and Consumer Economics University of Illinois, Urbana-Champaign
A Review of Missing Data Treatment Methods
A Review of Missing Data Treatment Methods Liu Peng, Lei Lei Department of Information Systems, Shanghai University of Finance and Economics, Shanghai, 200433, P.R. China ABSTRACT Missing data is a common
EXAM #1 (Example) Instructor: Ela Jackiewicz. Relax and good luck!
STP 231 EXAM #1 (Example) Instructor: Ela Jackiewicz Honor Statement: I have neither given nor received information regarding this exam, and I will not do so until all exams have been graded and returned.