Embed Size (px)
Citation preview
SpaceTimeAnalysis ofRegionalSystem
Quick Start GuideVersion 0.7.22004 July 4
Sergio J. ReyMark V. Janikas
Copyright c© 2004 Sergio J. Rey.Printing history:
July 2004: First edition.
Department of GeographySan Diego State UniversitySan Diego, CA 92182
Permission is granted to copy, distribute, and/or modify this document underthe terms of the GNU Free Documentation License, Version 1.1 or any later versionpublished by the Free Software Foundation. The GNU Free Documentation Licenseis available from www.gnu.org or by writing to the Free Software Foundation, Inc.,59 Temple Place, Suite 330, Boston, MA 02111-1307, USA.
The original form of this book is LATEX source code. Compiling this LATEXsource has the effect of generating a device-independent representation of a text-book, which can be converted to other formats and printed.
The LATEX source for this book is available from the author at [email protected]
Rey, Sergio J and Mark V. JanikasSTARS: Space-Time Analysis of Regional Systems
1st ed.p. cm.Includes index (RSN).ISBNLCCN
1. Dynamic Geographic Data Analysis 2. Visualization 3. Open Source
Contents
1 Introduction to STARS 11.1 What is STARS? . . . . . . . . . . . . . . . . . . . . . . . . 11.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 What is in this Guide and Where is the Rest of It? . . . . . 2
2 A Quick Tour of STARS 52.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Starting STARS . . . . . . . . . . . . . . . . . . . . . . . . . 52.3 STARS Menus . . . . . . . . . . . . . . . . . . . . . . . . . 62.4 Opening a Project . . . . . . . . . . . . . . . . . . . . . . . 72.5 Example Analysis . . . . . . . . . . . . . . . . . . . . . . . . 72.6 Quitting STARS . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Creating STARS Projects 153.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2 California Tutorial . . . . . . . . . . . . . . . . . . . . . . . 15
3.2.1 Selecting and Naming Your Project . . . . . . . . . . 153.2.2 Creating Time-Series Signatures . . . . . . . . . . . . 153.2.3 Reading in the GIS Shapefiles . . . . . . . . . . . . . . 163.2.4 Declaring Name and ID Fields . . . . . . . . . . . . . 163.2.5 Converting ArcView Variables to STARS Variables . . 173.2.6 Importing External Data . . . . . . . . . . . . . . . . 18
Order Matters . . . . . . . . . . . . . . . . . . . . . . 18Understanding Cross-Sectional and Panel Variables
Data Files . . . . . . . . . . . . . . . . . . . 18Understanding Time-Series Variables Data Files . . . 20Importing External Data . . . . . . . . . . . . . . . . 21
3.2.7 Plotting Your Project . . . . . . . . . . . . . . . . . . 21
iv Contents
3.2.8 Finishing Your Project . . . . . . . . . . . . . . . . . . 21
4 Getting Involved with STARS 234.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.2 End User Contributions . . . . . . . . . . . . . . . . . . . . 234.3 Resources for Developers . . . . . . . . . . . . . . . . . . . . 24
List of Figures
2.1 Starting STARS Under Mac OS X From a Terminal . . . . . 62.2 Open Project Dialog . . . . . . . . . . . . . . . . . . . . . . . 82.3 Project Summary . . . . . . . . . . . . . . . . . . . . . . . . . 92.4 ESDA Menu for Global Moran Analysis . . . . . . . . . . . . 102.5 Dialog for Global Moran Analysis . . . . . . . . . . . . . . . . 112.6 Views for Global Moran Analysis . . . . . . . . . . . . . . . . 122.7 Output for Global Moran Analysis . . . . . . . . . . . . . . . 13
List of Tables
2.1 STARS Menu System . . . . . . . . . . . . . . . . . . . . . . 7
Chapter 1
Introduction to STARS
1.1 What is STARS?Space-Time Analysis of Regional Systems (STARS) is an open source pack-age designed for the analysis of areal data measured over time. STARSbrings together a number of recently developed methods of space-time anal-ysis into a user-friendly graphical environment offering an array of dynami-cally linked graphical views. It is intended to be used as an exploratory dataanalysis tool. STARS can also be used from the command line to supportmore flexible and specialized types of analyses by advanced users. As such,STARS should appeal to a wide array of users.
1.2 HistoryLike many open source projects,1 STARS began as an attempt to scratchan itch. The itch involved the study of regional income inequality andconvergence in which the focus is on how the gap in levels of economic wellbeing between different regional economies may, or may not, close over time(Rey and Montouri, 1999). At the time mainstream time-series econometricsoffered a wealth of advanced methods for the analysis of the dynamics of aneconomy’s growth path. These methods, however, said precious little aboutthe geographic dimensions of the data. On the other hand, the literaturesin spatial statistics, spatial econometrics and geographic information science(GISc) offered a host of techniques to analyze the spatial dimensions of thedata used in convergence studies. The treatment of the dynamic aspects ofthe data, however, is often highly cumbersome with these latter tools.
1For an overview of recent research on open source approaches towards exploratoryspatial data analysis in the social sciences see Anselin and Rey (2002).
2 Introduction to STARS
Of course one could employ the two sets of techniques (time series andspatial) in complementary fashion in studying the convergence question.Such a strategy has two main drawbacks. First, it requires the use of multiplesoftware packages which can prove to slow the exploratory process. Even ifthis issue could be overcome, the focus on either the spatial or the temporaldimensions individually does not allow for simultaneous consideration of bothdimensions.
STARS was born out of a need to address these issues. Although STARSinitial focus was the study of regional income convergence, the methods thatit contains can be applied to many different types of phenomena which havedata recorded for areal units2 over regular time periods.
1.3 DesignSTARS has been implemented using the Python3 scripting language. Itconsists of a series of Python modules which can be used as stand alonecomponents or together in an interactive user interface. The use of Pythonoffers a number of advantages to the development of STARS:
• Highly portable/cross-platform
• Object orientation
• Low entry costs for new developers
These advantages mesh very well with the long term goals for STARS.The development team is striving to make STARS easy to install, easy touse, and easy to enhance and expand.
1.4 What is in this Guide and Where is the Restof It?
This short guide serves as the starting point for using STARS and is orga-nized as follows. The next chapter contains a short tutorial on STARS.4
This is followed by a chapter dedicated to the creation of STARS projects.
2The current version of STARS is designed for areal rather than point data. Arealunits are discrete spatial zones such as counties, census tracts, states or countries. Futureversions may include functionality for point data.
3http://www.python.org4See Rey and Janikas (2004) for a further discussion of STARS methods of analysis
and graphical interactivity capabilities.
1.4 What is in this Guide and Where is the Rest of It? 3
Lastly, some information is provided on how to get involved with the STARSProject.
This guide is a work in progress, and will continue to grow with STARS,eventually becoming the “Official Manual”. Our webpage:
http://stars-py.sf.net
should be used as a supplement to this guide. A series of QuickTime tu-torials are provided to familiarize the user with the various analysis andvisualization options in STARS. Please explore the website for additionalinformation.
Interested users should also subscribe to the STARS userlist:
http://lists.sourceforge.net/lists/listinfo/stars-py-users
This is a great way to get involved. Users can use the list to request futureenhancements, ask for assistance, and to report bugs.
Chapter 2
A Quick Tour of STARS
2.1 IntroductionThis chapter presents a brief tour of some of the components of STARS.After reading it you should be able to:
• start STARS
• navigate around STARS windows
• carry out an exploratory spatial data analysis
• interact with statistical graphics and the report window
• quit STARS
Further details on each of these are provided in subsequent chapters of themanual.
2.2 Starting STARSSTARS can be started in several ways depending on the operating systemand how you installed the program.1 There are three general approachestowards starting STARS:
• Clicking on a desktop icon
• Using an alias or batch file
• Through the Python interpreter1For installation instructions see the Install Guide available on our homepage.
6 A Quick Tour of STARS
Figure 2.1 shows an example of the second approach under Mac OS X. Inthe lower right portion of the figure the user has entered the name of an aliasto start STARS within an Aqua terminal. After entering this command, theuser would see a splash screen (not captured in the figure) followed by themain application window which is in the upper left portion of the figure.
Figure 2.1: Starting STARS Under Mac OS X From a Terminal
2.3 STARS Menus
In GUI mode, you interact with STARS through a series of menus. Themenu system consists of six top-level menu items, each of which has a seriesof sub-menus as summarized in Table 2.1. When STARS is loaded only theFile and Help top-level menus will be active. A STARS project must beloaded in order to activate the remaining top-level menus.
2.4 Opening a Project 7
ITEM DESCRIPTIONFile Opening projects and exiting STARSData Listing of variables and matrices, transformations of dataAnalysis Computational and statistical modulesVisualization Generation of statistical and mapping viewsWindows Control of active views and main application windowHelp On-line documentation and program information
Table 2.1: STARS Menu System
2.4 Opening a ProjectA STARS project can be opened with File-Open Project. This bringsup a dialog as depicted in Figure 2.2. STARS project files have a .prjextension. In the figure there is only one project file csiss.prj which willserve as the example project for this tutorial.2 Selecting csiss.prj loadsthe project into STARS and generates a project summary in the outputwindow seen in Figure 2.3.
This project contains data on per capita income in the lower 48 USstates on an annual basis from 1929-2000. The project has 10 variables, 5 ofwhich are purely cross-sectional (CS), four being cross-sectional time-series(CSTS) and 1 being a time-series (TS) variable. In addition the projecthas two matrices. The components and construction of STARS projects arecovered in more detail in Chapter 3.
2.5 Example AnalysisTo carry out a spatial autocorrelation analysis of per capita income, selectANALYSIS-ESDA-MORAN GLOBAL as depicted in Figure 2.4. Se-lecting this command generates the dialog box for this analysis as shown inFigure 2.5. There are four spinboxes at the top of the dialog, one for eachoption. The first provides for the selection of the variable on which the anal-ysis will be carried out. Clicking the up or down arrow will scroll throughthe available variables. For now, select pcr. For the spatial weights matrix,select states48 which is a simple first order contiguity matrix. Select 0 forthe number of permutations and Normality for the variance assumption.
Below the Statistical Option spinboxes are checkbuttons for specifyingwhich if any statistical or geographical views should be generated for theassociated analysis. Select each of these views and then click OK.
2This example project can also be loaded from Help-Example.
8 A Quick Tour of STARS
Figure 2.2: Open Project Dialog
STARS then carries out the analysis and generates the four views asshown in Figure 2.6. The upper left view is a quintile map of pcr for thefirst year of the project. Lighter colors are associated with the lower incomeclasses. To the left of each color is listed the upper bound of that incomeclass, while the values to the right of the color is the count of observationsfalling in that interval. To the right of the map is the Moran scatterplotfor the same year (1929). This plots the spatial lag of income for each stateagainst the states income.
In addition to generating the four views as part of the analysis, STARSalso reports summary statistics to the OUTPUT WINDOW. Currently theOUTPUT WINDOW is obscured by the four views. To uncover it, simplygive one of the views focus by clicking in the view, then issue a CONTROL-H to raise the main application window that contains the OUTPUT WIN-DOW, as shown in Figure 2.7.
2.6 Quitting STARS 9
Figure 2.3: Project Summary
2.6 Quitting STARSTo quit STARS select File-Quit. This will close any open statistical viewas well as the main application window.
10 A Quick Tour of STARS
Figure 2.4: ESDA Menu for Global Moran Analysis
2.6 Quitting STARS 11
Figure 2.5: Dialog for Global Moran Analysis
12 A Quick Tour of STARS
Figure 2.6: Views for Global Moran Analysis
Figure 2.7: Output for Global Moran Analysis
Chapter 3
Creating STARS Projects
3.1 IntroductionThis document takes you through a series of steps which creates a STARSproject out of shapefiles and a series of comma delimited data files. Allthe necessary files should be found in the data subdirectory of your STARShome directory. Here is the list of files used in this example: california.shp,california.shx, california.dbf, caPOP.csv, caPCR.csv, caTS.csv.
3.2 California TutorialExecute the “ProjectMaker.py” file. You will encounter the gui application.
3.2.1 Selecting and Naming Your Project
You first need to create a blank project:
• Select File → Create STARS Project
• You will be asked to provide a prefix for the project you will be cre-ating. In this case we will fill in ‘CA’ in the File name entry spaceprovided.
• Select Save
3.2.2 Creating Time-Series Signatures
After you name your new project you will be immediately prompted toprovide the time series information. The California project contains annualdata for each county for the years 1969 – 2001. Let’s put this informationinto the entry spaces provided:
16 Creating STARS Projects
• ‘Double left click’ the yellow entry space corresponding to Time Fre-quency.
• A listbox will pop-up which contains three choices: ANNUAL, DECADAL,IRREGULAR.
• Since our project contains annual data select ANNUAL.
• In the second entry box labeled Start End, you must type the startand end period separated by a space in the entry box → 1969 2001.
• Select OK
3.2.3 Reading in the GIS ShapefilesYou will then need to declare which shapefiles you will be using for yourproject. The GIS shapefiles needed for any project end with: *.shp, *.shx,*.dbf. In our case the * in these files is equal to california.
• Select Data → Import ArcView Project
• You will prompted to find the *.shp file you will be using for youproject.
• Select ‘california.shp’
• Select Open
3.2.4 Declaring Name and ID FieldsThis is a very important bookkeeping step in the STARS project makingprocess. Some cross-sectional units are made up of more than one shape. Inthis case, we need to find unique fields to identify what shape goes with whatdata. The ProjectMaker script will tell you if their are any field variablesthat have repeated in the data files. These are usually good candidatevariables for your STARS project. In our example, it turns out that the‘COUNTY’ and ‘NAME’ fields are the only ones that repeat. We will endup using these fields. It is important to note that many shapefiles have oneunique entry for every cross-sectional unit, so pretty much any field can beused to identify the region names and IDs. Lastly, you can always view thecontents of any field by:
• Select Data → List Variable Values
Returning to the tutorial:
3.2 California Tutorial 17
• Select Data → Create Region Names and IDs
• ‘Double Left Click’ the yellow entry space corresponding to ID Field
• Select COUNTY
• ‘Double Left Click’ the yellow entry space corresponding to NameField
• Select NAME
• Select OK
3.2.5 Converting ArcView Variables to STARS Variables
The ProjectMaker can create Cross-Sectional Variables from data fields inthe ArcView Project Files. We will only create one variable from the Ar-cView File to the STARS project in this example:
• Select Data → Convert ArcView Variables
• ‘Double Left Click’ the yellow entry space corresponding to Variableto Convert
• ’Left Click’ on AREA.
• You have the option of renaming this variable in the second entry boxentitled New Variable Name, or you could leave it blank.
• ‘Double Left Click’ the yellow entry space corresponding to Data Co-hesion Method
• When a cross-sectional unit is made of more than one shape, the Pro-jectMaker needs to know how to consolidate the data into a singleobservation. In the California example, Santa Barbara county is madeup of 4 separate shapes. Each of these shapes have a value for AREA.In this case it is rather simple, we want to sum up all of its valuesinto a single total, therefore we would choose to aggregate. But forother variables you may want to use the maximum, minimum, or theaverage as a method of consolidation.
• Select Aggregate
• Select OK
18 Creating STARS Projects
3.2.6 Importing External DataOrder MattersMost of our interesting data will NOT be in our ArcView projects. Thereforeit is necessary to merge data from outside the ArcView world into the STARSworld. The first task will be to find the order in which the cross-sectionalunits will be stored in STARS:
• Select Data → Print Region Names To File
• A new file named ‘CA.cso’ will be created. Opening up this file youwill find the order in which data needs to be organized:
SiskiyouDel NorteModoc..Imperial
• So the first county recorded is ‘Siskiyou’ and the last is ‘Imperial’.There are a total of 58 counties in California, and if you want the datain the STARS project to match, you will have to sort your data bythis ordered list.
Understanding Cross-Sectional and Panel Variables Data Files• Lets start by opening one of our data files for our California project.
They are all found in comma delimited format: ‘caPOP.csv’, ‘caPCR.csv’,‘caTS.csv’.
• Using your favorite text editor open up: ‘caPOP.csv’.
1 population2 333 33022.000,33258.000,33382.000,33514.000,34318.000,
34568.000,34947.000,35639.000,36581.000,37647.000,38781.000,39952.000,40757.000,41279.000,41212.000,41267.000,41586.000,41178.000,41403.000,42026.000,42851.000,43743.000,44035.000,44298.000,44150.000,44405.000,44760.000,44707.000,45030.000,44848.000,44424.000,44264.000,44015.000
....
3.2 California Tutorial 19
....60 73604.000,74795.000,74931.000,75869.000,79648.000,
81526.000,82989.000,85264.000,86981.000,88474.000,90086.000,92584.000,94301.000,95688.000,96890.000,97475.000,99199.000,98885.000,100077.000,101881.000,105166.000,110839.000,117143.000,123823.000,132758.000,134315.000,136986.000,137987.000,138624.000,139615.000,141056.000,142601.000,143926.000
• The first line contains the name of the variable in question: ‘popula-tion’.
• The second line contains the number of columns included for this vari-able: 33. There are really only two values that can be listed for ourproject: 33 and 1. We use 33 here because this is a panel data vari-able, and there are 33 time periods in our project (1969-2001). Usinga 1 would identify a purely cross-sectional variable which we will seein the following data set.
• The third line contains the 33 years of population information forSiskiyou County, the last line pertains to Imperial County because itis last in the ‘CA.cso’ file.
• Multiple variables can be placed into the same *.csv file. Lets move toanother example. Use you favorite text editor to open ‘caPCR.csv’.
1 pcr,area2 33,13 0.854,0.839,0.863,0.856,0.915,0.886,0.880,0.893,
0.838,0.845,0.823,0.833,0.765,0.747,0.745,0.730,0.715,0.727,0.726,0.720,0.732,0.725,0.736,0.731,0.754,0.758,0.738,0.736,0.738,0.723,0.704,0.691,0.693,1.25622
....
....60 0.815,0.796,0.760,0.895,0.865,0.882,0.853,0.845,
0.789,0.752,0.945,0.791,0.741,0.773,0.768,0.713,0.661,0.655,0.721,0.774,0.766,0.734,0.706,0.678,0.712,0.689,0.673,0.622,0.628,0.635,0.615,0.542,0.556,0.26118
20 Creating STARS Projects
• Their are two variables in this file: ‘pcr’ (per capita income relative tothe California average), and ‘area’.
• The second line indicates that the first 33 columns contain data for‘pcr’, and the last column is the purely cross-sectional variable ‘area’.
• You recall that we created AREA out of the ArcView fields, well weare going to create it again like this to show that pure cross-sectionalvariables can be created in more than one way.
• Again, the third line contains the data for Siskiyou County, and theorder continues as per the CA.cso file until the final entry (line 60)which contains the data for Imperial County.
Understanding Time-Series Variables Data FilesInputing time-series data has to be done separately from the cross-sectionaland panel data variables. Lets look at our example time-series data for ourCalifornia project with our favorite text editor. Open ‘caTS.csv’:
• The format here is a bit different from the cross-sectional variables:
1 population CA,pcincome CA2 TS3 19711000.000,4540.000........35 34600463.000,32655.000
• Again, the first line contains the names of the variables separatedby commas. In this case, we have ‘population CA’ which stands forCalifornia’s total population , and ‘pcincome CA’ which relates toCalifornia’s per capita income.
• The second line just contains a code ‘TS’ which stand for time-series.This tells the ProjectMaker to treat this variable in the proper manner.
• The third line contains the data for 1969. So, the value ‘19711000.000’was California’s total population in 1969.
• The value ‘32655.000’ in the last line was California’s per capita incomein 2001.
3.2 California Tutorial 21
Importing External DataNow that we are aware of the format of the data, it is time to read the datainto our project.
• Select Data → Merge ASCII Data
• ‘Left Click’ on ‘caPop.csv’
• Select Open
• That is all their is to it!!!! Now repeat this process for: ‘caPCR.csv’,and ‘caTS.csv’.
3.2.7 Plotting Your ProjectOnce all of your data files have been imported it is time to plot your map.
• Select Plot → Plot Shapes
• Wait for the ProjectMaker to work its magic, and your map will ap-pear.
3.2.8 Finishing Your ProjectNow that you have created your data, and viewed the map, it is time tofinish up your project.
• Select File → Save STARS Project
• This will create all the STARS project files.
• You have now completed the creation of a STARS project. Now fireup STARS, use the CA.prj, and get ready to explore the data!
Chapter 4
Getting Involved withSTARS
4.1 IntroductionThis chapter provides resources for individuals interested in contributing tothe ongoing development of STARS.
4.2 End User ContributionsUsers are a vital component of any open source project. STARS can benefitsignificantly from the input of users which can take multiple forms:
• requests for new features
• testing new releases and features
• bug reports
• contributing new data sets to the project
• artwork, documentation, web
• spreading the word about STARS
To facilitate these contributions users are encouraged to join the STARSusers mailing list at
http://lists.sourceforge.net/lists/listinfo/stars-py-users
which serves as the main community home for the STARS project.
24 Getting Involved with STARS
4.3 Resources for DevelopersOur project is managed by the Development Team. The project Devel-opment Team consists of individuals who guide the official development ofSTARS. In particular, members of the Development Team have the followingresponsibilities:
• Voting rights
• Read and write permissions on the project CVS tree
Individuals can become members of the Development Team through anomination by an existing member and a formal vote of approval by theentire Team.
STARS relies on the generous support of Sourceforge to host projectdevelopment. The project page is http://stars-py.sf.net.
Bibliography
Anselin, L. and Rey, S. J. (2002). New tools for spatial data analysis: pro-ceedings of the specialist meeting. Center for Spatially Integrated SocialSciences, University of California, Santa Barbara.
Rey, S. J. and Janikas, M. V. (2004). STARS: Space-Time Analysis ofRegional Systems. Geographical Analysis, Forthcoming.
Rey, S. J. and Montouri, B. D. (1999). U.S. regional income convergence:A spatial econometric perspective. Regional Studies, 33:143–156.
