US Census Spatial and Demographic Data in R
Contents
1. border Character string this selects the border color of the polygons legend List containing first where to place the legend and second a title for the legend type Character string can be either plot or spplot Only arguments available in plot function 14 UScensus2000 US Census Spatial and Demographic Data in R 2000 US Census California Tracts Population Count 0 3399 3399 4546 4546 5932 5932 36146 Quantiles equal frequency Figure 4 A choropleth map of California The default choropleth map may be called in the following way R gt choropleth california tract main 2000 US Census n California Tracts F border transparent and produces the graphic in Figure 4 6 4 Example 4 The MSA and city functions Researchers often prefer to analyze standard metropolitan units e g a census designated place CDP or metropolitan statistical area MSA Often cities or MSAs are of particular interest since most of the United States population live in urban centers For this example we examine the MSA of Los Angeles at the tract level we could of course use any of the aforementioned levels we perform this task by applying the function MSA from the UScensus2000 package MSA takes the following inputs msafips Character string takes a four digit MSA FIPS code e g 0040 of Texas Journal of Statistical Software msaname Character string this can either be in conjunctio2. CA level msa msaname Los Angeles R gt laDem pop2000 white black san bernardino county 1709434 1006960 155348 ventura county 193197 526721 14664 los angeles county 9519338 4637062 930957 riverside county 1545387 1013478 96421 orange county 2846289 1844652 47649 In the subsequent example using the cdp level the reader might notice a CDP with popu lation equal to zero While there are not many of CDPs with zero population it is possible CDPs are incorporated and unincorporated cities towns CDPs and so forth as defined by the US Census Bureau and local and state agencies they are not required to have either a maxi mum or minimum population size http www census gov geo www psapage htm1 CDP A few of the possible reasons why there might be areas with zero population include Resort towns and corporate towns Journal of Statistical Software R gt ca cdp lt demographics dem c pop2000 white black hh units hh vacant CA level cdp R gt ca cdplorder rownames ca cdp 1 10 pop2000 white black hh units hh vacant Acton 2390 2130 17 873 76 Adelanto 18130 9147 2377 5547 3833 Agoura Hills 20537 17858 272 6993 119 Alameda T2259 41148 4488 31644 1418 Alamo 15626 14119 T4 5497 91 Albany 16444 10078 675 7248 237 Alhambra 85804 25758 1437 30069 958 Aliso Viejo 40166 31395 828 16608 461 Almanor O O O T4 T4 Alondra Park 8622 3584 1088 2933 103 6 3 Example 3 Choropleth map
3. Journal of Statistical Software November 2010 Volume 37 Issue 6 http www jstatsoft org US Census Spatial and Demographic Data in R The UScensus2000 Suite of Packages Zack W Almquist University of California Irvine Abstract The US Decennial Census is arguably the most important data set for social science research in the United States The UScensus2000 suite of packages allows for convenient handling of the 2000 US Census spatial and demographic data The goal of this article is to showcase the UScensus2000 suite of packages for R to describe the data contained within these packages and to demonstrate the helper functions provided for handling this data The UScensus2000 suite is comprised of spatial and demographic data for the 50 states and Washington DC at four different geographic levels block block group tract and census designated place The UScensus2000 suite also contains a number of functions for selecting and aggregating specific geographies or demographic information such as metropolitan statistical areas counties etc These packages rely heavily on the spatial tools developed by Bivand Pebesma and G mez Rubio 2008 i e the sp and maptools packages This article will provide the necessary background for working with this data set helper functions and finish with an applied spatial statistics example Keywords spatial data spatial analysis spatial data handling US Census demography R
4. 1 Introduction The US Decennial Census is arguably the most important data set for social science research in the United States The US conducts a census of the entire population every ten years to de termine proper Congressional representation based on the population Along with population counts the US Census Bureau collects thousands of basic demographic characteristics and aggregates these into various geographical regions represented as polygons This paper will provide an overview of the UScensus2000 suite of packages The packages contain geographic representations of the 2000 US Census a common set of demographic variables and various helper functions These packages also provide easy access to the US Census data for R users including Improved accessibility polygon spatial data management detailed meta data and Z UScensus2000 US Census Spatial and Demographic Data in R conveniently sourced inbuilt documentation The UScensus2000 suite of packages integrates seamlessly with the geographical information system Bivand et al 2008 built for the R programing language R Development Core Team 2010 In their book GIS A Computing Perspective Worboys and Duckham 2004 explain that lal geographic information system GIS is a special type of computer based information system tailored to store process and manipulate geospatial data This type of geospatial data has proven to be extremely valuable to a diverse range of fi
5. lt 2e 16 Signif codes O 0 001 0 01 0 05 0 1 1 Residual standard error 10 62 on 881 degrees of freedom 1 observation deleted due to missingness Multiple R squared 0 6107 Adjusted R squared 0 6085 F statistic 276 4 on 5 and 881 DF p value lt 2 2e 16 The linear model weighted by inverse population results in a very different picture than our first two regressions Our race ethnicity hypothesis is not confirmed i e we see positive effects for both non hispanic asian and black In fact our hypothesis that race ethnicity has any effect is brought into question since none of the race covariates are significant on the other hand our married household with children hypothesis is again confirmed This model however does not take into account spatial autocorrelation Just as before we can perform a Moran s I test to see if any such spatial autocorrelation exists R gt lm morantest la lmw la nbw Global Moran s I for regression residuals data model lm formula pctowner pct nh white pct hispanic pct nh black pct nh asian pct marhh chd data losangeles tract weights pop2000 weights la nbw Moran I statistic standard deviate 22 3887 p value lt 2 2e 16 alternative hypothesis greater sample estimates Observed Moran s I Expectation Variance O 423094032 0 004956330 O 000365538 Again we see that the Moran s I test is significant and conclude that there is spatial auto
6. 3 A plot of the Pacific Northwest Combining data is another common activity and is made straight forward through the sp package There are two basic types of data integration spRbind for binding spatial data and unionSpatialPolygons for aggregating spatial data Take for example the case of the Pacific Northwest Oregon Washington and Idaho Figure 3 A user might want to have a single data object which contains all the spatial and demographic data of the Pacific Northwest or one might want to simply have the border of the Pacific Northwest The following example provides the necessary code R gt data washington tract R gt data idaho tract R gt pacificNW lt spRbind oregon tract washington tract R gt pacificNW lt spRbind pacificNW idaho tract R gt summary as pacificNW SpatialPolygons Object of class SpatialPolygons Coordinates min max x 124 73317 111 04356 y 41 98806 49 00249 Is projected FALSE proj4string proj longlat datum NAD83 ellps GRS80 towgs84 0 0 0 R gt gpclibPermit R gt pacNWol lt unionSpatialPolygons pacificlw rep x length slot pacificNW polygons R gt par mfrow c 1 2 par mfrow c 1 2 mar c 0 0O 4 0 0 1 8 UScensus2000 US Census Spatial and Demographic Data in R R gt plot pacificNw R gt title Pacific Northwest n with Tracts R gt plot pacNWol R gt title Pacific Northwest n without Tracts 4 The UScensus2000
7. 50 states and Washington DC as a collection of CDP polygons The data contained within each of the various geographic levels are saved in 51 SpatialPolygonsDataFrame objects Each state contains all the polygon files necessary to cover the state at a given level block block group tract CDP and the corresponding demographic data This data set comes with 86 standard demographic variables population race ethnicity age household information etc attached to each polygon for more informa tion use the help function on state and level of interest e g help california tract 5 The UScensus2000 and UScensus2000add packages The UScensus2000 contains the following functions county Allows the user to pull out one or more counties within a given state for any level including CDPs counties and MSAs Journal of Statistical Software MSA Allows the user to extract a single MSA from a given state at any level block block group tract This function handles three different types of inputs the MSA FIPS code the full MSA name this must be exact e g Abilene TX MSA and the state argument should be left NULL or an MSA city and one of the states in which it is contained e g msaname Portland state OR city Allows the user to extract a single CDP from a given state poly clipper Allows the user to extract all the blocks block groups or tracts contained within a CDP and compute the intersection of the CDP and
8. 900051 07 0 URL http www R project org Temple Lang D 2009 XML Tools for Parsing and Generating XML within R and S PLUS R package version 2 6 0 URL http CRAN R project org package XML US Census Bureau 2001 Census 2000 Summary File 1 United States Prepared by the US Census Bureau URL http www census gov prod cen2000 doc sf1 pdf US Census Bureau 2010a United States Census 1980 URL http www census gov prod www abs decennial 1980 htn Journal of Statistical Software 31 US Census Bureau 2010b United States Census 1990 URL http www census gov main www ceni990 html US Census Bureau 2010c United States Census 2010 URL http 2010 census gov Worboys M Duckham M 2004 GIS A Computing Perspective 2nd edition CRC Press Boca Raton FL Affiliation Zack W Almquist Department of Sociology University of California Irvine 3151 Social Science Plaza A Irvine CA 92697 United States of America E mail almquist uci edu URL http www socsci uci edu almquist Journal of Statistical Software http www jstatsoft org published by the American Statistical Association http www amstat org Volume 37 Issue 6 Submitted 2010 01 29 November 2010 Accepted 2010 10 04
9. An alternative model we might propose is one which uses the inverse population as weights he dependent variable in this case a population mean is expected to have an in trinsic variance that scales inversely with population i e we expect systematic heteroskedas ticity Since tracts vary in population it would seem wise to correct for this issue Weighting allows the model to avoid having low population tracts exert an untoward influence and or to prevent accidental correlation of error variance with covariates from generating spurious effects This process will appear very similar to the process outlined above but will include the weights variable in 1m and spautolnm R gt la lmw lt Im pctowner pct nh white pct hispanic pct nh black pcet nh asian pct marhh chd data losangeles tract weights pop2000 R gt summary la 1mw Call lm formula pctowner pct nh white pct hispanic pct nh black pct nh asian pct marhh chd data losangeles tract weights pop2000 Residuals Min 1Q Median 3Q Max 36 437 6 718 0 326 6 422 39 875 Coefficients Estimate Std Error t value Pr gt t Intercept 2 09347 0 39426 5 310 1 39e O7 Journal of Statistical Software 2T pct nh white 2 51193 0 41695 6 025 2 49e 09 pct hispanic 1 48667 0 40471 3 673 0 000254 pct nh black 2 45677 0 40878 6 010 2 71e 09 pct nh asian 2 03148 0 41617 4 881 1 25e 06 x pct marhh chd 2 29162 0 07894 29 028
10. class bbox Retrieves spatial bounding box from spatial data help bbox proj4string Sets or retrieves projection attributes on classes extending spatial data help proj4string Each of the four data packages of the UScensus2000 suite is broken down into 6 UScensus2000 US Census Spatial and Demographic Data in R 51 SpatialPolygonsDataFrame objects which are comprised in part of polygon and de mographic data see the example above One may directly access the list of polygon data through the slot polygons and the data frame object of demographic data via slot data There are two types of information stored within the data slot of each SpatialPolygonsDataFrame objects in the UScensus2000 suite ID variables which are stored as factors and demographic variables which are stored as numeric All SF1 data is count data and represents X number of the given variable at a given geography e g california tract white provides all counts of white individuals in each tract in Califor nia and california tract hh units provides all counts of household units in each tract in California Some useful functions provided in the sp package include summary bbox proj4string plot spplot spRbind unionSpatialPolygons and overlay The summary function pro vides a standard summary of the sp objects proj4string provides for some manipula tion of the CRS the bbox function pulls out the bounding box of the entire object
11. correlation We can again use the spautolm function this time also including the weights parameter R gt la sarw lt spautolm pctowner pct nh white pct hispanic pct nh black pct nh asian pct marhh chd data losangeles tract listw la nbw weights pop2000 R gt summary la sarw Call spautolm formula pctowner nw pet nh white pct hispanic pct nh black 28 UScensus2000 US Census Spatial and Demographic Data in R pct nh asian pct marhh chd data losangeles tract listw la nbw weights pop2000 Residuals Min 1Q Median 3Q Max 0 846746 0 063808 0 012976 0 088940 0 491317 Coefficients Estimate Std Error z value Pr gt lz Intercept 0 425640 0 070702 6 0202 1 742e 09 pet nh white 0 014744 081470 0 1810 0 85639 pet hispanic 1 068973 085979 12 4330 lt 2 2e 16 pet nh black 0 164167 081185 2 0221 0 04316 pet nh asian 0 580842 089983 6 4550 1 082e 10 pet marhh chd 1 976906 091879 21 5164 lt 2 2e 16 O OOOO Lambda 0 12303 LR test value 347 23 p value lt 2 22e 16 Log likelihood 448 7812 ML residual variance sigma squared 66 111 sigma 8 1309 Number of observations 887 Number of parameters estimated 8 AIC 881 56 The SAR model with population weights confirms our race ethnicity hypothesis i e all the minority coefficients are negative and significant and white is effectively zero and is not significant and again our married household with childre
12. e g bbox california tract where a bounding box is a rectangle of minimum and maximum coordinates of the sp object spRbind allows for combining two sp objects of the same type with the same data frame columns and unionSpatialPolygons allows one to combine sp polygons into larger polygons These functions are useful for summarizing plotting and or statistical techniques The overlay function is a particularly useful command and performs a type of point in polygon procedure i e overlay points polygons It is always good practice to run a summary on the data however users should be aware that running summary directly on a SpatialPolygonsDataFrame results in both the sum mary for data frame information and the SpatialPolygons information To generate the SpatialPolygons summary information one applies the following code R gt summary as oregon tract SpatialPolygons Object of class SpatialPolygons Coordinates min max ri 124 55244 116 4635 r2 41 99179 46 2710 Is projected FALSE proj4string proj longlat datum NAD83 ellps GRS80 towgs84 0 0 0 If one wants the bounding box information or the CRS information one may do the following R gt bbox oregon tract min max ri 124 55244 116 4635 r2 41 99179 46 2710 R gt proj4string oregon tract 1 proj longlat datum NAD83 ellps GRS80 towgs84 0 0 0 Journal of Statistical Software Pacific Northwest Pacific Northwest with Tracts without Tracts Figure
13. losangeles tract R gt plot nb losangeles nb coords coordinates losangeles tract add TRUE cex 0 5 R gt title Tracts of Los Angeles 2000 n polygon generated queen weights R gt la nbw lt nb2listw losangeles nb style B R gt 1Im morantest la 1lm la nbw Global Moran s I for regression residuals data model lm formula pctowner nw pet nh white pct hispanic pcet nh black pct nh asian pct marhh chd data losangeles tract weights la nbw Moran I statistic standard deviate 22 1729 p value lt 2 2e 16 alternative hypothesis greater sample estimates Observed Moran s I Expectation Variance 0 4210120096 0 0042431905 O 0003678362 The results of the Moran s I test suggest that a SAR model with spatial autocorrelation will fit the data better than a linear model without spatial autocorrelation taken into account We can fit this model using the function spautolm in the spdep package The code below performs this analysis R gt la sar lt spautolm pctowner pct nh white pct hispanic pct nh black pcet nh asian pct marhh chd data losangeles tract listw la nbw R gt summary la sar Call spautolm formula pctowner pct nh white pct hispanic pct nh black pct nh asian pct marhh chd data losangeles tract listw la nbw Residuals Min 1Q Median 30 Max 0 5175469 0 0681585 0 0059096 0 0843405 0 4262356 Coefficients Estimate Std Error z value P
14. packages The UScensus2000 suite is comprised of six packages which are organized in a hierarchal fashion with UScensus2000 and UScensus2000add at the top level and UScensus2000blk UScensus2000blkgrp UScensus2000tract UScensus2000cdp at the bottom level The US census2000 suite of packages can stand as a general model for how to build large data set packages for R especially other US Census data sets and equivalent data sets worldwide UScensus2000add is a separate package due to future developments and because it requires a number of extraneous packages to operate UScensus2000 Contains a number of helper functions for managing of these rather large data sets including functions to pull out county MSA and CDP level data UScensus2000add Contains a function to download add and attach one or more demo graphic variables to the sp objects at any of the discussed geographic levels A warning for users This function accesses the US Census FTP site and must download a fair bit of data to work This means it is only practical if the user has a lot of bandwidth UScensus2000blk Contains 51 sp objects representing the 50 states and Washington DC at the block level UScensus2000bkgrp Contains 51 sp objects representing the 50 states and Washington DC at the block group level UScensus2000tract Contains 51 sp objects representing the 50 states and Washington DC at the tract level UScensus2000cdp Contains 51 sp objects representing the
15. pct hispanic 0 80911 04686 17 268 lt 2e 16 pct nh black 0 04052 03621 1 119 0 263 pct nh asian 0 46423 04723 9 830 lt 2e 16 pct marhh chd 1 87897 07207 26 071 lt 2e 16 O OOOO O Signif codes 0O 0 001 0 01 0 05 0 1 1 Residual standard error 0 1747 on 881 degrees of freedom 1 observation deleted due to missingness Multiple R squared 0 5945 Adjusted R squared 0 5922 F statistic 258 3 on 5 and 881 DF p value lt 2 2e 16 Looking at this standard linear regression we see that our initial race ethnicity hypothesis is partially confirmed although percentage non hispanic white is not significant and that our married household with children hypothesis is also confirmed 24 UScensus2000 US Census Spatial and Demographic Data in R Tracts of Los Angeles 2000 polygon generated queen weights ES se REE NZ SA ayy eae FEET 3 ah NN LA 4 EOYs as 0 fey i Wr SPER De vra J PINEN ISAIN S aaan aS o Sa S ern eas TS 2 4 AAN T N zis A Ms Na A 3e Ce Vie 3 MN INT 8B K y A K area Ny ya aos A ROE ad PEA Spt cs z y 7 a 1 a as 1 z D e aii AT ch 2 j s RA a Ve a Ty TRT PA 3 i SRS l 7 a4 rote i yey ete a ne E Figure 9 A map of the tracts within Los Angeles city and queens distance weighting matrix However we might suspect there to be spatial autocorrelation in this data There are a num ber of arguments as
16. a homogenous allocation of covariates is often necessary however it is not always rational and can result in distortions in the data 1 Given the selected level of geography limit the possible intersecting units with the bounding box of the polygon of interest along with an expansion term Perform boolean polygon intersection with selected level of geography and the polygon of interest Save all the polygons which are fully contained with their original state Save only the portion of the polygon which falls within the boundary of the polygon of interest Drop any polygons with no intersection Impute the covariates where necessary with a proportion estimator based on area Area Geography N Polygon 1 Atea Geographyy N Polygon a Area Geography Note that we take the ceiling of this estimate We perform this operation because this is count data and we want to maintain integer numbers Journal of Statistical Software 19 2000 US Census Tracts Los Angeles NSS Cf RORY RSET IFES TE ROY pe a PSU HPL SE ABH 7 a Me Te eS Ny Bry TERA Y PY P es ny l AH RA TE 7 Eis ean a M7 ee al J A T RRA maSin or HHE v HEIA zm a il i a E da ieee a N aN ue Tar Ne naL Figure 8 A map of the tracts within Los Angeles city 7 Recombine all the polygon and covariate information into a new SpatialPolygonsDataFrame object Let us now focus on th
17. any blocks block groups or tracts not fully contained within the CDP including an estimate of demographic variables within that intersection using the proportion of the area contained within the CDP This function makes use of the gpclib Peng 2009 for performing the intersection between polygons demographics Allows the user to select out specific demographic variables at any level areaPoly Selects out the area parameter contained within each SpatialPolygon object within the SpatialPolygonDataFrame object and outputs a vector choropleth Allows for convenient plotting of choropleth maps of the 2000 US Census nameTofips Allows the user to look an individual county or MSA FIPS code by name install blk Allows for convenient installation of UScensus2000blk package Each of these functions results in the following class county SpatialPolygonsDataFrame object MSA SpatialPolygonsDataFrame object city SpatialPolygonsDataFrame object demographics matrix object areaPoly numeric vector choropleth Graphic nameTofips character install blk Installs UScensus2000blk The UScensus2000add contains the following function demographics add Allows the user to add arbitrary census demographic variables from the 100 percent Census data SF1 at any level This function takes in a list of desired variables from SF1 tech documentation list US Census Bureau 2001 and attaches it to the desired level sp object This function is
18. are 23 R gt library spdep R gt la pop tot lt losangeles tract pop2000 R gt la hh tot lt losangeles tract households R gt losangeles tract pctowner lt losangeles tract hh owner la hh tot R gt losangeles tract pct nh white lt losangeles tract nh white la pop tot R gt losangeles tract pct nh black lt losangeles tract nh black la pop tot R gt losangeles tract pct hispanic lt losangeles tractfhispanic la pop tot R gt losangeles tract pct nh asian lt losangeles tract asian la pop tot R gt losangeles tract pct marhh chd lt losangeles tract marhh chd la hh tot R gt losangeles tract lt losangeles tract c 1 297 299 length losangeles tract polygons To test our hypothesis a reasonable approach is to start by fitting a simple linear regression model with the aforementioned covariates We notice that we can treat the SpatialPolygonDataFrame objects just like the standard R data frame object R gt la lm lt Im pctowner pct nh white pct hispanic pct nh black pcet nh asian pct marhh chd data losangeles tract R gt summary la 1m Call lm formula pctowner pct nh white pct hispanic pct nh black pct nh asian pct marhh chd data losangeles tract Residuals Min 1Q Median 3Q Max 0 795045 0 115970 0 009655 0 106370 0 637660 Coefficients Estimate Std Error t value Pr tl Intercept 0 33941 02598 13 064 lt 2e 16 pct nh white 0 04386 03791 1 157 0 248
19. based on the XML package Temple Lang 2009 10 UScensus2000 US Census Spatial and Demographic Data in R This function results in a SpatialPolygonsDataFrame object 6 Examples This section explores the basics of the demographic data and demonstrates the capabilities of the UScensus2000 suite of packages The last example is a statistical example applied to this data set 6 1 Example 1 The basics and the county function Let us first load the UScensus2000 package which will also bring up the sp maptools US census2000tract and UScensus2000cdp packages one will need to load UScensus2000blk and UScensus2000blkgrp separately if desired All examples here will use the tract and CDP level data Next load the data and check and see what demographics are available names R gt library UScensus2000 R gt data california tract R gt names california tract 1 state county tract pop2000 5 white black ameri es asian 9 hawn pi other mult race hispanic 13 not hispanic t nh white nh black nh ameri es 17 nh asian nh hawn pi nh other hispanic t 21 h white h black h american es h asian 25 h hawn pi h other males females 29 age under5 age 5 17 age 18 21 age 22 29 33 age 30 39 age 40 49 age 50 64 age 65 up 37 med age med age m med age f households 41 ave hh sz hsehld i m hsehld 1 f marhh chd 45 marhh no c m
20. blicly available but are maintained in a series of disparate flat text files which include the actual demographic information shapefiles and relevant accompanying documentation explaining the organization of the files definitions of variables etc The UScensus2000 suite provides an intuitive organization of these separate entities into a single coherent package that includes inbuilt documentation help files exam ples and a series of general helper functions for identifying and extracting important and useful subsets of the spatial and demographic data These helper functions allow the user to extract and aggregate geographies demographic characteristics and also allow the user to add demographic data from the US Census Summary File 100 SF1 percent files US Census Bureau 2001 The US Census Bureau aggregates data at four basic geographic levels county tract block group and block In addition two other geographic conglomerations metropolitan statistical areas MSA and census designated places CDP are defined The first four geographic areas county tract block group and block exist in a hierarchical system US Census Bureau 2001 Figure 1 This is explained in great detail at either the US Census website http www census gov or the SF1 technical report US Census Bureau 2001 MSAs are composed of counties and census designated places are political entities defined by states and the US Census Bureau e g incorporated a
21. ckage Note for Windows users UScensus2000blk requires R 2 11 0 or greater to install R gt install packages UScensus2000 dependencies TRUE R gt install packages UScensus2000add dependencies TRUE R gt library UScensus2000 R gt install blk osx A general warning The UScensus2000blk is very large and should not be installed if one does not have a good internet connection Also for all systems the install is from source and may take a great deal of time Journal of Statistical Software 3 The sp and maptools packages The sp Pebesma and Bivand 2005 and maptools Lewin Koh and Bivand 2009 packages provide the backbone of the UScensus2000 suite of packages to be fully conversant in spatial analysis and spatial data in R one should read Bivand et al 2008 s book Applied Spatial Data Analysis with R All spatial data stored in the UScensus2000 suite are of the form SpatialPolygonsDataFrame e g california tract is a SpatialPolygonsDataFrame ob ject SpatialPolygonsDataFrame objects are a so called S4 class object in R and con tain detailed attribute data In general each SpatialPolygonsDataFrame object may be treated like a data frame object which means the standard data frame methods apply e g oregon tract pop2000 which is characterized by special attributes for spatial infor mation The two most important of these attributes are the bounding box and the coordinate reference system CRS The b
22. ckage regdal Lewin Koh NJ Bivand R 2009 maptools Tools for Reading and Handling Spatial Objects R package version 0 7 25 URL http CRAN R project org package maptools Massey DS Denton NA 1988a The Dimensions of Residential Segregation Social Forces 67 2 281 315 Massey DS Denton NA 1988b Suburbanization and Segregation in US Metropolitan Areas The American Journal of Sociology 94 3 592 626 Massey DS Denton NA 1993 American Apartheid Segregation and the Making of the Underclass Harvard University Press Cambridge Massey DS Fong E 1990 Segregation and Neighborhood Quality Blacks Hispanics and Asians in the San Francisco Metropolitan Area Social Forces 69 1 15 32 Minnesota Population Center 2010 Integrated Public Use Microdata Series International Version 6 0 University of Minnesota URL https international ipums org Neuwirth E 2007 RColorBrewer ColorBrewer Palettes R package version 1 0 2 URL http CRAN R project org package RColorBrewer Pebesma EJ Bivand RS 2005 Classes and Methods for Spatial Data in R R News 5 2 9 13 URL http CRAN R project org doc Rnews Peng RD 2009 gpclib General Polygon Clipping Library for R R package version 1 4 4 URL http CRAN R project org package gpclib R Development Core Team 2010 R A Language and Environment for Statistical Computing R Foundation for Statistical Computing Vienna Austria ISBN 3
23. cs A word of caution if one adds a covariate to the sp object which is not of a count type variable e g a ratio the resulting estimation performed should be ignored 6 7 Example 7 A statistical application of the UScensus2000 suite The last example is a statistical example based on the spdep package Bivand 2009 and motivated by the example given in Bivand et al 2008 Section 10 2 It will walk the reader through using this data in performing a common model based approach for handling spatial autocorrelation There are many different possible models for spatial autocorrelation however one of the most used in practice is that of the simultaneous autoregressive SAR models Bivand et al 2008 These are models of the form Y WY u e where W is a weight matrix u is estimated with the standard linear model u X 8 and e is assumed to be distributed N 0 One way to think about this model is to assume that the observations arise from the equilibrium of a linear diffusion process with a weight matrix W and initial state vector u e Say one is interested in modeling the percent of home owners in a given tract in Los Angeles There is a long history of the study of segregation and inequality in neighborhoods Massey and Fong 1990 Massey and Denton 1988a b 1993 much of which suggest the hypothesis that we will see lower home ownership rates in minority tracts One might endeavor to model this hypothesis by adding covariates f
24. ds ave hh sz hsehld 1 m 43 hsehld 1 f marhh chd marhh no c 46 mhh child fhh child hh units 49 hh urban hh rural hh occupied 52 hh vacant hh owner hh renter 55 hh iperson hh 2person hh 3person 58 hh 4person hh 5person hh 6person 61 hh 7person hh nh white 1p hh nh white 2p 64 hh nh white 3p hh nh white 4p hh nh white 5p 67 hh nh white 6p hh nh white 7p hh hisp ip 70 hh hisp 2p hh hisp 3p hh hisp 4p 73 hh hisp 5p hh hisp 6p hh hisp Tp 76 hh black 1p hh black 2p hh black 3p 79 hh black 4p hh black 5p hh black 6p 82 hh black 7p hh asian 1p hh asian 2p 85 hh asian 3p hh asian 4p hh asian 5p 88 hh asian 6p hh asian 7p PCTO16033 91 PCTO16034 PO05010 22 UScensus2000 US Census Spatial and Demographic Data in R Now that we have added the demographics PCT016033 PCT016034 P005010 we can build an sp object of Providence RI from this object with the following command R gt providence lt poly clipper name providence state RI level tract sp object rhode_island There is only a small difference between this code and that from Section 6 5 the sp object rhode_island argument The sp object tells the poly clipper function to use the rhode_island object for the base of the clipping this has the added benefit of perform ing the proportion estimator to the newly added demographi
25. e maptools and sp packages Lewin Koh and Bivand 2009 Pebesma and Bivand 2005 This article will concentrate on covering the relevant information needed to manipulate the 2000 US Census geographic and demographic data contained within the UScensus2000 suite This article will introduce functions for acquiring specific conglomerations of census data Counties MSA and cities county MSA and poly clipper as well as selecting de mographics demographics and functions for adding data to these packages spatial objects demographics add In addition to addressing examples of these functions and some stan dard uses of these functions a spatial statistics application using the spdp package Bivand 2009 is demonstrated 2 Data structure basics The UScensus2000 suite is composed of six packages four of which contain spatial and demographic data each of which maintains the same basic nomenclature and data struc 4 UScensus2000 US Census Spatial and Demographic Data in R Package e g UScensus2000tract State e g california tract data and polygons e g california tract data or california tract polygons Figure 2 Representation of the data structure and nomenclature of the UScensus2000 suite of software ture Breaking the UScensus2000 suite into smaller pieces is motivated by organizational requirements however this also has the added benefit of easier download and installa tion for users Additionally this o
26. e usage of the poly clipper function This function accepts the following arguments name Character string this string must be the name of CDP for a given state state Character string can either be the full name of a state e g oregon the abbre viation e g or or the FIPS code e g 41 note that if you are using the FIPS code you have to change statefips to TRUE This variable is insensitive to case statefips Logical by default statefips FALSE change to TRUE when providing state with a FIPS code 20 UScensus2000 US Census Spatial and Demographic Data in R level Character string takes in one of three values tract blk or blkgrp This defines the geographic level of data for the county bb epsilon Numeric by default bb epsilon 0 006 this value controls the number of blocks block groups tracts which are considered for clipping if the function is not selecting all blocks block groups tracts consider choosing a larger value for bb epsilon sp object SpatialPolygonsDataFrame default NULL allows the user to provide an sp object in which to perform this operation primarily for use with demographics add function proj CRS class takes a CRS object e g CRS proj utm zone 10 datum NAD83 This is simply a wrapper for the spTransform function in rgdal Warning Requires rgdal package Say we are interested in performing analysis on the CDP of Los Angeles we would then enter
27. elds from geology to economics any scientist who wishes to display and analyze spatial data Worboys and Duckham 2004 proceeds to write la t the heart of any GIS is the database which organizes data in a form that is easy to store and retrieve That is to say managing spatial data is a difficult task due to the large amount of mathematically complex polygon files and accompanying covariate information Consequently a competently built data management system for handling large scale geospatial data is an important enterprise crucial to enabling the analyst to perform his or her task at optimal efficiency These packages represent a template for the managing of spatial and demographic cen sus data which might be used for other US Censuses and similar types of data world wide e g http 2010 census gov 2010census https international ipums org http epp eurostat ec europa eu etc Specifically we take advantage of the rigid Census hierarchy of geographic scale and data attributes e g administrative borders to simplify common tasks in spatial analysis such as data acquisition plotting map overlay functions and statistical analysis The US Census geography maintains a strict hierarchy such that states contain counties counties contain tracts tracts contain block groups and block groups contain blocks Strictly speaking there should be no overlap between each con tainer level The US Census data files are pu
28. ensus2000 US Census Spatial and Demographic Data in R 2000 US Census Tracts Los Angeles MSA l 22 92120 35 5 N 35 N D A Q 34 5 N 5 4 34 N Los Angeles a 17 72308 17 20021 33 N 16 42721 119 W 118 W 117 W 116 W 115 W Longitude Log Density Figure 5 A choropleth map of the Los Angeles MSA 2000 US Census Tracts Los Angeles MSA 22 92120 34 4 N 34 2 N o o Tb 34 N pe 33 8 N 17 72308 33 6 N 17 20021 16 42721 119 W 118 5 W 118 W 117 5 W 117 W Longitude Log Density Figure 6 A choropleth map of the Los Angeles MSA be set to tract blkgrp or blk the last two require that the user has installed the UScensus2000blkgrp and UScensus2000blIk packages The example for generating the MSA for Los Angeles follows note that MSA will produce a SpatialPolygonsDataFrame object Journal of Statistical Software R gt losangeles msa lt MSA msaname Los Angeles state CA level tract R gt losangeles lt city name los angeles state ca The city function extracts a specified CDP from the entire list of CDPs for a given state The user needs to provide two variables the city CDP desired name and the state state Now we plot these two examples in a choropleth map of log population density applying the spplot mode in the choropleth function see Figures 5 and 6 First we calculate the log density as log population area where we access the
29. ge For example if one wants to retrieve all the tracts in Orange County one can apply the county function as follows R gt orange county lt county name orange state ca level tract This will return a SpatialPolygonsDataFrame object which contains only the tracts in the desired county s Changing the level will result in all the block groups or blocks in the county This might be desired for a number of reasons for example plotting or statistical analysis It is worth taking a moment to go over the basic argument nomenclature in this function as it will be similar in nature to functions discussed in following subsections The user can provide either a FIPS code for the county where a FIPS code is the standard identification number used by the Census and other governmental agencies in combination with the correct state e g fips 001 state 41 statefips TRUE or fips 001 state OR statefips FALSE The state and statefips arguments are to be used in tandem see the above example The sp object argument not used in this example allows the user to apply this function onto another SpatialPolygonsDataFrame object this is primarily for use 11 12 UScensus2000 US Census Spatial and Demographic Data in R with the demographics add function discussed in section 6 6 an example is also provided The proj argument not discussed in this article allows the user to re project the data using the spTransfo
30. hh child fhh child hh units 49 hh urban hh rural hh occupied hh vacant 53 hh owner hh renter hh iperson hh 2person 57 hh 3person hh 4person hh 5person hh 6person 61 hh 7person hh nh white 1p hh nh white 2p hh nh white 3p 65 hh nh white 4p hh nh white 5p hh nh white 6p hh nh white 7p 69 hh hisp 1ip hh hisp 2p hh hisp 3p hh hisp 4p 73 hh hisp 5p hh hisp 6p hh hisp p hh black 1p 77 hh black 2p hh black 3p ss hh black 4p hh black 5p 81 hh black 6p hh black 7p hh asian ip hh asian 2p 85 hh asian 3p hh asian 4p hh asian 5p hh asian 6p 89 hh asian 7p One thing to notice is that the first three variables in the above list are used for identification purposes state is the two digit state Federal Information Processing Standards FIPS http www itl nist gov fipspubs code county is the three digit county FIPS code and tract is the four or six digit FIPS code Each level has its own distinct set of identification Journal of Statistical Software codes CDP has a five digit FIPS code block group has all the identification characteristics as tracts plus a blkgrp FIPS code and the block level data contain a single identification variable fips that is a 13 digit FIPS code Alternatively the user can use the help function e g help california tract which will bring up the user manual on that state and level which includes informat
31. information about the area of each polygon using the function areaPoly The choropleth function is based off the spplot function and accepts a number of arguments for more detailed explanation of spplot please see Bivand et al 2008 R gt losangeles msa lt MSA msaname Los Angeles state CA level tract R gt losangeles lt city name los angeles state ca R gt losangeles msa lnden lt log losangeles msa pop2000 areaPoly losangeles msa R gt choropleth losangeles msa Inden main 2000 US Census Tracts n Los Angeles MSA sub Log Density type spplot object list list sp polygons losangeles first FALSE col blue list sp text c 119 33 9 Los Angeles R gt choropleth losangeles msa Inden main 2000 US Census Tracts n Los Angeles MSA sub Log Density type spplot object list list sp polygons losangeles first FALSE col blue list sp text c 118 33 9 Los Angeles xlim c 119 117 ylim c 33 5 34 5 6 5 Example 5 The poly clipper function A common problem in applications of spatial analysis is the lack of a one to one mapping between polygon sets One case of particular interest for those working with the US Census data is in identifying polygons which both fall within a boundary of interest e g all the blocks contained within a given city and which fail to align perfectly with the as
32. ion on all the demographic and identification codes attached A common aggregation used in the United States is that of the county Counties typically have stable borders and represent the next lower level of aggregation after the state level The county function allows the user to select one or more counties The county function takes the following arguments fips Character string takes a string of three characters i e a county FIPS code e g 001 name Character string this must be the name of an actual county in the state e g Baker county Oregon This variable is insensitive to case state Character string can either be the full name e g oregon the abbreviation e g or or the FIPS code e g 41 note that if you are using the FIPS code you have to change statefips to TRUE This variable is insensitive to case level Character string takes in one of three values tract blk or blkgrp This defines the geographic level of data for the county statefips Logical by default statefips FALSE change to TRUE when providing state with a FIPS code sp object SpatialPolygonsDataFrame default NULL allows the user to provide an sp object in which to perform this operation primarily for use with demographics add proj CRS class takes a CRS object e g CRS proj utm zone 10 datum NAD83 This is simply a wrapper for the spTransform function in rgdal Warning Requires rgdal packa
33. itive to case statefips Logical by default statefips FALSE set to TRUE if using the state FIPS codes level Character string takes in one of three values tract blk or blkgrp This defines the geographic level of data for the county Journal of Statistical Software 21 This example is chosen so that it takes a relatively minimal amount of bandwidth however it is still several megabytes of material to download The reader may look up what variables are available with the SF1 technical manual US Census Bureau 2001 For this example the following three variables will be added to the tract level data of rnode_island 1 College dormitories PCT016033 2 Military quarters PCT016034 3 Population of two or more races P005010 R gt library UScensus2000add R gt rhode_island lt demographics add dem c PCT016033 PCT016034 P005010 state R gt names rhode_island ri level tract 1 state county tract 4 pop2000 white black 7 ameri es asian hawn pi 10 other mult race hispanic 13 not hispanic t nh white nh black 16 nh ameri es nh asian nh hawn pi 19 nh other hispanic t h white 22 h black h american es h asian 25 h hawn pi h other males 28 females age under5 age 5 17 31 age 18 21 age 22 29 age 30 39 34 age 40 49 age 50 64 age 65 up 37 med age med age m med age f 40 househol
34. n hypothesis is confirmed We now have four different models none of which have the exact same results and in fact some of which have contradictory results What should we do A natural approach is to compare the models using the Akaike information criterion AIC Akaike 1974 The AIC is a common method for comparing models where we choose the model with the lowest AIC as the best fitting model The code to perform this comparison follows R gt aic model lt c AIC la 1m AIC la sar AIC la lmw AIC la sarw R gt coef model lt cbind la lm coef la sar fit coef la lmw coef la sarw fit coef R gt colnames coef model lt c LM SAR LMW SARW R gt model aic rank lt rbind aic model coef model R gt model aic rank order aic model SAR SARW LM LMW aic model 962 36330848 881 56247395 569 64381426 536 330961 Intercept 0 29271752 0 42564032 0 33940945 2 093474 pct nh white O 28465945 0 01474367 O 04386037 2 511933 pct hispanic 0 65503795 1 06897327 0 80910523 1 486667 pct nh black 0 04992822 0 16416682 0 04052084 2 456774 Journal of Statistical Software 29 pct nh asian 0 27646646 0 58084221 0 46422631 2 031482 pct marhh chd 1 32057946 1 97690601 1 87896648 2 291624 We see that the SAR model fits the best based on its AIC score The SAR model confirms both our race ethnicity hypothesis and our married household with children hypothesis T Summary The UScensus2000 suite
35. n with the variable state or not Case 1 Full MSA name state should be left NULL in this case e g Abilene TX MSA this must be exact Case 2 Takes one of the city names of the MSA and the one of the states which contain the MSA e g msaname Albany and state NY state Character string can either be the full name e g oregon the abbreviation e g or or the FIPS code e g 41 Note that if you are using the FIPS code you have to change statefips to TRUE This variable is insensitive to case This is used in conjunction with msaname see above for more details statefips Logical by default statefips FALSE change to TRUE when providing state with a FIPS code msaname must also be a FIPS code when using this mode level Character string takes in one of three values tract blk or blkgrp This defines the geographic level of data for the MSA proj CRS class takes a CRS object e g CRS proj utm zone 10 datum NAD83 This is simply a wrapper for the spTransform function in rgdal Warning Requires rgdal package To plot the boundary of Los Angeles proper on top of this MSA area we will use the following function city also from the UScensus2000 package The output can be seen in Figure 5 city takes the following inputs name Character string takes the value of a string or string vector and has to be the exact name or names of CDP s If you are unsure of the exact name a quick
36. nd unincorporated cities townships etc US Census Bureau 2001 The US Census Bureau provides polygon representations of the geographic data in a format known as shapefiles or ESRI shapefiles through the TIGER LINE data repository http www census gov geo www tiger and provides access to demographic data through the US Census Bureau s online data extractor American FactFinder http factfinder census Journal of Statistical Software Portland MSA Eight Counties AAEN Block Rae Block Group R AD e Tract RAE es BR e County e Portland CDP i x iS Spe at ae tn IDS Ls Se P t Lowy PR eae Neem me omg NY Nba AT Ra z WAT EEE AAN y ARNE Aa ee we fis 4 Vie alight N v Figure 1 Representation of block block group tract and county hierarchy in US Census geography Note that CDPs do not observe boundaries of the other polygons and that MSAs are composed of counties gov The shapefiles are meant for use in geographic information systems GIS software such as GRASS and ArcGIS A myriad of reasons exist to want such data readily available in an R based format including simulation modeling spatial statistics GIS style plotting and so forth however this data has not been imported into R on a large scale due to the complexity and size of the data set Fortunately a number of the basic tools necessary for this task have been implemented in R including th
37. of packages represents a modern data management system for a large multipurpose data set The added experience provided by having a single coherent series of packages which the user may download install and access on any platform offers useful tools for both didactic and research based endeavors within the social and statistical sciences These packages bring important and influential data into a single manageable system for use in simulation methods and models statistical models and statistical analysis There are a number of obvious extensions of these R packages either to additional US Cen suses or to similar data from other countries This model for documenting and handling the census data can be applied to the 1990 and 1980 US Censuses US Census Bureau 2010a b as well as the upcoming 2010 US Census US Census Bureau 2010c This model can also be applied to other countries which publicly release census and spatial data e g Europe Euro pean Commission 2010 Mexico Instituto Nacional De Estadistica Y Geografia 2010 China CHGIS Data 2010 and the myriad other countries basic administrative and demographic information directly available on IPUMS Minnesota Population Center 2010 The helper functions can be directly applied to past and future US Censuses and can be generalized to handle these other countries geographies Acknowledgments This material is based on research supported in part by the National Science F
38. or race ethnicity in the form of Percent non hispanic white non hispanic black non hispanic asian and hispanic where we would expect to see a positive effect for non hispanic white i e increase in home ownership and a decrease for all minorities Another potential hypothesis is that families with children are more likely to own houses so we might propose the hypothesis that the percentage of married households with children should increase the likelihood of home ownership We could test this by adding a covariate for the percent of married families with children To confirm our hypothesis we should see a positive effect All the necessary data to perform this test is available in the UScensus2000 suite however a certain amount of preprocessing needs to occur Since all the demographic data is stored as a character string it will be necessary to change the demographic values to numeric values Another issue is that all covariates stored in theses packages are in raw counts In our hypothesis we stated all variables in terms of percent X in a given tract so we need to build the necessary percentage vectors to perform our analysis In constructing a SAR model the losangeles tract object built in Section 6 5 will cause an error in the weight matrix function this is due to a disconnected polygon that will have to be remove to perform this type of analysis The code to perform these operations is contained below Journal of Statistical Softw
39. oundation NSF under award BCS 0827027 and by the Office of Naval Research ONR under award N00014 08 1 1015 The author would like in particular to thank Carter T Butts for his suggestions as well as the input of fellow lab members Ryan Acton Lorien Jasney Christopher Steven Marcum and Emma Spiro I would also like thank Nicholas Nagle and John Hipp for their suggestions and lastly I would like to thank both of the anonymous reviewers for their comments and suggestions References Akaike H 1974 A New Look at the Statistical Model Identification IEEE Transactions on Automatic Control 19 6 716 723 Bivand R 2009 spdep Spatial Dependence Weighting Schemes Statistics and Models R package version 0 4 52 URL http CRAN R project org package spdep Bivand RS Pebesma EJ G mez Rubio V 2008 Applied Spatial Data Analysis with R Springer Verlag New York NY 30 UScensus2000 US Census Spatial and Demographic Data in R CHGIS Data 2010 China DCW GIS Data Harvard University URL http www fas harvard edu chgis data dcw European Commission 2010 Eurotat URL http epp eurostat ec europa eu Instituto Nacional De Estadistica Y Geografia 2010 Statistical Yearbook of the United Mexican States URL http www inegi org mx inegi Keitt TH Bivand R Pebesma E Rowlingson B 2009 rgdal Bindings for the Geospatial Data Abstraction Library R package version 0 6 21 URL http CRAN R project org pa
40. ounding box which is used mostly for plotting represents the minimum and maximum values of the spatial polygons The CRS represents the projection of the data commonly this is Longitude and Latitude The sp and maptools packages also provide a number of routines so that R knows how to perform many common tasks such as plot and summary There are two basic methods for directly accessing polygon and demographic data in SpatialPolygonsDataFrame objects The first is the slot method accessed by either slot or symbol The second is through the standard method calls E and Take for example the SpatialPolygonsDataFrame object oregon tract R gt library UScensus2000tract R gt data oregon tract R gt slotNames oregon tract 1 data polygons plotOrder bbox proj4string The function slotNames provides us the names of the five objects which comprise each SpatialPolygonsDataFrame Excerpts describing each of these objects pulled from their respective help files Pebesma and Bivand 2005 Lewin Koh and Bivand 2009 are shown below data Object of class data frame the number of rows in data should equal the number of Polygons class objects help SpatialPolygonsDataFrame polygons Sets of spatial coordinates to create spatial data or retrieve spatial coordinates help polygons plotOrder Object of class integer integer array giving the order in which objects should be plotted help SpatialPolygons
41. r gt zl Intercept 0 292718 0 027362 10 6979 lt 2 2e 16 pcet nh white 0 284659 0 040294 7 0645 1 612e 12 pet hispanic 0 655038 0 046395 14 1187 lt 2 2e 16 26 UScensus2000 US Census Spatial and Demographic Data in R pet nh black 0 049928 0 043829 1 1392 0 2546 pet nh asian 0 276466 0 051743 5 3430 9 140e 08 pet marhh chd 1 320579 0 071883 18 3712 lt 2 2e 16 Lambda 0 12459 LR test value 394 72 p value lt 2 22e 16 Log likelihood 489 1817 ML residual variance sigma squared 0 01688 sigma 0 12992 Number of observations 887 Number of parameters estimated 8 AIC 962 36 Looking at this SAR model we see that our initial race ethnicity hypothesis is again confirmed although percentage non hispanic black is not significant and that our married household with children hypothesis is confirmed again as well It is worth noting that there is change in both what is significant i e non hispanic white goes from not significant to significant and non hispanic black goes from significant to not significant and there is a magnitude change non hispanic white goes from 0 04 to 0 28 Another aspect of this analysis that is worth noting is that the size of the effect for the percentage of non hispanic white increases and conversely decreases in the other minority groups and percentage married households with children This last model does not take into account the possibility of heterogeneous population within tracts
42. rganization may be advantageous to the user in actual application for example this allows the suite to be broken up by the user if say they are interested in only a subset of levels of the US Census data Each of the packages which contain spatial and demographic data UScensus2000blk UScensus2000blkgrp US census2000tract and UScensus2000cdp are composed of 51 SpatialPolygonsDataFrame objects Each SpatialPolygonsDataFrame object is named state name all lower case dot Census Bureau designation e g california blk Figure 2 Following this section there will be a more detailed coverage of the sp and maptools packages Section 3 However we will mention here that all demographics are stored as a data frame object e g califronia tract data within a slot within each state The demographics contained within the data object are stored as numeric vectors 2 1 Installing the UScensus2000 suite Installation of the UScensus2000 suite may be performed either directly from the Compre hensive R Archive Network CRAN http CRAN R project org or from the command line using R CMD INSTALL after downloading from either CRAN or the Networks Computa tion and Social Dynamics NCASD Lab website http www ncasd org census2000 Unfortunately the UScensus2000blk package is not available through CRAN due to its size One may however download it directly from NCASD website using the install b1k func tion available in UScensus2000 pa
43. rm function provided in the rgdal package Keitt Bivand Pebesma and Rowlingson 2009 6 2 Example 2 The demographics function While these packages are meant to be primarily for use in spatial analysis there are times when the user might want to access demographic information at various levels of aggregation This might occur when applying simpler statistical models analyzing demographic distributions and so forth All the data in the US Census is count data This example demonstrates the use of the demographics function The results obtained in the following examples are the same as those obtained from from US Census Bureau s website American Factfinder http factfinder census gov The demographics function takes the following arguments dem Character string takes one or more demographics named as discussed in Section 6 1 state Character string can either be the full name of a state e g oregon the abbre viation e g or or the FIPS code e g 41 note that if you are using the FIPS code you have to change statefips to TRUE This variable is insensitive to case statefips Logical by default statefips FALSE set to TRUE if using the state FIPS codes level Character string takes levels tract blk blkgrp cdp msa or county msaname Logical optional if level msa allows the use of the verbose MSA place name R gt laDem lt demographics dem c pop2000 white black
44. s A choropleth map a common method used in GIS style analysis is typically defined as a thematic map in which areas or polygons are shaded or patterned and displayed on a map in proportion to a measurement or outcome variable of interest standard examples include population counts population density income etc This allows the analyst to perform a quick visual analysis of the spatial homogeneity or heterogeneity in his or her data set Now one may plot a choropleth map of California s population A helper function for per forming this task is provided this can also be done using the plot function or RColorBrewer Neuwirth 2007 see help california tract or the spplot function see Section 6 4 for an example The helper function choropleth handles the necessary details for matching the colors to the legend as well as the call to plot for the user The output can be seen in Figure 4 choropleth takes the following arguments sp SpatialPolygonsDataFrame must be a SpatialPolygonsDataFrame object dem Character string this must be the name of one of the data frame objects contained within the SpatialPolygonsDataFrame e g pop2000 cuts List containing quantile and seq object from 0 to 1 color List containing a function and list of arguments for the function to produce the requested color scheme main a character string this will be the title of the plot sub Character string this will be the subtitle on the plot
45. signed boundary That is cases in which a single chosen polygon e g a city boundary school district border zip code are overlaid onto another level of geography e g block block group tract where the borders of the chosen polygon fail to line up with the chosen geographic level for an example of this problem see Figure 7 For a solution to this problem we perform a boolean intersection between polygons and eliminate the non intersecting portion As the area of interest is not only in the geographical unit e g the blocks within a given city but also the covariates associates with that unit e g population counts housing unites etc the question then arises how one should impute the covariate of the new resulting polygon While there are a number of ways to handle such a problem we have chosen to implement a very simple approach that should be sufficient for many purposes We apply a simple proportion estimator by area while assuming homogeneous allocation of covariates within 17 18 UScensus2000 US Census Spatial and Demographic Data in R 2000 US Census Tracts Bend OR f City Border Tract Border Intersection Figure 7 Demonstration of the intersection of tracts and a CDP in this case Bend OR the geography This is reasonable approach in this particular case as all covariates are count data this would not be a practical approach if the covariates were proportions themselves Warning Assuming
46. the following R commands A word of caution the bb epsilon is set particularly low this works well with large cities such as Los Angeles however it needs to be enlarged for smaller cities e g Bend OR We recommend playing with this value some to see the different results for Figure 7 we used a bb epsilon 0 5 R gt losangeles tract lt poly clipper name Los Angeles state ca level tract R gt plot losangeles tract R gt title 2000 US Census Tracts n Los Angeles The output can be seen in Figure 8 6 6 Example 6 The demographics add function While the UScensus2000 suite contains 86 different demographic variables there could be a situation where the user desires one or more demographic variables which are not readily available For this we have developed a function demographics add which take in SF1 vari able names and add the chosen variables to the desired geography and outputs a sp object A word of caution the following example requires access to the internet The demographics add function takes the following arguments dem Character string takes in a vector of one or more census variables as defined in SF 1 tech report US Census Bureau 2001 state Character string can either be the full name of a state e g oregon the abbre viation e g or or the FIPS code e g 41 note that if you are using the FIPS code you have to change statefips to TRUE This variable is insens
47. to why this might be the case we propose the following Direct zoning or development might create dependencies between home ownership in one area and other nearby areas with high ownership areas tending to encourage similar behavior nearby e g by resisting efforts to create high density rental housing in the vicinity and low ownership areas having a similar effect e g by development pressure erosion of ownership based neigh borhoods via pricing effects etc This leads us to perform one of the omnibus tests for spatial autocorrelation which could affect our model in ways that are non intuitive One of the most accepted of these tests for spatial correlation is the so called Moran s I test Bivand et al 2008 To perform a Moran s I test on the linear model in R we first need to build the spatial weights we will use in the SAR models We will build our weight matrix based on the concept of queen weights which is named for the ways in which a queen can move in chess e g up down left right and diagonally spdep package contains functions for building the queens weight adjacency matrix in which the polygons represent the nodes and edges represent any shared edge by two polygons We can also plot the the spatial network generated this can be seen in Figure 9 Journal of Statistical Software 25 R gt losangeles nb lt poly2nb losangeles tract R gt plot losangeles tract border gray R gt coords lt coordinates
48. way to find it is to load the library UScensus2000cdp and pull out the list of names for the state you are interested in see example below state Character string can either be the full name e g oregon the abbreviation e g or or the FIPS code e g 41 note that if you are using the FIPS code you have to change statefips to TRUE This variable is insensitive to case statefips Logical by default statefips FALSE change to TRUE when providing state with a FIPS code name must also be a FIPS code when using this mode sp object SpatialPolygonsDataFrame default NULL allows the user to provide an sp object in which to perform this operation primarily for use with demographics add function proj CRS class takes a CRS object e g CRS proj utm zone 10 datum NAD83 This is simply a wrapper for the spTransform function in rgdal Warning Requires rgdal package Let us breakdown these two functions MSA has three main ways of extracting a specified MSA By using the five digit MSA FIPS code via the variable msafips not used in this example by supplying the full MSA name to msaname e g Portland Salem OR WA CMSA or by providing the name of one of the cities from the full name of the MSA e g Portland and one of the state abbreviations from the full MSA name or one of the state FIPS codes e g OR in the state argument The level argument specifies the geographical level and can 16 USc
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