[R-sig-Geo] using spatialpolygonsdataframe in ppm (or, converting spatialpolygonsdataframe to pixel image or other object useful in ppm)

[Christopher W. Ryan]

Hello.

What is the best way to use a spatialpolygonsdataframe, with a numerical
variable of interest for each polygon (proportion of households in
poverty for US census tracts in the region of interest) as a predictor
in ppm() in spatstat?  I don't think I can use it directly on the RHS of
ppm(), because spatialpolygonsdataframe is not listed in the help file
for ppm() as an acceptable predictor.  So is there a way to convert the
census tract spatialpolygonsdataframe to an acceptable input object for
ppm(), such as a pixel image with each pixel having the numerical value
of poverty in its census tract polygon?

Or is there a better way to proceed?

Thank you.  Below is my sessionInfo

--Chris Ryan
Broome County Health Department, Binghamton, NY

=================================
R version 3.3.3 (2017-03-06)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
 [1] tmap_1.10          rgdal_1.2-6        RColorBrewer_1.1-2
maptools_0.9-2
 [5] sp_1.2-4           spatstat_1.50-0    rpart_4.1-10
nlme_3.1-131
 [9] shapefiles_0.7     foreign_0.8-67     stringr_1.2.0
dplyr_0.5.0

loaded via a namespace (and not attached):
 [1] viridisLite_0.2.0    jsonlite_1.4         splines_3.3.3
 [4] geojsonlint_0.2.0    foreach_1.4.3        R.utils_2.5.0
 [7] gtools_3.5.0         shiny_1.0.5          assertthat_0.2.0
[10] expm_0.999-2         stats4_3.3.3         LearnBayes_2.15
[13] lattice_0.20-35      digest_0.6.12        polyclip_1.6-1
[16] colorspace_1.3-2     plyr_1.8.4           htmltools_0.3.5
[19] httpuv_1.3.5         Matrix_1.2-8         R.oo_1.21.0
[22] XML_3.98-1.9         rmapshaper_0.3.0     raster_2.5-8
[25] gmodels_2.16.2       xtable_1.8-2         webshot_0.4.1
[28] scales_0.4.1         gdata_2.17.0         tensor_1.5
[31] satellite_1.0.0      spatstat.utils_1.4-1 tibble_1.3.0
[34] mgcv_1.8-17          gdalUtils_2.0.1.7    mapview_2.1.4
[37] magrittr_1.5         mime_0.5             deldir_0.1-12
[40] R.methodsS3_1.7.1    MASS_7.3-45          class_7.3-14
[43] tools_3.3.3          geosphere_1.5-5      V8_1.5
[46] munsell_0.4.3        compiler_3.3.3       e1071_1.6-8
[49] units_0.4-5          classInt_0.1-24      grid_3.3.3
[52] tmaptools_1.2-1      RCurl_1.95-4.8       dichromat_2.0-0
[55] iterators_1.0.8      htmlwidgets_0.8      goftest_1.1-1
[58] crosstalk_1.0.0      bitops_1.0-6         base64enc_0.1-3
[61] boot_1.3-18          codetools_0.2-15     abind_1.4-5
[64] DBI_0.6-1            jsonvalidate_1.0.0   curl_2.5
[67] R6_2.2.0             udunits2_0.13        rgeos_0.3-23
[70] spdep_0.6-12         KernSmooth_2.23-15   stringi_1.1.5
[73] osmar_1.1-7          Rcpp_0.12.10         sf_0.5-3
[76] png_0.1-7            leaflet_1.1.0        coda_0.19-1
>

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