Points, Polygons and Power Outages

December 27, 2013
By

(This article was first published on rud.is » R, and kindly contributed to R-bloggers)

Most of my free coding time has been spent tweaking a D3-based live power outage tracker for Central Maine Power customers (there’s also a woefully less-featured Shiny app for it, too). There is some R associated with the D3 vis, but it’s limited to a cron job that’s makes the CSV files for the sparklines in D3 vis by

  • reading historical outage data from a database of scraped readings I’ve been keeping
  • filling out the time series
  • reducing it to an hourly time series, and
  • trimming the data set to the last 30 days of records:

#!/usr/bin/Rscript
# running in a cron job so no spurious text output pls
options(warn=-1)
options(show.error.messages=FALSE)
 
suppressMessages(library(methods))
suppressMessages(library(zoo))
library(chron)
library(xts)
library(reshape2)
library(DBI)
library(RMySQL)
 
m <- dbDriver("MySQL");
con <- dbConnect(m, user='DBUSER', password='DBPASSWORD', host='localhost', dbname='DBNAME');
res <- dbSendQuery(con, "SELECT * FROM outage") # cld just pull the 2 fields I need
outages <- fetch(res, n = -1)
outages$ts <- as.POSIXct(gsub("\\:[0-9]+\\..*$","", outages$ts), format="%Y-%m-%d %H:%M")
 
# for each county we have data for
for (county in unique(outages$county)) {
 
  # get the 2 fields I need (shld prbly filter that in the SELECT)
  outage.raw <- outages[outages$county == county,c(1,4)]
 
  # make it a zoo object
  outage.zoo <- zoo(outage.raw$withoutpower, outage.raw$ts)
 
  # fill out the 15-minute readings
  complete.zoo <- merge(outage.zoo, zoo(, seq(start(outage.zoo), max(outages$ts), by="15 min")), all=TRUE)
  complete.zoo[is.na(complete.zoo)] <- 0
 
  # shrink to hourly and trim at 30 days
  hourly.zoo <- last(to.hourly(complete.zoo), "30 days")
 
  # crank out a CSV  
  df <- data.frame(hourly.zoo)
  df <- data.frame(ts=rownames(df), withoutPower=df$complete.zoo.High)
 
  write.csv(df, sprintf("OUTPOUT_LOCATION/%s.csv",county), row.names=FALSE)
 
}

I knew there were other power companies in Maine, but CMP is the largest, so I focused my attention on getting data from it. After hearing an outage update on MPBN I decided to see if Bangor Hydro Electric had scrape-able content and it turns out there was a lovely (well, as lovely as XML can be) XML file delivered on the page with this “meh” Google push-pin map:

Bangor_Hydro_Electric_-_Outage_Map

The XML file is used to build the markers for the map and has marker tags that look like this:

<marker name="Exeter" outages="18" 
        lat="44.96218" lng="-69.12253" 
        streets="BUTTERS  RD;CHAMPEON  RD;GREENBUSH  RD;" 
        reflabels="87757-1;84329-1;85032-1;"/>

I’m really only tracking county-level data and BHE does not provide that, even in the huge table of street-level outages that you’ll see on that outage page. I decided to use R to aggregate the BHE data to the county level via the “point-in-polygon” method.

Getting Right To the Point

To perform the aggregation in R, I needed county-level polygons for Maine. I already had that thanks to the previous work, but I wanted to optimize the search process, so I took the US counties shapefile and used OGR from the GDAL (Geospatial Data Abstraction Library) suite to extract just the Maine county polygons:

ogr2ogr -f "ESRI Shapefile" \
        -where "STATE_NAME = 'MAINE'" maine.shp counties.shp

You can see both a reduction in file size and complexity via ogrinfo:

$ll *shp
-rwxr-xr-x@ 1 bob  staff  1517624 Jan  8  2010 counties.shp
-rw-r--r--  1 bob  staff    12588 Dec 26 23:03 maine.shp
$ ogrinfo -sql "SELECT COUNT(*) FROM counties" counties.shp
INFO: Open of 'counties.shp'
      using driver 'ESRI Shapefile' successful.
 
Layer name: counties
Geometry: Polygon
Feature Count: 1
Layer SRS WKT:
(unknown)
COUNT_*: Integer (0.0)
OGRFeature(counties):0
  COUNT_* (Integer) = 3141
$ ogrinfo -sql "SELECT COUNT(*) FROM maine" maine.shp
INFO: Open of 'maine.shp'
      using driver 'ESRI Shapefile' successful.
 
Layer name: maine
Geometry: Polygon
Feature Count: 1
Layer SRS WKT:
(unknown)
COUNT_*: Integer (0.0)
OGRFeature(maine):0
  COUNT_* (Integer) = 16

The conversion reduces the file size from 1.5MB to ~12K and shrinks the number of polygons from 3,141 to 16. The counties.shp and maine.shp shapefiles were built from U.S. census data and have scads more information that you might want to use (i.e. perhaps, for correlation with the outage info, though storms are the prime causal entity for the outages :-):

$ ogrinfo -al -so counties.shp
INFO: Open of 'counties.shp'
      using driver 'ESRI Shapefile' successful.
 
Layer name: counties
Geometry: Polygon
Feature Count: 3141
Extent: (-176.806138, 18.921786) - (-66.969271, 71.406235)
Layer SRS WKT:
GEOGCS["GCS_WGS_1984",
    DATUM["WGS_1984",
        SPHEROID["WGS_84",6378137.0,298.257223563]],
    PRIMEM["Greenwich",0.0],
    UNIT["Degree",0.0174532925199433]]
NAME: String (32.0)
STATE_NAME: String (25.0)
STATE_FIPS: String (2.0)
CNTY_FIPS: String (3.0)
FIPS: String (5.0)
POP2000: Integer (9.0)
POP2007: Integer (9.0)
POP00_SQMI: Real (10.1)
POP07_SQMI: Real (7.1)
WHITE: Integer (9.0)
BLACK: Integer (9.0)
AMERI_ES: Integer (9.0)
ASIAN: Integer (9.0)
HAWN_PI: Integer (9.0)
OTHER: Integer (9.0)
MULT_RACE: Integer (9.0)
HISPANIC: Integer (9.0)
MALES: Integer (9.0)
FEMALES: Integer (9.0)
AGE_UNDER5: Integer (9.0)
AGE_5_17: Integer (9.0)
AGE_18_21: Integer (9.0)
AGE_22_29: Integer (9.0)
AGE_30_39: Integer (9.0)
AGE_40_49: Integer (9.0)
AGE_50_64: Integer (9.0)
AGE_65_UP: Integer (9.0)
MED_AGE: Real (9.1)
MED_AGE_M: Real (9.1)
MED_AGE_F: Real (9.1)
HOUSEHOLDS: Integer (9.0)
AVE_HH_SZ: Real (9.2)
HSEHLD_1_M: Integer (9.0)
HSEHLD_1_F: Integer (9.0)
MARHH_CHD: Integer (9.0)
MARHH_NO_C: Integer (9.0)
MHH_CHILD: Integer (9.0)
FHH_CHILD: Integer (9.0)
FAMILIES: Integer (9.0)
AVE_FAM_SZ: Real (9.2)
HSE_UNITS: Integer (9.0)
VACANT: Integer (9.0)
OWNER_OCC: Integer (9.0)
RENTER_OCC: Integer (9.0)
NO_FARMS97: Real (11.0)
AVG_SIZE97: Real (11.0)
CROP_ACR97: Real (11.0)
AVG_SALE97: Real (7.2)
SQMI: Real (8.1)
OID: Integer (9.0)

With the new shapefile in hand, the basic workflow to get BHE outages at the county level is:

  • Read and parse the BHE outages XML file to get the lat/long pairs
  • Build a SpatialPoints object out of those pairs
  • Make a SpatialPolygonsDataFrame out of the reduced Maine counties shapefile
  • Overlay the points in the polygons and get the feature metadata intersection (including county)
  • Aggregate the outage data

The R code (below) does all that and is liberally commented. One has to appreciate how succinct the XML parsing is and the beautiful simplicity of the over() function (which does all the really hard work).

library(XML)
library(maptools)
library(sp)
library(plyr)
 
# Small script to get county-level outage info from Bangor Hydro
# Electric's town(-ish) level info
#
# BHE's outage google push-pin map is at
#   http://apps.bhe.com/about/outages/outage_map.cfm
 
# read BHE outage XML file that was intended for the google map
# yep. One. Line. #takethatpython
 
doc <- xmlTreeParse("http://apps.bhe.com/about/outages/outage_map.xml", 
                    useInternalNodes=TRUE)
 
# xmlToDataFrame() coughed up blood on that simple file, so we have to
# resort to menial labor to bend the XML to our will
 
doc.ls <- xmlToList(doc)
doc.attrs <- doc.ls$.attrs
doc.ls$.attrs <- NULL
 
# this does the data frame conversion magic, tho it winces a bit
 
suppressWarnings(doc.df <- data.frame(do.call(rbind, doc.ls), 
                                      stringsAsFactors=FALSE))
 
# need numbers for some of the columns (vs strings)
 
doc.df$outages <- as.numeric(doc.df$outages)
doc.df$lat <- as.numeric(doc.df$lat)
doc.df$lng <- as.numeric(doc.df$lng)
 
# SpatialPoints likes matrices, note that it's in LON, LAT order
# that always messes me up for some reason
 
doc.m <- as.matrix(doc.df[,c(4,3)])
doc.pts <- SpatialPoints(doc.m)
 
# I trimmed down the country-wide counties file from
#   http://www.baruch.cuny.edu/geoportal/data/esri/usa/census/counties.zip
# with
#   ogr2ogr -f "ESRI Shapefile" -where "STATE_NAME = 'MAINE'" maine.shp counties.shp
# to both save load time and reduce the number of iterations for over() later
 
counties <- readShapePoly("maine.shp", repair=TRUE, IDvar="NAME")
 
# So, all the above was pretty much just for this next line which does  
# the "is this point 'a' in polygon 'b' automagically for us. 
 
found.pts <- over(doc.pts, counties)
 
# steal the column we need (county name) and squirrel it away with outage count
 
doc.df$county <- found.pts$NAME
doc.sub <- doc.df[,c(2,7)]
 
# aggregate the result to get outage count by county
 
count(doc.sub, c("county"), wt_var="outages")
 
##      county freq
##1    Hancock 4440
##2  Penobscot  869
##3      Waldo   28
##4 Washington  545
##5       <NA>  328

Astute readers will notice unresolved points (the NAs). I suspect they are right on coastal boundaries that were probably missed in these simplified county polygons. We can see what they are by looking at the NA entries in the merged data frame:

doc.df[is.na(doc.df$county),c(1:4)]
           name outages      lat       lng
35    Deer Isle       1 44.22451 -68.67778
38   Harborside     166 44.34900 -68.81555
39     Sorrento      43 44.47341 -68.17723
62    Bucksport      71 44.57369 -68.79562
70    Penobscot      40 44.44552 -68.81780
78      Bernard       1 44.24119 -68.35585
79   Stonington       5 44.15619 -68.66669
80 Roque Bluffs       1 44.61286 -67.47971

But a map would be more useful for those not familiar with Maine geography/extents:

Plot_Zoom

library(ggplot2)
 
ff = fortify(counties, region = "NAME")
 
missing <- doc.df[is.na(doc.df$county),]
 
gg <- ggplot(ff, aes(x = long, y = lat))
gg <- gg + geom_path(aes(group = group), size=0.15, fill="black")
gg <- gg + geom_point(data=missing, aes(x=lng, y=lat), 
                      color="#feb24c", size=3)
gg <- gg + coord_map(xlim=extendrange(range(missing$lng)), ylim=extendrange(range(missing$lat)))
gg <- gg + theme_bw()
gg <- gg + labs(x="", y="")
gg <- gg + theme(plot.background = element_rect(fill = "transparent",colour = NA),
                 panel.border = element_blank(),
                 panel.background =element_rect(fill = "transparent",colour = NA),
                 panel.grid = element_blank(),
                 axis.text = element_blank(),
                 axis.ticks = element_blank(),
                 legend.position="right",
                 legend.title=element_blank())
gg

The “zoom in” is done by taking and slightly extending the range of the extracted points via range() and extendrange(), reproduced below:

range(missing$lng)
[1] -68.81780 -67.47971
range(missing$lat)
[1] 44.15619 44.61286
 
extendrange(range(missing$lng))
[1] -68.88470 -67.41281
extendrange(range(missing$lat))
[1] 44.13336 44.63569

It turns out my suspicion was right, so to use this in “production” I’ll need a more accurate shapefile for Maine counties (which I have, but Descent is calling me, so it will have to wait for another day).

I’ll leave you with a non-Google push-pin map of outages that you can build upon (it needs some tweaking):

Plot_Zoom-2

gg <- ggplot(ff, aes(x = long, y = lat))
gg <- gg + geom_polygon(aes(group = group), size=0.15, fill="black", color="#7f7f7f")
gg <- gg + geom_point(data=doc.df, aes(x=lng, y=lat, alpha=outages, size=outages), 
                      color="#feb24c")
gg <- gg + coord_map(xlim=c(-71.5,-66.75), ylim=c(43,47.5))
gg <- gg + theme_bw()
gg <- gg + labs(x="", y="")
gg <- gg + theme(plot.background = element_rect(fill = "transparent",colour = NA),
                 panel.border = element_blank(),
                 panel.background =element_rect(fill = "transparent",colour = NA),
                 panel.grid = element_blank(),
                 axis.text = element_blank(),
                 axis.ticks = element_blank(),
                 legend.position="right",
                 legend.title=element_blank())
gg

You can find all the R code in one, compact gist.

To leave a comment for the author, please follow the link and comment on his blog: rud.is » R.

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