states

i've been asked to generate some demographic reports (crime rates, birth/deaths, etc) based on state and cities for the USA. I have all the demographic data (provided by our client) but can't seem to find any places which have the boundaries (read: LAT/LONG's) of the USA States and their cities. Our data are Lat/Long points of data (eg. a crime, a birth, etc) and we want to get some mapped reports and also datamine using Sql server (we're using MS Sql 2008, but that shouldn't impact this question). So .. can anyone direct me to where there are some state and city boundary sources ? I know our

Are there states other than "running", "paused" and "stopped" or are these the only states available? No, there are other possible states. The Docker Remote API defines the following states: created A container that has been created (e.g. with docker create ) but not started restarting A container that is in the process of being restarted running A currently running container paused A container whose processes have been paused exited A container that ran and completed ("stopped" in other contexts, although a created container is technically also "stopped") dead A container that the daemon

In Linux, what happens to the state of a process when it needs to read blocks from a disk? Is it blocked? If so, how is another process chosen to execute? Tim Post While waiting for read() or write() to/from a file descriptor return, the process will be put in a special kind of sleep, known as "D" or "Disk Sleep". This is special, because the process can not be killed or interrupted while in such a state. A process waiting for a return from ioctl() would also be put to sleep in this manner. An exception to this is when a file (such as a terminal or other character device) is opened in O

mycars <- c("mpg",'hp','wt') head(mtcars[mycars]) summary(mtcars[mycars]) mystats <- function(x,na.omit=FALSE){ #偏度峰度 if (na.omit) x <- x[!is.na(x)] m <- mean(x) n <- length(x) s <- sd(x) skew <- sum((x-m)^3/s^3)/n kurt <- sum((x-m)^4/s^4)/n - 3 return(c(n=n,mean=m,stdev=s,skew=skew,kurtosis=kurt)) } sapply(mtcars[mycars], mystats) sapply(mtcars[mycars], mystats,na.omit=T) install.packages('Hmisc') library(Hmisc) describe(mtcars[mycars]) library(pastecs) # stat.desc(x,basic=T,desc=T,norm=F,p=0.95) stat.desc(mtcars[mycars]) library(psych) describe(mtcars[mycars]) head(mtcars) aggregate(mtcars