13. A computing device for determining topics and locations associated with a target document, comprising: a document store having a collection of documents of words, each word of a document associated with a location; a memory storing computer-implemented instructions of a component that generates collection level parameters for a latent Dirichlet allocation model for the collection of documents based on latent topics and the location of each document, the collection level parameters relating to probabilities of latent topics, locations, and words of the collection including a probability that each location of the collection relates to each latent topic, the collection level parameters indicating a probability that a document in the collection relates to each latent topic, a probability that each word of the collection relates to each latent topic, and a probability that each location of the collection relates to each latent topic, wherein a variational expectation maximization algorithm is used to estimate the collection level parameters that are a maximization of a lower bound on the collection level parameters represented by a summation for each document in the collection of the log of the conditional probability of the document and its location given the collection level parameters; and a component that estimates using the collection level parameters probabilities of topics and locations being associated with the target document; and a component that selects the location with the highest estimated probability for a target document as the location associated with that target document; and a processor for executing the computer-implemented instructions stored in memory.
13. A computing device for determining topics and locations associated with a target document, comprising: a document store having a collection of documents of words, each word of a document associated with a location; a memory storing computer-implemented instructions of a component that generates collection level parameters for a latent Dirichlet allocation model for the collection of documents based on latent topics and the location of each document, the collection level parameters relating to probabilities of latent topics, locations, and words of the collection including a probability that each location of the collection relates to each latent topic, the collection level parameters indicating a probability that a document in the collection relates to each latent topic, a probability that each word of the collection relates to each latent topic, and a probability that each location of the collection relates to each latent topic, wherein a variational expectation maximization algorithm is used to estimate the collection level parameters that are a maximization of a lower bound on the collection level parameters represented by a summation for each document in the collection of the log of the conditional probability of the document and its location given the collection level parameters; and a component that estimates using the collection level parameters probabilities of topics and locations being associated with the target document; and a component that selects the location with the highest estimated probability for a target document as the location associated with that target document; and a processor for executing the computer-implemented instructions stored in memory. 14. The computing device of claim 13 wherein each word of a document in the document store is associated with the same document.