Methods and apparatus to generate data for geographic areas are disclosed. An example method includes identifying a first geographic area for which a database does not include a model, determining a first data element of the first geographic area, identifying a first trained model corresponding to a second geographic area with the first data element, identifying a second trained model corresponding to a third geographic area with the first data element, mixing the first trained model and the second trained model to generate a composite model, and using the composite model to represent the first geographic area in the database.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: determining, via a processor, a data element of a first geographic area; creating in a database, via the processor, a first dataset representative of the first geographic area based on the data element, the first dataset having less than a threshold amount of information; identifying, via the processor, a second geographic area having the data element, the database including a second dataset for the second geographic area, the second dataset being a model trained with information known to be representative of the second geographic area; identifying, via the processor, a third geographic area having the data element, the database including a third dataset for the third geographic area, the third dataset being a model trained with information known to be representative of the third geographic area; combining, via the processor, the second and third dataset to generate a composite model; and profiling an unknown geographic area by populating, via the processor, the first dataset for the first geographic area with data from the composite model such that the amount of information in the first dataset satisfies the threshold amount of information. 2. A method as defined in claim 1, wherein at least one of the second dataset or the third dataset is trained using machine learning algorithms prior to the combining of the second and third dataset. 3. A method as defined in claim 1, wherein determining the data element of the first geographic area is based on at least one of satellite imagery of the first geographic area, a survey or census related to the first geographic area, applications monitoring the first geographic area, or Internet entries related to the first geographic area. 4. A method as defined in claim 1, wherein using the composite model to populate the first dataset for the first geographic area includes adding an estimation of an aspect of the first geographic area to the first dataset, the estimation based on the composite model. 5. A method as defined in claim 1, wherein the first data element is at least one of a type, a geography, a demographic, an inhabitant lifestyle, a wealth distribution, a size, or a shape. 6. A method as defined in claim 1, wherein the second and third dataset are combined by weighted averaging the second and third dataset, the second and third dataset weighted based on similarities between the first geographic area and the second and third geographic areas. 7. A method as defined in claim 1, wherein the threshold amount of information is an amount of information required for a learning algorithm to perform accurately on unseen tasks. 8. A tangible computer readable storage medium, comprising instructions that, when executed, cause a machine to at least: identify a first geographic area for which a database does not include a model; determine a first data element of the first geographic area; identify a first trained model corresponding to a second geographic area with the first data element; identify a second trained model corresponding to a third geographic area with the first data element; mix the first trained model and the second trained model to generate a composite model; and use the composite model to represent the first geographic area in the database. 9. A storage medium as defined in claim 8, wherein the determining of the first data element of the first geographic area is based on at least one of satellite imagery of the first geographic area, a survey or census related to the first geographic area, applications monitoring the first geographic area, or Internet entries related to the first geographic area. 10. A storage medium as defined in claim 8, wherein the first data element is at least one of a type, a geography, a demographic, an inhabitant lifestyle, a wealth distribution, a size, or a shape. 11. A storage medium as defined in claim 8, wherein the instructions further cause a machine to: determine a second data element of the first geographic area based on at least one of an image representative of the first geographic area, a survey from the first geographic area, applications monitoring the first geographic area, and the internet; and verify that the first trained model corresponding to the second geographic area has the second data element. 12. A storage medium as defined in claim 11, wherein the second trained model corresponding to the third geographic area does not have the second data element, the instructions further cause a machine to mix the first and second trained models by weighted averaging, the respective weights of the first and second trained models based on the number of matching data elements. 13. A storage medium as defined in claim 8, wherein the first and second trained models are mixed by aggregating the first and second trained models together. 14. A storage medium as defined in claim 8, wherein the composite model satisfies a threshold amount of information required for a learning algorithm to perform accurately on unseen tasks. 15. An apparatus, comprising: a geographic area manager to determine a first data element of a first geographic area; a model searcher to search a database for at least one of a model of the first geographic area, a first trained model corresponding to a second geographic area, and a second trained model corresponding to a third geographic area; a geographic area matcher to determine whether the second geographic area and the third geographic area match the first data element of the first geographic area; a trained model extractor to extract a first trained model corresponding to the second geographic area and a second trained model corresponding to the third geographic area; and a model mixer to blend the first trained model and the second trained model together to represent the model of the first geographic area. 16. An apparatus as defined in claim 15, wherein the first data element is at least one of a type, a geography, a demographic, an inhabitant lifestyle, a wealth distribution, a size, or a shape. 17. An apparatus as defined in claim 15, wherein the model mixer is to blend the first trained model and the second trained model using data fusion algorithms. 18. An apparatus as defined in claim 15, wherein the first trained model is a bootstrap aggregate decision tree learning algorithm and the second trained model is an artificial neural network learning algorithm. 19. An apparatus as defined in claim 15, wherein the geographic area manager is to determine a second data element of the first geographic area based on at least one of the image representative of the first geographic area, a survey from the first geographic area, applications monitoring the first geographic area, and the Internet, and further wherein the geographic area matcher is to verify that at least one of the second geographic area and the third geographic area have the second data element of the first geographic area. 20. An apparatus as defined in claim 15, wherein when the second geographic area has the second data element of the first geographic area and the third geographic area does not have the second data element of the first geographic area, the geographic area matcher determines the second geographic area is a better match to the first geographic area than the third geographic area.