Methods and apparatus to profile geographic areas of interest

Abstract

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.

1 - 20 . (canceled) 
     
     
         21 . An apparatus comprising:
 memory; and   at least one processor to:
 collect data for a first geographic area, the first geographic area an unknown geographic area; 
 perform a first comparison between the first geographic area and a second geographic area, the second geographic area associated with a first set of trained machine learning models, to identify a similarity based on a criterion, the second geographic area a known matching geographic area; 
 perform a second comparison between the first geographic area and a third geographic area, the third geographic area associated with a second set of trained machine learning models, to identify a similarity based on the criterion, the third geographic area a known matching geographic area; and 
 facilitate a reduction of costs associated with determining geographic information by, in response to identifying a similarity between the first and second geographic areas and a similarity between the first and third geographic areas based on the criterion, aggregating the set of trained machine learning models corresponding to the second geographic area and the third geographic area to create a composite machine learning model characteristic of the first geographic area, wherein the composite machine learning model is predictive of the geographical information. 
   
     
     
         22 . The apparatus of  claim 21 , wherein the at least one processor is to weight the second and third geographic areas based on a similarity threshold to the first geographic area. 
     
     
         23 . The apparatus of  claim 21 , wherein at least one processor causes the composite machine learning model to exclude a first subset of the set of trained machine learning models corresponding to the second geographic area and a second subset of the set of trained machine learning models corresponding to the third geographic area, the first subset conflicting with the second subset, wherein the second geographic area has a first affinity and the third geographic area has a second affinity, the first affinity higher than the second affinity. 
     
     
         24 . The apparatus of  claim 21 , wherein the criterion is at least one of a type, a geography, an inhabitant lifestyle, a demographic, a wealth distribution, or a size. 
     
     
         25 . The apparatus of  claim 21 , wherein the at least one processor is to, in response to a determination that the composite machine learning model of the first geographic area represents the first geographic area, populate a first dataset associated with the first geographic area with a second dataset associated with the composite machine learning model. 
     
     
         26 . The apparatus of  claim 21 , wherein the at least one processor is to determine that second and third geographic areas are known matching geographic areas based on data collected meeting or exceeding a threshold of information. 
     
     
         27 . The apparatus of  claim 21 , wherein the composite machine learning model includes a third subset of the set of trained machine learning models corresponding to the second geographic area and a forth subset of the set of trained machine learning models corresponding to the third geographic area, the third subset different than the forth subset. 
     
     
         28 . A tangible computer readable storage medium, comprising instructions that, when executed, cause at least one processor to at least:
 collect data for a first geographic area, the first geographic area an unknown geographic area;   perform a first comparison between the first geographic area and a second geographic area, the second geographic area associated with a first set of trained machine learning models, to identify a similarity based on a criterion, the second geographic area a known matching geographic area;   perform a second comparison between the first geographic area and a third geographic area, the third geographic area associated with a second set of trained machine learning models, to identify a similarity based on the criterion, the third geographic area a known matching geographic area; and   facilitate a reduction of costs associated with determining geographic information by, in response to identifying a similarity between the first and second geographic areas and a similarity between the first and third geographic areas based on the criterion, aggregating the set of trained machine learning models corresponding to the second geographic area and the third geographic area to create a composite machine learning model characteristic of the first geographic area, wherein the composite machine learning model is predictive of the geographical information.   
     
     
         29 . The storage medium as defined in  claim 28 , wherein the instructions, when executed, cause the at least one processor to weight the second and third geographic areas based on a similarity threshold to the first geographic area. 
     
     
         30 . The storage medium as defined in  claim 28 , wherein the instructions, when executed, cause the at least one processor to cause the composite machine learning model to exclude a first subset of the set of trained machine learning models corresponding to the second geographic area and a second subset of the set of trained machine learning models corresponding to the third geographic area, the first subset conflicting with the second subset, wherein the second geographic area has a first affinity and the third geographic area has a second affinity, the first affinity higher than the second affinity. 
     
     
         31 . The storage medium as defined in  claim 28 , wherein the criterion is at least one of a type, a geography, an inhabitant lifestyle, a demographic, a wealth distribution, or a size. 
     
     
         32 . The storage medium as defined in  claim 28 , wherein the instructions, when executed, cause the at least one processor to, in response to a determination that the composite machine learning model of the first geographic area represents the first geographic area, populate a dataset associated with the first geographic area with a dataset associated with the composite machine learning model. 
     
     
         33 . The storage medium as defined in  claim 28 , wherein second and third geographic areas are known matching geographic areas based on data collected meeting or exceeding a threshold of information. 
     
     
         34 . The storage medium as defined in  claim 28 , wherein the composite machine learning model includes a third subset of the set of trained machine learning models corresponding to the second geographic area and a forth subset of the set of trained machine learning models corresponding to the third geographic area, the third subset different than the forth subset. 
     
     
         35 . An apparatus comprising:
 means for collecting to collect data for a first geographic area, the first geographic area an unknown geographic area;   means for matching to:
 perform a first comparison between the first geographic area and a second geographic area, the second geographic area associated with a first set of trained machine learning models, to identify a similarity based on a criterion, the second geographic area a known matching geographic area; 
 perform a second comparison between the first geographic area and a third geographic area, the third geographic area associated with a second set of trained machine learning models, to identify a similarity based on the criterion, the third geographic area a known matching geographic area; and 
   means for mixing to facilitate a reduction of costs associated with determining geographic information by, in response to identifying a similarity between the first and second geographic areas and a similarity between the first and third geographic areas based on the criterion, aggregating the set of trained machine learning models corresponding to the second geographic area and the third geographic area to create a composite machine learning model characteristic of the first geographic area, wherein the composite machine learning model is predictive of the geographical information.   
     
     
         36 . The apparatus of  claim 35 , wherein the mixing means is to mix a plurality of machine learning techniques to create the composite machine learning model. 
     
     
         37 . The apparatus of  claim 35 , wherein the mixing means is to cause the composite machine learning model to exclude a first subset of the set of trained machine learning models corresponding to the second geographic area and a second subset of the set of trained machine learning models corresponding to the third geographic area, the first subset conflicting with the second subset, wherein the second geographic area has a first affinity and the third geographic area has a second affinity, the first affinity higher than the second affinity. 
     
     
         38 . The apparatus of  claim 35 , wherein the criterion is at least one of a type, a geography, an inhabitant lifestyle, a demographic, a wealth distribution, or a size. 
     
     
         39 . The apparatus of  claim 35 , further including a means for modeling, the modeling means to, in response to a determination that the composite machine learning model of the first geographic area represents the first geographic area, populate a first dataset associated with the first geographic area with a second dataset associated with the composite machine learning model. 
     
     
         40 . The apparatus of  claim 35 , wherein second and third geographic areas are known matching geographic areas based on data collected meeting or exceeding a threshold of information.