Abstract: Methods and apparatus to calculate granular data of a region based on another region for media audience measurement are disclosed. An example method for calculating, via a processor, granular data of a region includes determining aggregate behavioral data associated with a media audience measurement of a target region. The example method includes determining, via the processor, aggregate demographics data of the target region. The example method includes determining, via the processor, granular data of a source region. The example method includes calculating, via the processor, granular data of the target region to measure a media audience of the target region by apportioning the aggregate demographics data and the aggregate behavioral data of the target region based on the granular data of the source region.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to identify co-relationships between media using social media. An example method includes obtaining media-exposure social media messages based on keywords and posting timestamps, and identifying a media-pairing including first media and second media. The example method also includes estimating a first audience of the first media based on a first set of media-exposure social media messages referencing the first media, and estimating a second audience of the second media based on a second set of media-exposure social media messages referencing the second media.

Abstract: Methods and apparatus to apply household-level weights to audience measurement data at a household-member level are disclosed. An example method to determine demographics of populations to measure media audiences of populations includes determining demographics for members of a first household of a sub-population. First demographics of a first member of the first household are different than second demographics of a second member of the first household. The example method includes calculating a first household-level weight for the first household based on a demographics distribution of the sub-population and aggregate demographics of a population.

Abstract: Methods and apparatus to solve for a minimum variance solution for a constrained weight problem in a scalable and memory efficient manner are disclosed. An example method of reducing memory usage of a computing device disclosed herein comprises storing a constraint matrix and a population matrix into a memory, the population matrix representing a population related to data of the constraint matrix, determining, via a processor, a linear system based on a set of constraints including the constraint matrix and the population matrix, determining, via a processor, an inverse of a matrix of the linear system, the determination of the inverse including breaking down the matrix into blocks, the dimensions of the blocks being no larger than dimensions of the constraint matrix, and outputting a set of weights based on the inverse of the linear system, the set of weights satisfying the set of constraints.

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.

Abstract: Methods and apparatus to determine ratings data from population sample data having unreliable demographic classifications are disclosed. An example method includes receiving, at an audience measurement entity (AME), a first request sent from a first type of device via a communications network; sending a request for demographic information corresponding to requests received at the AME from the first type of device, the requests including the first request; obtaining a misattribution matrix; generating a multinomial distribution from the misattribution matrix; generating samples of the multinomial distribution; converting the samples to misattribution matrices; and applying a vector to the plurality of misattribution matrices to estimate a first number of audience members who are attributable to the second demographic group, the vector representing a second number of audience members who are associated with the first demographic group based on the demographic information.

Abstract: Methods and apparatus to partition data are discloses. An example method includes generating, with a processor, an indicator matrix for a set of panelist data based on a set of matrix criteria corresponding to the panelist data. The entries in the indicator matrix are determined based on a conditional probability of a panelist having one or more characteristics. An indicator is placed in a panelist vector of the matrix if the panelist has the one or more characteristics. A set of unique panelist vectors is determined and redundant panelist vectors that are not unique are removed to determine a minimum set of mutually exclusive partitions of the panelist data.

Abstract: Methods and apparatus to estimate demographics of a household are disclosed. An example method to determine demographics for non-panelist households includes calculating a first demographic constraint average and a second demographic constraint average based on a first demographic distribution of a first tuning event of a household and a second demographic distribution of a second tuning event of the household. The household is a non-panelist household. The example method also includes, based on the first demographic constraint average, determining a first likelihood of the household being associated with a first demographic constraint. The example method also includes, based on the second demographic constraint average, determining a second likelihood of the household being associated with a second demographic constraint. The example method also includes estimating a household characteristic of the household based on the first likelihood and the second likelihood.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve reach calculation efficiency. An example method includes estimating, with a processor, a sample distribution of marketing data to generate a maximum entropy distribution, generating, with the processor, a geometric distribution based on estimating a minimum cross entropy of (a) the maximum entropy distribution and (b) the sample distribution of marketing data, and improving calculation efficiency of the public reach of the sample distribution of marketing data by generating, with the processor, conserved quantity expressions of the geometric distribution.

Abstract: Methods, apparatus, systems and articles of manufacture to correct for bias in ratings data are disclosed. Example methods disclosed herein include accessing information specifying a set of population strata having corresponding strata demographic statistics capable of being combined to determine a first set of baseline demographic statistics for a sample population that corresponds to a second set of baseline demographic statistics for a target population. Disclosed example methods also include determining a set of weights corresponding respectively to the set of population strata that reduces bias between the second set of baseline demographic statistics for the target population and the first set of baseline demographic statistics for the sample population when the set of weights is applied to the strata demographic statistics to determine the first set of baseline demographic statistics for the sample population.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to estimate an unknown audience size from recorded impressions for an online media. The estimate of the unknown audience size for the online media is based on a total number of initial impressions and a frequency distribution of recorded demographic impressions across a partial audience size for the online media. The estimate of the unknown audience size is determined by modeling the probability of obtaining the frequency distribution of the recorded demographic impressions across the partial audience for different possible unknown audience sizes in a range of unknown audience sizes and determining an estimate for the unknown audience size by evaluating the models for the different possible unknown audience sizes.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for a viewing condition probability calculator. The viewing condition probability calculator enumerates a list of all viewing combinations for a tuning event, for a number of people N at a location. A probability for each of the viewing combinations is determined, using an independent probability of viewing for each of the N people at the location. A probability of a viewing condition is determined using the probabilities of a first set of viewing combinations that have a first common characteristic.

Abstract: Methods and apparatus to estimate a population of a consumer segment in a geographic area are disclosed. An example method includes: recognizing a first type of object in a first image of a first area, the first type of object being associated with a consumer segment; obtaining first measurements of a first set of characteristics for the first area, the first set of characteristics being associated with the segment; determining a first relationship between a first population of the segment in the first area and the first measurements of the first set of characteristics; recognizing the first type of object in a second image of a second area; obtaining second measurements of a second set of characteristics for the second area; and determining a second population of the segment in the second area based on applying the first relationship to the second measurements.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve decision tree execution. An example method includes retrieving, with a processor, a decision tree logic expression in a sum-of-products (SOP) form, the decision tree logic expression consuming a first duration to evaluate a dataset, eliminating, with the processor, redundant variables of the decision tree logic expression by transforming the decision tree logic expression into a product-of-sums (POS) form, and evaluating, with the processor, the data set with the decision tree logic expression in the POS form, the decision tree logic expression in the POS form consuming a second duration to evaluate the data set that is less than the first duration.

Abstract: Methods and apparatus to estimate market opportunities for an object class are disclosed. An example method includes obtaining first measurements of a set of characteristics for a first area, the set of characteristics being associated with an item class; determining a first relationship between a first probability of a population in the first area to purchase the item class and the first measurements of the set of characteristics; obtaining second measurements of the set of characteristics for a second area; and estimating a second probability of a population of the second area purchasing the item class based on applying the first relationship to the second measurements.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to correct segmentation errors. An example disclosed method includes identifying, with a processor, a segment group comprising observation data associated with two or more segments, respective ones of the two or more segments having a similar first characteristic and a dissimilar second characteristic, identifying first portions of the observation data having errors, generating a first matrix of binary indicators associated with the observation data, the binary indicators associating the first portions of the observation data with a first correction factor, and generating a value for the first correction factor by minimizing a residual sum of squares of the segment group observation data associated with the first matrix of binary indicators.

Abstract: Methods and apparatus to estimate commercial characteristics based on aerial images are disclosed. An example method includes identifying, using a computer vision technique, a feature in a first aerial image of a geographic location of interest, identifying a reference aerial image that includes the feature from a set of reference aerial images, the reference aerial image being associated with commercial characteristics, and associating a first one of the commercial characteristics with the location of interest.