Method and system for predictive modeling of signage location and pricing

1. A computer-implemented method for predictive modeling, the method comprising:

aggregating, by one or more processors using information gathering, sample data regarding a plurality of factors associated with income and geographic location;

performing, by one or more processors using machine intelligence, iterative analysis on the sample data using machine learning to construct a predictive model;

populating, by one or more processors using the predictive model, a database with predicted values of an encountered income for a selected set of predefined signage locations;

converting, by one or more processors using indexing program, the predicted values of the encountered income in the database into percentages of observed values of encountered income for signage locations within the selected set over a specified time period to create indices of encountered income; and

rank ordering, by one or more processors using indexing program, the signage locations within the selected set according to their indices of encountered income,

wherein the predictive model reduces errors in sign placement and advertisement pricing.

2. The method according to claim 1 , further comprising:

comparing, by one or more processors, rank ordering of encountered income for the selected set of predefined signage locations to observed encountered income for the predefined signage locations over a second specified time period;

aggregating, by one or more processors, updated sample data over the second specified time period; and

updating, by one or more processors, the predictive model using machine learning incorporating the updated sample data for the second specified time period.

3. The method according to claim 1 , wherein categories of the sample data applied to the machine learning to construct the predictive model include at least one of:

salary;

total payroll deductions;

tax filing status;

region of residence;

home values within said region;

rental costs within said region;

job type;

shift;

work location;

traffic data;

road construction/closures;

employer growth trends;

industry/sector growth trends;

regional employment; or

industry/sector diversity.

4. The method according to claim 1 , wherein the machine learning uses supervised learning to construct the predictive model.

5. The method according to claim 1 , wherein the machine learning uses unsupervised learning to construct the predictive model.

6. The method according to claim 1 , wherein the machine learning uses reinforcement learning to construct the predictive model.

7. A machine learning predictive modeling system, comprising:

a computer system; and

one or more processors running on the computer system, wherein the one or more processors aggregate, using information gathering, sample data regarding a plurality of factors associated with income and geographic location; perform, using machine intelligence, iterative analysis on the sample data using machine learning to construct a predictive model; populate, using the predictive model, a database with predicted values of encountered income for a selected set of predefined signage locations; convert, using indexing program, the predicted values of encountered income in the database into percentages of observed values of encountered income for signage locations within the selected set over a specified time period to create indices of encountered income; and rank order, using indexing program, the signage locations within the selected set according to their indices of encountered income,

wherein the predictive model reduces errors in sign placement and advertisement pricing.

8. The machine learning predictive modeling system according to claim 7 , wherein the one or more processors running on the computer system compare rank ordering of encountered income for the selected set of predefined signage locations to observed encountered income for said signage locations over a second specified time period; aggregate updated sample data over the second specified time period; and update the predictive model using machine learning incorporating the updated sample data for the second specified time period.

9. The machine learning predictive modeling system according to claim 7 , wherein the one or more processors comprise aggregated graphical processor units (GPU).

10. The machine learning predictive modeling system according to claim 7 , wherein the machine learning uses supervised learning to construct the predictive model.

11. The machine learning predictive modeling system according to claim 7 , wherein the machine learning uses unsupervised learning to construct the predictive model.

12. The machine learning predictive modeling system according to claim 7 , wherein the machine learning uses reinforcement learning to construct the predictive model.

13. A computer program product for machine learning predictive modeling, the computer program product comprising:

a computer-readable storage media;

first program code, stored on the computer-readable storage media, for aggregating, using information gathering, sample data regarding a plurality of factors associated with income and geographic location;

second program code, stored on the computer-readable storage media, for performing, using machine intelligence, iterative analysis on the sample data using machine learning to construct a predictive model;

third program code, stored on the computer-readable storage media, for populating, using the predictive model, a database with predicted values of encountered income for a selected set of predefined signage locations;

fourth program code, stored on the computer-readable storage media, for converting, using indexing program, the predicted values of encountered income in the database into percentages of observed values of encountered income for signage locations within the selected set over a specified time period to create indices of encountered income; and

fifth program code, stored on the computer-readable storage media, for rank ordering, using indexing program the signage locations within the selected set according to their indices of encountered income,

wherein the predictive model reduces errors in sign placement and advertisement pricing.

14. The computer program product according to claim 13 , further comprising:

sixth program code, stored on the computer-readable storage media, for comparing rank ordering of encountered income for the selected set of pre defined predefined signage locations to observed encountered income for said signage locations over a second specified time period;

seventh program code, stored on the computer-readable storage media, for aggregating updated sample data over the second specified time period; and

eighth program code, stored on the computer-readable storage media, for updating the predictive model using machine learning incorporating the updated sample data for the second specified time period.

15. The computer program product according to claim 13 , wherein categories of the sample data applied to the machine learning to construct the predictive model include at least one of:

salary;

total payroll deductions;

tax filing status;

region of residence;

home values within said region;

rental costs within said region;

job type;

shift;

work location;

traffic data;

road construction/closures;

employer growth trends;

industry/sector growth trends;

regional employment; or

industry/sector diversity.

16. The computer program product according to claim 13 , wherein the machine learning uses supervised learning to construct the predictive model.

17. The computer program product according to claim 13 , wherein the machine learning uses unsupervised learning to construct the predictive model.

18. The computer program product according to claim 13 , wherein the machine learning uses reinforcement learning to construct the predictive model.

19. A computer-implemented method for predictive modeling, the method comprising:

aggregating, by one or more processors using information gathering, sample data regarding a plurality of factors associated with income and geographic location;

performing, by one or more processors using machine intelligence, iterative analysis on the sample data using machine learning to construct a predictive model;

populating, by one or more processors using the predictive model, a database with predicted values of an encountered income for a selected set of time periods;

converting, by one or more processors using indexing program, the predicted values of the encountered income in the database into percentages of observed values of encountered income for time periods within the selected set at a predefined signage location to create indices of encountered income; and

rank ordering, by one or more processors using indexing program, the time periods within the selected set according to their indices of encountered income,

wherein the predictive model reduces errors in sign placement and advertisement pricing.

20. The method according to claim 19 , further comprising:

comparing, by one or more processors, rank ordering of encountered income for the selected set of time periods to observed encountered income for the time periods from a second sample period;

aggregating, by one or more processors, updated sample data from a second sample period; and

updating, by one or more processors, the predictive model using machine learning incorporating the updated sample data.

21. The method according to claim 19 , wherein categories of the sample data applied to the machine learning to construct the predictive model include at least one of:

salary;

total payroll deductions;

tax filing status;

region of residence;

home values within said region;

rental costs within said region;

job type;

shift;

work location;

traffic data;

road construction/closures;

employer growth trends;

industry/sector growth trends;

regional employment; or

industry/sector diversity.

22. The method according to claim 19 , wherein the machine learning uses supervised learning to construct the predictive model.

23. The method according to claim 19 , wherein the machine learning uses unsupervised learning to construct the predictive model.

24. The method according to claim 19 , wherein the machine learning uses reinforcement learning to construct the predictive model.

25. A machine learning predictive modeling system, comprising:

a computer system; and

one or more processors running on the computer system, wherein the one or more processors aggregate, using information gathering, sample data regarding a plurality of factors associated with income and geographic location; perform, using machine intelligence, iterative analysis on the sample data using machine learning to construct a predictive model; populate, using the predictive model, a database with predicted values of encountered income for a selected set of time periods; convert, using indexing program, the predicted values of encountered income in the database into percentages of observed values of encountered income for time periods within the selected set at a signage location to create indices of encountered income; and rank order, using indexing program, the time periods within the selected set according to their indices of encountered income,

wherein the predictive model reduces errors in sign placement and advertisement pricing.

26. The machine learning predictive modeling system according to claim 25 , wherein the one or more processors running on the computer system compare rank ordering of encountered income for the selected set of time periods to observed encountered income for said time periods from a second sample period; aggregate updated sample data from a second sample period; and update the predictive model using machine learning incorporating the updated sample data.

27. The machine learning predictive modeling system according to claim 25 , wherein the one or more processors comprise aggregated graphical processor units (GPU).

28. The machine learning predictive modeling system according to claim 25 , wherein the machine learning uses supervised learning to construct the predictive model.

29. The machine learning predictive modeling system according to claim 25 , wherein the machine learning uses unsupervised learning to construct the predictive model.

30. The machine learning predictive modeling system according to claim 25 , wherein the machine learning uses reinforcement learning to construct the predictive model.

31. A computer program product for machine learning predictive modeling, the computer program product comprising:

a computer-readable storage media;

first program code, stored on the computer-readable storage media, for aggregating, using information gathering, sample data regarding a plurality of factors associated with income and geographic location;

second program code, stored on the computer-readable storage media, for performing, using machine intelligence, iterative analysis on the sample data using machine learning to construct a predictive model;

third program code, stored on the computer-readable storage media, for populating, using the predictive model, a database with predicted values of encountered income for a selected set of time periods;

fourth program code, stored on the computer-readable storage media, for converting, using indexing program, the predicted values of encountered income in the database into percentages of observed values of encountered income for time periods within the selected set at a signage location to create indices of encountered income; and

fifth program code, stored on the computer-readable storage media, for rank ordering, using indexing program, the time periods within the selected set according to their indices of encountered income,

wherein the predictive model reduces errors in sign placement and advertisement pricing.

32. The computer program product according to claim 31 , further comprising:

sixth program code, stored on the computer-readable storage media, for comparing rank ordering of encountered income for the selected set of time periods to observed encountered income for said time periods from a second sample period;

seventh program code, stored on the computer-readable storage media, for aggregating updated sample data from a second sample period; and

eighth program code, stored on the computer-readable storage media, for updating the predictive model using machine learning incorporating the updated sample data.

33. The computer program product according to claim 31 , wherein categories of the sample data applied to the machine learning to construct the predictive model include at least one of:

salary;

total payroll deductions;

tax filing status;

region of residence;

home values within said region;

rental costs within said region;

job type;

shift;

work location;

traffic data;

road construction/closures;

employer growth trends;

industry/sector growth trends;

regional employment; or

industry/sector diversity.

34. The computer program product according to claim 31 , wherein the machine learning uses supervised learning to construct the predictive model.

35. The computer program product according to claim 31 , wherein the machine learning uses unsupervised learning to construct the predictive model.

36. The computer program product according to claim 31 , wherein the machine learning uses reinforcement learning to construct the predictive model.