Computer system with economic development decision support platform and method of use thereof

1 . A computer-implemented method of determining economic well-being geographically, the method comprising;

receiving from a user, at a user interface of a computing device having one or more processors and a display interface, a geographical region of interest;

receiving, at the computing device, historical observations taken over a predefined timeframe associated with image data and survey data for the geographical region;

compiling, at the computing device, the image data and the survey data such that the image data and the survey data collectively produce unique geospatial datasets related to (i) economic information, (ii) demographic information and (iii) infrastructure information based on the historical observations;

generating, at the computing device, a first categorical model having a first geospatial dataset related to the economic information based on the historical observations;

generating, at the computing device, a second categorical model having a second geospatial dataset related to the demographic information based on the historical observations;

generating, at the computing device, a third categorical model having a third geospatial dataset related to infrastructure information based on the historical observations;

displaying, graphically at the display interface, first, second and third drop down menus representing user selectable criteria related to the respective first, second and third categorical models;

receiving, at the user interface, a user defined selection from each of the first, second and third criteria;

generating, at the computing device and displaying on the display interface, a navigation map that displays clusters in the form of pixels that signify distinct values viewable by the user across the selected geographical region, the clusters representing geographical areas that satisfy the user defined selections from the first, second and third criteria for a previous or current timeframe;

forecasting, at the computing device, outcomes based on the first, second and third categorical models;

generating, at the computing device and displaying on the display interface, a prediction model map that forecasts a geographical location of growth for a future time based on the forecasted outcomes and the user defined selections from the first, second and third criteria, wherein the growth comprises at least one of economic growth and socioeconomic growth.

2 . The computer-implemented method of claim 1 , further comprising:

compiling the image data and survey data such that the image data and the survey data collectively produce a unique geospatial dataset related to economic information.

3 . The computer-implemented method of claim 2 wherein generating the categorical model related to economic information comprises assigning an asset wealth index (AWI) to the geospatial dataset and wherein the AWI is based on the survey data wherein the survey data comprises household surveys on economic well-being.

4 . The computer-implemented method of claim 2 wherein generating the categorical model related to economic information comprises assigning economic consumption estimates to the geospatial dataset.

5 . The computer-implemented method of claim 1 , further comprising:

compiling the image data and survey data such that the image data and the survey data collectively produce a unique geospatial dataset related to demographic information.

6 . The computer-implemented method of claim 5 wherein generating the categorical model related to land use comprises estimating crop area within the geospatial dataset.

7 . The computer-implemented method of claim 5 wherein generating the categorical model related to land use comprises estimating crop yield within the geospatial dataset.

8 . The computer-implemented method of claim 1 , further comprising:

compiling the image data and survey data such that the image data and the survey data collectively produce a unique geospatial dataset related to infrastructure.

9 . The computer-implemented method of claim 8 wherein generating the categorical model related to infrastructure comprises estimating electrification locations within the geospatial dataset based on a time series of illuminance values generated from the night light satellite image data.

10 . The computer-implemented method of claim 1 wherein the predetermined timeframe collectively comprises:

a first range of time from a first date to a second date;

a second range of time from the second date to a third date; and

a third range of time from the third date to a fourth date, wherein the first, second, third and fourth dates are sequential.

11 . The computer-implemented method of claim 10 wherein the historical observations received include a first data input of historical observations associated with the first range of time and a second data input of historical observations associated with the third range of time, the computer-implemented method further comprising:

generating a third data input of historical observations associated with the second range of time based on the first and second data inputs.

12 . The computer-implemented method of claim 1 wherein the historical observations associated with image data comprises satellite image data taken over the predefined timeframe.

13 . The computer-implemented method of claim 12 wherein the satellite image data includes night light satellite image data.

14 . The computer-implemented method of claim 13 wherein the night light satellite image data comprises visible infrared imaging radiometer suite (VIIRS).

15 . The computer-implemented method of claim 12 wherein the satellite image data includes at least one of Landsat-8 multispectral satellite image data, Sentinel-2 satellite image data, and radar satellite image data.

16 . The computer-implemented method of claim 12 wherein the satellite image data comprises a combination of night light satellite image data, Landsat-8 satellite image data, Sentinel-2 satellite image data and radar image data.

17 . The computer-implemented method of claim 1 wherein the survey data comprises at least one of data taken over a predetermined timeframe and sourced from one of Demographic and Health Surveys (DHS), household survey data, The World Bank and Gallup, Inc.

18 . The computer-implemented method of claim 1 wherein generating the categorical model comprises compiling multiple historical observations for correspondence with multiple pixels over the predefined timeframe.

19 . The computer-implemented method of claim 1 , further comprising;

receiving, at the computing device, population datasets related to the geographical region.

20 . The computer-implemented method of claim 1 wherein the historical observations further include at least one of Open Street Map data taken over the predetermined timeframe, internet speed data taken over the predetermined timeframe, advertising data taken over the predetermined timeframe, and cellular phone data taken over the predetermined timeframe.

21 . The computer-implemented method of claim 20 wherein the historical observations include cellular phone data taken over the predetermined timeframe and wherein the cellular phone data includes Econet Wireless Zimbabwe Ltd. data taken over the predetermined timeframe.

22 . The computer-implemented method of claim 1 wherein the historical observations further include at least one of social media data taken over the predetermined timeframe, property data taken over the predetermined timeframe, and plant data taken over the predetermined timeframe.

23 . The computer-implemented method of claim 22 wherein the historical observations include plant data taken over the predetermined timeframe and wherein plant data includes Plantix data.

24 . The computer-implemented method of claim 1 wherein generating a prediction model map comprises:

identifying multiple factors related to an outcome;

determining at least one factor of the multiple factors that influences the outcome;

performing an intervention by modifying the at least one factor;

measuring the impact of the intervention on the outcome; and

modifying the at least one factor to influence an alternate outcome.

25 . The computer-implemented method of claim 24 wherein generating a prediction model map comprises:

identifying a first set of criteria for application to a first geographic situation having a known outcome;

identifying a second set of criteria for application to a second geographic situation having an unknown outcome; and

applying the first set of criteria for the second set of criteria to influence an outcome similar to the known outcome at the second geographic location.

26 . The computer-implemented method of claim 1 wherein generating a navigation map comprises at least one of generating a navigation map having 2 kilometer by 2 kilometer resolution, generating a navigation map having 1 kilometer by 1 kilometer resolution based on the economic information, and generating a navigation map having 10 meter by 10 meter resolution based on the land use.

27 . A computer-implemented method of determining economic well-being geographically, the method comprising;

receiving from a user, at a user interface of a computing device having one or more processors and a display interface, a geographical region of interest;

receiving, at the computing device, historical observations taken over a predefined timeframe associated with image data and survey data for the geographical region, wherein the predetermined timeframe collectively comprises:

a first range of time from a first date to a second date;

a second range of time from the second date to a third date; and

a third range of time from the third date to a fourth date, wherein the first, second, third and fourth dates are sequential, wherein the historical observations received include a first data input of historical observations associated with the first range of time and a second data input of historical observations associated with the third range of time;

generating a third data input of historical observations associated with the second range of time based on the first and second data inputs;

storing, at the computing device, the historical observations;

generating a first categorical model having a first geospatial dataset related to one of economic, demographic and infrastructure information based on the historical observations;

displaying, graphically at the display interface, a first drop down menu representing a user selectable criteria related to the first categorical model;

receiving, at the user interface, a user defined selection from the first criteria; and

generating, at the computing device and displaying on the display interface, a navigation map that displays clusters in the form of pixels that signify distinct values viewable by the user across the selected geographical region, the clusters representing geographical areas that satisfy the user defined selection from the first criteria for a previous or current timeframe.

28 . The computer-implemented method of claim 27 , further comprising:

generating, at the computing device and displaying on the display interface, a prediction model that forecasts a geographical location of growth based on the categorical model, wherein the growth comprises at least one of economic growth and socioeconomic growth; and

generating, at the computing device and displaying on the display interface, a map that displays the prediction model.

29 . The computer-implemented method of claim 28 wherein generating a prediction model comprises:

identifying multiple factors related to an outcome;

determining at least one factor of the multiple factors that influences the outcome;

performing an intervention by modifying the at least one factor;

measuring the impact of the intervention on the outcome; and

modifying the at least one factor to influence an alternate outcome.

30 . The computer-implemented method of claim 24 wherein generating a prediction model comprises:

identifying a first set of criteria for application to a first geographic situation having a known outcome;

identifying a second set of criteria for application to a second geographic situation having an unknown outcome; and

applying the first set of criteria for the second set of criteria to influence an outcome similar to the known outcome at the second geographic location.