CA2561939A1 - Method and system for forecasting events and results based on geospatial modeling - Google Patents
Method and system for forecasting events and results based on geospatial modeling Download PDFInfo
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- CA2561939A1 CA2561939A1 CA002561939A CA2561939A CA2561939A1 CA 2561939 A1 CA2561939 A1 CA 2561939A1 CA 002561939 A CA002561939 A CA 002561939A CA 2561939 A CA2561939 A CA 2561939A CA 2561939 A1 CA2561939 A1 CA 2561939A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/02—Reservations, e.g. for tickets, services or events
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B29/00—Maps; Plans; Charts; Diagrams, e.g. route diagram
- G09B29/003—Maps
- G09B29/006—Representation of non-cartographic information on maps, e.g. population distribution, wind direction, radiation levels, air and sea routes
- G09B29/007—Representation of non-cartographic information on maps, e.g. population distribution, wind direction, radiation levels, air and sea routes using computer methods
Abstract
A forecasting engine (10) and method assists in forecasting occurrences of identifiable events and/or results threats on signature and/or pattern matching (29). The present invention derives signature (23) for event-types based on a comparison of actual event data (14) with pre-established representational surfaces. The surfaces represent proximitry measurements and analysis associated with elements of the geospatial boundary (12) being considered. The measurements and analysis can consist of a vast array of potential variables of interest in order to provide a comprehensive, robust forecasting engine (10). In one embodiment, the present invention considers past data associated with several event-types (35) in order to arrive at an assessment.
Claims (59)
1. A geospatial event forecasting engine, comprising:
a spatial database storing geospatial boundary information for one or more areas of interest, and further storing geospatial characteristic information pertaining to at least one variable of interest in the form of one or more variable layers associated with said one or more areas of interest;
a boundary component for establishing a geospatial boundary pertaining to a first area of interest and a grid containing a plurality of cells within said boundary;
a function component for identifying a functional measurement of a cell element for each cell to said at least one variable of interest for each of said one or more layers associated with said first area of interest, and for indexing said functional measurement for each cell;
a signature derivation component for receiving geospatial information related to one or more past events of at least one event type, including location information for said one or more past events, said signature derivation component further identifying and indexing an event functional measurement from the location information for each of said one or more events to the at least one variable of interest for each of said layers, and deriving a signature pattern for said event type; and an event likelihood determinant component for determining a level of signature match between said derived signature pattern and at least one cell of said plurality of cells, whereby a forecast of an event-type likelihood can be displayed on at least a portion of a display of said geospatial boundary based on said determined level of signature match.
a spatial database storing geospatial boundary information for one or more areas of interest, and further storing geospatial characteristic information pertaining to at least one variable of interest in the form of one or more variable layers associated with said one or more areas of interest;
a boundary component for establishing a geospatial boundary pertaining to a first area of interest and a grid containing a plurality of cells within said boundary;
a function component for identifying a functional measurement of a cell element for each cell to said at least one variable of interest for each of said one or more layers associated with said first area of interest, and for indexing said functional measurement for each cell;
a signature derivation component for receiving geospatial information related to one or more past events of at least one event type, including location information for said one or more past events, said signature derivation component further identifying and indexing an event functional measurement from the location information for each of said one or more events to the at least one variable of interest for each of said layers, and deriving a signature pattern for said event type; and an event likelihood determinant component for determining a level of signature match between said derived signature pattern and at least one cell of said plurality of cells, whereby a forecast of an event-type likelihood can be displayed on at least a portion of a display of said geospatial boundary based on said determined level of signature match.
2. The engine of claim 1 wherein said functional measurement is not a nearest neighbor value and is one from the group of measurements including: density, concentration, average distance by actual path, Manhattan distance, visibility.
3. The engine of claim 2 wherein said functional measurement is average distance by actual path, wherein said actual path is artificial.
4. The engine of claim 2 wherein said functional measurement is average distance by actual path, wherein said actual path is a natural path.
5. The engine of claim 1 wherein said functional measurement is a manual function.
6. The engine of claim 1 wherein said at least one variable of interest is non-static.
7. The engine of claim 1 wherein said event likelihood determinant component determines whether there is a signature match by determining a score indicative of said at least one cell's compatibility with the event type signature.
8. The engine of claim 7 wherein said event likelihood determinant component determines whether there is a signature match for each of said cells within said boundary and further plots said scores on a choropleth graph.
9. The engine of claim 1 wherein said at least one variable of interest is provided with a plurality of feature types individually indexed by a respective feature identifier.
10. The engine of claim 1 wherein said plurality of cells are of irregular shape.
11. The engine of claim 10 wherein said cell irregular shapes are determined by political boundaries.
12. The engine of claim 10 wherein said cell irregular shapes are determined by natural boundaries.
13. The engine of claim 10 wherein said cell irregular shapes are determined randomly.
14. The engine of claim 1 wherein said signature derivation component represents said received geospatial information in a non-point format.
15. The engine of claim 14 wherein said non-point format is one of the formats from the group consisting of polygon, line, three-dimensional.
16. The engine of claim 1 wherein said event likelihood determination component determines, for each event, the individual cell element nearest said event and associates the functional measurement for said nearest individual cell element with said event.
17. The engine of claim 1 wherein said event likelihood determination component determines said level of signature match between said derived signature pattern and at least one cell of said plurality of cells through comparison with said indexed functional measurement.
18. The engine of claim 1 wherein said event likelihood determination component determines said level of signature match between said derived signature pattern and each of said plurality of cells.
19. The engine of claim 1 wherein said established boundary is a first boundary, and wherein said engine further includes a signature transfer component for applying said derived signature to indexed functional measurements of a second boundary, said second boundary having a plurality of cells therein, and determining a level of signature match between said derived signature and at least one cell of said plurality of cells in said second boundary.
20. The engine of claim 19 wherein said indexed functional measurements of said second boundary are measured from a cell element for each cell in said second boundary to at least one variable of interest, the at least one variable of interest within said first boundary being different from the at least one variable of interest within said second boundary.
21. The engine of claim 1 wherein said functional measurement is not a probability density function.
22. The engine of claim 1 further including a layer selection component for selecting at least one of said plurality of layers for imposing upon said grid.
23. The engine of claim 1 wherein said one or more variable layers are each indicative of one or more variables of interest which are different from the one or more variables of interest of the remaining layers.
24. The engine of claim 1 wherein said one or more variable layers are indicative of the same variable or variables of interest, but wherein each of said layers represents a time frame which is different from the remaining layers.
25. The engine of claim 1 wherein said cell element is a centroid.
26. The engine of claim 1 wherein at least one of said cells includes a plurality of cell elements and wherein said function component identifies and indexes a functional measurement for each cell element for each cell to said at least one variable of interest for each of said layers.
27. The engine of claim 1 further including an alert component for communicating said signature match determination to at least one remote agent operating within said geospatial boundary.
28. A method of forecasting geospatial events, comprising the steps of:
storing geospatial boundary information for one or more areas of interest;
storing geospatial characteristic information pertaining to at least one variable of interest in the form of one or more variable layers associated with said one or more areas of interest;
establishing a geospatial boundary pertaining to a first area of interest and a grid containing a plurality of cells within said boundary;
identifying a functional measurement of a cell element for each cell to said at least one variable of interest for each of said one or more layers associated with said first area of interest, and indexing said functional measurement for each cell;
receiving geospatial information related to one or more past events of at least one event type, including location information for said one or more past events;
identifying and indexing an event functional measurement from the location information for each of said one or more events to the at least one variable of interest for each of said layers, and deriving a signature pattern for said event type; and determining a level of signature match between said derived signature pattern and at least one cell of said plurality of cells, whereby a forecast of an event-type likelihood can be displayed on at least a portion of a display of said geospatial boundary based on said determined level of signature match.
storing geospatial boundary information for one or more areas of interest;
storing geospatial characteristic information pertaining to at least one variable of interest in the form of one or more variable layers associated with said one or more areas of interest;
establishing a geospatial boundary pertaining to a first area of interest and a grid containing a plurality of cells within said boundary;
identifying a functional measurement of a cell element for each cell to said at least one variable of interest for each of said one or more layers associated with said first area of interest, and indexing said functional measurement for each cell;
receiving geospatial information related to one or more past events of at least one event type, including location information for said one or more past events;
identifying and indexing an event functional measurement from the location information for each of said one or more events to the at least one variable of interest for each of said layers, and deriving a signature pattern for said event type; and determining a level of signature match between said derived signature pattern and at least one cell of said plurality of cells, whereby a forecast of an event-type likelihood can be displayed on at least a portion of a display of said geospatial boundary based on said determined level of signature match.
29. The method of claim 28 wherein said functional measurement is not a nearest neighbor value and is one from the group of measurements including: density, concentration, average distance by actual path, Manhattan distance, visibility.
30. The method of claim 28 wherein said functional measurement is a manual function.
31. The method of claim 28 wherein said step of determining a level of signature match determines a score indicative of said at least one cell's compatibility with the event type signature.
32. The method of claim 31 wherein said step of determining a level of signature match determines whether there is a signature match for each of said cells within said boundary and further plots said scores on a choropleth graph.
33. The method of claim 28 wherein said at least one variable of interest is provided with a plurality of feature types individually indexed by a respective feature identifier.
34. The method of claim 28 wherein said plurality of cells are of irregular shape.
35. The method of claim 34 wherein said cell irregular shapes are determined by political boundaries.
36. The method of claim 34 wherein said cell irregular shapes are determined by natural boundaries.
37. The method of claim 34 wherein said cell irregular shapes are determined randomly.
38. The method of claim 28 wherein said received geospatial information is represented in a non-point format.
39. The method of claim 38 wherein said non-point format is one of the formats from the group consisting of: polygon, line, three-dimensional.
40. The method of claim 28 wherein said step of determining a level of signature match determines, for each event, the individual cell element nearest said event and associates the functional measurement for said nearest individual cell element with said event.
41. The method of claim 28 wherein said step of determining a level of signature match determines said level of signature match between said derived signature pattern and at least one cell of said plurality of cells through comparison with said indexed functional measurement.
42. The method of claim 28 wherein said step of determining a level of signature match determines said level of signature match between said derived signature pattern and each of said plurality of cells.
43. The method of claim 28 wherein said established boundary is a first boundary, and wherein said method further includes the steps of applying said derived signature to indexed functional measurements of a second boundary, said second boundary having a plurality of cells therein, and determining a level of signature match between said derived signature and at least one cell of said plurality of cells in said second boundary.
44. The method of claim 43 wherein said indexed functional measurements of said second boundary are measured from a cell element for each cell in said second boundary to at least one variable of interest, the at least one variable of interest within said first boundary being different from the at least one variable of interest within said second boundary.
45. The method of claim 28 wherein said functional measurement is not a probability density function.
46. The method of claim 28 further including a layer selection component for selecting at least one of said plurality of layers for imposing upon said grid.
47. The method of claim 28 wherein said one or more variable layers are each indicative of one or more variables of interest which are different from the one or more variables of interest of the remaining layers.
48. The method of claim 28 wherein said one or more variable layers are indicative of the same variable or variables of interest, but wherein each of said layers represents a time frame which is different from the remaining layers.
49. The method of claim 28 wherein at least one of said cells includes a plurality of cell elements and wherein said function component identifies and indexes a functional measurement for each cell element for each cell to said at least one variable of interest for each of said layers.
50. The method of claim 28 further including the step of providing an alert component for communicating said signature match determination to at least one remote agent operating within said geospatial boundary.
51. A method for determining an event type based on an event signature, comprising the steps of:
establishing a geospatial boundary and a grid containing a plurality of cells within said boundary;
establishing at least one layer imposed upon the grid, the layer being indicative of geospatial characteristics of at least one variable of interest;
identifying a functional measurement of a cell element for each cell to the variable of interest, and for indexing said functional measurement for each cell;
receiving geospatial information pertaining to one or more past events of a given event type, including location information for said one or more past events;
identifying an event functional measurement to the at least one variable of interest for each of said events;
determining a likelihood associating said event type's relative proximity to the variable of interest;
establishing a signature for said event type based on said determined likelihood;
receiving an outside event signature associated with said boundary;
determining a level of signature match between said established signature and said outside event signature.
establishing a geospatial boundary and a grid containing a plurality of cells within said boundary;
establishing at least one layer imposed upon the grid, the layer being indicative of geospatial characteristics of at least one variable of interest;
identifying a functional measurement of a cell element for each cell to the variable of interest, and for indexing said functional measurement for each cell;
receiving geospatial information pertaining to one or more past events of a given event type, including location information for said one or more past events;
identifying an event functional measurement to the at least one variable of interest for each of said events;
determining a likelihood associating said event type's relative proximity to the variable of interest;
establishing a signature for said event type based on said determined likelihood;
receiving an outside event signature associated with said boundary;
determining a level of signature match between said established signature and said outside event signature.
52. A decision support system, comprising:
means for establishing a group of options associated with a system decision;
means for associating each option of said group of options with an area of interest (AOI);
means for collecting training data associated with each of said options;
means for establishing a result forecast for each option; and means for scoring said result forecasts so as to determine which option in said group of options is optimal.
means for establishing a group of options associated with a system decision;
means for associating each option of said group of options with an area of interest (AOI);
means for collecting training data associated with each of said options;
means for establishing a result forecast for each option; and means for scoring said result forecasts so as to determine which option in said group of options is optimal.
53. The system of claim 52 wherein said means for establishing a result forecast for each option comprises:
a boundary component for establishing a geospatial boundary associated with said AOI and a grid containing a plurality of cells within said boundary;
a layer component for establishing at least one layer imposed upon the grid, the layer being indicative of geospatial characteristics of at least one variable of interest;
a function component for identifying a functional measurement of a cell element for each cell to said at least one variable of interest, and for indexing said functional measurement for each cell;
a signature derivation component for receiving geospatial information related to one or more past events of at least one event type, including location information for said one or more past events, said signature derivation component further identifying and indexing an event functional measurement from the location information for each of said one or more events to the at least one variable of interest, and deriving a signature pattern for said event type;
an event likelihood determinant component for determining a level of signature match between said derived signature pattern and at least one cell of said plurality of cells.
a boundary component for establishing a geospatial boundary associated with said AOI and a grid containing a plurality of cells within said boundary;
a layer component for establishing at least one layer imposed upon the grid, the layer being indicative of geospatial characteristics of at least one variable of interest;
a function component for identifying a functional measurement of a cell element for each cell to said at least one variable of interest, and for indexing said functional measurement for each cell;
a signature derivation component for receiving geospatial information related to one or more past events of at least one event type, including location information for said one or more past events, said signature derivation component further identifying and indexing an event functional measurement from the location information for each of said one or more events to the at least one variable of interest, and deriving a signature pattern for said event type;
an event likelihood determinant component for determining a level of signature match between said derived signature pattern and at least one cell of said plurality of cells.
54. The system of claim 52 wherein said group of options is a group of potential retail establishment locations.
55. The system of claim 52 wherein said group of options is a group of potential insurance candidates.
56. A decision support system, comprising:
means for establishing at least one geospatial area of interest (AOI) comprising a two-dimensional, cell-based, geospatial boundary;
means for imposing at least one layer of geospatial information on said AOI, said geospatial information including at least one variable of interest;
means for determining the proximity of each cell in said boundary to each of said at least one variable of interest;
means for determining a location of a past meaningful result;
means for determining the proximity of the past meaningful result to said at least one variable of interest;
means for establishing a signature associated with said result; and means for determining a closest match with said result signature among said cells.
means for establishing at least one geospatial area of interest (AOI) comprising a two-dimensional, cell-based, geospatial boundary;
means for imposing at least one layer of geospatial information on said AOI, said geospatial information including at least one variable of interest;
means for determining the proximity of each cell in said boundary to each of said at least one variable of interest;
means for determining a location of a past meaningful result;
means for determining the proximity of the past meaningful result to said at least one variable of interest;
means for establishing a signature associated with said result; and means for determining a closest match with said result signature among said cells.
57. A system for forecasting an event, comprising:
an interface for receiving a problem definition;
a forecasting component having a plurality of areas of interest, a plurality of variable layers, and a plurality of training data elements representing at least one discrete event;
a selection interface for receiving a selection of at least one area of interest and at least one variable layer, a training data interface for receiving a selection of training data;
an assessment component for applying said training data to said selected area of interest and said selected variable layer to determine a forecast for one or more locations within said area of interest where a future event related to said at least one discrete event may occur.
an interface for receiving a problem definition;
a forecasting component having a plurality of areas of interest, a plurality of variable layers, and a plurality of training data elements representing at least one discrete event;
a selection interface for receiving a selection of at least one area of interest and at least one variable layer, a training data interface for receiving a selection of training data;
an assessment component for applying said training data to said selected area of interest and said selected variable layer to determine a forecast for one or more locations within said area of interest where a future event related to said at least one discrete event may occur.
58. The system of claim 57 wherein said training data selection is made by said user.
59. The system of claim 58 wherein said training data selection is made by said system according to said problem definition.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US55865404P | 2004-04-02 | 2004-04-02 | |
| US60/558,654 | 2004-04-02 | ||
| PCT/US2005/011388 WO2005098720A2 (en) | 2004-04-02 | 2005-04-04 | Forecasting based on geospatial modeling |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CA2561939A1 true CA2561939A1 (en) | 2005-10-20 |
| CA2561939C CA2561939C (en) | 2011-10-18 |
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2561939A Active CA2561939C (en) | 2004-04-02 | 2005-04-04 | Method and system for forecasting events and results based on geospatial modeling |
Country Status (5)
| Country | Link |
|---|---|
| US (2) | US7346597B2 (en) |
| AU (1) | AU2005232219B2 (en) |
| CA (1) | CA2561939C (en) |
| GB (1) | GB2429313A (en) |
| WO (1) | WO2005098720A2 (en) |
Families Citing this family (63)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3367268A1 (en) * | 2000-02-22 | 2018-08-29 | Nokia Technologies Oy | Spatially coding and displaying information |
| US7765319B1 (en) | 2003-07-30 | 2010-07-27 | Gorman Sean P | System and method for analyzing the structure of logical networks |
| JP2006031442A (en) * | 2004-07-16 | 2006-02-02 | Toshiba Corp | Space data analysis apparatus, space data analysis method, and space data analysis program |
| US7529195B2 (en) | 2004-07-30 | 2009-05-05 | Fortiusone, Inc. | System and method of mapping and analyzing vulnerabilities in networks |
| US8255262B2 (en) * | 2005-01-21 | 2012-08-28 | Hntb Holdings Ltd | Methods and systems for assessing security risks |
| US7801842B2 (en) * | 2005-04-04 | 2010-09-21 | Spadac Inc. | Method and system for spatial behavior modification based on geospatial modeling |
| US8200676B2 (en) | 2005-06-28 | 2012-06-12 | Nokia Corporation | User interface for geographic search |
| DE102005061690A1 (en) * | 2005-12-21 | 2007-07-05 | Solmic Gmbh | Metallurgical silicon producing method for manufacturing e.g. multi-crystalline silicon solar cells, involves forming crystallization front during directed freezing process, where front has shape of sector of spherical surface |
| WO2007084924A2 (en) * | 2006-01-17 | 2007-07-26 | Carbon Project, Inc. | Methods for using geospatial information as geospatial session files |
| AU2007215162A1 (en) | 2006-02-10 | 2007-08-23 | Nokia Corporation | Systems and methods for spatial thumbnails and companion maps for media objects |
| US20070203759A1 (en) * | 2006-02-27 | 2007-08-30 | Guy Carpenter & Company | Portfolio management system with gradient display features |
| WO2007146298A2 (en) | 2006-06-12 | 2007-12-21 | Metacarta, Inc. | Systems and methods for hierarchical organization and presentation of geographic search results |
| US8027868B1 (en) * | 2006-06-21 | 2011-09-27 | Sprint Communications Company L.P. | Trade area analyzer |
| US9721157B2 (en) | 2006-08-04 | 2017-08-01 | Nokia Technologies Oy | Systems and methods for obtaining and using information from map images |
| US20080140348A1 (en) * | 2006-10-31 | 2008-06-12 | Metacarta, Inc. | Systems and methods for predictive models using geographic text search |
| US7616828B2 (en) * | 2006-07-20 | 2009-11-10 | Harris Corporation | Geospatial modeling system providing geospatial model data target point filtering based upon radial line segments and related methods |
| WO2008031085A2 (en) | 2006-09-08 | 2008-03-13 | Fortiusone, Inc. | System and method for web enabled geo-analytics and image processing |
| US20080077474A1 (en) * | 2006-09-20 | 2008-03-27 | Dumas Mark E | Method and system for global consolidated risk, threat and opportunity assessment |
| US20080109196A1 (en) * | 2006-11-06 | 2008-05-08 | The Remme Corporation | Tools and Methods for Range Management |
| WO2009075689A2 (en) | 2006-12-21 | 2009-06-18 | Metacarta, Inc. | Methods of systems of using geographic meta-metadata in information retrieval and document displays |
| EP2122490B1 (en) * | 2007-02-13 | 2020-11-25 | ESRI Technologies, LLC | A method and system for integrating a social network and data repository to enable map creation |
| US20080221978A1 (en) * | 2007-02-26 | 2008-09-11 | Samuel Richard I | Microscale geospatial graphic analysis of voter characteristics for precise voter targeting |
| US20080250337A1 (en) * | 2007-04-05 | 2008-10-09 | Nokia Corporation | Identifying interesting locations based on commonalities in location based postings |
| US20090198641A1 (en) * | 2007-10-12 | 2009-08-06 | Enforsys, Inc. | System and method for forecasting real-world occurrences |
| US20090099862A1 (en) * | 2007-10-16 | 2009-04-16 | Heuristic Analytics, Llc. | System, method and computer program product for providing health care services performance analytics |
| US20100070309A1 (en) * | 2008-04-15 | 2010-03-18 | Deede Martin W | Method and System for Assessing Insurance Risk |
| US20100036793A1 (en) * | 2008-07-18 | 2010-02-11 | Willis Ruth P | Method and System for Geospatial Forecasting of Events Incorporating Data Error and Uncertainty |
| US8924332B2 (en) | 2009-05-29 | 2014-12-30 | Purdue Research Foundation | Forecasting hotspots using predictive visual analytics approach |
| US9165304B2 (en) | 2009-10-23 | 2015-10-20 | Service Management Group, Inc. | Analyzing consumer behavior using electronically-captured consumer location data |
| US8204886B2 (en) * | 2009-11-06 | 2012-06-19 | Nokia Corporation | Method and apparatus for preparation of indexing structures for determining similar points-of-interests |
| US20110202326A1 (en) * | 2010-02-17 | 2011-08-18 | Lockheed Martin Corporation | Modeling social and cultural conditions in a voxel database |
| US20110231336A1 (en) * | 2010-03-18 | 2011-09-22 | International Business Machines Corporation | Forecasting product/service realization profiles |
| US8938115B2 (en) | 2010-11-29 | 2015-01-20 | The Regents Of The University Of California | Systems and methods for data fusion mapping estimation |
| US8589389B2 (en) | 2011-02-17 | 2013-11-19 | International Business Machines Corporation | Characterizing and selecting providers of relevant information based on quality of information metrics |
| US20120297331A1 (en) * | 2011-05-19 | 2012-11-22 | Sumathi Chutkay | Systems and methods for intelligent decision support |
| US8670782B2 (en) | 2011-06-10 | 2014-03-11 | International Business Machines Corporation | Systems and methods for analyzing spatiotemporally ambiguous events |
| US20140032271A1 (en) * | 2012-07-20 | 2014-01-30 | Environmental Systems Research Institute (ESRI) | System and method for processing demographic data |
| GB201506927D0 (en) | 2012-10-01 | 2015-06-10 | Service Man Group Inc | Consumer analytics system that determines, offers, and monitors use of rewards incentivizing consumers to perform tasks |
| US10248700B2 (en) | 2013-03-15 | 2019-04-02 | Remote Sensing Metrics, Llc | System and methods for efficient selection and use of content |
| US9965528B2 (en) | 2013-06-10 | 2018-05-08 | Remote Sensing Metrics, Llc | System and methods for generating quality, verified, synthesized, and coded information |
| US9542627B2 (en) | 2013-03-15 | 2017-01-10 | Remote Sensing Metrics, Llc | System and methods for generating quality, verified, and synthesized information |
| US9654570B2 (en) | 2013-12-20 | 2017-05-16 | International Business Machines Corporation | Providing a sensor composite service based on operational and spatial constraints |
| US9911129B2 (en) | 2014-05-06 | 2018-03-06 | At&T Mobility Ii Llc | Facilitating demographic assessment of information using targeted location oversampling |
| US9838858B2 (en) | 2014-07-08 | 2017-12-05 | Rapidsos, Inc. | System and method for call management |
| MX2018005568A (en) | 2015-11-02 | 2018-11-09 | Rapidsos Inc | Method and system for situational awareness for emergency response. |
| US10664808B2 (en) * | 2015-12-14 | 2020-05-26 | Shift Technologies, Inc. | System and method for managing on-demand test drives |
| EP3391632A4 (en) | 2015-12-17 | 2019-06-12 | Rapidsos Inc. | Devices and methods for efficient emergency calling |
| US9986404B2 (en) | 2016-02-26 | 2018-05-29 | Rapidsos, Inc. | Systems and methods for emergency communications amongst groups of devices based on shared data |
| CA3021803A1 (en) | 2016-04-26 | 2017-11-02 | Rapidsos, Inc. | Systems and methods for emergency communications |
| US20180033109A1 (en) * | 2016-07-26 | 2018-02-01 | International Business Machines Corporation | Using public safety data to manage a criminal event response |
| WO2018039142A1 (en) | 2016-08-22 | 2018-03-01 | Rapidsos, Inc. | Predictive analytics for emergency detection and response management |
| JP2019082937A (en) * | 2017-10-31 | 2019-05-30 | パナソニックＩｐマネジメント株式会社 | Proposed site evaluation system and proposed site evaluation method |
| EP3721402A4 (en) | 2017-12-05 | 2021-08-04 | Rapidsos Inc. | Social media content for emergency management |
| US10820181B2 (en) | 2018-02-09 | 2020-10-27 | Rapidsos, Inc. | Emergency location analysis system |
| EP3803774A4 (en) | 2018-06-11 | 2022-03-09 | Rapidsos, Inc. | Systems and user interfaces for emergency data integration |
| US11917514B2 (en) | 2018-08-14 | 2024-02-27 | Rapidsos, Inc. | Systems and methods for intelligently managing multimedia for emergency response |
| US10977927B2 (en) | 2018-10-24 | 2021-04-13 | Rapidsos, Inc. | Emergency communication flow management and notification system |
| WO2020172612A1 (en) | 2019-02-22 | 2020-08-27 | Rapidsos, Inc. | Systems & methods for automated emergency response |
| CA3135274C (en) | 2019-03-29 | 2024-01-16 | Rapidsos, Inc. | Systems and methods for emergency data integration |
| US11146680B2 (en) | 2019-03-29 | 2021-10-12 | Rapidsos, Inc. | Systems and methods for emergency data integration |
| US11228891B2 (en) | 2019-07-03 | 2022-01-18 | Rapidsos, Inc. | Systems and methods for emergency medical communications |
| US11330664B1 (en) | 2020-12-31 | 2022-05-10 | Rapidsos, Inc. | Apparatus and method for obtaining emergency data and providing a map view |
| US11756063B2 (en) * | 2021-01-14 | 2023-09-12 | Spectrum Communications & Consulting, LLC | Sales and marketing assistance system using predictive analytics and method |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5805446A (en) * | 1994-08-19 | 1998-09-08 | Hitachi, Ltd. | Method for facility location |
| US5832187A (en) * | 1995-11-03 | 1998-11-03 | Lemelson Medical, Education & Research Foundation, L.P. | Fire detection systems and methods |
| CA2187704C (en) * | 1996-10-11 | 1999-05-04 | Darcy Kim Rossmo | Expert system method of performing crime site analysis |
| US7447509B2 (en) * | 1999-12-22 | 2008-11-04 | Celeritasworks, Llc | Geographic management system |
| US6606494B1 (en) * | 2000-05-10 | 2003-08-12 | Scoreboard, Inc. | Apparatus and method for non-disruptive collection and analysis of wireless signal propagation |
| US20020035432A1 (en) * | 2000-06-08 | 2002-03-21 | Boguslaw Kubica | Method and system for spatially indexing land |
| US6574561B2 (en) * | 2001-03-30 | 2003-06-03 | The University Of North Florida | Emergency management system |
| US6999876B2 (en) * | 2001-03-30 | 2006-02-14 | University Of North Florida | Modular architecture for rapid deployment and coordination of emergency event field surveillance |
| US6442483B1 (en) * | 2001-08-21 | 2002-08-27 | Arthur George Doglione | System and method for defining and creating surrogate addresses for township and range quarter sections |
| US7130865B2 (en) * | 2001-12-19 | 2006-10-31 | First Data Corporation | Methods and systems for developing market intelligence |
| AU2003263989A1 (en) * | 2002-08-05 | 2004-02-23 | Metaedge Corporation | Spatial intelligence system and method |
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