AU2013201826B2 - System and method for web enabled geo-analytics and image processing - Google Patents

System and method for web enabled geo-analytics and image processing Download PDF

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AU2013201826B2
AU2013201826B2 AU2013201826A AU2013201826A AU2013201826B2 AU 2013201826 B2 AU2013201826 B2 AU 2013201826B2 AU 2013201826 A AU2013201826 A AU 2013201826A AU 2013201826 A AU2013201826 A AU 2013201826A AU 2013201826 B2 AU2013201826 B2 AU 2013201826B2
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data
method
map
analysis
user
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AU2013201826A1 (en
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Sean Gorman
Christopher Allen Ingrassia
Pramukta Satya Kumar
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Esri Technologies LLC
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Fortiusone Inc
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Abstract

A method for providing mapping, data management and analysis, comprising: initiating creation of a map with a desired Gaussian aggregation and desired color map parameters; loading data to be utilized in the map; rasterizing the data; converting the 5 data to a certain scale; performing a convolution operation on the data; applying convolution results to a color ramp; and creating the map based on the color ramp and the convolution results. co co 0 Li) w - J <:)w LLI w CY 0 W w 0 > U)) C0 L w I :z Lu > LLI z0 r C) LUF-- C F- U) w r

Description

1 TITLE SYSTEM AND METHOD FOR WEB ENABLED GEO-ANALYTICS AND IMAGE PROCESSING 5 RELATED APPLICATION This application is a divisional application of Australian application no. 2007294516, the disclosure of which is incorporated herein by reference. Most of the disclosure of that application is also included herein, however, reference may be made to the specification 10 of application no. 2007294516 as filed to gain further understanding of the invention claimed herein. SUMMARY OF THE INVENTION According to an aspect of the present invention, there is provided a computerized 15 method for providing mapping, data management and analysis, comprising: receiving at a server a user request for creation of a map with a desired Gaussian aggregation and desired color map parameters; loading vector geographic data at the server comprising location data, the location data comprising at least one attribute; rasterizing the vector geographic data to create image data composed of pixels; converting the image data to a 20 certain scale greyscale image data; performing a convolution operation on the greyscale image data to provide an aggregation of the data using a kernel radius wherein the kernel radius adjusted in accordance with the desired zoom level; applying convolution results to a color ramp; creating the map for the location data based on the color ramp and the convolution results; and providing the map to the user. 25 According to another aspect of the present invention, there is provided a system for providing mapping, data management and analysis, comprising: a server coupled to a network; a database accessible by the server; and an application coupled to the server, the application configured for implementing the method of any one of the previous claims. Aspects of the invention also include computer software configured to implement 30 the method of the first aspect when executed by a computer, and computer readable storage media comprising computer code configured to implement the method of the first aspect when executed by a computer. BRIEF DESCRIPTION OF THE FIGURES 35 An embodiment, incorporating all aspects of the invention, will now be described by way of example only with reference to the accompanying drawings in which 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 2 FIGURE 1 is a system diagram illustrating a mapping, data management and analysis system 100, according to one embodiment. FIGURE 2 provides additional details on the core platform 1 of FIGURE 1, according to one embodiment. 5 FIGURES 3-4, and 14-17 are workflow diagrams utilizing the mapping, data management, and analysis system 100, according to several embodiments. FIGURES 5-6 are examples of how a user may create attribute data, according to several embodiments. FIGURES 7-13 are examples of different maps, according to several 10 embodiments. DETAILED DESCRIPTION OF EMBODIMENTS FIGURE 1 is a system diagram illustrating a mapping, data management and analysis system 100, according to one embodiment. Using the system 100, geo-analytics 15 can be delivered utilizing a Web browser. Geo-analytics performs mathematical computations and/or analysis on geographic information. Geo-analytics delivered using a Web browser can enable entities to utilize geo-spatial applications (i.e., applications which gather, store, process and deliver geographical information) with Web 1.0 or Web 2.0 applications. Web 2.0 applications are applications that use a second generation of Web 20 based communications and hosted services, and facilitate collaboration and sharing between users. The system 100 can include a core platform 1 (explained further in FIGURE 2 and its accompanying explanation). The core platform 1 can provide many core functions of applications data management, including, but not limited to, data dictionaries/data wikis 25 21, user generated content 19, search capabilities 18, database federation capabilities 17 (i.e., data pulled from outside system 100), authentication and identification 15, and analysis functions 22 (e.g., heatmap generation 23, intersection analysis 24, spatial concentration indexing (SCI) 25, spatial correlation 26, and temporal analysis 27). The core platform 1 can manage all the data in the system and can link it to analysis modules 30 that serve up results to the system 100. All external data can be loaded through and managed by the core platform 1, and all analysis of that data can be provided by the core platform 1 and delivered to the rest of the system 100. The exposure of the core platform functions to the rest of system 100 can be handled by the portal 2, which can serve as a gateway to the services in the core platform 1. The portal 2 can then be linked to the 35 application server instances 3, which can handle the middleware connecting the core platform 1 to the outside world. The application server instances 3 can be managed by a balanced proxy front end server 4, which can control the flow of traffic from the user 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 3 interface 6 and Web services 5 (which can communicate using APIs (Application Programming Interfaces), SOAP (Simple Object Access Protocol) XML-RCP (eXtensible Markup Language-Remote Procedure Call), REST (REpresentational State Transfer), Javascript and/or any other technique). The user interface 6 enables a user to access 5 the system 100 and the services it provides. The Web services 5 are the standard means by which a developer can access the services provided by the core platform 1. Through either the user interface 6 or Web services 5, the outside world can interact with the system 100 by sending requests routed through the balanced proxy server 4. These requests can be for raster analysis 7 (including, but not limited to, heatmaps 23, 10 intersection 24, spatial concentration 25, spatial correlation 26, and/or temporal analysis 27). (More detail on the raster analysis requests 7 is provided in FIGURE 2 and its accompanying explanation.) The raster analysis request 7 is routed to the core platform 1 which can locate the appropriate dataset from the database 10 for the analysis request and can process that data through the appropriate raster analysis module and then serve 15 up the results using the service pools 11 for raster analysis 12 to create a portable network graphic (PNG) (or other compatible format such as GIFF, TIFF) of the results along with numeric output, if appropriate. Those outside the system 100 can also request to import a data set 8 to the core platform 1 using the service pools 11 for data import 13 that place the data in the spatial database 10. (Details on how the data is managed is 20 provided in FIGURE 2 and its accompanying explanation). In addition to requesting analysis and adding data to the system, third party applications can be implemented in the system through Web service requests 9. For instance a third party mapping application can be used to provide a layer in the application for the geo-referenced raster analysis 12, as well as a reference upon which the data in the core platform 1 can be represented. 25 Web service requests 9 are sent through the balanced proxy frontend server 4 to initiate Web services 5. As pointed out above, the system 100 set forth in FIGURE 1 can enable entities to utilize geo-spatial, Web 1.0, and Web 2.0 technologies. Adding geo-analytics to media rich Web applications allows expanded computational and processing abilities. The 30 system 100 can also be used with a variety of applications, including desktop applications and/or Web enabled systems. The analysis that is created can be used for one or more geographic scales. The user can zoom into the image to gain further detail and get an expanded picture of the original raster surface and/or additional detail regarding what specifically is causing the variation in attribute valuation. The user is also able to perform 35 additional analysis using mathematical formulas the add data (e.g., SCI), subtract data (e.g., temporal analysis), multiply data (e.g., intersection), etc. 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 4 In addition, a broad, easy to use application can be provided to accommodate a non-technical user base. An understanding of the geo-spatial and mathematical concepts underlying the mapping application is thus not required. Many geo-analytics can be leveraged and simplified from the user's perspective to solve imminent real world 5 problems for this new user set. In one embodiment, the system 100 can be built with a geo-database back end using MY Structured Query Language (MY SQL) that allows geo-referenced data to be stored and queried. The data can be rated and tagged similar to what is implemented with Flickr (Flickr is an application that can provide photo sharing to consumers). Note 10 that other tagging and rating systems can also be utilized. The tagging and rating system allows data to be to easily managed, pushing the most relevant and accurate data to the top of the hierarchy. The middleware of the application can be developed, in one embodiment, using Ruby on Rails (Ruby on Rails is a middleware development platform that allows rapid building of media rich Web applications), providing a robust architecture 15 for quickly building media rich Web applications to utilize a geo-database. Note that systems other than Ruby on Rails can be utilized. The front end can be developed, in one embodiment, in OpenLaszlo (OpenLaszlo is a middleware development platform that can integrate Macromedia Flash with html and dynamic html in a browser deployable environment without requiring plug-ins to be downloaded). Note that systems other than 20 OpenLaszlo can also be used. FIGURE 2 provides additional details on the core platform 1 of FIGURE 1, according to one embodiment. The core platform 1 can provide both the analysis functions and database functions for the system. Data that is loaded into the system can be managed and served through the core platform 1. When data is loaded with the 25 service pools through the portal 2, the user may be asked to provide tags/ratings 20 and a data dictionary of uploaded attributes and/or wiki descriptions 21 for each data set. The uploaded data can be either third party formats that already have attributes and features specified, or they can be user generated content 19 where the user defines the attributes and fills in the feature data for each attribute. These datasets can then be rated by other 30 users of the system based on, for example, their accuracy, usefulness, and/or popularity. The combination of tags/ratings 20 and data dictionaries and/or wiki descriptions 21 provide a set of key words that can searched 18, and the tags/ratings 20 can provide a means by which to rank the search results. Once data is loaded into the system 100, analysis can be performed on it by the analysis modules 22, which are explained in detail 35 below in FIGURES 3-4 and 7-17 and their accompanying explanations. If the user selects a heatmap 23 (explained in more detail in FIGURES 3, 7, and 8 and their accompanying explanations), the analysis can produce a colored map illustrating where there are high 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 5 number values for the data attribute selected. If the user selects an intersection analysis 24 (explained in more detail in FIGURES 11 and 15 and their accompanying explanations), the analysis can show the location where two data sets intersect each other. If the user selects the spatial concentration analysis 25 (explained in more detail in 5 FIGURES 12 and 16 and their accompanying explanations), the analysis can use integration to illustrate how closely located different data attributes (e.g., infrastructures) are. If the user selects the temporal analysis 27 (explained in more detail in FIGURES 9, 10, and 14 and their accompanying explanations), the analysis can show the difference in values from one time period to another for the same set of data for a selected data 10 attribute. If the user selects the spatial correlation analysis 26 (explained in more detail in FIGURES 13 and 17 and their accompanying explanations), the analysis can show how the data attribute from one data set is related to a data attribute from another data set. As described in FIGURES 3-4 and 7-17, many of these analysis results can be communicated out of the system as a raster image. Accompanying numeric results can 15 be communicated to external users and applications 16 because the raw data can be formatted in a variety of ways (e.g., tabular, unique instance). In addition to data in the core platform 1, the system 100 can use database federation 17 to add data from a third party database that can be analyzed by the analysis modules. FIGURE 3 is a workflow diagram 300 utilizing the mapping, data management, 20 and analysis system 100, according to one embodiment. The workflow diagram 300 illustrates how a raster based analysis can be performed which can pertain to any of the analysis functions 22 outlined in FIGURE 2 and its accompanying explanation. In 305, heat map creation is initiated with a desired Gaussian aggregation (e.g., search radius) and desired color map parameters. In 310, the desired vector data source is loaded from 25 the data management object 14 in the core platform 1. In 315, the vector data source can be turned into a grid using rasterization. Rasterization is the conversion of images described in terms of mathematical elements (such as points and lines) to equivalent images composed of pixel patterns that can be stored and manipulated as sets of bits. In 320, conversion to a certain scale (e.g., 32 bit grayscale) is performed. Once it is 30 converted, in 325, convolution can be applied to the grid/matrix. Convolution can be applied by taking the grid/matrix and applying a kernel distance decay function. The kernel distance decay function can be based one of many mathematical applications, including, but not limited to, a Gaussian distribution, an exponential formula, a linear formula, a power law formula, a logarithmic formula or a step function formula. The speed 35 at which the kernel distance decay formula is applied can be enhanced by several mathematical applications, including, but not limited to Fourier transformations for convolution, and separable kernels (i.e., if the grid/matrix is one column and one row, 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 6 when separated, the resulting matrix is expressed as a product of all columns and all rows). In addition, values between attributed data can be interpolated using approaches including, but not limited to, nearest neighbor approach, inverse distance approach, kriging, and splining. In 330, once the convolution operation has been performed on the 5 grid/matrix, the results of the operation can be applied to a color ramp where the color indicates a range of number values resulting from the mathematic operation. In 335, the resulting image can then be created based on the color ramp and mathematical output. In 340, the external visualization and processing can be done to produce a PNG or other suitable graphics interchange format. 10 FIGURE 4 is a vector density analysis setting forth details of inputting vector data sources 310, according to one embodiment. Example analysis include temporal analysis 27 (using subtraction), intersection analysis 24 (using multiplication), spatial concentration analysis 25 (using addition/integration), and spatial correlation analysis 26 (using a linear correlation coefficient). FIGURE 4 illustrates a sample process where output from a 15 network analysis system is integrated. The raw network data 405 is imported 420 as edge inputs 440, which are points that are connected to each other (e.g., edge inputs 440 can be connections between electrical power substations 1, 4, 7, and 8). Each one of these edge inputs 440 can have one or more attributes 410 mapped to it (e.g., attributes of substations can be maximum voltage, number of lines, etc.). Based on the edge inputs 20 440 and the attributes 410 that are imported, a frequency analysis 445 can be run. In such an analysis, different routes can be run across the network and each time an edge is used it is counted as part of its frequency utilization (e.g., counting how many times edges connecting substations 1, 4, 7, and 8 are used). The edge results 450 of the frequency analysis can then be joined 435 to the original geometry file identifying which edge 25 frequency 425 the result belongs to. The edge frequency is the number of times the edge is used (e.g., how many times the edge connecting 1 and 4 is used). The edge frequency 425 can then be utilized to perform a raster analysis with the heatmap generator 430 and sent out to the system 100 as an raster heatmap image 315. As pointed out above with respect to FIGURE 3, data is entered in 310 that is used 30 to generate the heat map. Many formats can be utilized to allow data (e.g., a geometric location) to be specified and described. It is sometimes useful to allow multiple attributes to be associated with a location or data point. In one embodiment, data structures can be feature attributes, so that quantifiable metrics can be parsed from the data to analyze. This can be done by placing the feature attribute data in the schema tag of any XML 35 based language in a structured way that allows it to be machine parsable. For instance: <Schema name="City" parent=" Placemark"> 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 7 <SimpleField name="Name" type="string" /> <SimpleField name="Population" type="int" /> <SimpleField name="Temperature" type="int" /> <SimpleField name="Crime Rate" type="int" /> 5 </Schema> <City> <Name>Nowheretown</Name> 10 <Population>300</Population> <Temperature>76</Temperature> <Crime Rate>25</Crime Rate> </City> 15 From this code the system knows that geometry is a placemark on the map. That placemark is a city with the name Nowheretown and it has the following attributes - a population of 300, a temperature of 76, and a crime rate of 25. The city has a location and string of attributes that allow analysis to be done by the system. 20 While the above code is useful for a machine to read, in another embodiment, the system 100 allows a user to easily create attribute data to describe the numeric and textual features of a location. The following two methods, for example, can be utilized. For the first method (illustrated in FIGURE 5), the user creates fields for the attributes they would like add to a geometry (e.g., point, line, or polygon) or set of geometries. For 25 example, the fields State name, Bush Votes, and Kerry Votes can be created. The user then fills in the fields for each point created on the map. For the second method (illustrated in FIGURE 6), the user loads a set of predefined geometries with attributes in an easy spreadsheet interface. The user then selects from a list of predefined geometric boundaries (e.g., countries, states, counties, 30 zip codes, provinces) or they upload a file with geometric boundaries they would like to use as a reference. From the geometric boundary file/selection, the user chooses the field (unique identifier) they would like to join data to (e.g., State name, city Name, FIPS code, identification number etc.). The user then adds the additional fields they would like to supply attribute data for. The user can enter the attributes by hand or cut and paste the 35 attributes from another source. Similar configuration data from other formats can also be converted to this structure, so that information resident in several systems can be put into a single format 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 8 that is easily transferable over a network. Once the data is in an open standard single file format that is easily parsable, the data can be easily served up and analyzed in a Web browser environment. The data can be stored in a variety of database configurations and accessed through any number of middleware languages and served to the analytics 5 engine. Once the data has been entered in 310, the remainder of the process set forth in FIGURE 3 generates a heat map. The heat map can then be dynamically regenerated as the image is zoomed into, panned across, or as the attribute data changes (e.g., the heat map for weather would change as new temperature values were uploaded to the system). 10 For example, a user can zoom to a certain level of specificity or pan across an area by changing the desired Gaussian aggregation in 305 of FIGURE 3. An example of the dynamic zooming and/or panning capability is provided in FIGURE 7. FIGURE 7 illustrates real time pipeline flows in a particular city. One color (such as yellow) can be utilized to indicate the areas of the city where there is the most frequent use of the 15 pipelines. Another color (such as purple) can indicate the areas of the city where there is less frequent user of the pipelines. Zooming can be performed by setting a kernel radius at each geographic level of granularity. When the user zooms in to a level of specificity, the kernel radius is set to match the geographic level of interest or the radius can stay the same and the number of pixels can change due to the size of the window viewed. 20 The map can also change based on the original attributes in the data source 310 of FIGURE 3 changing. An example of the attribute changing capability is provided in FIGURE 8. The addition of buffered bounding boxes allows dynamic rendering and refactoring of heat maps by the user. When the attributes change the pixel valuation determination is run again to produce a new image. The speed at which the image 25 processing occurs can be enhanced by, for example, Fourier transforms for convolution and/or separable kernels. As shown in FIGURE 8, an image is given geographic bounding boxes such as 805 (which is illustrated in FIGURE 8 by an inside box, which can be green) or 810 (which is illustrated in FIGURE 8 by an inside box, which can be green) based on the original coordinates of the attributed data. In addition to the bounding boxes 30 805 and 810 , a buffer 815 (which is illustrated in FIGURE 8 by an outside box, which can be yellow) and 820 (which is illustrated in FIGURE 8 by an outside box, which can be red) for each box is set based on the Gaussian distance decay function to produce an image outside of the viewing bounding boxes 805 and 810. Thus, when the viewer moves to the next tile or zooms in and out there is a continuous buffer 815 and 820 creating a seamless 35 image. Since the result of the analysis is an image (e.g., PNG, JPEG, TIFF, BITMAP), it can be created as a single overlay onto a variety of geo-referenced maps or surfaces following the process described in FIGURE 3. This image can be easily viewed in a Web 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 9 browser after being sent to a user's Web browser following a request from the user. Each time the user pans, zooms, etc., a new request is sent from the user, and a new image is sent to the user's browser The heat maps can also be analyzed based on applying additional mathematical 5 processes to the method illustrated in FIGURE 3, according to several embodiments of the invention. Example analysis include temporal analysis (using subtraction), intersection analysis (using multiplication), special concentration analysis (using addition/integration), and spatial correlation analysis (using a linear correlation function). FIGURES 9, 10 and 14 illustrates data that changes in a temporal fashion. The 10 attributed features of a geometry or set of geometries can have a temporal data aspect to it such as a date or time stamp. Using the generic XML style example previously cited temporal data could follow the following form: <Schema name="City" parent=" Placemark"> 15 <SimpleField name="Name" type="string" /> <SimpleField name="Population 1900" type="int" /> <SimpleField name="Population 1920" type="int" /> <SimpleField name="Population 1940" type="int" /> <SimpleField name="Population 1960" type="int" /> 20 <SimpleField name="Population 1980" type="int" /> <SimpleField name="Population 2000" type="int" /> </Schema> <City> 25 <Name>Nowheretown</Name> <Population>300</Population> <Population>400</Population> <Population>500</Population> <Population>600</Population> 30 <Population>700</Population> <Population>800</Population> </City> 35 From the user interface side the data would have the following visual layout 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 10 City Name Populati Populati Populati Population Populatio Population on 1900 on 1920 on 1940 1960 n 1980 2000 Nowhereto 300 400 500 600 700 800 wn Enter Enter Location Attribute Now that the system has a set of a series of temporal data points the map can visualize the change over time either as a series of static maps (e.g., FIGURE 9) or a map showing the difference (e.g., FIGURE 10). If a series of maps are created, the maps can 5 be animated, for example, sequenced as a timed animation using any number of standard methods. The dynamic heat mapping approach can also be used to re-render heat maps on the fly as real time data changes or is dynamically updated. FIGURE 9 illustrates the changing heat map of real time pipeline flows at three different time periods, according to one embodiment. For each map (e.g., one, two, or three), the method illustrated in 10 FIGURE 3 is followed using the data points for the particular time period (e.g., one, two or three) as the vector data source 310. In addition to dynamically visualizing real time data flows, the approach can also provide temporal analytics for change over time. For instance, did a certain geography or asset increase or decrease between the two time periods. FIGURE 10 is a map 15 illustrating map 1015, which is the difference in flow between two time periods (map 1005 representing pipeline flow of time period one, and map 1010 representing pipeline flow of time period two). For map 1015, geographies that gained flow (e.g., there was an increase in pipeline use) can be illustrated in one color (such as green) and those that lost flow (e.g., there was a decrease in pipeline flow) can be illustrated in another color (such 20 as red). Map 1005 is time period one, map 1010 is time period two, and map 1015 is the difference between map one 1005 and map two 1010. FIGURE 14 is a method illustrating how map 1015 is generated, according to one embodiment. In 1405, 305-325 of the method illustrated in FIGURE 3 are followed, using the data points for time period one as the vector data source 310. In 1410, 305-325 of the method illustrated in FIGURE 3 are 25 followed, using the data points for time period two as the vector data source 310. In 1415, the grid/vector results of 325 for time period one is subtracted from the grid/vector results of 325 for time period two. The map 1015 for the temporal analysis can then be created by following 330-340 of the method illustrated in FIGURE 3. 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 11 Another example of analysis that can be applied is illustrated in FIGURES 11 and 15, which illustrate the intersection of data, according to one embodiment. For example, if two or more images for two or more different data sets (weighted or unweighted by an attribute) are added, the results could be an image illustrating where those two data sets 5 intersected each other and the color would indicate the proximity and magnitude (if weighted by an attribute) of those two or more data sets. FIGURE 11 illustrates intersection map 1115. For example, map 1115 could illustrate areas where pipelines and possible earthquake locations intersect, thus illustrating areas of possible flooding. FIGURE 15 is a method illustrating how map 1115 is generated, according to one 10 embodiment. In 1505, the grid/vector result for the first data set can be created by following 305-325 of the method illustrated in FIGURE 3, and using the data points for the first data set as the vector data source 310. In 1510, the grid/vector result for the second data set can be created by following 305-325 of the method illustrated in FIGURE 3, and using the data points for the second data set as the vector data source 310. In 1515, map 15 1115 for the intersection can be created by multiplying the grid/vector result of for the first data set by the grid/vector result of the second data set. The map 1115 for the intersection analysis can then be created by following 330-340 of the method illustrated in FIGURE 3. FIGURES 12 and 16 illustrate a spatial concentration index (SCI) analysis, 20 according to one embodiment. FIGURE 12 is an example of a spatial concentration index (SCI) for a city for five attributed infrastructures (electric power, natural gas, crude oil, refined products, and telecommunications). The SCI calculates the geographic concentration of any number of attributed data by measuring the geographic space separating the various assets and weighting it by the appropriate attribute. In 1605, 305 25 325 of FIGURE 3 is followed for each attribute data set. In 1615, the grid/vector results of 325 for each attribute data set are integrated together. Then 330-340 of FIGURE 3 are applied to the result in order to map the SCI. Thus, based on the calculation of distances between attributed data and what the values of those attributes are, a concentration index can be calculated. If all the attributed data in the study space are directly on top of each 30 other the index would be 100. If no attributed data is in the study space the index would be 0, and various distributions of attributed data will results in varying indexes based on the concentration of the attributed data. FIGURE 12 illustrates an SCI of 18.21. Note that one color 1205 (such as yellow) could illustrate the most concentrated area, another color 1215 (such as purple) could illustrate the least concentrated area, and varying 35 distributions 1210 between the two colors could be illustrated by other colors. The SCI can also integrate the real time data analytics discussed above. For instance the SCI could be determined in real time based on the actual flows though the 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 12 infrastructure. Also, a risk index could be calculated based on real time hazard data like wind speed or storm surge from an approaching hurricane. As the threat ebbs and flows in magnitude or direction the SCI would dynamically recalculate to indicate the risk exposure as the real time threat evolves. 5 The SCI approach is not confined to calculating only risk parameters, but can also be used on a wide variety geospatial data providing geo-analytic decision support across a number of verticals. Consumers can hook in local searches, such as Yahoo Local and Google Local, and determine which location has a higher SCI of businesses they find attractive. This data can then be mashed up with demographic information to provide 10 geo-analytics to support consumer and business decisions. For instance does neighborhood "A" or neighborhood "B" have a high concentration of young singles and highly rated bars? From the business perspective does neighborhood "A" or neighborhood "B" have a low concentration of competing coffee shops and a high concentration of individuals making over $100,000 per year? 15 FIGURES 13 and 17 illustrate a correlation analysis, according to one embodiment. Suppose there are two or more sets of sufficiently overlapping geometries, each ranked by a different attribute. It is possible to compute a score that measures how related those attributes are, for the area in question. For example, if a data set was loaded with 25 store locations and second data set was loaded with the location of 20 households who make over $100,000, an analysis could be run to see what the spatial correlation was between the stores and people who make over $100,000. To compute the correlation score (note that a numerical output and not a map output is provided in the correlation analysis), in 1705, 305-325 of FIGURE 3 are followed for the data set for the first attribute. In 1710, 305-325 of FIGURE 3 are followed for the 25 data set for the second attribute. In 1715, a numerical output is created using the grid/vector results of the first and second attributes. At this point, we can compute a score by using the traditional linear correlation coefficient: r = - i < > Y - Y n-1 sX s, 30 In the above equation, in one embodiment, two data sets, X and Y, are compared. X includes a set of values x;, i=1 to n. Similarly Y includes a set of values y;, i=1 to n. The values <x> and <y> represent the average value from the data sets X and Y, respectively. The value sx and sy represent the standard deviation of the data sets X and Y, 35 respectively. The correlation coefficient r can thus be used to see to what degree the two 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 13 (or more) attributes are partially correlated. Thus, FIGURE 13 illustrates a map of locations that can represent both a map of attribute one (e.g., where music venue stores are located, which can be illustrated using one dot color, such as red) and a map of attribute two (where drinking establishments are located, which can be illustrated using 5 another dot color, such as green). The correlation index can be calculated to be a value such as 33.7. Conclusion While various embodiments have been described above, it should be understood 10 that they have been presented by way of example, and not limitation. It will be apparent to persons skilled in the relevant art(s) that various changes in form and detail can be made therein without departing from the spirit and scope. In fact, after reading the above description, it will be apparent to one skilled in the relevant art(s) how to implement alternative embodiments. Thus, the present embodiments should not be limited by any of 15 the above described exemplary embodiments. In addition, it should be understood that any figures which highlight the functionality and advantages, are presented for example purposes only. The disclosed architecture is sufficiently flexible and configurable, such that it may be utilized in ways other than that shown. For example, the steps listed in any flowchart may be re-ordered 20 or only optionally used in some embodiments. As another example, more than two data sets could be used in each analysis. In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word "comprise" or variations such as "comprises" or "comprising" is used in an 25 inclusive sense, i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention. It is to be understood that, if any prior art publication is referred to herein, such reference does not constitute an admission that the publication forms a part of the common general knowledge in the art, in Australia or any other country. 30 7209684_1 (GHMatters) P80252.AU.1 SANDRAP

Claims (18)

1. A computerized method for providing mapping, data management and analysis, comprising: 5 receiving at a server a user request for creation of a map with a desired Gaussian aggregation and desired color map parameters; loading vector geographic data at the server comprising location data, the location data comprising at least one attribute; rasterizing the vector geographic data to create image data composed of pixels; 10 converting the image data to a certain scale greyscale image data; performing a convolution operation on the greyscale image data to provide an aggregation of the data using a kernel radius wherein the kernel radiusis adjusted in accordance with the desired zoom level; applying convolution results to a color ramp; creating the map for the location data 15 based on the color ramp and the convolution results; and providing the map to the user.
2. A method as claimed in claim 1, wherein an additional mathematical operation is performed to analyze the data. 20
3. A method as claimed in claim 2, wherein the mathematical operation is: addition/integration; subtraction; multiplication; or 25 a linear correlation; or any combination thereof.
4. A method as claimed in claim 3, further comprising employing the multiplication to perform an intersection analysis. 30
5. A method as claimed in claim 3 or claim 4, further comprising employing the subtraction to perform a temporal analysis.
6. A method as claimed in any one of claims 3 to 5, further comprising employing the 35 addition/integration to perform a special concentration analysis. 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 15
7. A method as claimed in any one of claims 3 to 6, further comprising employing a linear correlation function to perform a correlation analysis.
8. A method as claimed in any one of claims 1 to 7, further comprising delivering the 5 map through a Web browser.
9. A method as claimed in any one of claims 1 to 8, wherein the map is dynamically re-created as users zoom in and out or as temporal data feeds change.
10 10. A method as claimed in any one of claims 1 to 9, wherein the method utilizes a bounding box and tiling technique to provide panning of raster analysis across the map.
11. A method as claimed in any one of claims 1 to 10, wherein the data comprises user-generated vector data. 15
12. A method as claimed in any one of claims 1 to 11, wherein the convolution is a Gaussian aggregation and the kernel radius is a Gaussian distance decay function.
13. A method as claimed in any one of claims 1 to 12, wherein the convolution 20 operation is performed on the grayscale image data within the confines of a viewing boundary box around part of the grayscale image data in accordance with zoom and panning operations performed by the user.
14. A method as claimed in claim 13, wherein the viewing boundary box comprises a 25 buffer around its periphery based on the kernel radius to produce an additional region of image around the viewing boundary box.
15. A method as claimed in claim 14, further comprising: receiving a request from the user to pan the map; and 30 providing an updated map to the user including at least some of the image from the buffer.
16. A system for providing mapping, data management and analysis, comprising: a server coupled to a network; 35 a database accessible by the server; and an application coupled to the server, the application configured for implementing the method of any one of the previous claims. 7209684_1 (GHMatters) P80252.AU.1 SANDRAP 16
17. Computer software configured to implement the method of any one of claims 1 to 15 when executed by a computer. 5
18. Computer readable storage media comprising computer code configured to implement the method of any one of claims 1 to 15 when executed by a computer. 7209684_1 (GHMatters) P80252.AU.1 SANDRAP
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