Patent Publication Number: US-2023161813-A1

Title: Geographic population health information system

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation of U.S. Nonprovisional application Ser. No. 17/333,167, filed May 28, 2021, which is a continuation of U.S. Nonprovisional application Ser. No. 16/882,406, filed May 22, 2020, which issued as U.S. Pat. No. 11,023,563 on Jun. 1, 2021, which is a continuation of U.S. Nonprovisional application Ser. No. 15/336,466, filed Oct. 27, 2016, which issued as U.S. Pat. No. 10,664,570 on May 26, 2020, which claims the benefit of U.S. Provisional Application No. 62/247,190, filed Oct. 27, 2015, all of which are incorporated herein by reference in their entirety. 
    
    
     BACKGROUND 
     This application relates generally to the field of healthcare data analysis, and more specifically to systems and methods for displaying geographic population health information. 
     Existing systems for analyzing population healthcare data based on population distribution, disease state, and access to options to improve health requires software residing on the user&#39;s computing device or system to store, compute, manipulate, integrate multiple data sources, and display the results of massive datasets. Most likely, such software residing on the user&#39;s computing device or system includes database management software to perform data management functions. As new datasets are made available, software on the user&#39;s computing device or system requires updates to enable access to the new datasets as such software updates are released. In addition, to render a spatial distribution on a user&#39;s computing device or system, existing methods and systems require software on the user&#39;s computing device or system to facilitate integration of the disparate data assets with geographical information system software to produce a composite rendering on the graphical user interface. 
     The proposed software and associated system of the instant disclosure addresses these and other limitations of existing systems by using a client device&#39;s web browser to display geographical population health information data that the system collects, assembles, and preformats from disparate data sources for on-demand, dynamic, interactive display in the web browser. 
     SUMMARY 
     A geographic population health information system is disclosed, comprising: (a) a map server configured to receive client identification information from a client device, the map server connected to a custom geographic data selection manager which is connected to a geo-spatial database server, and (b) a web server configured to receive client user ID information, the web server connected to a user security tabular data assessor which is connected to a custom data selection manager, wherein the custom data selection manager is connected to a plurality of databases, wherein the geographic population information system is configured to asynchronously match the data from the geo-spatial database server with data from the plurality of databases so as to render a user customized, dynamic visualization of healthcare data in a web browser of the client device connected to the map server and the web server. 
     A geographic population health information system is disclosed comprising: (i) one or more first servers configured to: receive a request from a client system for a geographical area and a desired segmenting of the geographical area; command a geo-spatial database to deliver one or more shapefiles based on the geographical area and the desired segmenting of the geographical area, the one or more shapefiles including polygons with vertices having coordinates located within the outer boundaries; serve the shapefiles to the client system; (ii) the client system, which is configured to: receive one or more sets of health data after receiving the served shapefiles; match each portion of the received health data to at least one of the served shapefiles; generate shading instructions for at least some of the polygons of the one or more shapefiles based on the matched health data; produce a plurality of image tiles based on the served shapefiles and the shading instructions; arrange and display the plurality of image tiles on a graphical user interface of the client system. 
     The geographical area of the request may comprise coordinates of a center of the geographical area and a zoom level. 
     The one or more servers may be configured to command the geo-spatial database to deliver the one or more shapefiles based on the center of the geographical area and the zoom level. 
     The zoom level may correspond to a current zoom level of the graphical user interface of the client system. 
     The system may further comprise the one or more second servers, the one or more second servers storing hypertension prevalence data for each of a plurality of zip codes. 
     The client system may be configured to receive the hypertension prevalence data as the one or more sets of health data and generate the shading instructions for the at least some of the polygons based on the received hypertension prevalence data. 
     The one or more second servers may be configured to serve at least some of the one or more sets of health data to the client system in a tabular format. 
     The client system may be configured to display the at least some of the one or more sets of health data in the tabular format in response to a user selection. 
     The client system may be configured to map a user selection of a pixel of at least one of the one or more image tiles to the portion the received one or more sets of health data matched with a polygon encompassing the pixel. 
     The client system may be configured to display the matched portion of the received one or more sets of health data in tabular form in response to the user selection of the pixel. 
     A geographic population health information system is disclosed comprising: one or more first servers configured to: receive a request from a client system for a layer including (a) one or more sets of health data (b) a geographical area, and (c) a desired segmenting of the geographical area; command a geo-spatial database to deliver one or more shapefiles based on the geographical area and the desired segmenting of the geographical area, the one or more shapefiles including polygons with vertices having coordinates located within the outer boundaries; receive the one or more sets of health data from one or more second servers; match the each portion of the one or more sets of health data with at least one of the polygons; generate shading instructions for at least some of the polygons of the one or more shapefiles based on the received one or more sets of health data matched with the at least some of the polygons; produce a plurality of image tiles based on the shapefiles and the shading instructions; serve the plurality of image tiles to the client system. 
     The geographical area of the request may comprise coordinates of a center of the geographical area and a zoom level. 
     The one or more servers may be configured to command the geo-spatial database to deliver the one or more shapefiles based on the center of the geographical area and the zoom level. 
     The zoom level may correspond to a current zoom level of a graphical user interface of the client system. 
     The system may further comprise the one or more second servers, the one or more second servers storing hypertension prevalence data for each of a plurality of zip codes. 
     The one or more first servers may be configured to receive the hypertension prevalence data as the one or more sets of health data and generate the shading instructions for the at least some of the polygons based on the received hypertension prevalence data. 
     The one or more first servers may be configured to serve at least some of the one or more sets of health data to the client system in a tabular format. 
     The system may further comprise the client system, which is configured to display the at least some of the one or more sets of health data in the tabular format in response to a user selection. 
     The system may further comprise the client system, which is configured to assemble and arrange the plurality of image tiles received from the one or more first servers in an array of image tiles. 
     The one or more first servers may be configured to serve tile arrangement instructions to the client system along with the plurality of image tiles and the client system is configured to arrange the plurality of image tiles into the array based on the tile arrangement instructions. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates one embodiment of a system architecture of the instant disclosure in block diagram form. 
         FIG.  2    illustrates a detailed block diagram of the client system shown in  FIG.  1   . 
         FIG.  3    illustrates a detailed block diagram of the customer geographic data selection manager shown in  FIG.  1   . 
         FIG.  4    illustrates a detailed block diagram of the user security tabular data assessor shown in  FIG.  1   . 
         FIGS.  5  to  52    illustrate various embodiments of geographic population health information data displayed on a graphical user interface according to various constraints and filters. 
     
    
    
     DETAILED DESCRIPTION 
     Although the figures and the instant disclosure describe one or more embodiments, one of ordinary skill in the art would appreciate that the teachings of the instant disclosure would not be limited to these embodiments. Various embodiments of the instant disclosure relate to integrating, analyzing, and visualizing geographic, population-based, health data on computer displays associated with one or more remote client computing devices without requiring client-side software on the client device other than a web browser. The system herein disclosed eliminates the need for enterprise-wide software installations on client devices, and of the need to modify client-side computing devices to access the requested data. The system herein disclosed also avoids data capacity limitations that would prevent the client computing device from rendering the requested topographical data. The system provides users with on-demand, user customized data analysis through a server system that may include a cloud-based server system. 
     Turning now to the drawings wherein like reference numerals refer to like elements,  FIG.  1    shows an illustration of one embodiment of a system architecture that solves the problems associated with existing systems. In this embodiment, system  10  includes server system  110  hosting application software that responds to requests from client system  100 . 
     Server system  110  may include any web server such as a cloud-based server or a dedicated server system. Server system  110  in this embodiment includes various hardware and software elements, including map server  111 , custom geographic data selection manager  112 , community health management (CHM) web server  116 , user security tabular data assessor  117 , and custom data selection manager  120 . Referring to  FIG.  3   , custom geographic data selection manager  112  may also include map identifier  113  and layer identifier  114 . Referring to  FIG.  4   , user security tabular data assessor  117  may include group security identifier  118  and tabular layer identifier  119 . 
     Server system  110  in this embodiment also includes connections to various databases, including geo-spatial database  115 , claims database  121 , nutrition database  122 , physical fitness database  123 , and social determinants of health database  124 . One of ordinary skill would appreciate that in other embodiments, server system  110  may implement multiple other servers and databases or fewer than what is shown in  FIG.  1   . Server system  110  may also include one or more computer processors, one or more data memory storage elements, one or more input/output buses and data ports to enable the communication of data to and from server system  110  via one or more wired, wireless, wifi, cellular, and satellite networks via the Internet, the world wide web, or any other communications protocol. 
     Geo-spatial database  115  may include and store a plurality of shapefiles. Each shapefile may include one or more points or vertices, one or more lines connecting at least two vertices, and/or one or more polygons connecting at least three vertices. Geo-spatial database  115  may include and store at least one shapefile including a plurality of polygons, each corresponding to a respective zip code. Claims database  121  may include geolocation data, organization data, and bar code scan data files. Nutrition database  122  may include healthcare administrative claims data. Physical fitness database  123  may include geological data and image data of impervious surfaces. Social determinants of health database  124  may include alphanumeric descriptive data. 
     Client system  100  may include graphical user interface (GUI)  101 , such as a display, and Internet web browser  102 , such as Internet Explorer, Safari, Firefox, or Google Chrome, to name a few. Client system  100  may also include client ID  108  representing a unique identifier that may be registered with server system  110  so that server system  110  recognizes and returns requested data back to the same client device. Referring to  FIG.  2   , web browser  102  of client system  100  may include dynamic display  103  including graphical image viewer  104 , and web page renderer  105  including image converter  106  and graphical composite assembler  107 . 
     In one embodiment, map server  111  authenticates client ID  108  after receiving a service token from web browser  102 . The custom geographic data selection manager  112  receives the client input/selection (i.e. query) for a layer and retrieves, via layer identifier  114 , one or more shapefiles (or portions thereof) from geo-spatial database  115 . The one or more shapefiles (or portions thereof) retrieved from the geo-spatial database  115  may be referred to as the appropriate shapefiles. To retrieve the appropriate shapefiles, map identifier  113  of custom geographic data selection manager  112  uses a map identification routine to select the requested map, and layer identifier  114  of geographic data selection manager  112  selects the appropriate layers from the selected map. The layers from the selected map include vertices files that are combined to form the shapefile. The selected shapefile is sent to the map server  111  as a response to the original request from the web browser  102 . 
     The custom data selection manager  112  may further retrieve, via map identifier  113 , one or more reference shapefiles from an external database. The one or more reference shapefiles may be used to build an underlying reference map upon which the appropriate shapefiles are overlaid. 
     To retrieve the appropriate shapefiles and/or the reference shapefiles, layer identifier  114  and/or map identifier  113  of custom geographic data selection manager  112  may determine outer coordinates of a displayed portion of the reference map. For example, the displayed portion of the reference map in  FIG.  7    is almost the entire United States and small portions of the Pacific and Atlantic Oceans, whereas the displayed portion of the reference map in  FIG.  8    is Chicago. According to one embodiment, map identifier  113  retrieves the reference shapefiles from the external server before the layer identifier  114  retrieves the appropriate shapefiles from the geo-spatial database  115 . 
     To determine the outer coordinates layer identifier  114  may determine (a) a zoom level of a reference map currently displayed on GUI  101  and (b) a center of the reference map currently displayed on GUI  101 . Layer identifier  114  may learn the zoom level of the reference map by querying the client system  100 . Alternatively, layer identifier  114  may have previously learned the zoom level of the reference map in order to retrieve appropriate reference shapefiles of the reference map. Based on the zoom level and the center of the reference map, layer identifier  114  may determine outer coordinates of a displayed portion of the reference map. Alternatively or in addition, layer identifier  114  may determine the outer coordinates of the displayed portion of the reference map by determining outer-most vertices, lines, or polygons of reference shapefiles being used to render the displayed portion of the reference map. 
     Layer identifier  114  may determine which sets of shapefiles stored in the geo-spatial database  115  include the appropriate shapefiles based on a user selection of one or more layers on a drop-down menu displayed on GUI  101 . For example, the geo-spatial database  115  may include (a) one set of one or more shapefiles representing zip codes of the United States, (b) one set of one or more shapefiles representing state lines of the United States, (c) one set of one or more shape files representing city boundaries of the United States. Layer identifier  114  may only retrieve shapefiles from the applicable sets of shapefiles. For example, if the user-selected layer is segmented according to zip code, but not according to city boundaries, then the layer identifier  114  may only retrieve shapefiles from sets (a) and/or (b), but not from set (c). 
     It should thus be appreciated that layer identifier  114  may determine the applicable sets of shapefiles of geo-spatial database  115  and that layer identifier  114  may further determine the appropriate portions of the applicable sets. Put differently, layer identifier  114  may retrieve the appropriate shapefiles from the one or more applicable sets of shapefiles. The appropriate shapefiles may include polygons having one or more vertices with coordinates falling within the displayed portion of the reference map, lines having one or more vertices with coordinates falling within the displayed portion of the reference map, and/or vertices with coordinates falling within the displayed portion of the reference map. The appropriate shapefiles may be sent to the map server  111  as a response to the original request from the web browser  102 . 
     According to one embodiment, the map server  111  may serve the appropriate shapefiles (retrieved by the layer identifier  114 ) and/or the reference shapefiles (retrieved by the map identifier  113 ) to the client system  100 . The graphical composite assembler  107  may receive the shapefiles from the map server  111  while waiting to receive the customized data from the CHM web server  116 . Upon receiving the customized data, the graphical composite assembler  107  may match the appropriate shapefiles (but not the reference shapefiles) with the tabular data received from the CHM web server  116 . The image converter  106  may render a series of image tiles or raster files having the polygons, lines, and/or vertices included in the reference shapefiles and the appropriate shapefiles. The polygons, lines, and/or vertices of the appropriate shapefiles may be overlaid on the polygons, lines, and/or vertices of the reference shapefiles and be shaded according to the tabular data received from the CHM web server  116 . The graphical image viewer  104  may assemble the rendered image tiles or raster files into an array according to assembly instructions produced by the image converter  106 . The reference shapefiles may be rendered according to instructions received from the external server. 
     According to another embodiment, the map server  111  may convert the appropriate shapefiles and the reference shapefiles into raster files or image tiles. Each raster file or image tile may include polygons from the appropriate shapefiles, shaded according to the health data received from the CHM web server  116  and polygons from the reference shapefiles, shaded according to instructions received from the external server. To perform the conversion, the map server  111  may fill in or shade each polygon included in the appropriate shapefiles based on tabular data received from the CHM web server  116 . Put differently, the shading of each polygon of the appropriate shapefiles may be based on data retrieved from CHM Web Server  116 . As in the above embodiment, the shaded polygons of the appropriate shapefiles are overlaid on the polygons of the reference shapefiles during rendering. The map server  111  may deliver the raster files or image tiles (which may be images of the shaded polygons of the appropriate shapefiles overlaid onto the polygons of the reference shapefiles), instead of the appropriate shapefiles and the reference shapefiles, to the client system  100 . The size of each polygon may based, at least in part, on a zoom level of the GUI  101 . 
     In one embodiment, graphical image viewer  104  is configured to display graphical images in various graphical file formats. Dynamic display  103  of graphical image viewer  104  responds to inputs, such as macros, to dynamically change the display of responsive data on the web browser  102  in accordance with a user&#39;s commands. The dynamic display  103 , for example, is configured to enable a user to move or pan the image displayed in web browser  102  and to zoom the image in or out. As the user moves, pans, and/or zooms, the dynamic display  103  may update or cause map identifier  113  to update the reference shapefiles being used to render the underlying reference map. As explained above, as the reference shapefiles used to display the underlying reference map are updated, the appropriate shapefiles may also be updated. As the user moves, pans, and/or zooms, the dynamic display  103  may update or cause map server  111  to serve (a) new shapefiles and/or (b) new raster files or image tiles. Web page renderer  105 , or similar rendering engine, of web browser  102  ensures the image is properly displayed on the web page in the web browser  102 . The web page renderer  105  combines various formats, including HTML and CSS. In one embodiment, the image is in HTML format and the navigation on the web page is performed via CSS. 
     The graphical composite assembler  107  may receive the data in an asynchronous manner. According to one embodiment, the graphical composite assembler  107  receives the appropriate shapefiles from the map server  111  and then instructs the image converter  106  to render the appropriate shapefiles according to health data later received from the CHM web server  116 . According to another embodiment, the graphical composite assembler  107  receives the raster files or image tiles from map server  111  while waiting to receive customized data in tabular format from the CHM web server  116 . Upon receiving the customized data in tabular format, the graphical composite assembler  107  may match the raster files or image tiles with the appropriate tabular data into a graphical composite. Image converter  106  may convert the assembled graphical composite into an image that can be viewed by the graphical image viewer  104 . 
     Server system  110  is configured to receive entry by a user of server system  110  of a user ID  109  or other login credentials for ensuring that the user is provided access to only that information to which that user is allowed. Upon logging into the software hosted by server system  110 , based upon the client identification and the login credentials of the user, server system  110  automatically modifies the menu of selectable options displayed on graphical user interface  101  of client system  100  for selection by the user. The user may then select from among one or more data analysis from the menu that are pre-customized for that user. 
     In operation, a user using a client device, such as a desktop computer, a laptop, a tablet, or a mobile device, connected to server system  110  would be provided by server system  110  with a login screen through which the user would enter the user&#39;s login credentials, such as login ID and password. Any variation of user authentication may be implemented by server system  110  as may be known in the art. Server system  110  is configured to provide a menu of data analyses from which the user may select. Such data analyses may include the preparation by server system  110  of a composite two-dimensional topographic map for display on the display of the user&#39;s device. Server system  110  may provide the user with various options or combination of elements from which the user may select that are associated with the requested data analysis. 
     Upon receipt of the user&#39;s request using web browser  102  connected to server system  110 , in one embodiment, cloud-based map server  111  of server system  110  separately sends a request to CHM Web server  116 . Map server  111  verifies the client ID  108  of the user&#39;s client computing device and sends the request to custom geographic data selection manager  112 . Upon receipt of the request, geographic data selection manager  112  uses the map identifier  113  and/or the layer identifier  114  to select the appropriate shapefiles from the geo-spatial database  115  to render a map. Geographic data selection manager  112  sends the appropriate shapefiles to the map server  111 . According to one embodiment, and as previously stated, the map server  111  serves the appropriate shapefiles to the client system  100 . According to another embodiment, and as previously stated, the map server  111  serves the raster files or image tiles to web browser  102  of client system  100  for integration by web browser  102  into a map comprising a composite visualization of the requested data analysis. 
     Simultaneously, the CHM Web server  116  sends the request to the user security data assessor  117  to verify the user&#39;s group ID  109  and the data layer requested by the user. Upon verification that the user permissions are satisfied, the user request is received by the custom data selection manager  120 . The custom data selection manager  120  queries the various databases  121 , 122 , 123 , 124  to retrieve the disparate data. Upon receipt of the disparate data components from the custom data selection manager  120 , the CHM Web server  116  serves the data components to web browser  102  of client system  100  for integration by web browser  102  into a map comprising a composite visualization of the requested data analysis. In this way, server system  110  conducts the spatial and statistical analysis to match the user&#39;s request, and presents that data in a format that web browser  102  can interpret and assemble to graphically display the results. 
     Web browser  102  uses graphical composite assembler  107  to match the tabular data with (a) the shapefiles or (b) the raster files/image tiles to produce a composite graphic. The graphical composite assembler  107  may change the format of incoming data from the map server, which may be in the form of raster files, image files, and/or KML files. Image converter  106  converts the composite graphic into a Web page that can be viewed by graphical image viewer  104 . Graphical image viewer  104  displays the Web page through graphical user interface  101 , which may be any one of a number of displays associated with client system  100 . Web browser  102  stores the layer information to prevent the need for server system  110  to retrieve the same layer if the user selects the same layer with new topographical elements. 
     Example: User Selection of Customized Data Analysis 
     In one example, a user wishes to conduct data analysis of a national account with employees across the 43,000 zip codes in the United States. In particular, the user desires to prioritize geographic areas where the account&#39;s employees have higher prevalence of hypertension in comparison to the prevalence of the general commercial population. Furthermore, the user seeks to limit the data analysis by controlling for social determinants of health. Finally, the user needs to identify local providers, community health centers, farmers markets, and social services that may be ideal partners to support population health management. 
     The user may log into the software hosted by server system  110  using a web browser  102  displayed on the client device of client system  100 . The server system  110  presents a customized menu  130  on the web page displayed in the web browser  102  that is appropriate and/or customized for the user&#39;s user ID. The user may select the account and the hypertension prevalence layer. In addition, the user may select the layer that controls for the social determinants of health. Lastly, the user may select the local resources of interest. 
     The server system  110  automatically responds on the fly to the user&#39;s request by facilitating the collection and integration of data stored on disparate data sources, and preformats the results in a way that is interpretable and displayable by the user&#39;s web browser without any additional client-side software. The server system  110  pushes that formatted data to the user&#39;s web browser, which renders the account-specific information for hypertension prevalence in a composite visualization that allows the user to scan the map of the United States. The visualization renders an image of shape files for each zip code symbolized and/or represented in different colors that reflect zip codes with higher prevalence among the account population. Because the user controlled for social determinants of health, the visualization shades the shape files for the geographic areas controlled for by the user. The user may select or “click” on an area of the map and the shape file produces a pop-up display of the comparative data of the employee population to the general commercial population. The file size for the image could exceed 125 MB. By selecting providers and resources, the user can click on the displayed community assets to reveal the services provided by the local entity or hours of operation. Upon reviewing the services, the user can receive directions to the local entity by entering an address associated with the user&#39;s geographic location. 
       FIGS.  5 - 52    provide the results displayed in graphical user interface (GUI)  101  of Internet browser  102  of various exemplary searches on server system  110  that integrates community mapping data and social determinants of health data overlaid on a graphical map of the United States. In these embodiments, the data is configured for display by zip code. In other embodiments, the data may be displayed according to any other actual, artificial, or synthetic geographic or other boundary or grouping. For example, the data may be displayed by community, by city/town, by township, by county, by state, and by region, or any combination or subcombination of the foregoing. 
     Community mapping data may include data concerning medical condition prevalence, medical condition frontier, food access frontier, nutrition, population density centers, and health care providers, as shown by the references positioned on the bottom ribbon on  FIG.  7    after selecting the tab  126  of graphical user interface (GUI)  101  of Internet browser  102 . 
     Social determinants of health data may include data concerning minority population prevalence, race/ethnicity prevalence, linguistic isolation prevalence, prevalence of a foreign language as the primary language, level of education, per capita income, income distribution, unemployment prevalence, prevalence of population not in the labor force, poverty prevalence, prevalence of disabled people living in poverty, marital status, prevalence of female only householders, prevalence of no access to a vehicle, prevalence of the population enrolled in Supplemental Nutritional Assistance Program (SNAP), and family size, as shown by the references positioned on the bottom ribbon on  FIG.  20    after selecting tab  127  of graphical user interface (GUI)  101  of Internet browser  102 . One of ordinary skill would appreciate that other community mapping data and/or social determinants of health data may be utilized. 
     Condition prevalence in this disclosure refers to the proportion of the commercial population that presents in a reporting period, for example, the preceding 12-month period, with one inpatient claim of a particular health condition of interest or 2 outpatient claims of the condition of interest in the reporting period. For example, a user seeking to know the proportion of the commercial population that presents in a reporting period, for example, the preceding 12-month period, with one inpatient claim of a particular health condition of interest or 2 outpatient claims of the condition of interest in the reporting period may query system  110 , as shown in  FIGS.  8 - 9   . 
     In this embodiment, as shown in  FIG.  8   , after entering a location, such as the name of a city, an address, or a zip code, into search box  128 , a user may select the “hypertension prevalence” dropdown option  132  from among one or more selectable medical conditions in customized menu  130  to obtain the percentage of the population in the geographic location who is considered to have hypertension. The prevalence of this condition is presented to the user in graphical map image  134  where a different color or shading style is assigned to each zip code shown in the image  134  according to the prevalence of the item being searched. The geographic breadth of the image  134  may default to a particular zoom level, which zoom level may be adjustable by the user. 
     The image  134  is dynamically interactive with the user. Consequently, the user may select a particular color coded region corresponding to a particular zip code to obtain further statistics on the prevalence of the condition being searched. As shown in  FIG.  9   , for example, the user selected the geographic tile  135  corresponding to zip code “36108” (item  137 ), after which system  110  responds by displaying text box  136  with detailed statistical data corresponding to that zip code and for the selected health condition, which in this example, is hypertension prevalence, including community characteristics for the general population in that zip code. Legend  138  may be configured to dynamically update with information corresponding to the zoom level of image  134 . 
     Condition frontier in this disclosure refers to, for any given medical condition selected above, the observed-to-expected ratio of the commercial population compared to the general population. Populations in underperforming zip codes may be identified relative to nationwide zip codes with these characteristics. Social Determinants of Health (SDOH) include cultural factors (e.g., race/ethnicity and language), economic factors (e.g., per capita income), educational factors (e.g., completion of primary education) and social factors (e.g., female head of household and access to vehicle). 
       FIGS.  10  and  11    take the foregoing example a step further by identifying one or more social factors that may explain the prevalence of hypertension in the selected location. For example, once hypertension prevalence displayed in the user&#39;s browser, the user may select “Hypertension Frontier” in the dropdown option  142  from among one or more selectable options in customized menu  130  to identify underperforming zip codes relative to nationwide zip codes with similar community characteristics. The prevalence of hypertension (or any condition that is previously selected) is presented to the user in graphical map image  144  where a different color or shading style is assigned to each zip code shown in the image  144  according to the prevalence of the item being searched. The geographic breadth of the image  144  may default to a particular zoom level, which zoom level may be adjustable by the user. 
     The image  144  is dynamically interactive with the user. Consequently, the user may select a particular color coded region corresponding to a particular zip code to obtain further statistics on the item being searched (in this example, the hypertension frontier). As shown in  FIG.  11   , for example, the user selected the geographic tile  145  corresponding to zip code “36117” (item  147 ), after which system  110  responds by displaying text box  146  with detailed statistical data corresponding to that zip code and for the selected health condition, which in this example, is hypertension prevalence and the hypertension observed-to-expected ratio (O/E) associated with that condition for that zip code, including community characteristics for the general population in that zip code. Legend  148  may be configured to dynamically update with information corresponding to the zoom level of image  144 . 
     Food access frontier in this disclosure refers to the availability of food options and access thereof, by zip code. The food access frontier comparison considers relatively low areas of healthy food options (e.g., “food deserts”) and high areas of unhealthy food options (e.g., “food swamps”). The methodology employed by server system  110  may control for median household income and commuting patterns, which may be described as part of the economic sphere of daily activity. The calculated results may be arrayed by deciles, as shown in  FIGS.  12 - 13   . 
     For example, after entering a location, such as the name of a city, an address, or a zip code, into search box  128 , a user may select the “food access frontier” dropdown option  152  from among one or more selectable nutritional options in customized menu  130  to obtain the availability by the population to different food options in that geographic location. 
     As discussed above, the results of the food access frontier search for the particular geographic location entered into search box  128  are presented to the user in graphical map image  154  where a different color or shading style is assigned to each zip code shown in the image  154  according to the prevalence of the item being searched. The geographic breadth of the image  154  may default to a particular zoom level, which zoom level may be adjustable by the user. 
     As discussed above, the image  154  is dynamically interactive with the user. Consequently, the user may select a particular color coded region corresponding to a particular zip code to obtain further statistics on the food access frontier. As shown in  FIG.  13   , for example, the user selected the geographic tile  155  corresponding to zip code “36117” (item  157 ), after which system  110  responds by displaying text box  156  with detailed statistical data corresponding to that zip code and for the selected nutritional dropdown item. In this example, the food access frontier (in deciles), average distance to the nearest grocer, food options within the daily commuting sphere, number of food options, and median household income may provide an indication of the environmental and socioeconomic impact on health of the population represented by the selected geographic location. Legend  158  may be configured to dynamically update with information corresponding to the zoom level of image  154 . 
     Nutrition in this disclosure refers to a nutritional performance measure, which may compare scanned grocery store purchases at the zip code level for both chain and independent stores. The comparison may consider fruits, whole grains, and vegetables relative to total purchases. The methodology employed by server system  110  may control for median household income and commuting patterns, which may be described as part of the economic sphere of daily activity. The calculated results may be arrayed by deciles, as shown in  FIGS.  14 - 15   . 
     For example, after entering a location, such as the name of a city, an address, or a zip code, into search box  128 , a user may select the “nutrition (decile)” dropdown option  162  from among one or more selectable nutritional options in customized menu  130  to obtain food purchasing patterns at the selection location. 
     As discussed above, the results of the nutrition search for the particular geographic location entered into search box  128  are presented to the user in graphical map image  164  where a different color or shading style is assigned to each zip code shown in the image  164  according to the prevalence of the item being searched. The geographic breadth of the image  164  may default to a particular zoom level, which zoom level may be adjustable by the user. 
     As discussed above, the image  164  is dynamically interactive with the user. Consequently, the user may select a particular color coded region corresponding to a particular zip code to obtain further statistics on nutrition for the selected geographic location. As shown in  FIG.  15   , for example, the user selected the geographic tile  165  corresponding to zip code “02134” (item  167 ), after which system  110  responds by displaying text box  166  with detailed statistical data corresponding to that zip code and for the selected nutritional dropdown item. In this example, the nutritional performance (in deciles) and the observed-to-expected (O/E) ratio are presented in text box  166 , which may provide an indication of the environmental and socioeconomic impact on health of the population represented by the selected geographic location. Legend  168  may be configured to dynamically update with information corresponding to the zoom level of image  164 . 
     Population density center in this disclosure refers to a measure of a community characteristic to provide the spatial location of the population density. This measure may aggregates the location of the population from the block level to the zip code. The measure calculation may utilize US Census data at the block level to determine population per square mile, as shown in  FIGS.  16 - 17   . 
     For example, after entering a location, such as the name of a city, an address, or a zip code, into search box  128 , a user may select the “population density center” dropdown option  172  from among one or more selectable community characteristics options in customized menu  130  to obtain the density of the population, among other statistics, at a selected location. 
     As discussed above, the results of the population density search for the particular geographic location entered into search box  128  are presented to the user in graphical map image  174  where a selectable icon corresponding to population density and other statistics for that geographical location overlays image  174 . The geographic breadth of the image  174  may default to a particular zoom level, which zoom level may be adjustable by the user. 
     As discussed above, the image  174  is dynamically interactive with the user. Consequently, the user may select a particular icon to obtain further statistics on the geographic location. As shown in  FIG.  17   , for example, the user selected the icon  175  corresponding to zip code “19143” (item  177 ), after which system  110  responds by displaying text box  176  with detailed statistical data corresponding to people residing in that zip code and for the selected community characteristics dropdown item. 
     Providers in this disclosure refers to the spatial location of health care providers in the community. In combination with other layers, the health care provider location layer allows users to identify potential partners to address population health. 
     As shown in  FIGS.  18 - 19   , after entering a location, such as the name of a city, an address, or a zip code, into search box  128 , one or more health care providers  182  may be displayed in image  184  that correspond to the selected location. For example, a user may select the “hospitals” or the “hospitals” and the “pharmacies” dropdown option  182  from among one or more selectable provider options in customized menu  130  to obtain information concerning health care providers and/or pharmacies that serve that particular location or geographic region. 
     As discussed above, the results of the provider search for the particular geographic location entered into search box  128  are presented to the user in graphical map image  184  where a selectable icon corresponding to the selected item being searched and other statistics for that item are overlaid onto image  184 . The geographic breadth of the image  184  may default to a particular zoom level, which zoom level may be adjustable by the user. 
     As discussed above, the image  184  is dynamically interactive with the user. Consequently, the user may select a particular icon to obtain further statistics on the geographic location. As shown in  FIGS.  18  and  19   , for example, the user selected the icon  185  corresponding to “Novant Health Franklin Medical Center” (item  187  in  FIG.  18   ) and “Walgreens” (item  187  in  FIG.  19   ), after which system  110  responds by displaying text box  186  with detailed statistical data corresponding to the selected health care provider. 
     Turning now to  FIGS.  20 - 52   , there is shown various exemplary search options for various exemplary social determinants of health, such minority population prevalence, race/ethnicity prevalence, linguistic isolation prevalence, prevalence of a foreign language as the primary language, level of education, per capita income, income distribution, unemployment prevalence, prevalence of population not in the labor force, poverty prevalence, prevalence of disabled people living in poverty, marital status, prevalence of female only householders, prevalence of no access to a vehicle, prevalence of the population enrolled in Supplemental Nutritional Assistance Program (SNAP), and family size, among others. 
     For example,  FIGS.  21 - 22    show that after entering a location, such as the name of a city, an address, or a zip code, into search box  128 , a user may select the “minority (non-white)” dropdown option  192  from among one or more cultural factors options in customized menu  130  to obtain minority statistics of the population, among other statistics, at the selected location. 
     As discussed above, the results of the minority search for the particular geographic location entered into search box  128  are presented to the user in graphical map image  194  where a different color or shading style is assigned to each zip code shown in the image  194  according to the prevalence of the item being searched. The geographic breadth of the image  194  may default to a particular zoom level, which zoom level may be adjustable by the user. 
     As discussed above, the image  194  is dynamically interactive with the user. Consequently, the user may select a particular color coded region corresponding to a particular zip code to obtain further statistics on nutrition for the selected geographic location. As shown in  FIG.  22   , for example, the user selected the geographic tile  195  corresponding to zip code “60085” (item  197 ), after which system  110  responds by displaying text box  196  with detailed statistical data corresponding to that zip code and for the selected cultural factors dropdown item. Legend  198  may be configured to dynamically update with information corresponding to the zoom level of image  194 . 
     Similarly,  FIGS.  23 - 24    show that after entering a location, such as the name of a city, an address, or a zip code, into search box  128 , a user may select the “Black (Race/Ethnicity)” dropdown option  202  from among one or more cultural factors options in customized menu  130  to obtain minority statistics of the population, among other statistics, at the selected location. 
     As discussed above, the results of the black (race/ethnicity) search for the particular geographic location entered into search box  128  are presented to the user in graphical map image  204  where a different color or shading style is assigned to each zip code shown in the image  204  according to the prevalence of the item being searched. The geographic breadth of the image  204  may default to a particular zoom level, which zoom level may be adjustable by the user. 
     As discussed above, the image  204  is dynamically interactive with the user. Consequently, the user may select a particular color coded region corresponding to a particular zip code to obtain further statistics on race/ethnicity for the selected geographic location. As shown in  FIG.  24   , for example, the user selected the geographic tile  205  corresponding to zip code “60636” (item  207 ), after which system  110  responds by displaying text box  206  with detailed statistical data corresponding to that zip code and for the selected cultural factors dropdown item. Legend  208  may be configured to dynamically update with information corresponding to the zoom level of image  204 . 
     Similarly,  FIGS.  25 - 26    show representative cultural factor data associated with linguistics isolation, which is defined in this embodiment as referring to the prevalence of families and/or the proportion of the population in the selected geographic location that have no one over the age of 14 having English as the primary language.  FIGS.  27 - 28    show representative cultural factor data associated with speaking primarily a foreign language, which is defined in this embodiment as referring to the identities and prevalence of the primary or preferred foreign languages spoken within the selected geographic location. Each of these cultural factor options for a selected geographic location may be displayed in the same way as discussed above. 
       FIGS.  29 - 30    show representative educational factor data associated with the proportion of people having a selected level of education in a selected geographic location. For example, a user using system  110  may identify zip codes having a higher proportion of people that have completed only primary education, along with other socioeconomic statistics about the population. 
       FIGS.  31 - 32    show representative economic factor data associated with the per capita income of people in a selected geographic location.  FIGS.  33 - 34    show another example of economic factor data reflecting the distribution of income in the selected geographic location. System  110  may use a zip code Gini coefficient, which is a measure of inequality of a distribution (in this example, wealth) defined as a ratio with values between 0 and 1. The numerator is the area between the Lorenz curve of the distribution and the uniform distribution line. The denominator is the area under the uniform distribution line. System  110  may display a pop-up window representing an income distribution layer, which may contains additional detailed information for the categorical distribution of income-to-poverty ratio. In one embodiment, the income-to-poverty ratio is a family&#39;s or person&#39;s income divided by their poverty threshold, as defined by the US Census Bureau. The income-to-poverty ratio categories shown in  FIG.  34    represent variations of the poverty level within a zip code or geographic location. Ratios below 1.00 (below 100% of poverty) are below the official poverty definition provided by the US Census Bureau, while ratios of 1.00 or greater (100% of poverty or greater) indicate income above the poverty level. Ratios below 0.50 (50% of poverty) may be described as “severe poverty”, while those with ratios at/or above 1.00 but less than 1.25 may be described as “near poverty”. 
       FIGS.  35 - 36    show representative economic factor data associated with the rate of unemployment in a selected geographic location.  FIGS.  37 - 38    show representative economic factor data associated with the prevalence and/or proportion of people not in the labor force in a selected geographic location.  FIGS.  39 - 40    show representative economic factor data associated with the prevalence of families living in poverty in a selected geographic location. In one embodiment, poverty is defined by the US Census Bureau as money income, before taxes, relative to family size and composition and do not vary geographically.  FIGS.  41 - 42    show representative economic factor data associated with the prevalence and/or proportion of disabled people who are living in poverty in a selected geographic location. This layer may identify the relationship between disability and poverty and the impact of the foregoing on health. 
       FIGS.  43 - 44    show representative social factor data associated with the prevalence and/or proportion of people of a specific marital status, such as divorced, separated, widowed, and married, in a selected geographic location.  FIGS.  45 - 46    show representative social factor data associated with the prevalence and/or proportion of families having a female as the householder in a selected geographic location. In one embodiment, a family household contains at least 2 persons—the householder and at least one other person related to the householder by birth, marriage, or adoption.  FIG.  46    shows representative detailed social factor data for female as the householder together with the distribution by race/ethnic groups.  FIGS.  47 - 48    show representative social factor data associated with the prevalence and/or proportion of people without access to a vehicle in a selected geographic location.  FIGS.  49 - 50    show representative economic factor data associated with the proportion of the population enrolled in Supplemental Nutritional Assistance Program (SNAP) in a selected geographic location.  FIGS.  51 - 52    show representative social factor data associated with the prevalence and/or proportion of families exceeding the average family size in a selected geographic location. 
     To display one or more of the images described above in graphical user interface (GUI)  101 , system  110  may be configured as one or more layers positioned on top of the reference map powered by the external server (e.g., Google®). Any of the community mapping factors or social determinants of health factors described above may be combined with one another to express or filter the data in any one of a number of ways. The resulting image tiles, raster files, and/or shapefiles may be stored/saved for future recall to avoid having to retrace one&#39;s steps to arrive at the same window sizing and selection of factors and location. Any of the resulting images may be captured, bookmarked, and or transmitted to another user via email, chat, or any other connectivity tool. 
     One or more aspects of server system  110  are operable by one or more computers and/or one or more programmable logic controllers (PLC&#39;s). The computers and/or PLC&#39;s may be connected to one another and to other computers or devices via a wired or wireless network. These devices may be connected to one or more remote computers and/or web servers via a wired or wireless connection to the Internet. 
     The computers and one or more PLC&#39;s include a processor, such as a central processing unit (CPU), for executing software, particularly software stored in memory or on any computer readable medium, for use by or in connection with any computer related system or method. 
     A computer readable medium includes any electronic, magnetic, optical, or other physical device or apparatus that can contain or store a computer program for use by or in connection with a computer related system or method. Memory can include any one or a combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.). Moreover, memory may incorporate electronic, magnetic, optical, and/or other types of storage media. Memory can have a distributed architecture where various components are situated remote from one another, but are still accessed by a processor. 
     The software may include one or more separate programs comprising ordered listings of executable instructions for implementing logical functions. The software stored in memory or on any computer readable medium may include one or more computer programs, each including executable instructions executed by the processor. An operating system may control the execution of other computer programs and can provide scheduling, input-output control, file and data management, memory management, and communication control and related services. 
     In one embodiment, the PLC may include a computer processor such as a central processing unit (CPU), memory, operating software stored in memory, and various input and output (I/O) devices or data paths. The I/O devices may include input devices, such as a keyboard, mouse, touch screen, and/or any other user interface. The I/O devices may also include output devices, such as a computer display, a modem, a router, serial and parallel wired and wireless communication components and any other elements needed to connect to, for example, another computer or device via a local network or the Internet whether wired or wirelessly. 
     While specific embodiments have been described in detail, it will be appreciated by those skilled in the art that various modifications and alternatives to those details could be developed in light of the overall teachings of the disclosure. Accordingly, the disclosure herein is meant to be illustrative only and not limiting as to its scope and should be given the full breadth of the appended claims and any equivalents thereof.