Method and system for assessment of environmental and/or social risks

A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:

• receiving environmental data from a plurality of environmental data sources, wherein the environmental data includes data corresponding to a plurality of categories of environmental conditions or social factors;
• identifying environmental risks based on the environmental data;
• obtaining profile information relating to a user or an entity;
• based on the obtaining the profile information and the identifying the environmental risks, generating a recommendation for one or more locations of possible interest, wherein the generating involves risk calculations for the one or more locations that are performed using a reinforcement learning algorithm, wherein the reinforcement learning algorithm is trained at least in part on a periodic basis based on tracking of actions or activities associated with the user or the entity, wherein the actions or the activities associated with the user or the entity include user or entity feedback submitted via a user interface (UI), and
• wherein the recommendation comprises different grades across different areas of a geographic region;
• causing the recommendation to be presented to the user or the entity, thereby providing location-based environmental risk information that is customized on a per user or per entity basis;
• and
• after the causing the recommendation to be presented to the user or the entity:
• detecting particular user or entity feedback that is submitted via the UI regarding accuracy associated with the recommendation,
• detecting a relocation of the user or the entity from one location at which the user or the entity has resided or conducted operations to a different location, wherein the relocation is detected based on a communication device associated with the user or the entity establishing a Bluetooth connection or a Wi-Fi connection at the different location,
• causing the reinforcement learning algorithm to be re-trained based on the detected relocation of the user or the entity and the detected user or entity feedback, resulting in a re-trained reinforcement learning algorithm that provides improved risk calculations relative to the reinforcement learning algorithm, and
• utilizing the re-trained reinforcement learning algorithm to perform one or more risk calculations for the user or the entity in relation to another location responsive to receiving a request to evaluate other environmental data that includes data associated with the another location.

A method, comprising:

• receiving, by a processing system comprising a processor and from a user interface (UI) executing on a computing device, a user request for environmental-based information that is personalized to a user, wherein the user request identifies a particular location;
• responsive to the receiving the user request, obtaining, by the processing system, environmental data from a plurality of environmental data sources, accessing, by the processing system, user profile information, performing, by the processing system, an evaluation of the environmental data in accordance with the user profile information, and generating, by the processing system and based on the evaluation, a report or a recommendation with the environmental-based information,
• wherein the environmental data includes data corresponding to a plurality of categories of environmental conditions or social factors, and wherein the generating involves risk calculations for the particular location that are performed using a reinforcement learning algorithm, wherein the reinforcement learning algorithm is trained at least in part on a periodic basis based on tracking of actions or activities associated with the user, wherein the actions or the activities associated with the user include user feedback submitted via the UI, and
• wherein the report or the recommendation comprises different grades across different areas of a geographic region;
• outputting, by the processing system, the report or the recommendation to the UI for presentation on the computing device;
• and
• after the outputting the report or the recommendation to the UI:
• detecting, by the processing system, particular user feedback that is submitted via the UI regarding accuracy associated with the report or the recommendation,
• detecting, by the processing system, a relocation of the user from one location at which the user has resided or conducted operations to a different location, wherein the relocation is detected based on a communication device associated with the user establishing a Bluetooth connection or a Wi-Fi connection at the different location,
• causing, by the processing system, the reinforcement learning algorithm to be re-trained based on the detected relocation of the user and the detected user feedback, resulting in a re-trained reinforcement learning algorithm that provides improved risk calculations relative to the reinforcement learning algorithm, and
• utilizing, by the processing system, the re-trained reinforcement learning algorithm to perform one or more risk calculations for the user in relation to another location responsive to receiving another user request to evaluate other environmental data that includes data associated with the another location.