System, method and computer program product for path computing based on unpleasant data

1. A computer-implemented path computing method, the method comprising:

extracting unpleasant data from a database to create a plurality of multi-variate spatio-temporal density functions including a reference to a geographic location of the unpleasant data with respect to a time that the unpleasant data occurred at the geographic location;

collecting a tolerance level of a user; and

computing a path for the user at a travel time of the user in reference to the time in the unpleasant data based on the tolerance level and the plurality of density functions,

wherein the computing computes the path based on a method of transportation of the user, and

wherein, around a geolocation of centroids defining an incident in the unpleasant data for each of the plurality of density functions, a decay function is created with decay rates that are weighted based on a type of the incident and the time of day associated with the incident, the decay being in relation to a distance from a location having a highest value risk of the unpleasant data.

2. The computer-implemented method of claim 1 , wherein the unpleasant data comprises a relationship between a type of incident and a location of the type of the incident.

3. The computer-implemented method of claim 1 , wherein the unpleasant data is semantically combined in the database from multiple data sources selected from a group consisting of:

a government-provided crime database;

a social network;

crowd sourcing data; and

a city planning map.

4. The computer-implemented method of claim 1 , wherein the plurality of density functions are created by running k-means and finding the centroids based on a location associated with an incident in the unpleasant data to obtain the geolocation of the centroids.

5. The computer-implemented method of claim 4 , wherein the k-means are weighted based on a predetermined weighting for each type of incident.

6. The computer-implemented method of claim 1 , further comprising learning a safety score based on a user's safety tolerance while on the computed path and based on prior data about the tolerance level.

7. The computer-implemented method of claim 1 , wherein a plurality of paths are computed by the computing, the method further comprising:

learning a safety score to update the tolerance level of the user based on a path of the plurality of paths selected by the user.

8. The computer-implemented method of claim 1 , embodied in a cloud-computing environment.

9. A computer program product for path computing, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform:

extracting unpleasant data from a database to create a plurality of multi-variate spatio-temporal density functions including a reference to a geographic location of the unpleasant data with respect to a time that the unpleasant data occurred at the geographic location;

collecting a tolerance level of a user; and

computing a path for the user at a travel time of the user in reference to the time in the unpleasant data based on the tolerance level and the plurality of density functions,

wherein the computing computes the path based on a method of transportation of the user, and

wherein, around a geolocation of centroids defining an incident in the unpleasant data for each of the plurality of density functions, a decay function is created with decay rates that are weighted based on a type of the incident and the time of day associated with the incident, the decay being in relation to a distance from a location having a highest value risk of the unpleasant data.

10. The computer program product of claim 9 , wherein the unpleasant data comprises a relationship between a type of incident and a location of the type of the incident.

11. The computer program product of claim 9 , wherein the unpleasant data is semantically combined in the database from multiple data sources selected from a group consisting of:

a government-provided crime database;

a social network;

crowd sourcing data; and

a city planning map.

12. The computer program product of claim 9 , wherein the plurality of density functions are created by running k-means and finding the centroids based on a location associated with an incident in the unpleasant data to obtain the geolocation of the centroids.

13. The computer program product of claim 12 , wherein the k-means are weighted based on a predetermined weighting for each type of the incident.

14. A path computing system, said system comprising:

a processor; and

a memory, the memory storing instructions to cause the processor to:

extracting unpleasant data from a database to create a plurality of multi-variate spatio-temporal density functions including a reference to a geographic location of the unpleasant data with respect to a time that the unpleasant data occurred at the geographic location;

collecting a tolerance level of a user; and

computing a path for the user at a travel time of the user in reference to the time in the unpleasant data based on the tolerance level and the plurality of density functions,

wherein the computing computes the path based on a method of transportation of the user, and

wherein, around a geolocation of centroids defining an incident in the unpleasant data for each of the plurality of density functions, a decay function is created with decay rates that are weighted based on a type of the incident and the time of day associated with the incident, the decay being in relation to a distance from a location having a highest value risk of the unpleasant data.

15. The system of claim 14 , embodied in a cloud-computing environment.