Graphical user interface for presenting geographic boundary estimation
Disclosed are systems, computer-program products, and computer-implemented methods for the automatic estimation of geographic boundaries. Implementations of the foregoing may be useful for automatedly determining the geographic boundary of a community. In some embodiments, a computing device may receive birth location data related to the community. Enriched birth locations for the community may be determined based on likelihood metrics. An estimated geographic boundary of the community may be determined. The estimated geographic boundary may correspond to a probability density of the enriched birth locations represented using the geographical coordinates. A graphical user interface may present a map and the estimated geographic boundary of the community overlaying the map.
1. A computer-implemented method, comprising:
defining a community comprising a plurality of individuals;
receiving birth location data of at least a subset of individuals selected from the plurality of individuals in the community, the birth location data comprising geographic coordinates of a plurality of birth locations for the subset of individuals;
determining likelihood metrics for the plurality of birth locations, wherein a likelihood metric for a particular birth location corresponds to a likelihood that a member of the community is associated with the particular birth location;
identifying enriched birth locations for the community based on the likelihood metrics, the enriched birth locations being birth locations that are statistically significantly associated with the community;
determining an estimated geographic boundary of the community, the estimated geographic boundary corresponding to a probability density of the enriched birth locations represented using the geographical coordinates; and
causing a graphical user interface to present the estimated geographic boundary of the community, the estimated geographic boundary overlaying a map presented in the graphical user interface.
2. The computer-implemented method of claim 1 , wherein determining the estimated geographic boundary for the community comprises:
generating a three-dimensional map comprising the geographic coordinates of the enriched birth locations plotted in x and y dimensional space and the likelihood metrics associated with the enriched birth locations plotted in a z-dimension;
determining a distribution of an overall probability density of the enriched birth locations; and
defining the estimated geographic boundary as a x,y-plane cross-section of the distribution at a selected z-value.
3. The computer-implemented method of claim 2 , wherein determining the distribution of the overall probability density of the enriched birth locations comprises using kernel density estimation.
4. The computer-implemented method of claim 1 , wherein identifying the enriched birth locations for the community comprises:
filtering the enriched birth locations based at least in part on a range of birth years or a geographical range of birth locations.
5. The computer-implemented method of claim 1 , wherein the likelihood metrics for the particular birth location corresponds to an odds ratio of a match frequency to a background frequency, the match frequency is the frequency of members of the community having the particular birth location, and the background frequency is the frequency of individuals outside of the community having the particular birth location.
6. The computer-implemented method of claim 1 , wherein the estimated geographical boundary is adjustable manually through the graphical user interface.
7. The computer-implemented method of claim 6 , wherein at least part of the estimated geographical boundary is adjusted to a county line that defines one of the enriched birth locations.
8. The computer-implemented method of claim 1 , further comprising receiving birth year data and the enriched birth locations are filtered by a range of birth years.
9. The computer-implemented method of claim 1 , wherein identifying enriched birth locations comprises using a Fisher's Exact Test to determine whether a candidate enriched birth location is statistically significantly associated with the community.
10. The computer-implemented method of claim 1 , wherein the community is a genetic community.
11. The computer-implemented method of claim 10 , wherein determining the genetic community comprising:
representing a population of individuals in a graph and connecting the individuals of the population based on identity-by-descent (IBD) match DNA segments of the individuals;
grouping the individuals represented in the graph into one or more clusters; and
defining one of the clusters as the genetic community.
12. A system comprising:
a computing server comprising memory and one or more processors, the memory configured to store code comprising instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform steps comprising:
defining a community comprising a plurality of individuals;
receiving birth location data of at least a subset of individuals selected from the plurality of individuals in the community, the birth location data comprising geographic coordinates of a plurality of birth locations for the subset of individuals;
determining likelihood metrics for the plurality of birth locations, wherein a likelihood metric for a particular birth location corresponds to a likelihood that a member of the community is associated with the particular birth location;
identifying enriched birth locations for the community based on the likelihood metrics, the enriched birth locations being birth locations that are statistically significantly associated with the community; and
determining an estimated geographic boundary of the community, the estimated geographic boundary corresponding to a probability density of the enriched birth locations represented using the geographical coordinates; and
a graphical user interface in communication with the computing server, the graphical user interface configured to present a map and the estimated geographic boundary of the community, the estimated geographic boundary overlaying the map.
13. The system of claim 12 , wherein determining the estimated geographic boundary for the community comprises:
generating a three-dimensional map comprising the geographic coordinates of the enriched birth locations plotted in x and y dimensional space and the likelihood metrics associated with the enriched birth locations plotted in a z-dimension;
determining a distribution of an overall probability density of the enriched birth locations; and
defining the estimated geographic boundary as a x,y-plane cross-section of the distribution at a selected z-value.
14. The system of claim 13 , wherein determining the distribution of the overall probability density of the enriched birth locations comprises using kernel density estimation.
15. The system of claim 12 , wherein identifying the enriched birth locations for the community comprises:
filtering the enriched birth locations based at least in part on a range of birth years or a geographical range of birth locations.
16. The system of claim 12 , wherein the likelihood metrics for the particular birth location corresponds to an odds ratio of a match frequency to a background frequency, the match frequency is the frequency of members of the community having the particular birth location, and the background frequency is the frequency of individuals outside of the community having the particular birth location.
17. The system of claim 12 , wherein the estimated geographical boundary is adjustable manually through the graphical user interface.
18. The system of claim 12 , wherein the community is a genetic community and wherein determining the genetic community comprising:
representing a population of individuals in a graph and connecting the individuals of the population based on identity-by-descent (IBD) match DNA segments of the individuals;
grouping the individuals represented in the graph into one or more clusters; and
defining one of the clusters as the genetic community.
19. A non-transitory computer-readable medium configured to store code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to perform steps comprising:
defining a community comprising a plurality of individuals;
receiving birth location data of at least a subset of individuals selected from the plurality of individuals in the community, the birth location data comprising geographic coordinates of a plurality of birth locations for the subset of individuals;
determining likelihood metrics for the plurality of birth locations, wherein a likelihood metric for a particular birth location corresponds to a likelihood that a member of the community is associated with the particular birth location;
identifying enriched birth locations for the community based on the likelihood metrics, the enriched birth locations being birth locations that are statistically significantly associated with the community;
determining an estimated geographic boundary of the community, the estimated geographic boundary corresponding to a probability density of the enriched birth locations represented using the geographical coordinates; and
causing a graphical user interface to present the estimated geographic boundary of the community, the estimated geographic boundary overlaying a map presented in the graphical user interface.
20. The non-transitory computer-readable medium of claim 19 , wherein determining the estimated geographic boundary for the community comprises:
generating a three-dimensional map comprising the geographic coordinates of the enriched birth locations plotted in x and y dimensional space and the likelihood metrics associated with the enriched birth locations plotted in a z-dimension;
determining a distribution of an overall probability density of the enriched birth locations; and
defining the estimated geographic boundary as a x,y-plane cross-section of the distribution at a selected z-value.