US 11,232,115 B2 · Identifying functional zones within a geographic region

Abstract

A computer system may use machine learning to determine functional zones within a geographical region. The computer system may determine position data and functional data for locations within a geographical region. The computer system may use machine learning to determine functional zones with a two-stage clustering approach. During the first stage, the computer system may determine geoclusters by clustering locations based on the position data. During the second stage, the computer system may determine functional zones by clustering the geoclusters based on the functional data.

Drawings

Drawings are temporarily unavailable. Open the PDF at USPTO

Drawings

Claims20

Dependent claims

An apparatus comprising

at least one processor;
and
at least one memory including computer program code;
the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to perform:
identifying a geographic region;
determining locations within the geographic region;
determining position data for the locations by accessing at least one database over a network;
determining functional data for the locations by accessing the at least one database over the network, wherein the functional data is obtained from preexisting records in the at least one database indicating at least a type of activity performed at each of the locations;
and
using machine learning to determine functional zones within the geographic region with a two-stage clustering approach by:
determining, for a first stage, geoclusters of the locations based on the position data for the locations, wherein each of the geoclusters includes a number of the locations grouped together based on the position data;
clustering, for a second stage, the geoclusters into the functional zones based on the functionality of the locations within each of the geoclusters;
and
outputting the functional zones by displaying the functional zones on a user interface;
wherein determining the geoclusters comprises:
determining a corresponding set of nearest neighbors for each of the locations, wherein, for each of the locations, the corresponding set of nearest neighbors comprises a predetermined number of the other locations nearest to the location;
and
determining a similarity value for each pair of the locations, wherein, for each of the pairs, the similarity value indicates a commonality between the sets of nearest neighbors corresponding to the locations of the pair.

15 more elements

A method comprising:

identifying, by one or more computing devices, a geographic region;
determining, by the one or more computing devices, locations within the geographic region;
determining, by the one or more computing devices, position data for the locations by accessing at least one database over a network;
determining, by the one or more computing devices, functional data for the locations by accessing the at least one database over the network, wherein the functional data is obtained from preexisting records in the at least one database indicating at least a type of activity performed at each of the locations;
and
using machine learning to determine functional zones within the geographic region with a two-stage clustering approach by:
determining, by the one or more computing devices for a first stage, geoclusters of the locations based on the position data for the locations, wherein each of the geoclusters includes a number of the locations grouped together based on the position data;
clustering, by the one or more computing devices for a second stage, the geoclusters into the functional zones based on the functionality of the locations within each of the geoclusters;
and
outputting, by the one or more computing devices, the functional zones by displaying the functional zones on a user interface;
wherein determining the geoclusters comprises:
determining a corresponding set of nearest neighbors for each of the locations, wherein, for each of the locations, the corresponding set of nearest neighbors comprises a predetermined number of the other locations nearest to the location;
and
determining a similarity value for each pair of the locations, wherein, for each of the pairs, the similarity value indicates a commonality between the sets of nearest neighbors corresponding to the locations of the pair.

11 more elements

Dependencies are inferred from the claim text.

Description6,750 words

Summary765 words

#Various example embodiments relate to machine learning for identifying functional zones within a geographic region.

#A geographic region may comprise multiple functional zones such as business, residential, entertainment and shopping areas. Identifying functional zones can assist in understanding the spatial structure, land usage, and future development of a geographic region.

#This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the various embodiments, nor is it intended to be used to limit the scope of the claims.

Brief description of the drawings6 figures

#Some example embodiments are shown by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which:

#FIG. 1 is a block diagram of an example computer system in which one or more embodiments may be implemented.

#FIG. 2 is a flow chart showing an example method of determining functional zones within a geographic region through machine learning.

Detailed description5,856 words

#In the following description of various illustrative embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which various embodiments are shown by way of example. It is to be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the present disclosure.

#FIG. 1 is a block diagram of an example computer system in which one or more embodiments may be implemented. As seen in FIG. 1, the system may include an access node (e.g., access point (AP)) 130 and a number of wireless stations (STAs) 105, 110, 115, and 120. The STAs may be computing devices. The system may also include additional computing devices such as, e.g., a server 125, a workstation 126, and databases 175 and 180.

#The STAs may include, for example, a mobile communication device 105, mobile phone 110, personal digital assistant (PDA) or mobile computer 120, computer work station (for example, personal computer (PC)) 115, or other portable or stationary device having a wireless interface capable of communicating with an access node (e.g., access point) 130. The STAs in the system may communicate with a network 100 or with one another through the AP 130. Network 100 may include wired and wireless connections and network elements, and connections over the networks may include permanent or temporary connections. Communication through the AP 130 is not limited to the illustrated devices and may include additional mobile or fixed devices. Additional devices, such as server 125, workstation 126, and databases 175 and 180 may communicate over wired connections.

Facts

Application

15/950,667Filed 11 Apr 2018

Published

US 2019/0318028 A117 Oct 2019

Granted

25 Jan 2022

Term ends

17 Sep 2039

Estimated · how

20 years from earliest known nonprovisional domestic filing plus recorded patent term adjustment, including 524 days of adjustment. Maintenance-fee lapses, terminal disclaimers and statutory extensions have not been checked. The continuity chain may be incomplete; this is not an enforceability determination.

Classification9 codes

G06F 16/2465
Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries: query processing support for facilitating data mining…
G06Q 10/00
Administration
G06F 16/248
Querying: presentation of query results
G06F 16/29
Of structured data, e.g. relational data: geographical information databases

Timeline

11 Apr 2018Application filedUS 15/950,667
2 May 2018Assigned to Nokia Technologies Oyassignment of assignor's interest
17 Oct 2019PublishedUS 2019/0318028 A1
25 Jan 2022GrantedUS 11,232,115 B2
12 Oct 2026TodayWithin its term
17 Sep 2039Term endsEstimated, includes 524 days of adjustment

Identifying functional zones within a geographic region

Life of this patent

Identifying functional zones within a geographic region

Life of this patent

Life of this patent

Abstract

Drawings

Drawings

Claims20

Description6,750 words

Drawings

Classification9 codes

Life of this patent

Description6,750 words