U.S. patent application number 17/138241 was filed with the patent office on 2022-06-30 for method, apparatus, and computer program product for quantifying human mobility.
The applicant listed for this patent is HERE GLOBAL B.V.. Invention is credited to Jerome BEAUREPAIRE, Dmitry KOVAL.
Application Number | 20220210609 17/138241 |
Document ID | / |
Family ID | |
Filed Date | 2022-06-30 |
United States Patent
Application |
20220210609 |
Kind Code |
A1 |
KOVAL; Dmitry ; et
al. |
June 30, 2022 |
METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR QUANTIFYING
HUMAN MOBILITY
Abstract
Provided herein is a method for quantifying and measuring human
mobility within defined geographic regions and sub-regions. Methods
may include: identifying sub-regions within a region; identifying
static information associated with the sub-regions from one or more
static information sources; obtaining dynamic information
associated with the sub-regions from one or more dynamic
information sources; determining correlations between elements of
the static information associated with a respective sub-region and
elements of the dynamic information associated with the respective
sub-regions; generating a mobility score for the respective
sub-region based, at least in part, on the correlations between the
elements of the static information and the elements of the dynamic
information associated with the respective sub-region; and
providing the mobility score to one or more clients for guiding an
action relative to the mobility score.
Inventors: |
KOVAL; Dmitry; (Berlin,
DE) ; BEAUREPAIRE; Jerome; (Berlin, DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
HERE GLOBAL B.V. |
Eindhoven |
|
NL |
|
|
Appl. No.: |
17/138241 |
Filed: |
December 30, 2020 |
International
Class: |
H04W 4/029 20060101
H04W004/029; G06K 9/00 20060101 G06K009/00; H04W 4/021 20060101
H04W004/021; H04W 4/024 20060101 H04W004/024; H04W 4/23 20060101
H04W004/23 |
Claims
1. 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 processor, cause
the apparatus to at least: identify sub-regions within a region;
identify static information associated with the sub-regions from
one or more static information sources; obtain dynamic information
associated with the sub-regions from one or more dynamic
information sources; determine correlations between elements of the
static information associated with a respective sub-region of the
identified sub-regions and elements of the dynamic information
associated with the respective sub-region; generate a mobility
score for the respective sub-region based, at least in part, on the
correlations between the elements of the static information and the
elements of the dynamic information associated with the respective
sub-region; and provide the mobility score to one or more clients
for guiding an action relative to the mobility score.
2. The apparatus of claim 1, wherein causing the apparatus to
identify sub-regions within the region comprises causing the
apparatus to: obtain map data identifying road segments in the
region containing the sub-regions; and partition the map data based
on the identified road segments in the region into the
sub-regions.
3. The apparatus of claim 2, wherein the sub-regions comprise city
blocks.
4. The apparatus of claim 1, wherein the static information
associated with the sub-regions comprise point-of-interest
information and building information, wherein the dynamic
information associated with the sub-regions comprises mobility data
representative of movement of people.
5. The apparatus of claim 4, wherein the dynamic information
sources comprise one or more of: a mobile device data source, a
ride-share data source, a public transit data source, a shared
mobility data source, a financial transaction data source, and an
event-based data source.
6. The apparatus of claim 1, wherein causing the apparatus to
provide the mobility score to one or more clients for guiding the
action relative to the mobility score comprises causing the
apparatus to: provide the mobility score to a marketing client for
predicting mobility patterns of people in at least one sub-region
of the sub-regions based on the mobility score.
7. The apparatus of claim 1, wherein the apparatus is further
caused to: isolate the static information associated with at least
one sub-region of the sub-regions for at least one static
information element; isolate the dynamic information associated
with the at least one sub-region for at least one dynamic
information element; and determine a cross-correlation between the
at least one static information element and the at least one
dynamic information element to identify a value representing a
degree of the cross-correlation between the at least one static
information element and the at least one dynamic information
element, wherein the value contributes to the mobility score.
8. The apparatus of claim 1, wherein the apparatus is further
caused to: temporally partition the dynamic information; and
identify temporal patterns in the dynamic information for the
sub-regions, wherein causing the apparatus to determine
correlations between elements of the static information associated
with the respective sub-region and elements of the dynamic
information associated with the respective sub-region comprises
causing the apparatus to determine correlations between the
elements of the static information and elements of the temporally
partitioned dynamic information of the respective sub-region, and
wherein causing the apparatus to generate a mobility score for the
respective sub-region from the correlations between the elements of
the static information and the elements of the dynamic information
associated with the respective sub-region comprises causing the
apparatus to generate a mobility score from the correlations
between the elements of the static information and the elements of
the temporally partitioned dynamic information associated with the
respective sub-region.
9. The apparatus of claim 1, wherein the mobility score represents
a degree of uncertainty associated with mobility patterns of people
within the respective sub-region.
10. The apparatus of claim 1, wherein the apparatus is further
caused to determine, from the dynamic information sources
associated with the sub-regions, mobility data inflow, mobility
data outflow, and mobility data pass-through for the sub-regions,
wherein the mobility score for the respective sub-region comprises
a confidence that the correlations explain the mobility data
inflow, mobility data outflow, and mobility data pass-through.
11. A computer program product comprising at least one
non-transitory computer-readable storage medium having
computer-executable program code instructions stored therein, the
computer-executable program code instructions comprising program
code instructions to: identify sub-regions within a region;
identify static information associated with the sub-regions from
one or more static information sources; obtain dynamic information
associated with the sub-regions from one or more dynamic
information sources; determine correlations between elements of the
static information associated with a respective sub-region of the
identified sub-regions and elements of the dynamic information
associated with the respective sub-region; generate a mobility
score for the respective sub-region based, at least in part, on the
correlations between the elements of the static information and the
elements of the dynamic information associated with the respective
sub-region; and provide the mobility score to one or more clients
for guiding an action relative to the mobility score.
12. The computer program product of claim 11, wherein the program
code instructions to identify sub-regions within the region
comprise program code instructions to: obtain map data identifying
road segments in the region containing the sub-regions; and
partition the map data based on the identified road segments in the
region into the sub-regions.
13. The computer program product of claim 12, wherein the
sub-regions comprise city blocks.
14. The computer program product of claim 11, wherein the static
information associated with the sub-regions comprise
point-of-interest information and building information, wherein the
dynamic information associated with the sub-regions comprises
mobility data representative of movement of people.
15. The computer program product of claim 14, wherein the dynamic
information sources comprise one or more of: a mobile device data
source, a ride-share data source, a public transit data source, a
shared mobility data source, a financial transaction data source,
and an event-based data source.
16. The computer program product of claim 11, wherein the program
code instructions to provide the mobility score to one or more
clients for guiding the action relative to the mobility score
comprise program code instructions to: provide the mobility score
to a marketing client for predicting mobility patterns of people in
at least one sub-region of the sub-regions based on the mobility
score.
17. The computer program product of claim 11, further comprising
program code instructions to: isolate the static information
associated with at least one sub-region for at least one static
information element; isolate the dynamic information associated
with the at least one sub-region for at least one dynamic
information element; and determine a cross-correlation between the
at least one static information element and the at least one
dynamic information element to identify a value representing a
degree of the cross-correlation between the at least one static
information element and the at least one dynamic information
element, wherein the value contributes to the mobility score.
18. The computer program product of claim 11, further comprising
program code instructions to: temporally partition the dynamic
information; and identify temporal patterns in the dynamic
information for the sub-regions, wherein the computer program
product to determine correlations between elements of the static
information associated with the respective sub-region and elements
of the dynamic information associated with the respective
sub-region comprise program code instructions to determine
correlations between the elements of the static information and
elements of the temporally partitioned dynamic information of the
respective sub-region, and wherein the program code instructions to
generate a mobility score for the respective sub-region from the
correlations between the elements of the static information and the
elements of the dynamic information associated with the respective
sub-region comprise program code instructions to generate a
mobility score from the correlations between the elements of the
static information and the elements of the temporally partitioned
dynamic information associated with the respective sub-region.
19. The computer program product of claim 11, wherein the mobility
score represents a degree of uncertainty associated with mobility
patterns of people within the respective sub-region.
20. The computer program product of claim 11, further comprising
program code instructions to determine, from the dynamic
information sources associated with the sub-regions, mobility data
inflow, mobility data outflow, and mobility data pass-through for
the sub-regions, wherein the mobility score for the respective
sub-region comprises a confidence that the correlations explain the
mobility data inflow, mobility data outflow, and mobility data
pass-through.
21. A method comprising: identifying sub-regions within a region;
identifying static information associated with the sub-regions from
one or more static information sources; obtaining dynamic
information associated with the sub-regions from one or more
dynamic information sources; determining correlations between
elements of the static information associated with a respective
sub-region of the identified sub-regions and elements of the
dynamic information associated with the respective sub-region;
generating a mobility score for the respective sub-region based, at
least in part, on the correlations between the elements of the
static information and the elements of the dynamic information
associated with the respective sub-region; and providing the
mobility score to one or more clients for guiding an action
relative to the mobility score.
22. The method of claim 21, wherein identifying sub-regions within
the region comprises: obtaining map data identifying road segments
in the region containing the sub-regions; and partitioning the map
data based on the identified road segments in the region into the
sub-regions.
Description
TECHNOLOGICAL FIELD
[0001] Example embodiments described herein relate generally to
quantifying and measuring human mobility, and more particularly, to
quantifying and measuring human mobility within defined geographic
regions and sub-regions.
BACKGROUND
[0002] Population estimation and mobility measurement for a region
is difficult based on the unique behavior of individuals within a
population and often unpredictable movement. Census data provides
population estimates for a region; however, census data is
generally periodic, static population counts. Thus, census data
only provides a static snapshot of population information. Further,
census data does not provide information regarding where people
actually are and instead relies upon residential addresses to
establish head counts.
[0003] Population data is valuable for a variety of reasons ranging
from demographic representation of a population to identifying
where people are in order to target advertising. Further,
population data over time or mobility data reveals migratory
patterns of people through a region and travel patterns of people
over time. More frequent population data that changes over shorter
periods of time may further be useful for a variety of reasons,
including the planning of roadways, public transit, or
communication base station placement, among other uses.
BRIEF SUMMARY OF EXAMPLE EMBODIMENTS
[0004] At least some example embodiments are directed to
quantifying and measuring human mobility within defined geographic
regions and sub-regions. Embodiments may provide an apparatus
including at least one processor and at least one memory including
computer program code, the at least one memory and the computer
program code may be configured to, with the processor, cause the
apparatus to at least: identify sub-regions within a region;
identify static information associated with the sub-regions from
one or more static information sources; obtain dynamic information
associated with the sub-regions from one or more dynamic
information sources; determine correlations between elements of the
static information associated with a respective sub-region and
elements of the dynamic information associated with the respective
sub-region; generate a mobility score for the respective sub-region
based, at least in part, on the correlations between the elements
of the static information and the elements of the dynamic
information associated with the respective sub-region; and provide
the mobility score to one or more clients for planning for guiding
an action relative to the mobility score.
[0005] According to some embodiments, causing the apparatus to
identify sub-regions within a region includes causing the apparatus
to: obtain map data identifying road segments in the region
containing the sub-regions; and partition the map data based on
identified road segments in the region into the sub-regions. The
sub-regions may include city blocks. Static information associated
with the sub-regions may include point-of-interest information and
building information, while dynamic information associated with the
sub-regions may include mobility data representative of movement of
people. The dynamic information sources may include one or more of
a mobile device data source, a ride-share data source, a public
transit data source, a shared mobility data source, a financial
transaction data source, and an event-based data source. According
to some embodiments, causing the apparatus to provide the mobility
score to one or more clients for guiding an action relative to the
mobility score may include causing the apparatus to provide the
mobility score to a marketing client for predicting mobility
patterns of people in at least one sub-region based on the mobility
score.
[0006] The apparatus of some embodiments is further caused to:
isolate static information associated with at least one sub-region
for at least one static information element; isolate dynamic
information associated with the at least one sub-region for at
least one dynamic information element; and determine a
cross-correlation between at least one static information element
and the at least one dynamic information element to identify a
value representing a degree of cross-correlation between the at
least one static information element and the at least one dynamic
information element, where the value contributes to the mobility
score. The apparatus of some embodiments is further caused to:
temporally partition the dynamic information and identify temporal
patterns in the dynamic information for the sub-regions, where
causing the apparatus to determine correlations between elements of
the static information associated with a respective sub-region and
elements of the dynamic information associated with the respective
sub-region includes causing the apparatus to determine correlations
between the elements of the static information and elements of the
temporally partitioned dynamic information of the respective
sub-regions, and where causing the apparatus to generate a mobility
score for the respective sub-region from the correlations between
the elements of the static information and the elements of the
dynamic information associated with the respective sub-region may
include causing the apparatus to generate a mobility score from the
correlations between the elements of the static information and the
elements of the temporally partitioned dynamic information
associated with the respective sub-region.
[0007] The mobility score may represent a degree of uncertainty
associated with mobility patterns of people within the sub-region.
According to an example embodiment, the apparatus may further be
caused to determine, from the dynamic information sources
associated with the sub-regions, mobility data inflow, mobility
data outflow, and mobility data pass-through for the sub-regions,
where the mobility score for the respective sub-region includes a
confidence that the correlations explain the mobility data inflow,
mobility data outflow, and mobility data pass-through.
[0008] Embodiments of the present disclosure may provide a computer
program product including at least one non-transitory
computer-readable storage medium having computer-executable program
code instructions stored therein, the computer-executable program
code instructions including program code instructions to: identify
sub-regions within a region; identify static information associated
with the sub-regions from one or more static information sources;
obtain dynamic information associated with the sub-regions from one
or more dynamic information sources; determine correlations between
elements of the static information associated with a respective
sub-region and elements of the dynamic information associated with
the respective sub-region; generate a mobility score for the
respective sub-region based, at least on part, on correlations
between the elements of the static information and the elements of
the dynamic information associated with the respective sub-region;
and provide the mobility score to one or more clients for guiding
an action relative to the mobility score.
[0009] The program code instructions to identify sub-regions within
a region may include program code instructions to: obtain map data
identifying road segments in the region containing the sub-regions;
and partition the map data based on the identified road segments in
the region into sub-regions. The sub-regions may include city
blocks. According to some embodiments, static information
associated with the sub-regions includes point-of-interest
information and building information, where dynamic information
associated with the sub-regions includes mobility data
representative of movement of people. The dynamic information
sources may include one or more of: a mobile device data source, a
ride-share data source, a public transit data source, a shared
mobility data source, a financial transaction data source, and an
event-based data source.
[0010] The program code instructions to provide the mobility score
to one or more clients for guiding an action relative to the
mobility score may include program instructions to provide the
mobility score to a marketing client for predicting mobility
patterns of people in at least one sub-region based on the mobility
score. Embodiments may include program code instructions to:
isolate static information associated with at least one sub-region
for at least one static information element; isolate dynamic
information associated with the at least one sub-region for at
least one dynamic information element; determine a
cross-correlation between the at least one static information
element and the at least one dynamic information element to
identify a value representing a degree of cross-correlation between
the at least one static information element and the at least one
dynamic information element, where the value contributes to the
mobility score.
[0011] According to some embodiments, the computer program product
may further include program code instructions to: temporally
partition the dynamic information; and identify temporal patterns
in the dynamic information for the sub-regions, where the computer
program product to determine correlations between elements of the
static information associated with a respective sub-region and
elements of the dynamic information associated with a respective
sub-region include program code instructions to determine
correlations between the elements of the static information and
elements of the temporally partitioned dynamic information of the
respective sub-region, and where the program code instructions to
generate a mobility score for the respective sub-region from the
correlations between the elements of the static information and the
elements of the dynamic information associated with the respective
sub-region include program code instructions to generate a mobility
score from the correlations between the elements of the static
information and the elements of the temporally partitioned dynamic
information associated with the respective sub-region.
[0012] The mobility score may represent a degree of uncertainty
associated with mobility patterns of people within the respective
sub-region. According to some embodiments, the computer program
product may further include program code instructions to determine,
from the dynamic information sources associated with the
sub-regions, mobility data inflow, mobility data outflow, and
mobility data pass-through for the sub-regions, where the mobility
score for the respective sub-region includes a confidence that the
correlations explain the mobility data inflow, the mobility data
outflow, and the mobility data pass-through.
[0013] Embodiments of the present disclosure provide a method
including: identifying sub-regions within a region; identifying
static information associated with the sub-regions from one or more
static information sources; obtaining dynamic information
associated with the sub-regions from one or more dynamic
information sources; determining correlations between elements of
the static information associated with a respective sub-region and
elements of the dynamic information associated with the respective
sub-regions; generating a mobility score for the respective
sub-region based, at least in part, on the correlations between the
elements of the static information and the elements of the dynamic
information associated with the respective sub-region; and
providing the mobility score to one or more clients for guiding an
action relative to the mobility score. Identifying sub-regions
within the region may include: obtaining map data identify road
segments in the region containing the sub-regions; and partitioning
the map data based on the identified road segments in the region
into the sub-regions.
[0014] Embodiments of the present disclosure provide an apparatus
including: means for identifying sub-regions within a region; means
for identifying static information associated with the sub-regions
from one or more static information sources; means for obtaining
dynamic information associated with the sub-regions from one or
more dynamic information sources; means for determining
correlations between elements of the static information associated
with a respective sub-region and elements of the dynamic
information associated with the respective sub-regions; means for
generating a mobility score for the respective sub-region based, at
least in part, on the correlations between the elements of the
static information and the elements of the dynamic information
associated with the respective sub-region; and means for providing
the mobility score to one or more clients for guiding an action
relative to the mobility score. The means for identifying
sub-regions within the region may include: means for obtaining map
data identify road segments in the region containing the
sub-regions; and means for partitioning the map data based on the
identified road segments in the region into the sub-regions.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Having thus described certain example embodiments in general
terms, reference will hereinafter be made to the accompanying
drawings, which are not necessarily drawn to scale, and
wherein:
[0016] FIG. 1 is a block diagram showing an example architecture of
an example embodiment described herein;
[0017] FIG. 2 is a block diagram of an apparatus that may be
specifically configured in accordance with an example embodiment of
the present disclosure;
[0018] FIG. 3 illustrates sources of dynamic information according
to an example embodiment of the present disclosure;
[0019] FIG. 4 depicts an N-squared chart including elements of
static information and elements of dynamic information and
illustrating the cross-correlation thereof according to an example
embodiment of the present disclosure;
[0020] FIG. 5 illustrates a region with defined sub-regions
according to an example embodiment of the present disclosure;
[0021] FIG. 6 illustrates defined sub-regions and static
information elements associated therewith according to an example
embodiment of the present disclosure;
[0022] FIG. 7 illustrates defined sub-regions and dynamic
information elements associated therewith according to an example
embodiment of the present disclosure;
[0023] FIG. 8 illustrates mobility data representing flux of a
sub-region according to an example embodiment of the present
disclosure; and
[0024] FIG. 9 is a flowchart of a method for generating a mobility
score for one or more sub-regions of a region according to an
example embodiment of the present disclosure.
DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS
[0025] Some embodiments will now be described more fully
hereinafter with reference to the accompanying drawings, in which
some, but not all, embodiments of the invention are shown. Indeed,
various embodiments of the invention may be embodied in many
different forms and should not be construed as limited to the
embodiments set forth herein; rather, these embodiments are
provided so that this disclosure will satisfy applicable legal
requirements. Like reference numerals refer to like elements
throughout. As used herein, the terms "data," "content,"
"information," and similar terms may be used interchangeably to
refer to data capable of being transmitted, received and/or stored
in accordance with embodiments of the present invention. Thus, use
of any such terms should not be taken to limit the spirit and scope
of embodiments of the present invention.
[0026] Methods, apparatus and computer program products are
provided in accordance with an example embodiment in order to
quantify and measure human mobility within defined geographic
regions and sub-regions based on a plurality of static and dynamic
data sources and the correlations therebetween. Census data can
only provide a snapshot of population information for geographical
areas of a geographic region. However, dynamic population
estimation for finite geographic sub-regions including temporal
population shifts and movement can be useful to a variety of
industries and location-based services and providers. Embodiments
provided herein estimate human mobility over time within geographic
regions and, more granularly, within geographic sub-regions.
Further, embodiments use static information from static information
sources and dynamic information from dynamic information sources to
determine an explained mobility score for a sub-region, where
correlations between elements of static information and/or dynamic
information inform the explained mobility score. Example
embodiments are particularly useful within urban environments where
the sub-regions may be defined as city blocks. Within urban
environments, dynamic mobility data may not be available, may not
be accurate (e.g., due to urban canyon effects on location
signals), and may not be dense enough to be representative.
Embodiments disclosed herein employ a combination of static
information and dynamic information and the correlation between the
static and dynamic information to quantify and measure human
mobility and population density change over time within geographic
regions and sub-regions. Embodiments further enable the generation
of a model sub-region and using the model sub-region to be applied
to infer mobility information for a similar sub-region that may
lack dynamic information or lack sufficient dynamic information to
establish mobility information for the similar sub-region. The
similar sub-region may be established based on static information
relating to the sub-region, such as a volume and type of buildings
within the sub-region, types of points of interest, or other static
information as described below.
[0027] Dynamic information may be generated by any of a plurality
of dynamic information sources. Some dynamic information sources
may include mobile devices (e.g., cell phones), vehicles, personal
navigation devices, public transit vehicles, traffic monitoring,
etc. Each of these examples of dynamic information sources may
produce dynamic information establishing an identified location of
a person or object associated with one or more people otherwise
referred to herein as mobility data. Mobility data may
fundamentally include location information of the person or object
to which it is associated. Dynamic information is data that is
regularly changing and is updated frequently, such as in real-time,
upon receipt of new data (near real-time), or periodically in terms
of seconds, minutes, or hours, typically. An instance of dynamic
information information/data may comprise, among other information,
location information/data, heading information/data, etc. For
example, the dynamic information/data may include a geophysical
location (e.g., latitude and longitude) indicating the location of
a person or object at the time that the dynamic information/data is
generated and/or provided (e.g., transmitted). The dynamic
information/data may optionally include a heading or direction of
travel. In an example embodiment, an instance of dynamic
information/data may comprise a source identifier identifying the
person or object that generated and/or provided the dynamic
information/data, a timestamp corresponding to when the probe
information/data was generated, and/or the like.
[0028] Further, based on the source identifier and the timestamp, a
sequence of instances of dynamic information/data may be
identified. For example, the instances of dynamic information of
data corresponding to a sequence of instances of dynamic
information/data may each comprise the same source identifier or an
anonymized identifier indicating that the data is from the same,
anonymous source. In an example embodiment, the instances of
dynamic information/data in a sequence of instances of dynamic
information/data are ordered based on the timestamps associated
therewith to form a path.
[0029] Dynamic information as described herein may also include
information associated with an environment (e.g., weather
information) or event information (e.g., a scheduled sporting
event, parade, or the like). Dynamic information therefore includes
mobility data of people/objects/devices and information associated
with a geographic region or sub-region that changes over time.
[0030] Dynamic information sources that produce mobility data,
whether they include mobile devices, vehicles, personal navigation
devices, public transit, etc., are referred to herein as probes
producing dynamic information in the form of probe data. The
gathered dynamic information in the form of probe data may be
associated with geographic sub-regions of a geographic region.
Associating the dynamic information with a geographic sub-area may
include matching a location of the gathered dynamic information
with the area represented by a geographic sub-area. As dynamic
information may have a discrete location associated with each data
point, each data point may be individually available to associate
with any arbitrary geographic division generated, such that a
geographic sub-region boundary may be established and the dynamic
information within that boundary at a specific time period is
associated with that geographic sub-region.
[0031] Static information for a geographic region and geographic
sub-region relates to information that does not change over time,
or is substantially similar over periods of time. For example,
building sizes (square footage, number of floors, etc.) may change
throughout time, but building sizes are substantially static for
substantial amounts of time. Static information, as described
herein, may include data that is not real-time data and is only
updated on a periodic basis. Static information pertaining to a
building may include the size, the type of use (residential,
commercial, industrial, etc.) and proportions of the building used
for each type of use, particularly in buildings that may include
both apartments and short-term rentals (e.g. hotels) or mixed-use
buildings such as residential buildings with commercial use on the
street level. Static information may include information pertaining
to points-of-interest such as point-of-interest types.
Point-of-interest types may include categories of
points-of-interest, such as restaurants, which may have
sub-categories such as type of food, dine-in, take-out, delivery,
price point, etc. Point-of-interest categories may broadly include
retail stores, types of retail stores, businesses, museums, parks,
service providers, automated teller machines, etc.
Points-of-interest may, in some circumstances, be mobile, such as a
food truck, whereby a food truck location would be established as
dynamic information provided the food truck moves at least
periodically.
[0032] Static information may be produced by a plurality of static
information sources. For example, building sizes and locations may
be provided by a municipality that has records of all buildings in
a geographic region. Points-of-interest information may be from a
service provider information source, such as a map services
provider.
[0033] Static information may optionally include static population
data, such as census data. Static population data generally
includes establishing population count based on residential
addresses of the population such that the static population data
does not reflect any movement of the population during a
day/month/year. Static population may include population data that
is updated only periodically, and less frequently than a predefined
amount of time, such as weekly, monthly, yearly, or longer.
Further, static population data may be generated for a geographic
region and the static population data may be broken down within
that region into geographical areas. These geographical areas may
correspond to boundaries such as zip codes, cities, counties, or
other defined boundaries, for example.
[0034] Embodiments provided herein use static information and
dynamic information associated with geographic regions and
sub-regions to quantify and measure human mobility. Further,
embodiments correlate different elements of static and/or dynamic
information to establish an explained mobility score using a
weighted correlation between the data sources.
[0035] To provide an improved manner of quantifying and measuring
human mobility, a system as illustrated in FIG. 1 may be used. FIG.
1 illustrates a communication diagram of an example embodiment of a
system for implementing example embodiments described herein. The
illustrated embodiment of FIG. 1 includes a map services provider
11, a processing server 12, and a map database 18. As shown, the
map services provider 11 may be in communication via a network 14,
such as a wide area network, such as a cellular network, the
Internet, or a local area network. However, the map services
provider 11 may be in communication with the other elements of the
system in other manners, such as via direct connection through
direct communications between the map services provider 11 and data
sources.
[0036] Examples of the data sources as described herein include a
static information source 10 and a dynamic information source 16.
The dynamic information source 16 may be a source of mobility data,
such as a mobile device and may be embodied by a number of
different devices including mobile computing devices, such as a
personal digital assistant (PDA), mobile telephone, smartphone,
laptop computer, tablet computer, vehicle navigation system,
infotainment system, in-vehicle computer, or any combination of the
aforementioned. The dynamic information source 16 may optionally be
a server or network device configured to provide information such
as weather information. Thus, the dynamic information source 16 may
in some embodiments be a mobile device indicative of the movement
of a person, while in other embodiments it may be a source of
information that may or may not be mobile.
[0037] The static information source 10 may be any computing device
configured to provide information to the map services provider 11
regarding static information relating to geographic regions and
sub-regions. The static information source 10 may include a
municipal information server, an archive of information (e.g., a
database), or may even be embodied by a map services provider, such
as the illustrated map services provider 11 or another similar
entity. A map services provider may provide information such as
point-of-interest location, type, hours of operation, or other data
that is considered static information.
[0038] The processing server 12 of the map services provider 11 may
also be embodied by a computing device and, in one embodiment, is
embodied by a web server. Additionally, while the system of FIG. 1
depicts a single map services provider 11 and two information
sources, systems of example embodiments may include any number of
static or dynamic information sources, any number of map services
providers, any number of databases, and any number of processing
servers, which may operate independently or collaborate to support
activities of the embodiments described herein.
[0039] The map database 18 may include one or more databases and
may include information such as geographic information relating to
road networks, points-of-interest, buildings, etc. Further, the map
database 18 may store therein static population data, such as
census data relating to populations of geographical sub-regions of
a geographic region. The static population information may be
provided by, for example, a municipality or governmental entity.
The map database 18 may also include historical dynamic population
or mobility data, such as historical traffic data, mobile device
data, monitored area data (e.g., closed-circuit television), or the
like. Thus, the map database 18 may be used to facilitate the
quantifying and measuring of human mobility within defined
geographic regions and sub-regions.
[0040] Regardless of the type of device that embodies the static
data source 10 or the dynamic data source 16, the data source may
include or be associated with an apparatus 20 as shown in FIG. 2.
In this regard, the apparatus 20 may include or otherwise be in
communication with a processor 22, a memory device 24, a
communication interface 26 and a user interface 28. As such, in
some embodiments, although devices or elements are shown as being
in communication with each other, hereinafter such devices or
elements should be considered to be capable of being embodied
within the same device or element and thus, devices or elements
shown in communication should be understood to alternatively be
portions of the same device or element.
[0041] In some embodiments, the processor 22 (and/or co-processors
or any other processing circuitry assisting or otherwise associated
with the processor) may be in communication with the memory device
24 via a bus for passing information among components of the
apparatus. The memory device 24 may include, for example, one or
more volatile and/or non-volatile memories. In other words, for
example, the memory device 24 may be an electronic storage device
(e.g., a computer readable storage medium) comprising gates
configured to store data (e.g., bits) that may be retrievable by a
machine (e.g., a computing device like the processor). The memory
device 24 may be configured to store information, data, content,
applications, instructions, or the like for enabling the apparatus
20 to carry out various functions in accordance with an example
embodiment of the present invention. For example, the memory device
24 could be configured to buffer input data for processing by the
processor 22. Additionally or alternatively, the memory device
could be configured to store instructions for execution by the
processor.
[0042] The processor 22 may be embodied in a number of different
ways. For example, the processor 22 may be embodied as one or more
of various hardware processing means such as a coprocessor, a
microprocessor, a controller, a digital signal processor (DSP), a
processing element with or without an accompanying DSP, or various
other processing circuitry including integrated circuits such as,
for example, an ASIC (application specific integrated circuit), an
FPGA (field programmable gate array), a microcontroller unit (MCU),
a hardware accelerator, a special-purpose computer chip, or the
like. As such, in some embodiments, the processor may include one
or more processing cores configured to perform independently. A
multi-core processor may enable multiprocessing within a single
physical package. Additionally or alternatively, the processor 22
may include one or more processors configured in tandem via the bus
to enable independent execution of instructions, pipelining and/or
multithreading.
[0043] In an example embodiment, the processor 22 may be configured
to execute instructions stored in the memory device 24 or otherwise
accessible to the processor 22. Alternatively or additionally, the
processor 22 may be configured to execute hard coded functionality.
As such, whether configured by hardware or software methods, or by
a combination thereof, the processor 22 may represent an entity
(e.g., physically embodied in circuitry) capable of performing
operations according to an embodiment of the present invention
while configured accordingly. Thus, for example, when the processor
22 is embodied as an ASIC, FPGA or the like, the processor 22 may
be specifically configured hardware for conducting the operations
described herein. Alternatively, as another example, when the
processor 22 is embodied as an executor of software instructions,
the instructions may specifically configure the processor 22 to
perform the algorithms and/or operations described herein when the
instructions are executed. However, in some cases, the processor 22
may be a processor of a specific device (e.g., a head-mounted
display) configured to employ an embodiment of the present
invention by further configuration of the processor 22 by
instructions for performing the algorithms and/or operations
described herein. The processor 22 may include, among other things,
a clock, an arithmetic logic unit (ALU) and logic gates configured
to support operation of the processor 22. In one embodiment, the
processor 22 may also include user interface circuitry configured
to control at least some functions of one or more elements of the
user interface 28.
[0044] Meanwhile, the communication interface 26 may include
various components, such as a device or circuitry embodied in
either hardware or a combination of hardware and software that is
configured to receive and/or transmit data between a computing
device (e.g. user device 10 or 16) and a server 12. In this regard,
the communication interface 26 may include, for example, an antenna
(or multiple antennas) and supporting hardware and/or software for
enabling communications wirelessly. Additionally or alternatively,
the communication interface 26 may include the circuitry for
interacting with the antenna(s) to cause transmission of signals
via the antenna(s) or to handle receipt of signals received via the
antenna(s). For example, the communications interface 26 may be
configured to communicate wirelessly with a head-mounted display,
such as via Wi-Fi (e.g., vehicular Wi-Fi standard 802.11p),
Bluetooth, mobile communications standards (e.g., 3G, 4G, or 5G) or
other wireless communications techniques. In some instances, the
communication interface 26 may alternatively or also support wired
communication. As such, for example, the communication interface 26
may include a communication modem and/or other hardware/software
for supporting communication via cable, digital subscriber line
(DSL), universal serial bus (USB) or other mechanisms. For example,
the communication interface 26 may be configured to communicate via
wired communication with other components of a computing
device.
[0045] The user interface 28 may be in communication with the
processor 22, such as the user interface circuitry, to receive an
indication of a user input and/or to provide an audible, visual,
mechanical, or other output to a user. As such, the user interface
28 may include, for example, a keyboard, a mouse, a joystick, a
display, a touch screen display, a microphone, a speaker, and/or
other input/output mechanisms. In some embodiments, a display may
refer to display on a screen, on a wall, on glasses (e.g.,
near-eye-display), in the air, etc. The user interface 28 may also
be in communication with the memory 24 and/or the communication
interface 26, such as via a bus.
[0046] The communication interface 26 may facilitate communication
between different user devices and/or between the server 12 and
user devices 10 or 16. The communications interface 26 may be
capable of operating in accordance with various first generation
(1G), second generation (2G), 2.5G, third-generation (3G)
communication protocols, fourth-generation (4G) communication
protocols, Internet Protocol Multimedia Subsystem (IMS)
communication protocols (e.g., session initiation protocol (SIP)),
and/or the like. For example, a mobile terminal may be capable of
operating in accordance with 2G wireless communication protocols
IS-136 (Time Division Multiple Access (TDMA)), Global System for
Mobile communications (GSM), IS-95 (Code Division Multiple Access
(CDMA)), and/or the like. Also, for example, the mobile terminal
may be capable of operating in accordance with 2.5G wireless
communication protocols General Packet Radio Service (GPRS),
Enhanced Data GSM Environment (EDGE), and/or the like. Further, for
example, the mobile terminal may be capable of operating in
accordance with 3G wireless communication protocols such as
Universal Mobile Telecommunications System (UMTS), Code Division
Multiple Access 2000 (CDMA2000), Wideband Code Division Multiple
Access (WCDMA), Time Division-Synchronous Code Division Multiple
Access (TD-SCDMA), and/or the like. The mobile terminal may be
additionally capable of operating in accordance with 3.9G wireless
communication protocols such as Long Term Evolution (LTE) or
Evolved Universal Terrestrial Radio Access Network (E-UTRAN) and/or
the like. Additionally, for example, the mobile terminal may be
capable of operating in accordance with fourth-generation (4G)
wireless communication protocols and/or the like as well as similar
wireless communication protocols that may be developed in the
future.
[0047] The apparatus 20 of example embodiments, particularly when
embodying a dynamic information source, may further include one or
more sensors 30 which may include location sensors, such as global
positioning system (GPS) sensors, sensors to detect wireless
signals for wireless signal fingerprinting, sensors to identify an
environment of the apparatus 20 such as image sensors for
identifying a location of the apparatus 20, or any variety of
sensors which may provide the apparatus 20 with an indication of
location.
[0048] While the apparatus 20 is shown and described to correspond
to an information source, be it dynamic or static, embodiments
provided herein may include a user device that may be used for a
practical implementation of embodiments of the present disclosure.
For example, such an apparatus may include a laptop computer,
desktop computer, tablet computer, mobile phone, or the like. Each
of which may be capable of providing a graphical user interface
(e.g., presented via display or user interface 28) to a user for
interaction with a map providing human mobility information and
explained mobility scores for geographic sub-regions within a
mapped region as described further below. Embodiments of the
apparatus may include components similar to those as shown in FIG.
2 through which a user may interact with dynamic mobility data
presented on the display of a user interface for a device, such as
apparatus 20.
[0049] Embodiments described herein relate quantifying and
measuring human mobility, and more particularly, to quantifying and
measuring human mobility within defined geographic regions and
sub-regions. By fusing available static and dynamic data sources
and establishing correlations therebetween, human mobility may be
quantified and measured for finite, defined sub-regions of a
geographical region. The results of such quantification and
measurement may be provided in a visual representation on a user
interface and made user-friendly through a service that provides
dynamic mobility data for consumption by various industries and
applications that may benefit from dynamic mobility measurement and
quantification, in addition to establishing the veracity of such
mobility data.
[0050] Static information may be received from sources such as a
census bureau, local, regional, or national governmental entities,
private population data collection/estimation services, map data
service providers, or the like. This data, while useful, does not
provide sufficient detail with regard to the fluidity of the
movement of people throughout a day, week, month, season, or year,
for example. However, static information may be useful in
establishing an explained mobility score when combined with dynamic
information.
[0051] Dynamic information data may be gathered through various
sources. For example, probe data from a dynamic information source
10 may be collected from user's mobile devices such as cell phones
which can report location and movement of a user. This data may be
real-time probe data or historical probe data from users. Other
probes such as probes associated with vehicles may provide dynamic
information in the form of traffic data, which may also be
real-time or historical traffic data. Historical traffic data can
be considered dynamic information as it tracks the ebb and flow of
a population as it moves over short periods of time and for
specific time instances. Thus, it is not static population data
identifying a static, unchanging location of a person. Dynamic
information in the form of probe data may provide accurate location
through positioning mechanisms employed by the probes, which may
include GPS sensors, wireless fingerprinting, access point
identifiers, etc. Other dynamic information may be collected
through social media, such as through user check-ins at locations,
users self-identifying locations or enabling location access within
social media, attendance at events identified within social media,
or the like. Dynamic information my further be gathered from
parking events, shared mobility devices such as scooters,
bike-shares, shared vehicles, etc. Public transit (e.g., trains,
buses, etc.) and semi-private transit (e.g., taxis, rideshares,
etc.) may further provide dynamic information that represents the
movement of people through mobility data. Dynamic information
sources such as public transit may include ridership information
along with headcounts of people boarding and exiting a public
transit vehicle at different stops. Another source of dynamic
information may include a financial institution, where financial
transactions represent dynamic information associated with a user
performing the financial transaction at a specific location to
conduct the financial transaction without providing identifying
information regarding the individual.
[0052] Still further, dynamic information may be provided by
devices monitoring specific locations, such as closed-circuit
television cameras or security cameras that capture individuals in
the field of view and may recognize individual people through image
recognition software to provide a count of population in a field of
view or a count of population passing through a field of view, such
as in a particular direction to capture movement of the population
toward or away from a location. Dynamic information may also be
established by cameras on roadways such as at toll points along a
roadway, along a road segment, or at an intersection. Other devices
may be used to identify dynamic information such as near-field
communication stations, such as radio-frequency identification
antennas that may read the presence of a person through their
identification, their mobile device, a key card, etc. Thus, data
regarding dynamic information may be gathered from a wide variety
of devices using infrastructure that is presently in place.
[0053] While dynamic information may be in the form of mobility
data representing the movement of people, dynamic information may
further include dynamically changing conditions and circumstances
that may correlate with mobility data. For example, weather is
dynamically changing and may have substantial impact on the
mobility data within a geographic region. A park may experience a
dramatic decrease in people within the park during adverse weather
conditions. Dynamic information may also include event data, such
as sporting events, parades, or other events that influence the
population of a geographic region or sub-region.
[0054] Dynamic information is provided from a dynamic information
source, and includes a plurality of dynamic information elements.
Each dynamic information element corresponds to a type of
information from the dynamic information source. Types of
information for dynamic information elements may include, for
example, mobile phone location from a global positioning system,
and separately, mobile phone location from a different positioning
means, such as from wireless fingerprinting. Types of information
can include public transit vehicle location and movement, a
specific social media platform (e.g., check-ins from Facebook),
parking event information, pick up and drop off information from a
rideshare service, etc. Each of these types of information is a
dynamic information element from a dynamic information source. One
dynamic information source may provide multiple dynamic information
elements, such as when a mobile device provides dynamic information
in the form of GPS location, wireless fingerprint location, social
media information, etc. Thus, a dynamic information source may
provide multiple dynamic information elements of dynamic
information data.
[0055] FIG. 3 illustrates different dynamic information sources and
dynamic information elements from those sources. As depicted,
dynamic information may be in the form of traffic information data
304 that is from a dynamic information source 302, which may
include a municipality, a map services provider, or a mobile device
provider, for example. In this case, the traffic information is the
dynamic information element. Pedestrian data 308 is another form of
a dynamic information element from a dynamic information source 306
that may include a closed-circuit camera system that produces
pedestrian data, for example. A dynamic information source 310 may
provide dynamic information in the form of public transit movement
312 within a region. The dynamic information source 310 associated
with public transit may provide dynamic information elements such
as location of public transit vehicles (e.g., buses and trains),
ridership numbers, rider intake/exit from the respective vehicles,
etc. A dynamic information source of a mobile device 314 may
provide a wide range of dynamic information elements. As
illustrated, those dynamic information elements may include a
location 316 generated by GPS, location 318 generated by Wi-Fi
locating means, and a travel path 320 generated by a navigation
application of the mobile device 314.
[0056] Various other dynamic information sources are available,
such as weather information sources. These may be in the form of a
National Weather Service (NWS), National Oceanic and Atmospheric
Administration (NOAA), a non-governmental source, a local weather
source, etc. FIG. 3 illustrates a weather information source 322 as
a form of dynamic information source providing weather data 324 as
the dynamic information. Dynamic information elements from a
weather information source may include things like precipitation
locations, warning/watch areas, etc. Dynamic information may be in
the form of temporal events, such as sporting events. FIG. 3
illustrates an event 328 as dynamic information received from an
event host 326 as the dynamic information source. As understood by
one of ordinary skill in the art, dynamic information is widely
varied and may be in the form of virtually any information that
changes with time and reflects information that can inform the
mobility of a population or individual person.
[0057] Static information sources, as described above, may be
municipalities, city planners, census data, point-of-interest data
sources (e.g., a map services provider), or the like. However,
while mobile devices are generally dynamic information sources,
they may also function as static information sources. Mobile
devices may provide static information based on observations, such
as identifying static objects, buildings, points-of-interest, or
the like. Further the crowd sourcing of mobile device information
may inform changes, albeit infrequent changes, to
points-of-interest, such as when a restaurant changes names,
closes/opens, changes cuisine or hours, etc. These static
information elements may change occasionally, but not often enough
to be dynamic information sources.
[0058] According to example embodiments described herein, a
cross-correlation may be established between the various elements
of dynamic information and static information. The
cross-correlation may be in the form of an N-squared chart that
represents the functional interfaces between different elements of
information. All information elements, whether dynamic or static,
may be represented on each axis of the N-squared chart to
cross-correlate the effects of each data element on one another.
The dynamic information elements and static information elements
are inputs to the N-squared chart and the output generated
indicates the correlation between each pair of information
elements. This cross correlation may be summed for all information
element pairs in order to establish an explained mobility
score.
[0059] FIG. 4 illustrates a simplified example embodiment of an
N-squared chart including along both axes the dynamic and static
information elements 402. Where the same elements cross in the
chart, the correlation is 1.00 or 100%. Some elements of
information have strong relationships, while others do not. For
example, restaurants, coffee shops, and office buildings all have a
relatively low cross-correlation to location from a GPS source,
noted in the chart as "Location (GPS)". This may be due to GPS
performing poorly indoors. On the other hand, location based on
Wi-Fi noted in the chart as "Location (Wi-Fi)" has a much stronger
cross-correlation as many office buildings, restaurants, and coffee
shops offer Wi-Fi connectivity. Social media has a generally strong
cross-correlation with locations and POIs as a user may actively
check-in affirming a location. Weather, as a form of dynamic
information, appears to have a mild correlation with a restaurant
or coffee shop, as people may be more reluctant to venture out
during poor weather and more likely in good weather. However, the
cross-correlation between weather and the office building is very
low as office workers generally do not change behaviors based on
weather. FIG. 4 is an example embodiment and certainly not
exhaustive of all types of dynamic information elements or static
information elements that may be considered. Further, the
cross-correlation of the N-squared chart of FIG. 4 may be temporal.
The correlations between different information elements may change
with time. For instance, during a weekday in the middle of a
workday correlations may be different than a weekend evening. Thus,
N-squared charts may be used for various epochs that may be defined
based on a repetitive pattern, such as 9 am-5 pm weekdays may have
a first N-squared chart while 5 pm-2 am on Friday and Saturday may
have a different N-squared chart.
[0060] While gathering and cross-correlating dynamic information
and static information may aid in measuring human mobility, the
information must be correlated to a geographic region to be of use.
Example embodiments described herein define sub-regions within a
geographic region within which the static information and dynamic
information are used to establish an explained mobility score at a
relatively high level of granularity. Embodiments of the present
disclosure enable the measurement and quantification of human
mobility level at a city block level by cross-correlating multiple
data sources available at this level of spatial granularity.
[0061] Embodiments of the present disclosure use a geospatial
partition scheme to segment a geographic region into small
sub-areas. Arbitrary geometry boundaries, a city, or a particular
spatial area may be partitioned into sub-regions. The mobility data
for a given geographic sub-region may be dynamic in that it changes
over time. The mobility data for a given area is not only broken
down by geographic segments and sub-areas, but segmented
temporally. A temporal partition scheme may be used, such as
fifteen minute or one-hour time bins, for example. Embodiments
provided herein establish a score to illustrate the reliability of
mobility data for a sub-region. Further, embodiments can establish
a score that that varies based on the time period for a sub-region,
as mobility data for a sub-region may be more reliable at different
times of the day where the correlation between data elements is
different.
[0062] Using city blocks as a spatial partition allows the
establishment of correlation between physical city structure (e.g.,
buildings, street network) and sources of data (point-of-interest
density, mobile devices/probes, events at locations, etc.).
Further, as the average GPS error is around five meters and
cellular data error may be as high as 200 meters, using city blocks
as the sub-regions described herein draw more accurate conclusions
about mobility data in the area when contrasted to a larger
sub-region, such as a district or square kilometer, for instance.
Partitioning of a region into sub-regions using city blocks as the
sub-regions capitalizes on geographic information that is already
available. Map data for road networks, particularly in larger towns
and cities, is readily available and can be implemented to divide a
region into sub-regions. These sub-regions can then use multiple
independent information sources, both static and dynamic, to
calculate an explained mobility score for each sub-region. This
explained mobility score can identify human mobility relative to
the sub-region and identify the confidence with which the mobility
information is formed. The use of city blocks delineated by the
road network also permits aggregation and correlation of multiple
sources of data with a common spatial denominator.
[0063] FIG. 5 illustrates an example embodiment of a map of a
region 500 with sub-regions illustrated as 502-514, where the
sub-regions are city blocks delineated by streets of a road
network. Each of these sub-regions have associated therewith static
information. The static information may include the mix of
residential, commercial, and industrial occupancy of the
sub-region, the volume of the buildings and/or a total floor area
of the buildings, the category of the buildings (e.g., office
building, residential building, mixed-use space, hotel, etc.), the
points-of-interest, such as by point-of-interest category (e.g.,
restaurants, types of restaurants, retail store, grocery store,
etc.). The static information for a sub-region or city block may
represent a profile for that city block. That profile may be a
measure or fingerprint of the various categories of buildings that
can be present and various types of points-of-interest. The profile
of the sub-region may be used to correlate one sub-region with
another for use in modeling sub-regions or planned changes to
sub-regions, as detailed further below.
[0064] FIG. 6 illustrates two sub-regions within the mapped region
500 and a sample profile or portion of a profile of the
sub-regions. As shown, sub-region 504 is primarily residential
having five restaurants and minimal on-street parking. Sub-region
516 has a mixed use four-floor building occupying the entire city
block of the sub-region. The profiles of the two city block
sub-regions illustrated in FIG. 6 are merely illustrative of the
type of information that may be included in a profile of a
sub-region.
[0065] FIG. 7 illustrates an example embodiment of dynamic
information that may be gathered over a period of time for
sub-regions. As shown, dynamic information 524 is shown for city
block sub-region 504 illustrating a population count of the
sub-region over a week based on one or more dynamic information
sources. The population count may be established based on mobile
device GPS location, social media check-ins, closed-circuit camera
systems, or any dynamic data source capable of measuring a
headcount within a predefined geographic sub-region. Further, the
dynamic information 524 may be a cumulation of these data sources
to identify the estimated total population of the sub-region based
on the dynamic information available. The dynamic information for a
period of time may be captured and used as historical mobility data
for the sub-region and may facilitate the estimation of a future
mobility model for the sub-region. Dynamic information 522
illustrates the mobility data for city block sub-region 512
illustrating the population over the prior week.
[0066] Embodiments of the present disclosure provide a mechanism by
which mobility data quality is quantified and measured to provide a
useful measure of how reliable mobility data is for a region and
sub-regions therein. While an explained mobility score may be
provided for a sub-region, the data behind the explained mobility
score in the form of static data and dynamic data may have other,
more specific applications. According to an example embodiment, a
user of a system employing the explained mobility score may want to
customize their view of the data or look deeper into the available
data. In such an embodiment, a user, through a user interface, may
be able to select sub-regions and view static and dynamic
information elements and sources. A user may select a sub-region
and view the N-squared chart for that sub-region to identify what
cross-correlations factor in to the explained mobility score.
Further, a user may be able to select certain information sources
(both dynamic and static) and elements of dynamic and static
information that the user wants to contribute to a customized
explained mobility score. A user may select or exclude information
elements from the N-squared chart that may not apply to them. For
example, a user may want to isolate certain elements of information
to establish cross-correlation between a subset of elements of
information that relate to their interest. A user may want to
isolate dynamic mobility data from mobile devices in an area and
restaurants and coffee shops to identify trends relating to food
services in a sub-region.
[0067] According to some embodiments, a user may import data
elements for a sub-region that can provide the user specific
information in which they are interested. A data element may be
added to the N-squared chart or data elements within the N-squared
chart may be manually adjusted to determine how an explained
mobility score changes with customized cross-correlation scores.
This may be desirable for a user planning a new business venture in
a sub-region, where the user wants to establish how their business
will impact mobility within the sub-region and how the sub-region
may react to the new business venture. A user may adjust values for
cross-correlation in an N-squared chart to understand or predict
how different factors may affect mobility for that sub-region. In
this way, the data collected through the dynamic and static data
elements described herein can be employed in a custom user
interface whereby users can manipulate data to learn, understand,
and predict changes in mobility.
[0068] Embodiments described herein cross-correlate the static
information, such as that found in city block sub-region profiles,
with dynamic information, such as mobility data of people entering
and leaving a sub-region. The cross-correlation between the dynamic
information elements and the static information elements provides
an indication of the validity of human mobility data relative to
the sub-region. If data elements (both static and dynamic) for a
sub-region have strong correlations, the mobility data for the
sub-region may be estimated with a high degree of accuracy, such
that the explained mobility score may be very high. The explained
mobility score is a function of map features and the number of
correlated independent sources of information, both static and
dynamic. The cross-correlation provides a weighted factor that
influences the total explained mobility score.
[0069] Embodiments provided herein not only compute the explained
mobility score and quantify mobility data, but provide an
indication of the flux of a region and sub-region. The flux relates
to a number of people enter and exit a sub-region as a function of
time. The flux can also relate to any type of dynamic mobility data
source entering or exiting a sub-region, such as public transit
vehicles, bicycles, cars, mobile phones, etc. However, the flux of
people entering and exiting as well as persisting within a
sub-region can be estimated through example embodiments provided
herein along with a measure of confidence of the estimate based on
the explained mobility score. The explained mobility score can be
associated with the flux and provide a measure of the degree of
confidence of the estimate of people entering (inflow) into a
sub-region, the estimate of people exiting (outflow) from a
sub-region, and the estimate of people persisting (remaining) in
the sub-region. This provides an indication of the pass-through
mobility data for a sub-region as well as the persistent,
non-pass-through mobility data.
[0070] According to an example embodiment, if there is an event
such as a concert within a given sub-region, an influx of people
may be observed from mobility data (e.g., from vehicles, people,
other transit means, etc.). The influx from the mobility data may
be cross-correlated with the event with a high degree of
correlation. The event would explain this influx and the explained
mobility score would reflect this strong correlation, providing a
high degree of confidence in the observed influx of mobility data.
FIG. 8 illustrates a graphical representation of this flux with
mobility data inflow 560, mobility data outflow 550, and mobility
data pass through traffic 570 for a geographic sub-region 540.
[0071] The explained mobility score as established herein may
provide a robust indicator of the reliability of mobility data for
a region. Further, as noted above, since the cross-correlation
between data elements (both static and dynamic) may change during
different time periods (e.g., different times of the day, different
days of the week, different seasons of the year, etc.), the
explained mobility score for a sub-region may also vary over time.
The explained mobility score may be codified to illustrate the
reliability of mobility data for an area. For example, if an
explained mobility score is relatively high, indicating a
substantial cross-correlation between different data sources, the
explained mobility score may convey a "good" score where the
mobility data can be relied upon. If the explained mobility score
is relatively low, indicating a low degree of cross-correlation
between data elements, the explained mobility score may convey a
"neutral" score indicating that while mobility data is present in
sufficient volume, the cross-correlation is low such that the
mobility data may not be of high accuracy. If the volume of
mobility data itself is low, such as if there is relatively little
dynamic information, the explained mobility score may be a "poor"
score indicating that there is insufficient mobility data to
provide a reasonably accurate estimate for the sub-region.
[0072] While dynamic information data may provide a robust
indicator of the presence of people, dynamic information data may
also provide too much data from multiple dynamic information
sources or dynamic information elements and may result in
individuals being counted multiple times by different devices or
different information elements, such as a user traveling in a
vehicle functioning as a probe while also carrying a mobile device
functioning as a probe. The fusion of static population data and
dynamic information sources as described in example embodiments
herein may provide a robust and reliable estimate of mobility data,
particularly when the cross-correlation of information sources is
robust and points to a high degree of reliability for mobility
data.
[0073] Processing mobility data collected through different means
may require an approach that can make reasonable estimations about
the amount of people in small geographic sub-regions. Different
data sources may provide different mobility estimations and may
have different degrees of reliability and accuracy based on the
features of an area within which a population is being evaluated.
For example, probe data points from mobile devices may be reliable
and relatively accurate when a population is in an open area with
few obstructions, such as in a park or a suburban residential area.
However, some areas may have rather sparse coverage, with few or no
mobile devices providing probe data. For example, in dense urban
environments, mobile devices within tall buildings may not provide
probe data or may not provide reliable probe data. Further, in
areas of poor signal coverage, mobile devices may not be able to
report probe data. In other cases, mobility data may be
over-estimated such as when one person who carries one or more
smart phones or connected devices and is traveling in a connected
vehicle sending probe data, and may be checked-in on social media.
In such an embodiment, that single person may be counted three,
four, five or more times based on their connected devices
functioning as probes to generate probe data. Embodiments described
herein use the cross-correlation of the different data elements,
both static and dynamic, to evaluate the reliability of mobility
data. This explained mobility score is used to provide a robust
indicator of the reliability of mobility data.
[0074] Population data from dynamic data sources may be able to
capture movement of persons from one area to another; however,
probe data from dynamic data sources may be anonymized to preclude
this depending on national or regional laws relating to data
privacy, or due to user preferences with regard to data sharing.
Probe data from dynamic data sources is not configured to be able
to identify individuals; however, probe data may include random
identifiers to identify data source which may enable
differentiation between different data source types.
[0075] Embodiments described herein may be useful for a wide
variety of practical implementations, such as for establishing
where people are at a given time, or how people move throughout a
day. Such information may be beneficial to advertisers so they
understand where to target specific advertisements and at what
times to do so. Other use cases may include aviation where a city
may be sensitive to the noise generated by aircraft approaching and
departing an airport due to noise issues. Embodiments may provide
an indication of preferred flight paths where flight paths are more
desirable to be over less-dense areas. Census data may suggest that
populations are static in residential areas. However, embodiments
described herein may demonstrate that it is undesirable to fly over
businesses or industrial areas during the day, and instead to fly
over residential areas of lower population to disrupt the fewest
number of people. Embodiments may also be used to plan for
emergency services and staffing such that emergency services
proximate low population areas at certain times of the day may
require lower staffing levels than during times of day in which
those same areas have a high population.
[0076] Given that mobility data may be used for city planning,
advertising, business planning (e.g. opening a new business in a
sub-region), the accuracy of mobility data is important such that
low-reliability mobility data is not used to inform costly and
extensive plans that may adversely affect a population based in
erroneous data. Thus, embodiments provided herein using the
explained mobility score can inform consumers of mobility data as
to the reliability of the data on which decisions may be made. The
explained mobility score may be used to inform
marketing/advertising to identify customer density and guide
advertising spend and locations. The explained mobility data may be
used for site planning for a new business, such as a new restaurant
or retail shop, where mobility data can be used from different
sub-regions to identify the most beneficial location for a new
business. Embodiments may optionally be used for predicting demand
of public transit, occupancy of buildings, success of businesses,
or the like. Wireless service providers may use the explained
mobility score to identify reliable mobility data for network
planning to understand which areas are more heavily trafficked and
for planning capacity.
[0077] Example embodiments described herein may further be used to
estimate mobility data for sub-regions that do not have mobility
data or have insufficient mobility data. As detailed above,
sub-regions and particularly city block sub-regions of a city may
have a profile that identifies a plurality of static information
elements used to define the sub-region. If a sub-region in another
part of the city or a sub-region in a different city has a similar
profile of static information elements, the mobility data for the
sub-region having sufficient mobility data may be used to estimate
the mobility data for the sub-region lacking sufficient mobility
data. The similarity between sub-regions may be established based
on a defined similarity function that may be user defined, such as
a similarity function that focuses on the building type and volume
within a sub-region, for example. A distance function may
optionally be employed to help identify similar sub-regions based
on proximity and the similarity of the profiles of the sub-regions.
These similar sub-regions may be used to model mobility data for
one another based on mobility data available within each
sub-region.
[0078] FIG. 9 illustrates a flowchart of a method for quantifying
and measuring human mobility, and more particularly, to quantifying
and measuring human mobility within defined geographic regions and
sub-regions according to an example embodiment of the present
disclosure. As shown at 610, sub-regions within a region are
identified. These sub-regions may be, for example, city blocks
within a city defined by a network of roads within the region.
Static information associated with the sub-regions is identified at
620 from one or more static information sources. Dynamic
information associated with the sub-regions is obtained from one or
more dynamic information sources as shown at 630. Correlations are
determined at 640 between elements of the static information and
elements of the dynamic information associated with a respective
sub-region. A mobility score is generated at 650 for the respective
sub-region based, at least in part, on the correlations between the
elements of the static information and the elements of the dynamic
information associated with the respective sub-region. The mobility
score is provided to the one or more clients for guiding an action
relative to the mobility score at 660.
[0079] As described above, FIG. 9 illustrates a flowchart of
apparatuses 20, methods, and computer program products according to
an example embodiment of the disclosure. It will be understood that
each block of the flowchart, and combinations of blocks in the
flowchart, may be implemented by various means, such as hardware,
firmware, processor, circuitry, and/or other devices associated
with execution of software including one or more computer program
instructions. For example, one or more of the procedures described
above may be embodied by computer program instructions. In this
regard, the computer program instructions which embody the
procedures described above may be stored by the memory device 24 of
an apparatus employing an embodiment of the present invention and
executed by the processor 22 of the apparatus. As will be
appreciated, any such computer program instructions may be loaded
onto a computer or other programmable apparatus (e.g., hardware) to
produce a machine, such that the resulting computer or other
programmable apparatus implements the functions specified in the
flowchart blocks. These computer program instructions may also be
stored in a computer-readable memory that may direct a computer or
other programmable apparatus to function in a particular manner,
such that the instructions stored in the computer-readable memory
produce an article of manufacture the execution of which implements
the function specified in the flowchart blocks. The computer
program instructions may also be loaded onto a computer or other
programmable apparatus to cause a series of operations to be
performed on the computer or other programmable apparatus to
produce a computer-implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide operations for implementing the functions specified in the
flowchart blocks.
[0080] Accordingly, blocks of the flowcharts support combinations
of means for performing the specified functions and combinations of
operations for performing the specified functions for performing
the specified functions. It will also be understood that one or
more blocks of the flowcharts, and combinations of blocks in the
flowcharts, can be implemented by special purpose hardware-based
computer systems which perform the specified functions, or
combinations of special purpose hardware and computer
instructions.
[0081] In an example embodiment, an apparatus for performing the
method of FIG. 9 above may comprise a processor (e.g., the
processor 22) configured to perform some or each of the operations
(610-660) described above. The processor may, for example, be
configured to perform the operations (610-660) by performing
hardware implemented logical functions, executing stored
instructions, or executing algorithms for performing each of the
operations. Alternatively, the apparatus may comprise means for
performing each of the operations described above. In this regard,
according to an example embodiment, examples of means for
performing operations 610-660 may comprise, for example, the
processor 22 and/or a device or circuit for executing instructions
or executing an algorithm for processing information as described
above.
[0082] In some embodiments, certain ones of the operations above
may be modified or further amplified. Furthermore, in some
embodiments, additional optional operations may be included.
Modifications, additions, or amplifications to the operations above
may be performed in any order and in any combination.
[0083] Many modifications and other embodiments of the inventions
set forth herein will come to mind to one skilled in the art to
which these inventions pertain having the benefit of the teachings
presented in the foregoing descriptions and the associated
drawings. Therefore, it is to be understood that the inventions are
not to be limited to the specific embodiments disclosed and that
modifications and other embodiments are intended to be included
within the scope of the appended claims. Moreover, although the
foregoing descriptions and the associated drawings describe example
embodiments in the context of certain example combinations of
elements and/or functions, it should be appreciated that different
combinations of elements and/or functions may be provided by
alternative embodiments without departing from the scope of the
appended claims. In this regard, for example, different
combinations of elements and/or functions than those explicitly
described above are also contemplated as may be set forth in some
of the appended claims. Although specific terms are employed
herein, they are used in a generic and descriptive sense only and
not for purposes of limitation.
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