Content uploaded by Michael Yost
Author content
All content in this area was uploaded by Michael Yost on Nov 28, 2015
Content may be subject to copyright.
Environmental Health Perspectives
•
VOLUME 111 | NUMBER 1 | January 2003
115
Time–Location Analysis for Exposure Assessment Studies of
Children Using a Novel Global Positioning System Instrument
Kai Elgethun, Richard A. Fenske, Michael G. Yost, and Gary J. Palcisko
Department of Environmental Health, School of Public Health and Community Medicine, University of Washington,
Seattle, Washington, USA
Evaluation of children’s exposure to environ-
mental health hazards is essential for both epi-
demiology and risk assessment and has
become a recent focus of national concern (1).
An essential component of exposure assess-
ment is knowledge of where individuals spend
their time. Such time–location information
can be linked with pollutant concentration
data to produce exposure estimates for well-
defined environments, often called microenvi-
ronments (2). Conventional time–location
analysis has relied on interviews or diaries
(3–6). Efforts have been made recently to
improve the validity of these methods, includ-
ing the “shadowing” of subjects with an
observer, and use of a beeper to prompt sub-
jects to record time–location data (7,8). Other
methods and technologies have been explored
but have not proven practical for human
exposure studies (9,10). The purpose of the
study reported in this article was to identify
and test a new method for tracking preschool
children throughout the course of a day.
The location of children has most often
been documented through parental interviews
and diaries (11–13). Although probably ade-
quate for gross location analysis (home/not
home), they are not considered reliable for
more detailed characterizations (time indoors
or outdoors at home or day care, time in vehi-
cle). Evaluation of children’s microactivities
(e.g., hand-to-mouth behavior) has used
videotaping at single locations (14,15), but
this approach cannot be applied realistically to
track children’s locations throughout the day.
Global positioning system technology. The
essential aspects of global positioning system
(GPS) technology have been described in a
report by the U.S. Environmental Protection
Agency (16). A summary of how GPS units
collect temporal and locational data is pro-
vided here. GPS satellites orbit the earth
twice every 24 hr transmitting a 50-W signal
at 1,575.42 mHz (the civilian frequency).
GPS receivers on the earth can detect this sig-
nal, which contains information necessary to
establish coordinates for location. The GPS
signal contains three components: a “pseudo-
random code,” ephemeris data, and almanac
data. The first identifies which satellites are
“seen” by the receiver. The second contains
current time and date information. The third
tells the GPS receiver where each GPS satel-
lite should be at any time throughout the day.
To determine location, the GPS receiver
compares the time a signal was transmitted by
satellite with the time it was received on the
earth. The receiver calculates how far away
that particular satellite is based on this time
difference. When signals from three or more
satellites are received simultaneously, the
receiver is able to calculate a coordinate posi-
tion on the earth. With four or more satellites
in view, a receiver can also provide altitude
information.
The U.S. Geodetic Survey manages a net-
work of beacons that transmit differential GPS
corrections from beacons across the country.
The correction data are available as public
domain information on the Internet from
many sources that maintain continuously
operating reference stations (CORS), such as
the U.S. Forest Service, the U.S. Coast Guard,
and the National Oceanic and Atmospheric
Administration (NOAA). Data for this study
were obtained from the closest station in
Seattle, Washington, operated by NOAA.
On 1 May 2000, the United States
stopped the intentional degradation, known
as “selective availability,” of GPS signals avail-
able to the public (17). This change allowed
civilian GPS users to receive location infor-
mation that is many times more accurate than
was previously possible. Differential correc-
tion is essential for improved resolution when
selective availability is in effect. When selec-
tive availability is not in effect, it provides a
less dramatic but still important improvement
in resolution. Renewal of selective availability
remains an option for the U.S. government
based on security concerns.
GPS signals can be received in all weather
conditions and in almost all environments.
Signal reception is impossible or limited
inside most buildings. Reception is generally
unaffected as long as there is some line of
Address correspondence to K. Elgethun, Department
of Environmental Health, Box 357234, University of
Washington, Seattle, WA 98195 USA. Telephone:
(206) 685-6629. Fax: (206) 616-2687. E-mail:
elgethun@u.washington.edu
We thank J. Kissel and S. Holland, who gave their
time and energy for this research, and the families
who participated in the pilot study.
This work was supported by grants from the
National Institute of Environmental Health Sciences
(PO1 ES09601), and the U.S. Environmental
Protection Agency (R826886) as part of the
University of Washington’s Center for Child
Environmental Health Risks Research. Additional
support was provided through National Institute for
Occupational Safety and Health Cooperative
Agreement no. U07/CCU012926 (Pacific Northwest
Agricultural Safety and Health Center). Although the
research described in this article has been funded by a
federal agency, it has not been subjected to agency-
required peer and policy review and therefore does
not necessarily reflect the views of the agencies and
no official endorsement should be inferred.
Received 19 November 2001; accepted 17 June 2002.
Global positioning system (GPS) technology is used widely for business and leisure activities and
offers promise for human time–location studies to evaluate potential exposure to environmental
contaminants. In this article we describe the development of a novel GPS instrument suitable for
tracking the movements of young children. Eleven children in the Seattle area (2–8 years old)
wore custom-designed data-logging GPS units integrated into clothing. Location data were trans-
ferred into geographic information systems software for map overlay, visualization, and tabular
analysis. Data were grouped into five location categories (in vehicle, inside house, inside school,
inside business, and outside) to determine time spent and percentage reception in each location.
Additional experiments focused on spatial resolution, reception efficiency in typical environments,
and sources of signal interference. Significant signal interference occurred only inside
concrete/steel-frame buildings and inside a power substation. The GPS instruments provided ade-
quate spatial resolution (typically about 2–3 m outdoors and 4–5 m indoors) to locate subjects
within distinct microenvironments and distinguish a variety of human activities. Reception experi-
ments showed that location could be tracked outside, proximal to buildings, and inside some
buildings. Specific location information could identify movement in a single room inside a home,
on a playground, or along a fence line. The instrument, worn in a vest or in bib overalls, was
accepted by children and parents. Durability of the wiring was improved early in the study to cor-
rect breakage problems. The use of GPS technology offers a new level of accuracy for direct quan-
tification of time–location activity patterns in exposure assessment studies. Key words: activity
pattern, behavior, children, exposure assessment, GIS, GPS, organophosphorous pesticides,
time–location, tracking. Environ Health Perspect 111:115–122 (2003).
doi:10.1289/ehp.5350 available via http://dx.doi.org/
Children’s Health
Articles
[Online 11 December 2002]
sight between receiver and satellite. Satellite
relative geometry can affect GPS accuracy, a
problem called positional dilution of preci-
sion (PDOP). Other errors can occur because
of signal deflection between the satellite and
the receiver and because of extremes in upper
atmospheric conditions (16).
Applications of GPS technology. GPS
technology is now in widespread use for busi-
ness and leisure activities. It is used to monitor
tractors as they plant fields and apply pesticides
to crops (18) and to measure short-term veloc-
ity of athletes (19) and has been employed to
gather time–location data on hunters by the
U.S. Forest Service (20). Commercial GPS
units were employed recently in an attempt to
validate 24-hr time–activity diaries in the
Oklahoma Urban Air Toxics Study (21). Poor
GPS instrument performance prevented col-
lection of sufficient data to realize this goal,
but the investigators concluded that GPS tech-
nology showed promise as a method for track-
ing research subjects in community-based
exposure studies. No studies to date have
employed GPS technology with children.
Data generated from GPS units can be
displayed effectively with a geographic infor-
mation system (GIS), a database system that
contains coordinate-correct maps and loca-
tions. For example, GIS has been used to
map data recorded by GPS receivers in preci-
sion agriculture to optimize fertilizer and pes-
ticide application (18). GIS has also been
used to predict historical exposures to agricul-
tural chemicals in a retrospective cohort study
of cancers among rural residents of Nebraska
(22). Use of GIS and GPS technologies in
tandem holds potential for new insights in
the field of human exposure assessment.
The use of GPS technology to evaluate
children’s locations throughout the day
requires equipment that differs substantially
from that available from commercial vendors
(23). No commercial GPS units meet all of
these criteria at present, although technologic
advances are occurring rapidly in this area.
The purpose of this study was to develop
and pilot test a novel GPS unit suitable for
studies of children’s exposure to environmen-
tal contaminants, particularly to pesticides.
Our previous work in agricultural communi-
ties has suggested that where young children
spend their time can play a critical role in
how and to what extent they are subject to
pesticide exposure (24). Time–location is not
used as a proxy of exposure, but rather as a
way to map exposures at the intersections
between humans and contaminated microen-
vironments. The GPS experiments reported
here focused on spatial resolution, reception
efficiency in several environments, and major
sources of signal interference. We then
employed the GPS units in a field study to
determine the feasibility of using GPS tech-
nology to track the movements of young chil-
dren over the course of a day.
Methods
Criteria for children’s GPS unit. The follow-
ing 10 features were deemed essential for an
instrument to be used with young children: a)
ability to log path data, b) ability to store raw
pseudo-random satellite code required to post-
process differential corrections, c) ability to
import data into GIS software, d) memory
and battery life capable of recording at least 24
hr of data at a frequent sampling rate (at least
once every 30 sec), e) external antenna that
can be positioned to optimize signal reception,
f) ability to be worn in a way that is acceptable
to both the child and parent, g) light weight
(< 300 g or < 0.75 lb), h) durable, i) tamper-
proof mechanism, and j) simplicity of opera-
tion. The following two performance
characteristics were also considered essential to
define location with sufficient accuracy and
precision: a) resolution of 3–5 m and b) recep-
tion under a wide range of field conditions.
GPS instrument. Our group worked with
Enertech Consultants (Campbell, CA) to
design a GPS “personal acquisition logger,”
or GPS-PAL. The GPS-PAL unit consists of
a battery pack, a central electronic unit, and
an antenna (Figure 1). The cost of each unit,
including software for downloading and post-
processing data, is estimated at US$1,000. All
components, including batteries, weigh
280 g. Separation of the antenna from the
central unit allows flexibility in antenna posi-
tioning. The unit was designed for use by a
subject with no supervision required and is
operated by one small on/off switch. The
GPS-PAL has enough memory to store 30 hr
of data when set to log data every 5 sec.
Battery life at the 5-sec sampling rate is 25 hr
using four AAA alkaline batteries. The GPS-
PAL is not an “off-the-shelf” tool, but a stan-
dard operating procedure (SOP) was
developed during the course of this pilot
study to expedite these functions.
Downloading, postprocessing, and mapping
of data can be accomplished by a user with
basic Windows software competency when
using the SOP.
Operating procedures. Two GPS-PAL
units were tested and used in feasibility studies.
The units were allowed to prime for approxi-
mately 5 min when first switched on until a
signal was received (indicated by a flashing
light on the unit). The units were set to record
time and location (latitude/longitude) data
every 5 sec. The time–location data that are
logged delineates the path traveled. The GPS-
PAL automatically deletes position data that
are the result of poor satellite geometry to
prevent spurious points from being included in
the path. At the end of each data collection
period, the units were connected to a desktop
computer with a communications cable, and
the GPS-PAL software was used to download
data. GPS-PAL software uses modules licensed
by Trimble Navigation Ltd. (Sunnyvale, CA).
Once downloaded, data were postprocessed to
correct for errors using differential signal data
obtained from the Seattle NOAA CORS
found on the CORS Internet site operated by
the National Geodetic Survey (25). The GPS-
PAL software automatically links to this site,
instructs the user how to download the
required differential correction data from the
nearest CORS site, and postprocesses the GPS
path data using the appropriate corrections.
After postprocessing, the coordinate
information was exported into ArcView GIS
software that included the Spatial Analyst
extension and the COS.Point Distance and
Nearest Features scripts (version 3.2; ESRI,
Redlands, CA). ArcView allows the user to
highlight points on a map by simply selecting
points in the data table, and vice versa. A GIS
of Seattle area aerial photographic maps was
used to visualize GPS-PAL data points and to
analyze both reception and resolution. Maps
used were U.S. Geological Survey (USGS)
Digital Orthophoto Quarter Quadrangles
(DOQQs) licensed by the City of Seattle to
the University of Washington. They are
ortho-rectified to attain the geometric proper-
ties of a map. The resolution of these maps is
±1 m. Registration errors of these DOQQs
are not expected to exceed 0.1 m and were
therefore not included in our analysis.
Field studies. Adult subjects and older
children wore GPS-PALs integrated into
nylon vests (Figure 2). Young children (< 4
years) wore GPS-PALs integrated into cotton
bib overalls, which are more durable and
more difficult to remove. Both types of cloth-
ing allowed for proper horizontal positioning
of the antenna, and both allowed for secure
attachment of the antenna cable inside the
garment. The battery and GPS unit were
concealed in closed pockets on the front of
the garments. Positioning of battery and GPS
unit was chosen to minimally encumber nor-
mal range of motion. The antenna was placed
on the top of the shoulder to optimize signal
reception. This design allowed research staff
to simply hand the clothing to the parent or
child and prevented tampering and instru-
ment removal.
Children’s Health
|
Elgethun et al.
116
VOLUME 111 | NUMBER 1 | January 2003
•
Environmental Health Perspectives
Figure 1. GPS-PAL antenna, electronics, and battery
pack (left to right).
Resolution experiments. In the first exper-
iment, GPS-PAL units were left in a station-
ary position for 12 hr in two urban locations:
outdoors in the open and inside a single-
story wood-frame house. The resulting coor-
dinate information was analyzed in ArcView
to determine what percentage of points was
recorded within 2 m, 3.5 m, and 5 m of the
true position of the unit. Lines were plotted
from each measured point to the true posi-
tion on the ortho-photo map in ArcView.
The true position was the center point of a 1-
m
2
landmark visible on the ortho-photo map.
True position coordinates were determined in
ArcView using the latitude and longitude
locator function. The number of points
logged by time and the resolution by time
were analyzed to investigate the existence of a
relationship between bias and time. The root
mean square (RMS) error distance, equal to
the root of the sum of the squares of all indi-
vidual errors, was computed for these data.
RMS is a standard expression of location
error for GPS receivers (16).
In a second experiment, GPS-PAL units
were carried by two pedestrians walking the
same 4-km path on a city sidewalk. A line
drawn down the center of the sidewalk was
considered the true path. True path coordi-
nates were determined in ArcView using the
“line theme” function. Coordinates were ana-
lyzed to determine what percentage of points
were recorded within 2, 3.5, and 5 m of the
true path walked. Parallel lines of these dis-
tances on either side, known as line buffers,
were drawn around the true path on the
ortho-photo map in ArcView. The variable
width of the sidewalk was accounted for
when determining the center line.
Reception experiments. The GPS-PAL
units were left stationary inside a wood-frame
house and inside a concrete school building
for 30 min, and were then worn by moving
individuals inside these two structures for 30
min. GPS-PALs were also worn by moving
individuals walking within 1–2 m of the
perimeter of these buildings directly adjacent
to the outside walls. The number of points
logged in each situation was compared with
30-min control data logged outside in an
open area. Stationary test data were compared
with stationary control data; mobile test data
were compared with mobile control data.
Interference experiments. Several known
sources of GPS signal interference (26) were
evaluated to test their effect on reception.
Sources were evaluated on two separate days.
One 10-min period was measured when the
interference source was proximal to the subject
wearing a GPS-PAL unit. Disruption of recep-
tion was quantified by dividing number of
points received by the number of points
expected during 10 min when no interference
is present. Interferences were evaluated sepa-
rately. The following personal interference
items were tested outdoors: a wool sweater and
a nylon raincoat worn over the vest containing
the unit; a 900-W Amana microwave oven
(Amana Appliances, Newton, IA) operating on
the high setting; a Motorola Fr60 Talkabout
465 mHz two-way radio (Motorola USA,
Schaumburg, IL); an Ericsson T19LX digital
cellular phone receiving full signal at
1,850–1,990 mHz (Sony Ericsson USA,
Plano, TX); and a V-Tech 900 mHz analog
cordless telephone 5 m away from its base (V-
Tech USA, Beaverton, OR). Electronic devices
were operated normally for 10 min.
Feasibility study. This study was designed
to evaluate child compliance and GPS-PAL
functionality over the course of a day.
Procedures were approved by the University
of Washington Human Subjects Division.
Eleven children (six female, five male) in the
Seattle area 2–8 years old (mean = 5.5) wore
GPS-PAL units for approximately 7–11 hr.
All parents involved in the study were faculty,
staff, or students in the Department of
Environmental Health at the University of
Washington. Recruited families responded to
an announcement sent via departmental elec-
tronic mail listserver. Children selected were
required to be toilet trained. Written consent
was obtained from parents, and verbal assent
was obtained from children. Three of the
children wore units to school on a weekday;
the other eight wore units on a weekend day.
Parents were allowed to select their child’s
monitoring period. Parents were asked to
record whether children complained about
the weight or fit of the GPS-PAL garments.
Parents were asked to switch the unit on
when their child got dressed in the morning,
and to turn the unit off at bedtime. Parents
also provided home addresses for verifying
location on the ortho-photo maps. Data from
the child study were grouped by the following
five location categories: in vehicle, inside
house, inside school, inside business, and out-
side. Time spent in each location and per-
centage reception in each location were
computed for each child.
Results
Resolution experiments. Table 1 shows the
number of points logged by stationary GPS-
PAL units over 12 hr and the RMS distance
of the points from the true location of each
unit. The units had RMS errors of 3 and 3.4
m outdoors, and 5.7 and 5.9 m inside the
wood-frame house. Analysis of resolution by
time showed a few short periods (< 1 min)
when the distance from true location sharply
increased (data not shown). Table 2 shows the
number of points logged and the resolution of
points logged by GPS-PAL units carried by
two pedestrians during a 50-min, 4- km walk
on city sidewalks. Figure 3 shows a closeup
view of the true path walked, the points
logged, and the line buffers that were used to
determine mobile resolution. Resolution was
measured by percentage of points lying within
2, 3.5, and 5 m of the true path. About 96%
Children’s Health
|
GPS time–location of children
Environmental Health Perspectives
•
VOLUME 111 | NUMBER 1 | January 2003
117
Figure 2. Child wearing GPS-PAL in a vest. Dashed
lines indicate location of components inside the
vest.
Battery
pack and cord
GPS
electronics
Antenna
cable
routing
Antenna
location
Table 1. Measurement error of two stationary GPS-PAL units over 12 hr.
Points
Distance from true position (m)
b
logged
a
Mean Median SD RMS
c
Outdoors
GPS-PAL 1 6,796 2.5 2.2 1.6 3.0
GPS-PAL 2 8,514 2.8 2.5 1.9 3.4
Indoors
d
GPS-PAL 1 3,920 4.8 4.0 3.2 5.7
GPS-PAL 2 4,812 4.9 4.1 3.3 5.9
a
Units log data every 5 sec for a maximum of 8,640 data points in 12 hr.
b
True position defined by locating the coordinates
of the units on the orthophotomap using GIS software.
c
Calculated by squaring each individual error, then taking the
square root of the mean of these numbers.
d
Indoors = inside a single-story wood-frame building, away from windows.
Table 2. Resolution of GPS-PAL units on a 4-km, 50-min walk in the city.
Points
Fraction of points within each buffer (%)
logged
a
±5 m ±3.5 m ± 2 m
GPS-PAL 1 540 96.2 89.9 78.6
GPS-PAL 2 575 96.3 90.7 79.1
a
Units log data every 5 sec for a maximum of 600 data points in 50 min.
of all points were logged within ± 5 m of the
true path, 90% were logged within ± 3.5 m,
and 79% were logged within ± 2 m.
Reception experiments. GPS-PAL recep-
tion data for a subject outdoors, inside two
types of buildings and proximal to two types of
buildings, are shown in Table 3. Data shown
are for 30 min of operation. Better reception
was attained next to a concrete/steel building
than next to a wood-frame building for one
unit, whereas the opposite was true for the
other unit. No points were logged inside the
concrete/steel frame building. Reception inside
the wood-frame building was reduced almost
2-fold by moving around inside the house
compared with remaining in one location.
Interference experiments. Reception inter-
ference experiment results are shown in Table
4. Walking within 20 m of power substation
transformers caused a complete blockage of
signal reception. Standing in front of an oper-
ating microwave oven caused a significant
(32%) reduction in reception. Talking on a
900 mHz cordless phone reduced reception by
7%. Other potential interference sources had
no effect or minimal effect on signal reception.
The performance of the GPS-PAL units
regarding resolution, reception, and interfer-
ence is illustrated in Figure 4. Figure 4A is an
ortho-photo image with GPS-PAL data
logged inside and proximal to a wood-frame
house (points shown in green). Figure 4B is a
3:1 scale drawing of this house showing the
same points. A 2-m
2
grid is superimposed on
this drawing. Based on data in Table 2, this
grid approximates an 80th percentile level of
resolution for the GPS-PAL. Figure 4 illus-
trates that locations within and around a
house can be defined so as to differentiate by
rooms or other microenvironments in and
around a residence.
Feasibility study. Data were obtained for
8 of the 11 study children. The first three
subjects had no data or minimal data logged
because of failure of wiring or connectors
leading from the battery pack. These prob-
lems were resolved, and no further wiring
problems were encountered. One parent
noted that the receiver was accidentally
turned off and then switched back on later,
yielding only 3 hr of data. This subject was
excluded from further analysis. Data from
another subject were logged without incident,
but postprocessing of the coordinates was not
feasible because of base file differential signal
errors recorded by the CORS station. The
unprocessed data were not comparable with
the postprocessed data and were excluded
from further analysis. The 11 parents all
responded that their children did not com-
plain about the weight or restrictiveness of
the GPS inside the custom clothes. Two
2-year-old children complained that they did
not like the color and style of the bib overalls.
Children’s Health
|
Elgethun et al.
118
VOLUME 111 | NUMBER 1 | January 2003
•
Environmental Health Perspectives
Figure 3. Sample of points logged by GPS-PAL units during the 50-min mobile test. Units were worn by two
pedestrians for 4 km in the city. Nine points per unit shown along a path with ideal GPS reception: on a
bridge, unobstructed. Complete 50-min data are shown in Table 2. True path is the center of the sidewalk.
Variations in sidewalk width were accounted for in this analysis.
Unit 1
Unit 2
True path
2 m buffer
3.5 m buffer
5 m buffer
Table 3. Reception of GPS-PAL units over 30 min under stationary and mobile conditions: outdoors,
indoors, and proximal to two types of buildings.
Test
Unit 1 reception Unit 2 reception
conditions Location Points logged
a
Percent of max Points logged
a
Percent of max
Outdoors
Stationary In the open 360 100.0 360 100.0
Mobile In the open 358 99.4 360 100.0
Mobile Proximal to CSF building
b
110 30.6 127 35.3
Mobile Proximal to WF building
b
76 21.1 170 47.2
Indoors
Stationary WF building 190 52.8 192 53.3
Stationary CSF building 0 0.0 0 0.0
Mobile WF building 113 31.4 85 23.6
CSF, concrete/steel frame; max, maximum; WF, wood frame.
a
Units log data every 5 sec for a maximum of 360 data points in 30 min.
b
Proximal = within 1–2 m of the outside wall of the
building.
Table 4. Interference to GPS-PAL unit reception.
Type of interference Notes Reception (% of max)
a
None
Outdoors, in the open > 5 m from any building 100
Spatial
Power substation < 20 m from transformers 0
High-tension power lines 30 m overhead 98
Large metal reflective surface Against galvanized steel 99
Personal
Clothing covering antenna Wool sweater and nylon raincoat 100
Microwave oven
b
0.5 m from oven on “high” 68
2-Way radio
c
Held to ear, transmit and receive 100
Digital cell phone
d
Held to ear while talking 100
Cordless phone
e
Held to ear while talking 93
max, maximum.
a
Units log data every 5 sec for a maximum of 120 data points in 10 min.
b
Amana 900 w.
c
Motorola Fr60 Talkabout 465 mHz.
d
Ericsson T19LX 1,850–1,990 mHz.
e
V-Tech analog 900 mHz.
Table 5 shows the efficiency of reception
by location for each child. Only two children
spent appreciable time outdoors, where
reception was high (79%). Reception inside
homes was greater than reception inside
vehicles (20% vs. 12%) and was lowest for
inside schools and businesses (6% and 9%,
respectively).
The fraction of time monitored for each
child by location is presented in Table 6. A
total of 2,964 min (49.4 hr) of data was col-
lected for the six children, with monitoring
times ranging from 387 to 700 min.
Figure 5 shows the path traveled by one
child (Child 1) during the hours of a normal
school day. Points on the street correspond to
the child walking from the school bus to the
school grounds. Points on the field near
the top of the picture correspond to two dis-
tinct recess breaks. Points near the school’s
entrance at the center of the picture were
logged before classes started in the morning
and after classes were over in the afternoon.
Points logged inside the school building, near
the bottom of the picture, are sporadic
because of the multilevel construction of the
building. Figure 6 is a time line illustrating
the progression of location by time for each
child throughout the day. Among the two
children monitored on a weekday, Child 2
spent all of her time at school indoors,
whereas Child 1 went outside three times for
recess. Among the four children monitored
on a weekend day, distribution of time in
each location varied greatly, except for Child
4 and Child 5. These two were together for
most of the day they were monitored.
Discussion
Once initial wiring problems were corrected,
time–location data were collected successfully
for the remaining eight study participants.
Data adequate for “all-day” analysis of
time–location patterns were obtained from
seven of these eight children. Accidental
receiver shut-off, which caused the collection
of only 3 hr of data from one child, was pre-
vented in subsequent trials by covering the
on/off switch. The CORS base file errors that
obstructed postprocessing of one child’s data
are not preventable. The raw data are still
readable but are lower in resolution when not
postprocessed (~15% greater RMS error). It
would be possible to obtain base files from a
private source if higher-resolution data were
deemed critical in future studies. Future
GPS-PAL studies with greater numbers of
subjects will incorporate solutions to data loss
discovered in this pilot study. Randomization
of children to either weekday or weekend
sampling groups would also strengthen future
studies and provide more insight into the
utility of the GPS-PAL. Overall, it appears
that the GPS-PAL is a practical tool for
Children’s Health
|
GPS time–location of children
Environmental Health Perspectives
•
VOLUME 111 | NUMBER 1 | January 2003
119
Figure 4. Representation of GPS-PAL capability to differentiate between distinct areas inside and outside a
house. Aerial photo of house (A) and 1:3 scale drawing of house floor plan overlaid on 2 m
2
grid (B). GPS-
PAL logged locations are shown by green circles on both photo and floor plan. Approximately 80% of points
logged by GPS-PAL fall within 2 m
2
. Therefore, it is possible to differentiate a person’s location in distinct
areas of a house and surrounding yard. Discriminating between indoors and outdoors for points close to
exterior walls is accomplished by comparing the time sequence of points to the location of exterior doors.
Table 5. Reception (% of maximum) by location and monitored time for children wearing GPS-PAL units.
Mean
Weekday Weekend
reception
a
Location Child 1 Child 2 Child 3 Child 4 Child 5 Child 6 by location CV (%)
b
Vehicle (inside) 26.9 8.8 1.1 15.9 11.0 NA 12.2 67.5
School (inside) 7.2 5.6 NA NA NA NA 6.3 12.6
Home (inside) 26.1 42.5 14.3 NA NA 20.9 19.8 43.6
Business (inside)
c
NA
e
NA 8.3 12.5 6.6 NA 9.3 27.0
Outdoors
d
86.0 NA NA NA NA 73.7 79.1 7.7
Mean reception
a
24.2 7.7 10.8 13.2 7.3 29.7 60.1
by child
Monitored time (min) 513 480 468 416 387 700
a
Units log data every 5 sec; maximum number of data points depends on monitored time for each child.
b
CV, coefficient of
variation = (SD/mean) × 100%.
c
Stores, restaurants, cinemas, and other large buildings.
d
Parks, playgrounds, sidewalks,
and yards.
e
NA = child spent no time in this location.
collection of children’s time–location data,
and that the technical criteria for this instru-
ment described above have been met. The
performance criteria of resolution and recep-
tion are addressed below.
Resolution. A critical factor for any
device intended for time–location analysis is
an assessment of the instrument position
accuracy. Position accuracy depends on
many factors, including the satellite constel-
lation geometry (geometric dilution of preci-
sion, or GDOP) and on biases or errors in
the GPS signal components or receiver (e.g.,
clock errors, ephemeris, and propagation
errors) (16). Although uniform position
accuracy under all conditions is desirable,
varying accuracy over time and space is
unavoidable because of GDOP and loss of
satellite data from interference. Often the
accuracy characteristic is summarized by the
range error relative to a known fixed loca-
tion. Because RMS error describes the
magnitude of all errors without regard to
direction and because typically it is much
greater than the mean error for a stationary
instrument, this provides a more conservative
estimate of the expected position accuracy of
a GPS receiver.
An alternative measure of position accu-
racy is the proportion of readings that fall
within a fixed range of a known location.
This measure of position accuracy, as we have
shown (Figure 3), can be applied to either
stationary or moving subjects along a defined
path. This metric is potentially more useful
for time–location studies because it also can
describe the ability of the instrument to cor-
rectly classify a location within a spatial
boundary, such as a schoolyard or a room in a
home (Figures 4 and 5).
Position accuracy is unit specific for each
GPS-PAL, probably due to random clock
errors in the receiver. The mean of the RMS
errors for the two GPS-PAL units was 3.2 m
outdoors and 5.8 m indoors, compared with a
typical outdoor RMS error for most portable
GPS units of 5–10 m (23). Published indoor
RMS values were not found. Usually only
large survey-quality GPS receivers are capable
of attaining a lower RMS error than the GPS-
PAL. The error of the map being used also
must be considered as an independent factor.
Thus, when GPS-PAL data was overlaid on
USGS DOQQ maps (nominal 1-m resolu-
tion), overall RMS error is about 3.4 m out-
doors and 5.9 m indoors, and the maximum
error is 3.2 m + 1.0 m = 4.2 m outdoors, 5.8
m + 1.0 m = 6.8 m indoors. Analysis of reso-
lution data by time (Table 1) showed a few
short (< 1 min) periods where resolution
waned. The existence of a relationship
between bias and time can be explained by
temporary loss of satellite signal or transient
shifts in high atmospheric conditions (16).
Children’s Health
|
Elgethun et al.
120
VOLUME 111 | NUMBER 1 | January 2003
•
Environmental Health Perspectives
Table 6. Where children went: Fraction of monitored time (%) in each location and total monitored time for
children wearing GPS-PAL units.
Weekday Weekend
Location Child 1 Child 2 Child 3 Child 4 Child 5 Child 6
Vehicle (inside) 4.8 15.0 9.7 21.4 19.0 0.0
School (inside) 52.7 80.4 0.0 0.0 0.0 0.0
Home (inside) 5.8 4.6 52.5 0.0 0.0 83.4
Business (inside)
a
0.0 0.0 37.8 78.6 81.0 0.0
Outdoors
b
36.7 0.0 0.0 0.0 0.0 16.6
Monitored time (min) 513 480 468 416 387 700
a
Stores, restaurants, cinemas, and other large buildings.
b
Parks, playgrounds, sidewalks, and yards.
Figure 5. Path traveled by one child on a weekday during school hours. The playing field is located near
the top of the picture, the school building is located near the bottom, and the main entrance is located at
the center. There is a street along the right side of the school grounds.
These data demonstrate that the position
accuracy achieved by the GPS-PAL instru-
ment under realistic conditions is sufficient
for human time–location analysis. Note in
Table 2 and Figure 3 that most points over
the 4-km, 50-min test were within 2–3 m of
the true path line. The 2-m grid in Figure 4
illustrates that location in and around a house
can be delineated at least 80% of the time
within a 2-m
2
area. At this scale, data based
on position and photo maps would allow
classification of activities such as entering a
retail store, walking on a sidewalk, traveling
by car or bus, playing on a schoolyard, or
playing in and around a house. This suggests
that the GPS-PAL units can locate subjects
with sufficient position accuracy to correctly
classify a large variety of human activities.
Reception and interference. Ideally, a GPS
device for time–location studies would provide
uninterrupted position data, regardless of the
subject’s location or activities. Certainly, build-
ings and other objects can compromise GPS
signal reception, so tracking subjects in and
around structures is constrained by the limita-
tions of current receiver (and antenna) technol-
ogy. The inconsistency of reception for
different children in similar locations can be
explained by the high number of variables
involved, including building materials, location
of a child within a building, type and location
of vehicle, and proximity of a child to windows
and other signal-permeable materials. This is a
limitation for being able to consistently locate
an individual in a specific microenvironment in
exposure analysis studies. Although consistent
time–location may not be feasible with GPS,
the percentage reception in most locations was
sufficient to define a child’s time–location. The
following examples using data shown in Table
3 and Figures 4 and 5 illustrate this point. In
Figure 5, signal is poorly received inside the
school building; however, the time and location
at which this child entered and exited the
building was precisely recorded, producing a
clear time–activity map. Reception within
wood-frame buildings and next to both wood-
frame and concrete/steel buildings was
adequate to characterize an individual’s posi-
tion in these locations (Table 3 and Figure 4).
For example, because 31.4% of points were
logged when the subject was moving inside
the house (Figure 4), and the sampling rate
was 5 sec, a location was logged about once
every 16 sec. This is sufficient to detect move-
ment between interior rooms, assuming that
temporal distribution in reception for a given
microenvironment is approximately uniform.
Further improvements can be gained by
careful review of the logged points to account
for the logical consistency of events in certain
microenvironments. When data points fell
close to the walls of a building (Figure 4), it
was possible to differentiate indoor from out-
door environments and eliminate ambiguous
data by examining the time sequence of
points and the location of exterior doors. It is
unlikely that a single point will fall outside a
house if points logged 16 sec before and 16
sec after are logged indoors, unless an exterior
door is immediately adjacent to the area.
Interference experiments examined a vari-
ety of potential sources, representing devices
that have become ubiquitous in our daily
environments operating at many frequencies.
The results suggest that electrical power distri-
bution equipment, or the associated electric or
magnetic fields from transformers or power
lines, cause a greater decrease in reception
than radio frequency equipment. The lack of
interference from clothing is especially impor-
tant, because this allows for total concealment
of the unit within garments worn by subjects.
Future applications. The GPS-PAL could
be used in many settings to contribute to a
refined exposure analysis of individuals. One
target group for application of this technology
is children living in rural agricultural commu-
nities. These subjects represent a potential
high-exposure group for spatial analysis,
because pesticides are used routinely in crop
production and may be dispersed over wide
areas. Children may come into contact with
pesticides through various scenarios, such as
playing in and around treated farmland,
accompanying their parents into the fields, and
by contact with pesticide residues brought into
the home by their parents (11,12,24). We have
also learned from more recent work that chil-
dren in these communities exhibit peak expo-
sures coincident with agricultural pesticide
applications (27), but we do not know the
pathways by which these spraying events pro-
duce elevated body burdens. GPS time–loca-
tion analysis could allow us to characterize
activities among these children so that we may
better understand pesticide exposure pathways.
Conclusion
The GPS-PAL instrument combines high–spa-
tial-resolution capabilities, a remote antenna,
and data-logging capability into a compact size
suitable for monitoring adults or children.
Spatial resolution is adequate to locate people
within distinct subenvironments and to distin-
guish a variety of human activities. Reception
is adequate for position determination outside,
proximal to buildings, and inside certain build-
ings. A subject’s position can be narrowed to a
single room in a home, a specific area of a play-
ground, or one side or another of a fence line.
This provides a new level of accuracy for defin-
ing time–location in relation to exposure and
eliminates recall bias and reporting errors
inherent with written subject-reported logs of
time–location. Signal interference from com-
mon sources did not appear to limit the utility
of the GPS devices in most environments.
Data are readily transferred into GIS software
for map overlays, allowing for linked visual
and tabular analysis. Compliance was good
among children 2–8 years old wearing the
GPS-PAL incorporated into their clothing.
The GPS-PAL is a promising new instrument
for quantification of time–location activity pat-
terns in exposure assessment studies. The
application of GPS and GIS technologies is the
logical next step in the characterization of
human time–location patterns.
REFERENCES AND NOTES
1. Olden K, Guthrie J. Children’s health: a mixed review.
Environ Health Perspect 108: 250–251 (2000).
2. Ott WR. Total human exposure. Environ Sci Technol
19:880–886 (1985).
3. Wallace LA Pellizzari ED, Hartwell TD, Sparacino C,
Whitmore R, Sheldon L, Zelon H, Perritt R. The TEAM
study: personal exposures to toxic substances in air,
drinking water, and breath of 400 residents of New
Jersey, North Carolina, and North Dakota. Environ Res
43:290–307 (1987).
4. Wallace L, Nelson W, Ziegenfus R, Pellizzari E, Michael L,
Whitmore R, Zelon H, Hartwell T, Perritt R, Westerdahl D.
The Los Angeles TEAM study: personal exposures,
indoor-outdoor air concentrations, and breath concentra-
tions of 25 volatile organic compounds. J Expos Anal
Environ Epidemiol 1:157–192 (1991).
5. Freeman NCG, Waldman JM, Lioy PJ. Design and evaluation
of a location and activity log used for assessing personal
exposure to air pollutants. J Expos Anal Environ Epidemiol
1:327–338 (1993).
6. Freeman NCG, Lioy PJ, Pellizzari E, Zelon H, Thomas K,
Clayton A, Quackenboss J. Responses to the Region 5
NHEXAS time-activity diary. J Expos Anal Environ
Epidemiol 9:414–426 (1999).
Children’s Health
|
GPS time–location of children
Environmental Health Perspectives
•
VOLUME 111 | NUMBER 1 | January 2003
121
Figure 6. Children’s monitored time–location during 1 day using GPS/GIS, 0800–2000 hr.
0800 0900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000
Child 6: weekend, 6-year-old male
Child 5: weekend, 6-year-old male
Child 4: weekend, 8-year-old male
Child 3: weekend, 4-year-old female
Child 2: weekday, 5-year-old female
Child 1: weekday, 7-year-old male
Time of day (hours)
Home
Vehicle
School
Outside
Business
7. Robinson JP, Godbey G. Time for Life. 2nd ed. State College,
PA:Pennsylvania State University Press, 1999;57–67.
8. Robinson JP, Silvers A. Measuring potential exposure to
environmental pollutants: time spent with soil and time spent
indoors. J Expos Anal Environ Epidemiol 10:341–354 (2000).
9. Moschandreas DJ, Relwani S. The shadow sensor: an
electronic activity pattern sensor. J Expos Anal Environ
Epidemiol 1:357–368 (1991).
10. Waldman JM, Bilder SM, Freeman NCG, Friedman M. A
portable datalogger to evaluate recall-based time-use
measures. J Expos Anal Environ Epidemiol 3:39–48 (1993).
11. Simcox NJ, Fenske RA, Wolz SA, Lee IC, Kalman DA.
Pesticides in household dust and soil: exposure pathways
for children of agricultural families. Environ Health
Perspect 103:1126–1134 (1995).
12. Loewenherz C, Fenske RA, Simcox NJ, Bellamy G, Kalman
D. Biological monitoring of organophosphorus pesticide
exposure among children of agricultural workers. Environ
Health Perspect 105:1344–1353 (1997).
13. Cohen Hubal EA, Sheldon LS, Burke JM, McCurdy TR,
Berry MR, Rigas ML, Zartarian VG, Freeman NC.
Children’s exposure assessment: a review of factors influ-
encing children’s exposure, and the data available to
characterize and assess that exposure. Environ Health
Perspect 108:475–486 (2000).
14. Zartarian VG, Streicker J, Rivera A, Cornejo CS, Molina S,
Valadez OF, Leckie JO. A pilot study to collect micro-activ-
ity data of 2–4 year old farm children in Salinas Valley,
California, J Expos Anal Environ Epidemiol 5:21–34 (1995).
15. Reed KJ, Jimenez M, Freeman NC, Lioy PJ. Quantification
of children’s hand and mouthing activities through a
videotaping methodology. J Expos Anal Environ Epidemiol
9:513–520 (1999).
16. U.S. EPA. GIS Technical Memorandum 3: Global Positioning
Systems Technology and Its Application in Environmental
Programs. US EPA/600/R-92/036. Washington, DC:U.S
Environmental Protection Agency, 1992.
17. Interagency GPS Executive Board. 2000. President Ends
Selective Availability. 1 May. Available: http://
www.igeb.gov/sa/ [cited 16 November 2001].
18. Holton WC. Farming from a new perspective: remote
sensing comes down to earth. Environ Health Perspect
108:A130–A133 (2000).
19. Schutz Y, Chambaz A. Could a satellite-based navigation
system (GPS) be used to assess the physical activity of
individuals on Earth? Eur J Clin Nutr 51:338–339 (1997).
20. Lyon LJ, Burcham MG. Tracking Elk Hunters with the
Global Positioning System. USFS RMRS-RP-3. Ogden,
UT:U.S. Department of Agriculture, Forest Service, 1998.
21. Phillips ML, Hall TA, Esmen NA, Lynch R, DL Johnson. Use
of global positioning system technology to track subject’s
location during environmental exposure sampling. J Expos
Anal Environ Epidemiol 11:207–215 (2001).
22. Ward M, Nuckols J, Weigel S, Maxwell S, Cantor K, Miller
R. Identifying populations potentially exposed to agricul-
tural pesticides using remote sensing and a geographic
information system. Environ Health Perspect 108:5–12
(2000).
23. GPS World. 2001 Buyer’s Guide. Cleveland, OH:Advanstar
Communications, 2001.
24. Lu C, Fenske RA, Simcox NJ, Kalman D. Pesticide expo-
sure of children in an agricultural community: evidence of
household proximity to farmland and take home exposure
pathways. Environ Res 84:290–302 (2000).
25. National Geodetic Survey. Continuously Operating Reference
Station (CORS). Available: http://www.ngs.noaa.gov/cgi-
cors/ufcors2.prl [cited 16 November 2001].
26. Johannessen R. Interference: sources and symptoms.
GPS World 8(9):44–46 (1997).
27. Koch D, Lu C, Fisker-Andersen J, Jolley L, Fenske RA.
Temporal association of children’s pesticide exposure
and agricultural spraying: report of a longitudinal biomoni-
toring study. Environ Health Perspect 110:829–833 (2002).
Children’s Health
|
Elgethun et al.
122
VOLUME 111 | NUMBER 1 | January 2003
•
Environmental Health Perspectives
the
Latest Word
on
Environmental Health
at your
Fingertips
.
VISIT US ON THE WEB TODAY! ehponline.org
